From 6f0b409c67694e2a364543422142f6f47eb10526 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Tue, 24 Feb 2026 15:39:34 +0000 Subject: [PATCH 01/17] uncommenting most of the build, adding aec build for vx4b, restructuring jenkins --- Jenkinsfile | 764 +++++++++++------- examples/app_aec/CMakeLists.txt | 6 +- lib_voice/lib_build_info.cmake | 107 +-- tests/lib_aec/aec_unit_tests/CMakeLists.txt | 7 +- .../aec_unit_tests/src/test_calc_coherence.c | 1 + .../src/test_calc_corr_factor.c | 1 + .../src/test_l2_unify_exponent.c | 2 +- 7 files changed, 522 insertions(+), 366 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 9a08723c..8032ff3c 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -1,6 +1,6 @@ // This file relates to internal XMOS infrastructure and should be ignored by external users -@Library('xmos_jenkins_shared_library@v0.45.0') _ +@Library('xmos_jenkins_shared_library@v0.46.0') _ def runningOn(machine) { println "Stage running on:" @@ -18,6 +18,11 @@ pipeline { defaultValue: '15.3.1', description: 'The XTC tools version' ) + string( + name: 'TOOLS_VX4_VERSION', + defaultValue: '-j --repo arch_vx_slipgate -b master -a XTC 112', + description: 'The XTC Slipgate tools version' + ) string( name: 'XMOSDOC_VERSION', defaultValue: 'v8.0.1', @@ -64,9 +69,17 @@ pipeline { checkoutScmShallow() createVenv(reqFile: "requirements.txt") } + // dir("${REPO}/examples") { + // withVenv { + // xcoreBuild(archiveBins: false) + // } + // } dir("${REPO}/examples") { withVenv { - xcoreBuild() + // only an aec example builds on vx4b so far + dir("app_aec") { + xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } } } } @@ -115,13 +128,59 @@ pipeline { } // stages + post { + cleanup { + xcoreCleanSandbox() + } + } + } // Build and Docs + + stage('vx4b build') { + when { + expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } + } + agent { + label 'x86&&linux' + } + stages { + stage('Get View') { + steps { + runningOn(env.NODE_NAME) + + dir("${REPO}") { + checkout scm + // need ai_tools for the build + // need numpy to generate aec tests, will get in from ai_tools + createVenv(reqFile: "requirements.txt") + } + } + } // Get View + + stage('Build tests vx4b') { + steps { + dir("${REPO}") { + withVenv { + dir("tests") { + dir("lib_aec/aec_unit_tests") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416 -DTEST_SPEEDUP_FACTOR=8") + } + stash name: 'vx4b_build_xcore', includes: '**/bin/**/*.xe' + } + } + } + } + } // Build tests vx4b + + } // stages + post { cleanup { xcoreCleanSandbox() } } } - stage('xcore.ai executables build, PartA') { + + stage('xs3a build, PartA') { when { expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } } @@ -147,15 +206,15 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - script { - if (env.FULL_TEST == "1") { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partA") - } - else { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partA") - } - } - stash name: 'xcommon_cmake_build_xcore_partA', includes: '**/bin/**/*.xe' + // script { + // if (env.FULL_TEST == "1") { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partA") + // } + // else { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partA") + // } + // } + // stash name: 'xcommon_cmake_build_xcore_partA', includes: '**/bin/**/*.xe' } } } @@ -168,8 +227,8 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - xcoreBuild(buildDir: "build_xcommon_cmake_native", archiveBins: false, cmakeOpts: "-DBUILD_NATIVE=ON") - stash name: 'xcommon_cmake_build_native', includes: '**/bin/**/', excludes: '**/bin/**/*.xe' + // xcoreBuild(buildDir: "build_xcommon_cmake_native", archiveBins: false, cmakeOpts: "-DBUILD_NATIVE=ON") + // stash name: 'xcommon_cmake_build_native', includes: '**/bin/**/', excludes: '**/bin/**/*.xe' } } } @@ -181,12 +240,12 @@ pipeline { sh "git clone git@github.com:xmos/xmos_cmake_toolchain.git --depth 1 --branch v1.0.0" // Do custom cmake, xcore build, from the tests/custom_cmake_build directory dir("${REPO}/tests/custom_cmake_build") { - withTools(params.TOOLS_VERSION) { - withVenv { - sh 'cmake -B build --toolchain=../../../xmos_cmake_toolchain/xs3a.cmake' - sh 'make -C build -j$(nproc)' - } - } + // withTools(params.TOOLS_VERSION) { + // withVenv { + // sh 'cmake -B build --toolchain=../../../xmos_cmake_toolchain/xs3a.cmake' + // sh 'make -C build -j$(nproc)' + // } + // } } } } @@ -196,8 +255,9 @@ pipeline { xcoreCleanSandbox() } } - } - stage('xcore.ai executables build, PartB') { + } // xs3a build, PartA + + stage('xs3a build, PartB') { when { expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } } @@ -223,15 +283,15 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - script { - if (env.FULL_TEST == "1") { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") - } - else { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") - } - } - stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' + // script { + // if (env.FULL_TEST == "1") { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") + // } + // else { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") + // } + // } + // stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' } } } @@ -244,366 +304,445 @@ pipeline { xcoreCleanSandbox() } } - } - } - } - stage('xcore.ai Verification') { - when { - expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } - } - agent { - label 'xcore.ai' - } - stages{ - stage('Get View') { - steps { - runningOn(env.NODE_NAME) - - sh "git clone --depth 1 --branch main git@github.com:xmos/amazon_wwe.git" - sh "git clone --depth 1 --branch master git@github.com:xmos/sensory_sdk.git" - - dir("${REPO}") { - checkout scm - dir("tests") { - createVenv(reqFile: "requirements_test.txt") - } - } + } // xs3a build, PartB + } // parallel + } // Build and Docs + + stage("Testing") { + parallel { + stage('vx4b Verification') { + when { + expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } } - } - stage('Make/get bins and libs'){ - steps { - dir("${REPO}/tests") { - withTools(params.TOOLS_VERSION) { - withVenv { - - sh "cmake -B build_xcommon_cmake" // to fetch lib_xcore_math - - // Build x86 versions locally as we had problems with moving bins and libs over from previous build due to brew - dir("custom_cmake_build") { - sh "cmake --version" - sh 'cmake -B build' - sh 'make -C build -j$(nproc)' - } - // We do this again on the NUCs for verification later, but this just checks we have no build error - dir("lib_ic/py_c_frame_compare") { - sh "python build_ic_frame_proc.py" - } - // We do this again on the NUCs for verification later, but this just checks we have no build error - dir("lib_vnr/test_vnr_cffi") { - sh "python build_vnr_cffi.py" + agent { + label 'vx4' + } + stages{ + stage('Get View') { + steps { + runningOn(env.NODE_NAME) + + sh "git clone --depth 1 --branch main git@github.com:xmos/amazon_wwe.git" + sh "git clone --depth 1 --branch master git@github.com:xmos/sensory_sdk.git" + + dir("${REPO}") { + checkout scm + dir("tests") { + withTools(params.TOOLS_VX4_VERSION) { + createVenv(reqFile: "requirements_test.txt") + } + unstash 'vx4b_build_xcore' } - dir("stage_b") { - sh "python build_c_code.py" + } + } + } // Get View + + stage('tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_aec") { + withTools(params.TOOLS_VERSION) { + withVenv { + dir("aec_unit_tests") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + } + } + } } - unstash 'xcommon_cmake_build_xcore_partA' - unstash 'xcommon_cmake_build_xcore_partB' - unstash 'xcommon_cmake_build_native' } } + } // tests + } // stages + post { + cleanup { + xcoreCleanSandbox() } } } - stage('Reset XTAGs'){ - steps{ - dir("${REPO}/tests") { - sh 'rm -f ~/.xtag/acquired' // Hacky but ensure it always works even when previous failed run left lock file present - withTools(params.TOOLS_VERSION) { - withVenv{ - sh "xtagctl reset_all XCORE-AI-EXPLORER" + + stage('xs3a Verification') { + when { + expression { !env.GH_LABEL_DOC_ONLY.toBoolean() } + } + agent { + label 'xcore.ai' + } + stages{ + stage('Get View') { + steps { + runningOn(env.NODE_NAME) + + sh "git clone --depth 1 --branch main git@github.com:xmos/amazon_wwe.git" + sh "git clone --depth 1 --branch master git@github.com:xmos/sensory_sdk.git" + + dir("${REPO}") { + checkout scm + dir("tests") { + createVenv(reqFile: "requirements_test.txt") + } } } } - } - } + stage('Make/get bins and libs'){ + steps { + dir("${REPO}/tests") { + withTools(params.TOOLS_VERSION) { + withVenv { - stage('MIPS are memory resource usage tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false) { - dir("${REPO}/tests") { - withTools(params.TOOLS_VERSION) { - withVenv { - dir("profile_memory") { - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - archiveArtifacts artifacts: "lib_voice_memory.json", fingerprint: true, onlyIfSuccessful: true - } - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("profile_mips") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - archiveArtifacts artifacts: "lib_voice_mips.json", fingerprint: true, onlyIfSuccessful: true + sh "cmake -B build_xcommon_cmake" // to fetch lib_xcore_math + + // Build x86 versions locally as we had problems with moving bins and libs over from previous build due to brew + dir("custom_cmake_build") { + sh "cmake --version" + sh 'cmake -B build' + sh 'make -C build -j$(nproc)' } + // We do this again on the NUCs for verification later, but this just checks we have no build error + dir("lib_ic/py_c_frame_compare") { + sh "python build_ic_frame_proc.py" + } + // We do this again on the NUCs for verification later, but this just checks we have no build error + dir("lib_vnr/test_vnr_cffi") { + sh "python build_vnr_cffi.py" + } + dir("stage_b") { + sh "python build_c_code.py" + } + // unstash 'xcommon_cmake_build_xcore' + // unstash 'xcommon_cmake_build_native' + } + } + } + } + } + stage('Reset XTAGs'){ + steps{ + dir("${REPO}/tests") { + sh 'rm -f ~/.xtag/acquired' // Hacky but ensure it always works even when previous failed run left lock file present + withTools(params.TOOLS_VERSION) { + withVenv{ + sh "xtagctl reset_all XCORE-AI-EXPLORER" } } } } } - } - } - stage('VNR tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_vnr") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("vnr_unit_tests") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_vnr_cffi") { - sh "python build_vnr_cffi.py" - sh "pytest -n 4 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('MIPS are memory resource usage tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false) { + dir("${REPO}/tests") { + withTools(params.TOOLS_VERSION) { + withVenv { + dir("profile_memory") { + sh "pytest -n 1 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + archiveArtifacts artifacts: "lib_voice_memory.json", fingerprint: true, onlyIfSuccessful: true + } + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("profile_mips") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + archiveArtifacts artifacts: "lib_voice_mips.json", fingerprint: true, onlyIfSuccessful: true + } + } } } } } } } - } - } - stage('NS tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_ns") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("compare_c_py"){ - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("ns_unit_tests"){ - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('VNR tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_vnr") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("vnr_unit_tests") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_vnr_cffi") { + sh "python build_vnr_cffi.py" + sh "pytest -n 4 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_vnr_profile") { + sh "pytest -s --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + } } } } } } } - } - } - stage('IC tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_ic") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("ic_unit_tests"){ - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("py_c_frame_compare"){ - sh "python build_ic_frame_proc.py" - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_ic_spec"){ - // This test compares the model and C implementation over a range of scenarious for: - // convergence_time, db_suppression, maximum noise added to input (to test for stability) - // and expected group delay. It will fail if these are not met. - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - sh "python print_stats.py > ic_spec_summary.txt" - // This script generates a number of polar plots of attenuation vs null point angle vs freq - // It currently only uses the python model to do this. It takes about 40 mins for all plots - // and generates a series of IC_performance_xxxHz.svg files which could be archived - //sh "python plot_ic.py" - } - dir("characterise_c_py"){ - // This test compares the suppression performance across angles between model and C implementation - // and fails if they differ significantly. It requires that the C implementation run with fixed mu - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - // This script sweeps the y_delay value to find what the optimum suppression is across RT60 and angle. - // It's more of a model develpment tool than testing the implementation so not run. It take a few minutes. - //sh "python sweep_ic_delay.py" - } - dir("test_calc_vnr_pred"){ - // This is a unit test for ic_calc_vnr_pred function. - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_bad_state"){ - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('NS tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_ns") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("test_ns_profile"){ + sh "pytest -n 1 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("compare_c_py"){ + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("ns_unit_tests"){ + sh "pytest -n 1 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + } } } } } } } - } - } - stage('Stage B tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/stage_b") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('IC tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_ic") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("ic_unit_tests"){ + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("py_c_frame_compare"){ + sh "python build_ic_frame_proc.py" + sh "pytest -s --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_ic_profile"){ + sh "pytest -s --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_ic_spec"){ + // This test compares the model and C implementation over a range of scenarious for: + // convergence_time, db_suppression, maximum noise added to input (to test for stability) + // and expected group delay. It will fail if these are not met. + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + sh "python print_stats.py > ic_spec_summary.txt" + // This script generates a number of polar plots of attenuation vs null point angle vs freq + // It currently only uses the python model to do this. It takes about 40 mins for all plots + // and generates a series of IC_performance_xxxHz.svg files which could be archived + //sh "python plot_ic.py" + } + dir("characterise_c_py"){ + // This test compares the suppression performance across angles between model and C implementation + // and fails if they differ significantly. It requires that the C implementation run with fixed mu + sh "pytest -s --junitxml=pytest_result.xml" + junit "pytest_result.xml" + // This script sweeps the y_delay value to find what the optimum suppression is across RT60 and angle. + // It's more of a model develpment tool than testing the implementation so not run. It take a few minutes. + //sh "python sweep_ic_delay.py" + } + dir("test_calc_vnr_pred"){ + // This is a unit test for ic_calc_vnr_pred function. + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_bad_state"){ + sh "pytest -s --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + } + } } } } } } - } - } - stage('ADEC tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_adec") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("de_unit_tests") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_delay_estimator") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - sh "python print_stats.py" - } - dir("test_adec_startup") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_adec") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('Stage B tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/stage_b") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + sh "pytest -n 1 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } } } } } } } - } - } - stage('AEC tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_aec") { - withTools(params.TOOLS_VERSION) { - withVenv { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("test_aec_schedule") { - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_aec_enhancements") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("aec_unit_tests") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_aec_spec") { - script { - if (env.FULL_TEST == "0") { - sh 'mv excluded_tests_quick.txt excluded_tests.txt' + stage('ADEC tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_adec") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("de_unit_tests") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_delay_estimator") { + sh 'mkdir -p ./input_wavs/' + sh 'mkdir -p ./output_files/' + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + sh "python print_stats.py" + } + dir("test_adec_startup") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_adec") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_adec_profile") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + // Testing bit exactness of the AEC scheduling + sh "diff output_1_2_2_10_5.wav output_2_2_2_10_5.wav" } } - sh "python generate_audio.py" - sh "pytest -n 2 --junitxml=results_process.xml test_process_audio.py" - catchError { - sh "pytest --junitxml=results_check.xml test_check_output.py" - } - sh "python parse_results.py" - sh "pytest --junitxml=results_final.xml test_evaluate_results.py" - junit "results_final.xml" } } } } } } - } - } - stage('AGC tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_agc/test_process_frame") { - withTools(params.TOOLS_VERSION) { - withVenv { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + stage('AEC tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_aec") { + withTools(params.TOOLS_VERSION) { + withVenv { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("test_aec_enhancements") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("aec_unit_tests") { + sh "pytest -n 2 --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("test_aec_spec") { + script { + if (env.FULL_TEST == "0") { + sh 'mv excluded_tests_quick.txt excluded_tests.txt' + } + } + sh "python generate_audio.py" + sh "pytest -n 2 --junitxml=results_process.xml test_process_audio.py" + catchError { + sh "pytest --junitxml=results_check.xml test_check_output.py" + } + sh "python parse_results.py" + sh "pytest --junitxml=results_final.xml test_evaluate_results.py" + junit "results_final.xml" + } + } + } + } } } } } - } - } - stage('Pipeline tests') { - steps { - catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/pipeline") { - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - withEnv(["PIPELINE_FULL_RUN=${PIPELINE_FULL_RUN}", "SENSORY_PATH=${env.WORKSPACE}/sensory_sdk/", "AMAZON_WWE_PATH=${env.WORKSPACE}/amazon_wwe/"]) { + + stage('AGC tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/lib_agc/test_process_frame") { withTools(params.TOOLS_VERSION) { withVenv { - echo "PIPELINE_FULL_RUN set as " + env.PIPELINE_FULL_RUN - - // Note we have 2 xcore targets and we can run x86 threads too. But in case we have only xcore jobs in the config, limit to 4 so we don't timeout waiting for xtags - sh "pytest -n 4 --junitxml=pytest_result.xml -vv" + sh "pytest -n 2 --junitxml=pytest_result.xml" junit "pytest_result.xml" - sh "python compare_keywords.py results_Avona_aec_ic_ns_agc_prev_arch_xcore.csv results_Avona_aec_ic_ns_agc_prev_arch_python.csv --pass-threshold=1" } } } } } } - } - } - stage('Benchmark Pipeline tests results') { - when { - expression { env.PIPELINE_FULL_RUN == "1" } - } - steps { - dir("${REPO}/tests/pipeline") { - withTools(params.TOOLS_VERSION) { - withVenv { - copyArtifacts filter: '**/results_*.csv', fingerprintArtifacts: true, projectName: '../lib_audio_pipelines/master', selector: lastSuccessful() - runPython("python plot_results.py lib_audio_pipelines/tests/pipelines/results_lib_ap_prev_arch_xcore.csv results_Avona_prev_arch_xcore.csv --single-plot --ww-column='0_2 1_2' --figname=results_benchmark_prev_arch") - runPython("python plot_results.py lib_audio_pipelines/tests/pipelines/results_lib_ap_alt_arch_xcore.csv results_Avona_alt_arch_xcore.csv --single-plot --ww-column='0_2 1_2' --figname=results_benchmark_alt_arch") + stage('Pipeline tests') { + steps { + catchError(stageResult: 'FAILURE', catchInterruptions: false){ + dir("${REPO}/tests/pipeline") { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + withEnv(["PIPELINE_FULL_RUN=${PIPELINE_FULL_RUN}", "SENSORY_PATH=${env.WORKSPACE}/sensory_sdk/", "AMAZON_WWE_PATH=${env.WORKSPACE}/amazon_wwe/"]) { + withTools(params.TOOLS_VERSION) { + withVenv { + echo "PIPELINE_FULL_RUN set as " + env.PIPELINE_FULL_RUN + + // Note we have 2 xcore targets and we can run x86 threads too. But in case we have only xcore jobs in the config, limit to 4 so we don't timeout waiting for xtags + sh "pytest -n 4 --junitxml=pytest_result.xml -vv" + junit "pytest_result.xml" + sh "python compare_keywords.py results_Avona_aec_ic_ns_agc_prev_arch_xcore.csv results_Avona_aec_ic_ns_agc_prev_arch_python.csv --pass-threshold=1" + } + } + } + } + } } } } + stage('Benchmark Pipeline tests results') { + when { + expression { env.PIPELINE_FULL_RUN == "1" } + } + steps { + dir("${REPO}/tests/pipeline") { + withTools(params.TOOLS_VERSION) { + withVenv { + copyArtifacts filter: '**/results_*.csv', fingerprintArtifacts: true, projectName: '../lib_audio_pipelines/master', selector: lastSuccessful() + runPython("python plot_results.py lib_audio_pipelines/tests/pipelines/results_lib_ap_prev_arch_xcore.csv results_Avona_prev_arch_xcore.csv --single-plot --ww-column='0_2 1_2' --figname=results_benchmark_prev_arch") + runPython("python plot_results.py lib_audio_pipelines/tests/pipelines/results_lib_ap_alt_arch_xcore.csv results_Avona_alt_arch_xcore.csv --single-plot --ww-column='0_2 1_2' --figname=results_benchmark_alt_arch") + } + } + } + } + } + }// stages + post { + always { + // AEC aretfacts + archiveArtifacts artifacts: "${REPO}/tests/lib_adec/test_adec_profile/**/adec_prof*.log", fingerprint: true + // IC artefacts + archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_profile/ic_prof.log", fingerprint: true + archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_spec/ic_spec_summary.txt", fingerprint: true + // NS artefacts + archiveArtifacts artifacts: "${REPO}/tests/lib_ns/test_ns_profile/ns_prof.log", fingerprint: true + // VNR artifacts + archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/*.png", fingerprint: true + archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/vnr_prof.log", fingerprint: true + // Pipelines tests + archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.csv", fingerprint: true + archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.png", fingerprint: true, allowEmptyArchive: true + archiveArtifacts artifacts: "${REPO}/tests/pipeline/keyword_input_*/*.npy", fingerprint: true, allowEmptyArchive: true + } + failure { + // archive wavs on failure only + archiveArtifacts artifacts: "${REPO}/tests/pipeline/keyword_input_*/*.wav", fingerprint: true + } + cleanup { + xcoreCleanSandbox() + } } - } - }// stages - post { - always { - // IC artefacts - archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_spec/ic_spec_summary.txt", fingerprint: true - // Pipelines tests - archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.csv", fingerprint: true - archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.png", fingerprint: true, allowEmptyArchive: true - archiveArtifacts artifacts: "${REPO}/tests/pipeline/keyword_input_*/*.npy", fingerprint: true, allowEmptyArchive: true - } - failure { - // archive wavs on failure only - archiveArtifacts artifacts: "${REPO}/tests/pipeline/keyword_input_*/*.wav", fingerprint: true - } - cleanup { - xcoreCleanSandbox() - } - } - }// stage xcore.ai Verification + } // xs3a Verification + } // parallel + } // Testing stage('🚀 Release') { when { @@ -613,5 +752,6 @@ pipeline { triggerRelease() } } // stage('🚀 Release') + } // stages } // pipeline diff --git a/examples/app_aec/CMakeLists.txt b/examples/app_aec/CMakeLists.txt index bf33f9a1..cbb02816 100644 --- a/examples/app_aec/CMakeLists.txt +++ b/examples/app_aec/CMakeLists.txt @@ -2,7 +2,11 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(app_aec) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() if(NOT BUILD_NATIVE) set(APP_COMPILER_FLAGS_1th -report -DAEC_THREADS=1) diff --git a/lib_voice/lib_build_info.cmake b/lib_voice/lib_build_info.cmake index 8888d5d5..ef0d51d8 100644 --- a/lib_voice/lib_build_info.cmake +++ b/lib_voice/lib_build_info.cmake @@ -1,76 +1,81 @@ set(LIB_NAME lib_voice) set(LIB_VERSION 1.0.0) -set(LIB_DEPENDENT_MODULES "lib_xcore_math(2.4.1)") +set(LIB_DEPENDENT_MODULES "lib_xcore_math(develop)") set(LIB_COMPILER_FLAGS -g -Os -DHEADROOM_CHECK=0) -if(BUILD_NATIVE) - list(APPEND LIB_COMPILER_FLAGS - -D__xtflm_conf_h_exists__ - -DNN_USE_REF - ) -endif() +# if(BUILD_NATIVE) +# list(APPEND LIB_COMPILER_FLAGS +# -D__xtflm_conf_h_exists__ +# -DNN_USE_REF +# ) +# endif() set(LIB_CXX_SRCS "") -include(${CMAKE_CURRENT_LIST_DIR}/vnr_model.cmake) -file(RELATIVE_PATH MODEL_OUT_DIR_REL ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_DIR}) +set(lib_ASM_SRCS "") +# include(${CMAKE_CURRENT_LIST_DIR}/vnr_model.cmake) +# file(RELATIVE_PATH MODEL_OUT_DIR_REL ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_DIR}) + +file(GLOB_RECURSE LIB_C_SRCS RELATIVE ${CMAKE_CURRENT_LIST_DIR} + "${CMAKE_CURRENT_LIST_DIR}/src/aec/*.c" + ) set(LIB_INCLUDES api - api/adec + # api/adec api/aec src/aec - api/agc - api/ic - src/ic - api/ns - src/ns - api/stage1 - api/vnr - src/vnr - ${MODEL_OUT_DIR_REL} + # api/agc + # api/ic + # src/ic + # api/ns + # src/ns + # api/stage1 + # api/vnr + # src/vnr + # ${MODEL_OUT_DIR_REL} ) -file(GLOB VNR_CXX_SOURCES RELATIVE ${CMAKE_CURRENT_LIST_DIR} CONFIGURE_DEPENDS "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.cpp") -file(RELATIVE_PATH VNR_MODEL_SOURCES ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_PATH}.cpp) +# file(GLOB VNR_CXX_SOURCES RELATIVE ${CMAKE_CURRENT_LIST_DIR} CONFIGURE_DEPENDS "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.cpp") +# file(RELATIVE_PATH VNR_MODEL_SOURCES ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_PATH}.cpp) -list(APPEND LIB_CXX_SRCS ${VNR_MODEL_SOURCES} ${VNR_CXX_SOURCES}) +# list(APPEND LIB_CXX_SRCS ${VNR_MODEL_SOURCES} ${VNR_CXX_SOURCES}) XMOS_REGISTER_MODULE() -include(${CMAKE_CURRENT_LIST_DIR}/aec_schedule.cmake) +# include(${CMAKE_CURRENT_LIST_DIR}/aec_schedule.cmake) -# Discover schedules -aec_collect_schedules(single_config_sched multi_config_name multi_config_sched) +# # Discover schedules +# aec_collect_schedules(single_config_sched multi_config_name multi_config_sched) -list(LENGTH single_config_sched singleconfig_list_len) -list(LENGTH multi_config_sched multi_config_list_len) +# list(LENGTH single_config_sched singleconfig_list_len) +# list(LENGTH multi_config_sched multi_config_list_len) -foreach(target ${APP_BUILD_TARGETS}) - # Apply AEC schedule if needed - if(singleconfig_list_len EQUAL 1) # App only does set(AEC_SCHEDULE_CONFIG ). Attach this schedule to all targets - list(GET single_config_sched 0 sched) - message(VERBOSE "Target ${target} using AEC_SCHEDULE_CONFIG schedule ${sched}") - generate_schedule(${target} "${sched}") - elseif(multi_config_list_len GREATER 0) - math(EXPR _last "${multi_config_list_len} - 1") - foreach(i RANGE 0 ${_last}) - list(GET multi_config_name ${i} config) - if(target MATCHES "${config}$") # target ends with - list(GET multi_config_sched ${i} sched) # Get the corresponding schedule for this config - message(VERBOSE "aec schedule config ${config}, matches target ${target} at index ${i}. schedule = ${sched}") - generate_schedule(${target} "${sched}") # Generate schedule and add to target sources and includes - break() - endif() - endforeach() - endif() +# foreach(target ${APP_BUILD_TARGETS}) +# # Apply AEC schedule if needed +# if(singleconfig_list_len EQUAL 1) # App only does set(AEC_SCHEDULE_CONFIG ). Attach this schedule to all targets +# list(GET single_config_sched 0 sched) +# message(VERBOSE "Target ${target} using AEC_SCHEDULE_CONFIG schedule ${sched}") +# generate_schedule(${target} "${sched}") +# elseif(multi_config_list_len GREATER 0) +# math(EXPR _last "${multi_config_list_len} - 1") +# foreach(i RANGE 0 ${_last}) +# list(GET multi_config_name ${i} config) +# if(target MATCHES "${config}$") # target ends with +# list(GET multi_config_sched ${i} sched) # Get the corresponding schedule for this config +# message(VERBOSE "aec schedule config ${config}, matches target ${target} at index ${i}. schedule = ${sched}") +# generate_schedule(${target} "${sched}") # Generate schedule and add to target sources and includes +# break() +# endif() +# endforeach() +# endif() - # Link aitools with the targets - target_link_libraries(${target} PRIVATE tflite_micro) -if(BUILD_NATIVE) - target_compile_features(${target} PRIVATE cxx_std_11) -endif() -endforeach() +# # Link aitools with the targets +# target_link_libraries(${target} PRIVATE tflite_micro) +# if(BUILD_NATIVE) +# target_compile_features(${target} PRIVATE cxx_std_11) +# endif() +# endforeach() diff --git a/tests/lib_aec/aec_unit_tests/CMakeLists.txt b/tests/lib_aec/aec_unit_tests/CMakeLists.txt index 8c34aa8e..001a797e 100644 --- a/tests/lib_aec/aec_unit_tests/CMakeLists.txt +++ b/tests/lib_aec/aec_unit_tests/CMakeLists.txt @@ -2,7 +2,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(aec_unit_tests) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../../../) include(${XMOS_SANDBOX_DIR}/lib_voice/tests/etc/build_options.cmake) diff --git a/tests/lib_aec/aec_unit_tests/src/test_calc_coherence.c b/tests/lib_aec/aec_unit_tests/src/test_calc_coherence.c index 4af48208..8e072223 100644 --- a/tests/lib_aec/aec_unit_tests/src/test_calc_coherence.c +++ b/tests/lib_aec/aec_unit_tests/src/test_calc_coherence.c @@ -2,6 +2,7 @@ // This Software is subject to the terms of the XMOS Public Licence: Version 1. #include "aec_unit_tests.h" #include +#include #include #include "aec.h" diff --git a/tests/lib_aec/aec_unit_tests/src/test_calc_corr_factor.c b/tests/lib_aec/aec_unit_tests/src/test_calc_corr_factor.c index 90d6f96e..1530b0e7 100644 --- a/tests/lib_aec/aec_unit_tests/src/test_calc_corr_factor.c +++ b/tests/lib_aec/aec_unit_tests/src/test_calc_corr_factor.c @@ -2,6 +2,7 @@ // This Software is subject to the terms of the XMOS Public Licence: Version 1. #include "aec_unit_tests.h" #include +#include #include #include "aec.h" diff --git a/tests/lib_aec/aec_unit_tests/src/test_l2_unify_exponent.c b/tests/lib_aec/aec_unit_tests/src/test_l2_unify_exponent.c index 2137803c..78102163 100644 --- a/tests/lib_aec/aec_unit_tests/src/test_l2_unify_exponent.c +++ b/tests/lib_aec/aec_unit_tests/src/test_l2_unify_exponent.c @@ -138,7 +138,7 @@ void test_bfp_s32_l2_unify_exponent() { int min_reqd_headroom[NUM_SUBGROUPS]; int null_mapping; //null_mapping = 1 => unify everything without looking at subgroups //null_mapping = 0 => unify according to subgroups - for(int iter=0; iter<1<<12; iter++) { + for(int iter=0; iter<(1<<12)/F; iter++) { null_mapping = pseudo_rand_uint32(&seed) % 2; for(int i=0; i Date: Wed, 25 Feb 2026 12:50:00 +0000 Subject: [PATCH 02/17] adding xtagctl and aec_unit_tests for vx4b --- Jenkinsfile | 19 ++++++++++-- tests/lib_aec/aec_unit_tests/conftest.py | 31 +++++++++++++++---- .../src/test_compare_filters_and_calc_mu.c | 6 ++-- tests/requirements_test.txt | 2 +- 4 files changed, 45 insertions(+), 13 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 8032ff3c..63fcd121 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -337,15 +337,28 @@ pipeline { } } // Get View + stage('Reset XTAGs'){ + steps{ + dir("${REPO}/tests") { + sh 'rm -f ~/.xtag/acquired' // Hacky but ensure it always works even when previous failed run left lock file present + withTools(params.TOOLS_VX4_VERSION) { + withVenv{ + sh "xtagctl reset_all XK-EVK-XU416" + } + } + } + } + } + stage('tests') { steps { catchError(stageResult: 'FAILURE', catchInterruptions: false){ dir("${REPO}/tests/lib_aec") { - withTools(params.TOOLS_VERSION) { + withTools(params.TOOLS_VX4_VERSION) { withVenv { dir("aec_unit_tests") { - // sh "pytest -n 2 --junitxml=pytest_result.xml" - // junit "pytest_result.xml" + sh "pytest --arch vx4b --junitxml=pytest_result.xml" + junit "pytest_result.xml" } } } diff --git a/tests/lib_aec/aec_unit_tests/conftest.py b/tests/lib_aec/aec_unit_tests/conftest.py index 737ff88a..4743e7bf 100644 --- a/tests/lib_aec/aec_unit_tests/conftest.py +++ b/tests/lib_aec/aec_unit_tests/conftest.py @@ -1,10 +1,17 @@ # Copyright 2021-2026 XMOS LIMITED. # This Software is subject to the terms of the XMOS Public Licence: Version 1. -from builtins import str import pytest import subprocess import xtagctl +def pytest_addoption(parser): + parser.addoption( + "--arch", + action = "store", + default = "xs3a", + help = "Architecture to run on", + choices = ["xs3a", "vx4b", "sim"], + ) def pytest_collect_file(parent, file_path): if(file_path.suffix == ".xe"): @@ -13,13 +20,17 @@ def pytest_collect_file(parent, file_path): class UnityTestSource(pytest.File): def collect(self): - yield UnityTestExecutable.from_parent(self, fspath=self.fspath, name=self.name) + selected_arch = self.config.getoption("arch") + yield UnityTestExecutable.from_parent( + self, fspath=self.fspath, name=self.name, arch=selected_arch + ) class UnityTestExecutable(pytest.Item): - def __init__(self, fspath, name, parent): + def __init__(self, fspath, name, parent, arch): super(UnityTestExecutable, self).__init__(name, parent) self.fspath = fspath + self.arch = arch self._nodeid = self.name # Override the naming to suit C better def runtest(self): @@ -27,9 +38,17 @@ def runtest(self): simulator_fail = False test_output = None try: - print("run xrun for executable ", self.fspath) - with xtagctl.acquire("XCORE-AI-EXPLORER") as adapter_id: - test_output = subprocess.check_output(['xrun', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + # NOTE: pytest calls runtest() with no parameters; pass data in via + # config/options during collection and store it on the item. + print(f"run executable {self.fspath} on arch {self.arch}") + if self.arch == "xs3a" or self.arch == "vx4b": + hw_target = "XCORE-AI-EXPLORER" if self.arch == "xs3a" else "XK-EVK-XU416" + with xtagctl.acquire(hw_target) as adapter_id: + test_output = subprocess.check_output(['xrun', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + elif self.arch == "sim": + test_output = subprocess.check_output(['xsim', self.fspath], text=True, stderr=subprocess.STDOUT) + else: + assert 0, f"Architecture {self.arch} not supported" except subprocess.CalledProcessError as e: # Unity exits non-zero if an assertion fails simulator_fail = True diff --git a/tests/lib_aec/aec_unit_tests/src/test_compare_filters_and_calc_mu.c b/tests/lib_aec/aec_unit_tests/src/test_compare_filters_and_calc_mu.c index 08c92e65..e224e371 100644 --- a/tests/lib_aec/aec_unit_tests/src/test_compare_filters_and_calc_mu.c +++ b/tests/lib_aec/aec_unit_tests/src/test_compare_filters_and_calc_mu.c @@ -912,10 +912,10 @@ void test_compare_filters_and_calc_mu() { } } } - for(int i=0; i Date: Wed, 25 Feb 2026 16:34:11 +0000 Subject: [PATCH 03/17] adding ns unit tests for vx4b --- Jenkinsfile | 11 +++++-- lib_voice/lib_build_info.cmake | 5 +-- tests/lib_ns/ns_unit_tests/CMakeLists.txt | 7 ++++- tests/lib_ns/ns_unit_tests/conftest.py | 31 +++++++++++++++---- .../test_subtract_lambda_from_frame.c | 3 +- .../test_update_lambda_hat.c | 2 +- 6 files changed, 45 insertions(+), 14 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 63fcd121..91870e7a 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -164,6 +164,9 @@ pipeline { dir("lib_aec/aec_unit_tests") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416 -DTEST_SPEEDUP_FACTOR=8") } + dir("lib_ns/ns_unit_tests") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } stash name: 'vx4b_build_xcore', includes: '**/bin/**/*.xe' } } @@ -353,10 +356,14 @@ pipeline { stage('tests') { steps { catchError(stageResult: 'FAILURE', catchInterruptions: false){ - dir("${REPO}/tests/lib_aec") { + dir("${REPO}/tests") { withTools(params.TOOLS_VX4_VERSION) { withVenv { - dir("aec_unit_tests") { + dir("lib_aec/aec_unit_tests") { + sh "pytest --arch vx4b --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + dir("lib_ns/ns_unit_tests"){ sh "pytest --arch vx4b --junitxml=pytest_result.xml" junit "pytest_result.xml" } diff --git a/lib_voice/lib_build_info.cmake b/lib_voice/lib_build_info.cmake index ef0d51d8..87af2ec5 100644 --- a/lib_voice/lib_build_info.cmake +++ b/lib_voice/lib_build_info.cmake @@ -21,6 +21,7 @@ set(lib_ASM_SRCS "") file(GLOB_RECURSE LIB_C_SRCS RELATIVE ${CMAKE_CURRENT_LIST_DIR} "${CMAKE_CURRENT_LIST_DIR}/src/aec/*.c" + "${CMAKE_CURRENT_LIST_DIR}/src/ns/*.c" ) set(LIB_INCLUDES @@ -31,8 +32,8 @@ set(LIB_INCLUDES # api/agc # api/ic # src/ic - # api/ns - # src/ns + api/ns + src/ns # api/stage1 # api/vnr # src/vnr diff --git a/tests/lib_ns/ns_unit_tests/CMakeLists.txt b/tests/lib_ns/ns_unit_tests/CMakeLists.txt index abe298fd..959b217a 100644 --- a/tests/lib_ns/ns_unit_tests/CMakeLists.txt +++ b/tests/lib_ns/ns_unit_tests/CMakeLists.txt @@ -3,7 +3,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(ns_unit_tests) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../../../) include(${CMAKE_CURRENT_LIST_DIR}/../../test_deps.cmake) diff --git a/tests/lib_ns/ns_unit_tests/conftest.py b/tests/lib_ns/ns_unit_tests/conftest.py index 4ba9f8bf..4a151743 100644 --- a/tests/lib_ns/ns_unit_tests/conftest.py +++ b/tests/lib_ns/ns_unit_tests/conftest.py @@ -1,10 +1,17 @@ # Copyright 2022-2026 XMOS LIMITED. # This Software is subject to the terms of the XMOS Public Licence: Version 1. -from builtins import str import pytest import subprocess import xtagctl +def pytest_addoption(parser): + parser.addoption( + "--arch", + action = "store", + default = "xs3a", + help = "Architecture to run on", + choices = ["xs3a", "vx4b", "sim"], + ) def pytest_collect_file(parent, file_path): if(file_path.suffix == ".xe"): @@ -13,13 +20,17 @@ def pytest_collect_file(parent, file_path): class UnityTestSource(pytest.File): def collect(self): - yield UnityTestExecutable.from_parent(self, fspath=self.fspath, name=self.name) + selected_arch = self.config.getoption("arch") + yield UnityTestExecutable.from_parent( + self, fspath=self.fspath, name=self.name, arch=selected_arch + ) class UnityTestExecutable(pytest.Item): - def __init__(self, fspath, name, parent): + def __init__(self, fspath, name, parent, arch): super(UnityTestExecutable, self).__init__(name, parent) self.fspath = fspath + self.arch = arch self._nodeid = self.name # Override the naming to suit C better def runtest(self): @@ -27,9 +38,17 @@ def runtest(self): simulator_fail = False test_output = None try: - print("run xrun for executable ", self.fspath) - with xtagctl.acquire("XCORE-AI-EXPLORER") as adapter_id: - test_output = subprocess.check_output(['xrun', '--xscope', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + # NOTE: pytest calls runtest() with no parameters; pass data in via + # config/options during collection and store it on the item. + print(f"run executable {self.fspath} on arch {self.arch}") + if self.arch == "xs3a" or self.arch == "vx4b": + hw_target = "XCORE-AI-EXPLORER" if self.arch == "xs3a" else "XK-EVK-XU416" + with xtagctl.acquire(hw_target) as adapter_id: + test_output = subprocess.check_output(['xrun', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + elif self.arch == "sim": + test_output = subprocess.check_output(['xsim', self.fspath], text=True, stderr=subprocess.STDOUT) + else: + assert 0, f"Architecture {self.arch} not supported" except subprocess.CalledProcessError as e: # Unity exits non-zero if an assertion fails simulator_fail = True diff --git a/tests/lib_ns/ns_unit_tests/src/test_subtact_lambda_from_frame/test_subtract_lambda_from_frame.c b/tests/lib_ns/ns_unit_tests/src/test_subtact_lambda_from_frame/test_subtract_lambda_from_frame.c index b0b3ca34..e234fbaa 100644 --- a/tests/lib_ns/ns_unit_tests/src/test_subtact_lambda_from_frame/test_subtract_lambda_from_frame.c +++ b/tests/lib_ns/ns_unit_tests/src/test_subtact_lambda_from_frame/test_subtract_lambda_from_frame.c @@ -82,8 +82,7 @@ TEST(ns_priv_subtract_lambda_from_frame, case0){ lambda_fl.exp = EXP; lambda_db[v] = float_s32_to_double(lambda_fl); - lut_index = sqrt(lambda_db[v]) / LUT_INPUT_MULTIPLIER; - lut_index = abs_Y_db[v] / lut_index; + lut_index = (abs_Y_db[v] * LUT_INPUT_MULTIPLIER) / sqrt(lambda_db[v]); r_data_int = (lut_index > (LUT_SIZE - 1)) ? 0 : LUT_TEST[lut_index]; r_data_fl.mant = r_data_int; diff --git a/tests/lib_ns/ns_unit_tests/src/test_update_lambda_hat/test_update_lambda_hat.c b/tests/lib_ns/ns_unit_tests/src/test_update_lambda_hat/test_update_lambda_hat.c index 6f7af2f5..37c3032c 100644 --- a/tests/lib_ns/ns_unit_tests/src/test_update_lambda_hat/test_update_lambda_hat.c +++ b/tests/lib_ns/ns_unit_tests/src/test_update_lambda_hat/test_update_lambda_hat.c @@ -84,7 +84,7 @@ TEST(ns_priv_update_lambda_hat, case0){ } double rel_error = fabs(abs_diff/(expected[id] + ldexp(1, -40))); - double thresh = ldexp(1, -26); + double thresh = ldexp(1, -25); TEST_ASSERT(rel_error < thresh); } } From 35f964d1f1789a6f3478c193b5922ed918ff7628 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 26 Feb 2026 15:18:49 +0000 Subject: [PATCH 04/17] adding agc unit tests to vx4b jenkins, switching lib_xcore_math to a fork --- Jenkinsfile | 7 +++++ lib_voice/lib_build_info.cmake | 5 ++-- .../lib_agc/test_process_frame/CMakeLists.txt | 7 ++++- tests/lib_agc/test_process_frame/conftest.py | 30 +++++++++++++++---- 4 files changed, 41 insertions(+), 8 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index 91870e7a..323aa5df 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -167,6 +167,9 @@ pipeline { dir("lib_ns/ns_unit_tests") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } + dir("lib_agc/test_process_frame") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416 -DTEST_SPEEDUP_FACTOR=8") + } stash name: 'vx4b_build_xcore', includes: '**/bin/**/*.xe' } } @@ -367,6 +370,10 @@ pipeline { sh "pytest --arch vx4b --junitxml=pytest_result.xml" junit "pytest_result.xml" } + dir("lib_agc/test_process_frame") { + sh "pytest --arch vx4b --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } } } } diff --git a/lib_voice/lib_build_info.cmake b/lib_voice/lib_build_info.cmake index 87af2ec5..e31e148b 100644 --- a/lib_voice/lib_build_info.cmake +++ b/lib_voice/lib_build_info.cmake @@ -1,6 +1,6 @@ set(LIB_NAME lib_voice) set(LIB_VERSION 1.0.0) -set(LIB_DEPENDENT_MODULES "lib_xcore_math(develop)") +set(LIB_DEPENDENT_MODULES "lib_xcore_math(lib_voice_fixes)") set(LIB_COMPILER_FLAGS -g @@ -21,6 +21,7 @@ set(lib_ASM_SRCS "") file(GLOB_RECURSE LIB_C_SRCS RELATIVE ${CMAKE_CURRENT_LIST_DIR} "${CMAKE_CURRENT_LIST_DIR}/src/aec/*.c" + "${CMAKE_CURRENT_LIST_DIR}/src/agc/*.c" "${CMAKE_CURRENT_LIST_DIR}/src/ns/*.c" ) @@ -29,7 +30,7 @@ set(LIB_INCLUDES # api/adec api/aec src/aec - # api/agc + api/agc # api/ic # src/ic api/ns diff --git a/tests/lib_agc/test_process_frame/CMakeLists.txt b/tests/lib_agc/test_process_frame/CMakeLists.txt index 59fa4aef..a1078cac 100644 --- a/tests/lib_agc/test_process_frame/CMakeLists.txt +++ b/tests/lib_agc/test_process_frame/CMakeLists.txt @@ -2,7 +2,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(test_agc_process_frame) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../../../) include(${XMOS_SANDBOX_DIR}/lib_voice/tests/etc/build_options.cmake) diff --git a/tests/lib_agc/test_process_frame/conftest.py b/tests/lib_agc/test_process_frame/conftest.py index 2d773699..d16e7c39 100644 --- a/tests/lib_agc/test_process_frame/conftest.py +++ b/tests/lib_agc/test_process_frame/conftest.py @@ -4,6 +4,14 @@ import subprocess import xtagctl +def pytest_addoption(parser): + parser.addoption( + "--arch", + action = "store", + default = "xs3a", + help = "Architecture to run on", + choices = ["xs3a", "vx4b", "sim"], + ) def pytest_collect_file(parent, file_path): if(file_path.suffix == ".xe"): @@ -12,13 +20,17 @@ def pytest_collect_file(parent, file_path): class UnityTestSource(pytest.File): def collect(self): - yield UnityTestExecutable.from_parent(self, fspath=self.fspath, name=self.name) + selected_arch = self.config.getoption("arch") + yield UnityTestExecutable.from_parent( + self, fspath=self.fspath, name=self.name, arch=selected_arch + ) class UnityTestExecutable(pytest.Item): - def __init__(self, fspath, name, parent): + def __init__(self, fspath, name, parent, arch): super(UnityTestExecutable, self).__init__(name, parent) self.fspath = fspath + self.arch = arch self._nodeid = self.name # Override the naming to suit C better def runtest(self): @@ -26,9 +38,17 @@ def runtest(self): simulator_fail = False test_output = None try: - print("run xrun for executable ", self.fspath) - with xtagctl.acquire("XCORE-AI-EXPLORER") as adapter_id: - test_output = subprocess.check_output(['xrun', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + # NOTE: pytest calls runtest() with no parameters; pass data in via + # config/options during collection and store it on the item. + print(f"run executable {self.fspath} on arch {self.arch}") + if self.arch == "xs3a" or self.arch == "vx4b": + hw_target = "XCORE-AI-EXPLORER" if self.arch == "xs3a" else "XK-EVK-XU416" + with xtagctl.acquire(hw_target) as adapter_id: + test_output = subprocess.check_output(['xrun', '--io', '--adapter-id', adapter_id, self.fspath], text=True, stderr=subprocess.STDOUT) + elif self.arch == "sim": + test_output = subprocess.check_output(['xsim', self.fspath], text=True, stderr=subprocess.STDOUT) + else: + assert 0, f"Architecture {self.arch} not supported" except subprocess.CalledProcessError as e: # Unity exits non-zero if an assertion fails simulator_fail = True From 8c33436e934692832f76e5310d3c938cebb324e7 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 26 Feb 2026 16:14:56 +0000 Subject: [PATCH 05/17] buildable vnr example --- Jenkinsfile | 3 + examples/app_vnr/CMakeLists.txt | 6 +- lib_voice/lib_build_info.cmake | 26 +- lib_voice/vnr_model.cmake | 56 +- new_ai_tools/include/fast_flash.h | 65 + new_ai_tools/include/flash_server.h | 79 + new_ai_tools/include/flatbuffers/allocator.h | 68 + new_ai_tools/include/flatbuffers/array.h | 243 + new_ai_tools/include/flatbuffers/base.h | 474 + .../include/flatbuffers/bfbs_generator.h | 43 + new_ai_tools/include/flatbuffers/buffer.h | 142 + new_ai_tools/include/flatbuffers/buffer_ref.h | 53 + .../include/flatbuffers/code_generators.h | 235 + .../include/flatbuffers/default_allocator.h | 64 + .../include/flatbuffers/detached_buffer.h | 114 + .../include/flatbuffers/flatbuffer_builder.h | 1197 + .../include/flatbuffers/flatbuffers.h | 270 + new_ai_tools/include/flatbuffers/flatc.h | 111 + .../include/flatbuffers/flexbuffers.h | 1897 ++ new_ai_tools/include/flatbuffers/grpc.h | 300 + new_ai_tools/include/flatbuffers/hash.h | 127 + new_ai_tools/include/flatbuffers/idl.h | 1232 + .../include/flatbuffers/minireflect.h | 419 + .../include/flatbuffers/pch/flatc_pch.h | 39 + new_ai_tools/include/flatbuffers/pch/pch.h | 38 + new_ai_tools/include/flatbuffers/reflection.h | 502 + .../flatbuffers/reflection_generated.h | 1449 + new_ai_tools/include/flatbuffers/registry.h | 128 + .../include/flatbuffers/stl_emulation.h | 509 + new_ai_tools/include/flatbuffers/string.h | 64 + new_ai_tools/include/flatbuffers/struct.h | 53 + new_ai_tools/include/flatbuffers/table.h | 168 + new_ai_tools/include/flatbuffers/util.h | 690 + new_ai_tools/include/flatbuffers/vector.h | 370 + .../include/flatbuffers/vector_downward.h | 271 + new_ai_tools/include/flatbuffers/verifier.h | 283 + new_ai_tools/include/ioserver.h | 44 + .../include/lib_nn/api/TransposeConv.h | 24 + new_ai_tools/include/lib_nn/api/add_int16.h | 27 + .../include/lib_nn/api/add_int16_transform.h | 42 + .../include/lib_nn/api/dequantize_int16.h | 22 + .../lib_nn/api/dequantize_int16_transform.h | 34 + .../include/lib_nn/api/expand_8_to_16.h | 8 + .../include/lib_nn/api/multiply_int16.h | 42 + .../lib_nn/api/multiply_int16_transform.h | 71 + new_ai_tools/include/lib_nn/api/nn_api.h | 15 + new_ai_tools/include/lib_nn/api/nn_arch.h | 17 + .../include/lib_nn/api/nn_bin_types.h | 14 + new_ai_tools/include/lib_nn/api/nn_config.h | 287 + .../include/lib_nn/api/nn_conv2d_structs.h | 72 + new_ai_tools/include/lib_nn/api/nn_image.h | 26 + new_ai_tools/include/lib_nn/api/nn_layers.h | 311 + .../include/lib_nn/api/nn_op_helper.h | 138 + new_ai_tools/include/lib_nn/api/nn_op_utils.h | 153 + new_ai_tools/include/lib_nn/api/nn_operator.h | 18 + new_ai_tools/include/lib_nn/api/nn_pooling.h | 551 + new_ai_tools/include/lib_nn/api/nn_types.h | 83 + .../include/lib_nn/api/nn_window_params.h | 55 + .../lib_nn/api/output_transform_fn_int16.h | 54 + ...tput_transform_fn_int16_kernel_transform.h | 37 + .../api/output_transform_fn_int16_mappings.h | 13 + .../lib_nn/api/quadratic_approximation.h | 83 + .../lib_nn/api/quadratic_interpolation.h | 23 + .../include/lib_nn/api/quantize_int16.h | 22 + .../lib_nn/api/quantize_int16_transform.h | 33 + new_ai_tools/include/lib_nn/api/version.h | 13 + .../lib_nn/api/vpu_memmove_word_aligned.h | 15 + .../include/lib_nn/api/vpu_memset_256.h | 55 + new_ai_tools/include/lib_nn/api/vpu_sim.h | 119 + new_ai_tools/include/lib_nn/api/xs3_vpu.h | 216 + .../include/lib_nn/api/xs3a_registers.h | 2869 ++ .../include/lib_nn/src/asm/asm_constants.h | 41 + .../include/lib_nn/src/asm/window_op_plan.h | 25 + .../lib_tflite_micro/api/inference_engine.h | 217 + .../lib_tflite_micro/api/load_weights.h | 64 + .../api/memory_parallel_transport.h | 52 + .../include/lib_tflite_micro/api/version.h | 13 + .../lib_tflite_micro/api/xcore_config.h | 18 + .../api/xcore_device_memory.h | 62 + .../api/xcore_shared_config.h | 48 + .../src/tflite-xcore-kernels/conv2d_float.h | 155 + .../xcore_custom_options.h | 28 + .../xcore_error_reporter.h | 32 + .../tflite-xcore-kernels/xcore_interpreter.h | 49 + .../src/tflite-xcore-kernels/xcore_ops.h | 89 + .../src/tflite-xcore-kernels/xcore_profiler.h | 49 + .../src/tflite-xcore-kernels/xcore_utils.h | 156 + .../lib_tflite_micro/src/thread_call.h | 95 + .../lib_xud/lib_xud/api/legacy/usb_defs.h | 4 + .../lib_xud/lib_xud/api/legacy/usb_device.h | 4 + .../lib_xud/api/legacy/usb_std_descriptors.h | 4 + .../lib_xud/api/legacy/usb_std_requests.h | 4 + .../include/lib_xud/lib_xud/api/xud.h | 518 + .../lib_xud/lib_xud/api/xud_conf_default.h | 11 + .../include/lib_xud/lib_xud/api/xud_device.h | 87 + .../lib_xud/lib_xud/api/xud_std_descriptors.h | 191 + .../lib_xud/lib_xud/api/xud_std_requests.h | 120 + .../lib_xud/src/user/XUD_USB_Defines.h | 70 + .../lib_xud/lib_xud/src/user/class/hid.h | 23 + .../lib_xud/src/user/class/usbaudio10.h | 30 + .../lib_xud/src/user/class/usbaudio20.h | 357 + .../lib_xud/src/user/class/usbaudiocommon.h | 168 + .../delay_flexbuffers_generated_data.h | 25 + .../energy_flexbuffers_generated_data.h | 28 + .../kernels/fft_flexbuffers_generated_data.h | 37 + .../filter_bank_flexbuffers_generated_data.h | 25 + ...lter_bank_log_flexbuffers_generated_data.h | 27 + ...l_subtraction_flexbuffers_generated_data.h | 26 + .../framer_flexbuffers_generated_data.h | 25 + .../include/signal/micro/kernels/irfft.h | 31 + .../overlap_add_flexbuffers_generated_data.h | 25 + .../kernels/pcan_flexbuffers_generated_data.h | 7 + .../include/signal/micro/kernels/rfft.h | 31 + .../stacker_flexbuffers_generated_data.h | 25 + .../window_flexbuffers_generated_data.h | 25 + .../include/signal/src/circular_buffer.h | 118 + new_ai_tools/include/signal/src/complex.h | 29 + new_ai_tools/include/signal/src/energy.h | 38 + .../include/signal/src/fft_auto_scale.h | 35 + new_ai_tools/include/signal/src/filter_bank.h | 69 + .../include/signal/src/filter_bank_log.h | 38 + .../src/filter_bank_spectral_subtraction.h | 73 + .../signal/src/filter_bank_square_root.h | 34 + new_ai_tools/include/signal/src/irfft.h | 84 + .../src/kiss_fft_wrappers/kiss_fft_common.h | 49 + .../src/kiss_fft_wrappers/kiss_fft_float.h | 31 + .../src/kiss_fft_wrappers/kiss_fft_int16.h | 30 + .../src/kiss_fft_wrappers/kiss_fft_int32.h | 31 + new_ai_tools/include/signal/src/log.h | 30 + new_ai_tools/include/signal/src/max_abs.h | 31 + new_ai_tools/include/signal/src/msb.h | 32 + new_ai_tools/include/signal/src/overlap_add.h | 46 + .../include/signal/src/pcan_argc_fixed.h | 41 + new_ai_tools/include/signal/src/rfft.h | 85 + new_ai_tools/include/signal/src/square_root.h | 32 + new_ai_tools/include/signal/src/window.h | 31 + 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new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/test_helpers.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h create mode 100644 new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h create mode 100644 new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h create mode 100644 new_ai_tools/include/tensorflow/lite/schema/schema_generated.h create mode 100644 new_ai_tools/include/tensorflow/lite/schema/schema_utils.h create mode 100644 new_ai_tools/include/tile_ram_server.h create mode 100644 new_ai_tools/libxtflitemicro.cmake create mode 100644 new_ai_tools/libxtflitemicro_vx4a.a create mode 100644 new_ai_tools/vnr_model/trained_model_xcore.tflite create mode 100644 new_ai_tools/vnr_model/trained_model_xcore.tflite.cpp create mode 100644 new_ai_tools/vnr_model/trained_model_xcore.tflite.h diff --git a/Jenkinsfile b/Jenkinsfile index 323aa5df..f697d541 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -80,6 +80,9 @@ pipeline { dir("app_aec") { xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } + dir("app_vnr") { + xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } } } } diff --git a/examples/app_vnr/CMakeLists.txt b/examples/app_vnr/CMakeLists.txt index 4943c68a..96e41c73 100644 --- a/examples/app_vnr/CMakeLists.txt +++ b/examples/app_vnr/CMakeLists.txt @@ -2,7 +2,11 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(app_vnr) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() if(NOT BUILD_NATIVE) set(APP_COMPILER_FLAGS -report) diff --git a/lib_voice/lib_build_info.cmake b/lib_voice/lib_build_info.cmake index e31e148b..42ae4774 100644 --- a/lib_voice/lib_build_info.cmake +++ b/lib_voice/lib_build_info.cmake @@ -5,6 +5,7 @@ set(LIB_DEPENDENT_MODULES "lib_xcore_math(lib_voice_fixes)") set(LIB_COMPILER_FLAGS -g -Os + -D__VX4A__ -DHEADROOM_CHECK=0) # if(BUILD_NATIVE) @@ -16,13 +17,14 @@ set(LIB_COMPILER_FLAGS set(LIB_CXX_SRCS "") set(lib_ASM_SRCS "") -# include(${CMAKE_CURRENT_LIST_DIR}/vnr_model.cmake) -# file(RELATIVE_PATH MODEL_OUT_DIR_REL ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_DIR}) +include(${CMAKE_CURRENT_LIST_DIR}/vnr_model.cmake) +file(RELATIVE_PATH MODEL_OUT_DIR_REL ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_DIR}) file(GLOB_RECURSE LIB_C_SRCS RELATIVE ${CMAKE_CURRENT_LIST_DIR} "${CMAKE_CURRENT_LIST_DIR}/src/aec/*.c" "${CMAKE_CURRENT_LIST_DIR}/src/agc/*.c" "${CMAKE_CURRENT_LIST_DIR}/src/ns/*.c" + "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.c" ) set(LIB_INCLUDES @@ -36,15 +38,15 @@ set(LIB_INCLUDES api/ns src/ns # api/stage1 - # api/vnr - # src/vnr - # ${MODEL_OUT_DIR_REL} + api/vnr + src/vnr + ${MODEL_OUT_DIR_REL} ) -# file(GLOB VNR_CXX_SOURCES RELATIVE ${CMAKE_CURRENT_LIST_DIR} CONFIGURE_DEPENDS "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.cpp") -# file(RELATIVE_PATH VNR_MODEL_SOURCES ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_PATH}.cpp) +file(GLOB VNR_CXX_SOURCES RELATIVE ${CMAKE_CURRENT_LIST_DIR} CONFIGURE_DEPENDS "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.cpp") +file(RELATIVE_PATH VNR_MODEL_SOURCES ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_PATH}.cpp) -# list(APPEND LIB_CXX_SRCS ${VNR_MODEL_SOURCES} ${VNR_CXX_SOURCES}) +list(APPEND LIB_CXX_SRCS ${VNR_MODEL_SOURCES} ${VNR_CXX_SOURCES}) XMOS_REGISTER_MODULE() @@ -56,7 +58,7 @@ XMOS_REGISTER_MODULE() # list(LENGTH single_config_sched singleconfig_list_len) # list(LENGTH multi_config_sched multi_config_list_len) -# foreach(target ${APP_BUILD_TARGETS}) +foreach(target ${APP_BUILD_TARGETS}) # # Apply AEC schedule if needed # if(singleconfig_list_len EQUAL 1) # App only does set(AEC_SCHEDULE_CONFIG ). Attach this schedule to all targets # list(GET single_config_sched 0 sched) @@ -75,9 +77,9 @@ XMOS_REGISTER_MODULE() # endforeach() # endif() -# # Link aitools with the targets -# target_link_libraries(${target} PRIVATE tflite_micro) + # Link aitools with the targets + target_link_libraries(${target} PRIVATE tflite_micro) # if(BUILD_NATIVE) # target_compile_features(${target} PRIVATE cxx_std_11) # endif() -# endforeach() +endforeach() diff --git a/lib_voice/vnr_model.cmake b/lib_voice/vnr_model.cmake index ff9e2598..a2436398 100644 --- a/lib_voice/vnr_model.cmake +++ b/lib_voice/vnr_model.cmake @@ -1,25 +1,26 @@ -set(CMD "\ -import os; \ -import xmos_ai_tools.runtime as rt; \ -print(os.path.dirname(rt.__file__)) \ -") +# set(CMD "\ +# import os; \ +# import xmos_ai_tools.runtime as rt; \ +# print(os.path.dirname(rt.__file__)) \ +# ") -execute_process( - COMMAND python -c "${CMD}" - WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR} - OUTPUT_VARIABLE XMOS_AITOOLSLIB_PATH - OUTPUT_STRIP_TRAILING_WHITESPACE -) +# execute_process( +# COMMAND python -c "${CMD}" +# WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR} +# OUTPUT_VARIABLE XMOS_AITOOLSLIB_PATH +# OUTPUT_STRIP_TRAILING_WHITESPACE +# ) # Add tflite_micro -set(XMOS_AITOOLSLIB_PATH_CMAKE "${XMOS_AITOOLSLIB_PATH}/buildfiles/aitoolslib.cmake") +# set(XMOS_AITOOLSLIB_PATH_CMAKE "${XMOS_AITOOLSLIB_PATH}/buildfiles/aitoolslib.cmake") +set(XMOS_AITOOLSLIB_PATH_CMAKE "${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/libxtflitemicro.cmake") -if(XMOS_AITOOLSLIB_PATH STREQUAL "") - message(FATAL_ERROR "Path to XMOS AI tools NOT found") -elseif(NOT EXISTS ${XMOS_AITOOLSLIB_PATH_CMAKE}) - message(FATAL_ERROR "Cmake file 'aitoolslib.cmake' NOT found in this path") -else() +# if(XMOS_AITOOLSLIB_PATH STREQUAL "") + # message(FATAL_ERROR "Path to XMOS AI tools NOT found") +# elseif(NOT EXISTS ${XMOS_AITOOLSLIB_PATH_CMAKE}) + # message(FATAL_ERROR "Cmake file 'aitoolslib.cmake' NOT found in this path") +# else() message(STATUS "Found python package xmos-ai-tools at: ${XMOS_AITOOLSLIB_PATH}") include(${XMOS_AITOOLSLIB_PATH_CMAKE}) if(NOT TARGET tflite_micro) @@ -31,19 +32,20 @@ else() IMPORTED_LOCATION ${XMOS_AITOOLSLIB_LIBRARIES} INTERFACE_INCLUDE_DIRECTORIES ${XMOS_AITOOLSLIB_INCLUDES}) endif() -endif() +# endif() ## Export model -set(MODEL_OUT_DIR ${CMAKE_CURRENT_BINARY_DIR}/src.autogen/vnr_model/) -set(MODEL_IN_PATH ${CMAKE_CURRENT_LIST_DIR}/src/vnr/model/trained_model.tflite) +# set(MODEL_OUT_DIR ${CMAKE_CURRENT_BINARY_DIR}/src.autogen/vnr_model/) +set(MODEL_OUT_DIR ${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/vnr_model/) +# set(MODEL_IN_PATH ${CMAKE_CURRENT_LIST_DIR}/src/vnr/model/trained_model.tflite) set(MODEL_OUT_PATH ${MODEL_OUT_DIR}/trained_model_xcore.tflite) -set(MODEL_N_CORES 1) -set(MODEL_TH 0.50) +# set(MODEL_N_CORES 1) +# set(MODEL_TH 0.50) file(MAKE_DIRECTORY ${MODEL_OUT_DIR}) -add_custom_command( - OUTPUT ${MODEL_OUT_PATH}.cpp ${MODEL_OUT_PATH}.h ${MODEL_OUT_PATH} - COMMAND xcore-opt ${MODEL_IN_PATH} -tc ${MODEL_N_CORES} -o ${MODEL_OUT_PATH} --xcore-conv-err-threshold ${MODEL_TH} - DEPENDS ${MODEL_IN_PATH} -) +# add_custom_command( +# OUTPUT ${MODEL_OUT_PATH}.cpp ${MODEL_OUT_PATH}.h ${MODEL_OUT_PATH} +# COMMAND xcore-opt ${MODEL_IN_PATH} -tc ${MODEL_N_CORES} -o ${MODEL_OUT_PATH} --xcore-conv-err-threshold ${MODEL_TH} --xcore-target-arch=VX4A +# DEPENDS ${MODEL_IN_PATH} +# ) diff --git a/new_ai_tools/include/fast_flash.h b/new_ai_tools/include/fast_flash.h new file mode 100644 index 00000000..1815b7da --- /dev/null +++ b/new_ai_tools/include/fast_flash.h @@ -0,0 +1,65 @@ +#ifndef _FAST_FLASH_H_ +#define _FAST_FLASH_H_ + +#ifdef __XC__ +#define chanend_t chanend +#else +#include +#endif + +#include + +// typedef struct { +// unsigned qspiCS; +// unsigned qspiSCLK; +// unsigned qspiSIO; +// unsigned qspiClkblk; +// } fl_QSPIPorts; + +// typedef struct { +// unsigned int byte_address; // Address in flash +// unsigned int byte_count; // Number of bytes to read/write +// } fl_QuadDeviceSpec; + +/** Fast flash library. + * Before calling any of the functions in here, lib_quad_flash must be initialised as normal by using + * fl_connectToDevice(qspi, flash_spec, n_flash_spec). + * After that, a call to fast_flash_init shall be made. + * After that, a sequence of calls to fast_flash_read can be made. + * + * The data partition must start with the following 32 bytes: **NOTE: REMOVE THE +4 in fast_flash_init** + * + * 0xff, 0x00, 0x0f, 0x0f, 0x0f, 0x0f, 0x0f, 0x0f, + * 0xff, 0x00, 0xff, 0x00, 0xff, 0x00, 0xff, 0x00, + * 0x31, 0xf7, 0xce, 0x08, 0x31, 0xf7, 0xce, 0x08, + * 0x9c, 0x63, 0x9c, 0x63, 0x9c, 0x63, 0x9c, 0x63 + * + * This pattern is designed to create maximum difficulties electrically and is used + * to calibrate the electrical settings. Note that this pattern must be nibble reversed + * before being written to flash; just like all other data. + * The rest of the data partition can be used as normal + */ + +/** Function that initialises the fast_flash library + * + * \param qspi ports that connect to flash + * + * \returns a negative value of -1..-5 if the window is too small (size 0..4) + * zero if successful + */ +int fast_flash_init(fl_QSPIPorts *qspi); + +/** Function that reads a sequential set of bytes from memory. + * This function assumes that nibbles have been reversed ((x << 4) & 0xf0 | (x >> 4) & 0x0f) + * before the data was written to flash. + * Note that reading 32 bytes from offset 0 shall yield the special pattern above. + * + * \param qspi ports that connect to flash + * \param addr address in flash data segment + * \param word_count Number of words to read + * \param read_data array to store data in to. + * \param c_out_data optional channel end over which data is out() instead. + */ +void fast_flash_read(fl_QSPIPorts *qspi, unsigned addr, unsigned word_count, unsigned read_data[], chanend_t c_data_out); + +#endif diff --git a/new_ai_tools/include/flash_server.h b/new_ai_tools/include/flash_server.h new file mode 100644 index 00000000..37abb1df --- /dev/null +++ b/new_ai_tools/include/flash_server.h @@ -0,0 +1,79 @@ +#ifndef _flash_server_h_ +#define _flash_server_h_ + +#include "fast_flash.h" + +#ifdef __cplusplus +#define EXTERN_C extern "C" +#else +#define EXTERN_C +#endif + +/** Struct holding the "file system" meta information for each client + * The flash is partitioned and each client has a section in the flash + * that stores data relevant to that particular client. For example, models + * parameters, code, etc. + * + * This struct caches all information necessary for a client for fast access. + * The main program must allocate this structure, one per client, prior to + * calling the flash server. + * + * If there is more than one flash device connected to the device, there can be + * multiple flash servers. + */ +typedef struct flash { + int model_start; ///< Start address for model. + int parameters_start; ///< Start address of parameters. + int operators_start; ///< Start address for operator-binaries. + int execute_in_place_start; ///< Start address for operator-binaries. +} flash_t; + +/** Type representing the commands that the flash server accepts */ +typedef enum flash_command { + FLASH_READ_PARAMETERS = + 0, ///< Read a set of parameters. // TODO: share with lib_tflite_micro + FLASH_READ_PARAMETERS_ASYNC = 1, ///< Read parameters asynchronously. + FLASH_READ_SYNCHRONIZE = 2, ///< Complete async read. + FLASH_READ_XIP = + 3, ///< Read code to execute-in-place throught L2 cache - future extension + FLASH_SERVER_QUIT = 4, + FLASH_SERVER_INIT = 5, // Initialize flash server with fast flash pattern speed match setup + //FLASH_READ_PARAMETERS_COMPRESSED_FLOAT = 6, // Read a set of compressed parameters +} flash_command_t; + +/** + * Function that runs a flash-server. A flash server is a thread that serves one + * or more clients. There is one flash server per flash-device, and the server + * can serve clients on one or more tiles. + * + * The flash server takes the following commands: + * - Read a whole model from the flash. + * - Read some parameters from the flash + * - (future extension) Read code for an operator from flash + * + * This function does, at present, never return. It could be made to return if + * all clients close their connection + * + * \param c_flash_clients Array of channels; one per client. + * Each client is served in turn + * \param headers Space to store a header for each client + * The header for the client describes the local + * "filesystem" for that client \param n_flash_clients Number of clients. The + * arrays in the first and second parameters should have this many entries + * \param qspi Structure holding the quad-flash ports. This contains + * three Ports and a clock-block, the CS_N port, the CLK port, the DATA port and + * a clock block to be used for the flash. \param flash_spec Array holding + * specificiations of flash devices, as per the libquadflash documentation + * \param n_flash_spec Number of elements in the spec array. + */ +#ifdef __XC__ +void flash_server(chanend c_flash_clients[], flash_t headers[], + int n_flash_clients, fl_QSPIPorts &qspi, + fl_QuadDeviceSpec flash_spec[], int n_flash_spec); +#else +EXTERN_C void flash_server(chanend_t *c_flash_clients, flash_t *headers, + int n_flash_clients, fl_QSPIPorts *qspi, + fl_QuadDeviceSpec *flash_spec, int n_flash_spec); +#endif + +#endif diff --git a/new_ai_tools/include/flatbuffers/allocator.h b/new_ai_tools/include/flatbuffers/allocator.h new file mode 100644 index 00000000..f4ef22db --- /dev/null +++ b/new_ai_tools/include/flatbuffers/allocator.h @@ -0,0 +1,68 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_ALLOCATOR_H_ +#define FLATBUFFERS_ALLOCATOR_H_ + +#include "flatbuffers/base.h" + +namespace flatbuffers { + +// Allocator interface. This is flatbuffers-specific and meant only for +// `vector_downward` usage. +class Allocator { + public: + virtual ~Allocator() {} + + // Allocate `size` bytes of memory. + virtual uint8_t *allocate(size_t size) = 0; + + // Deallocate `size` bytes of memory at `p` allocated by this allocator. + virtual void deallocate(uint8_t *p, size_t size) = 0; + + // Reallocate `new_size` bytes of memory, replacing the old region of size + // `old_size` at `p`. In contrast to a normal realloc, this grows downwards, + // and is intended specifcally for `vector_downward` use. + // `in_use_back` and `in_use_front` indicate how much of `old_size` is + // actually in use at each end, and needs to be copied. + virtual uint8_t *reallocate_downward(uint8_t *old_p, size_t old_size, + size_t new_size, size_t in_use_back, + size_t in_use_front) { + FLATBUFFERS_ASSERT(new_size > old_size); // vector_downward only grows + uint8_t *new_p = allocate(new_size); + memcpy_downward(old_p, old_size, new_p, new_size, in_use_back, + in_use_front); + deallocate(old_p, old_size); + return new_p; + } + + protected: + // Called by `reallocate_downward` to copy memory from `old_p` of `old_size` + // to `new_p` of `new_size`. Only memory of size `in_use_front` and + // `in_use_back` will be copied from the front and back of the old memory + // allocation. + void memcpy_downward(uint8_t *old_p, size_t old_size, uint8_t *new_p, + size_t new_size, size_t in_use_back, + size_t in_use_front) { + memcpy(new_p + new_size - in_use_back, old_p + old_size - in_use_back, + in_use_back); + memcpy(new_p, old_p, in_use_front); + } +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_ALLOCATOR_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/array.h b/new_ai_tools/include/flatbuffers/array.h new file mode 100644 index 00000000..d4b73fc9 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/array.h @@ -0,0 +1,243 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_ARRAY_H_ +#define FLATBUFFERS_ARRAY_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/stl_emulation.h" +#include "flatbuffers/vector.h" + +namespace flatbuffers { + +// This is used as a helper type for accessing arrays. +template class Array { + // Array can carry only POD data types (scalars or structs). + typedef typename flatbuffers::bool_constant::value> + scalar_tag; + typedef + typename flatbuffers::conditional::type + IndirectHelperType; + + public: + typedef uint16_t size_type; + typedef typename IndirectHelper::return_type return_type; + typedef VectorIterator const_iterator; + typedef VectorReverseIterator const_reverse_iterator; + + // If T is a LE-scalar or a struct (!scalar_tag::value). + static FLATBUFFERS_CONSTEXPR bool is_span_observable = + (scalar_tag::value && (FLATBUFFERS_LITTLEENDIAN || sizeof(T) == 1)) || + !scalar_tag::value; + + FLATBUFFERS_CONSTEXPR uint16_t size() const { return length; } + + return_type Get(uoffset_t i) const { + FLATBUFFERS_ASSERT(i < size()); + return IndirectHelper::Read(Data(), i); + } + + return_type operator[](uoffset_t i) const { return Get(i); } + + // If this is a Vector of enums, T will be its storage type, not the enum + // type. This function makes it convenient to retrieve value with enum + // type E. + template E GetEnum(uoffset_t i) const { + return static_cast(Get(i)); + } + + const_iterator begin() const { return const_iterator(Data(), 0); } + const_iterator end() const { return const_iterator(Data(), size()); } + + const_reverse_iterator rbegin() const { + return const_reverse_iterator(end()); + } + const_reverse_iterator rend() const { + return const_reverse_iterator(begin()); + } + + const_iterator cbegin() const { return begin(); } + const_iterator cend() const { return end(); } + + const_reverse_iterator crbegin() const { return rbegin(); } + const_reverse_iterator crend() const { return rend(); } + + // Get a mutable pointer to elements inside this array. + // This method used to mutate arrays of structs followed by a @p Mutate + // operation. For primitive types use @p Mutate directly. + // @warning Assignments and reads to/from the dereferenced pointer are not + // automatically converted to the correct endianness. + typename flatbuffers::conditional::type + GetMutablePointer(uoffset_t i) const { + FLATBUFFERS_ASSERT(i < size()); + return const_cast(&data()[i]); + } + + // Change elements if you have a non-const pointer to this object. + void Mutate(uoffset_t i, const T &val) { MutateImpl(scalar_tag(), i, val); } + + // The raw data in little endian format. Use with care. + const uint8_t *Data() const { return data_; } + + uint8_t *Data() { return data_; } + + // Similarly, but typed, much like std::vector::data + const T *data() const { return reinterpret_cast(Data()); } + T *data() { return reinterpret_cast(Data()); } + + // Copy data from a span with endian conversion. + // If this Array and the span overlap, the behavior is undefined. + void CopyFromSpan(flatbuffers::span src) { + const auto p1 = reinterpret_cast(src.data()); + const auto p2 = Data(); + FLATBUFFERS_ASSERT(!(p1 >= p2 && p1 < (p2 + length)) && + !(p2 >= p1 && p2 < (p1 + length))); + (void)p1; + (void)p2; + CopyFromSpanImpl(flatbuffers::bool_constant(), src); + } + + protected: + void MutateImpl(flatbuffers::true_type, uoffset_t i, const T &val) { + FLATBUFFERS_ASSERT(i < size()); + WriteScalar(data() + i, val); + } + + void MutateImpl(flatbuffers::false_type, uoffset_t i, const T &val) { + *(GetMutablePointer(i)) = val; + } + + void CopyFromSpanImpl(flatbuffers::true_type, + flatbuffers::span src) { + // Use std::memcpy() instead of std::copy() to avoid performance degradation + // due to aliasing if T is char or unsigned char. + // The size is known at compile time, so memcpy would be inlined. + std::memcpy(data(), src.data(), length * sizeof(T)); + } + + // Copy data from flatbuffers::span with endian conversion. + void CopyFromSpanImpl(flatbuffers::false_type, + flatbuffers::span src) { + for (size_type k = 0; k < length; k++) { Mutate(k, src[k]); } + } + + // This class is only used to access pre-existing data. Don't ever + // try to construct these manually. + // 'constexpr' allows us to use 'size()' at compile time. + // @note Must not use 'FLATBUFFERS_CONSTEXPR' here, as const is not allowed on + // a constructor. +#if defined(__cpp_constexpr) + constexpr Array(); +#else + Array(); +#endif + + uint8_t data_[length * sizeof(T)]; + + private: + // This class is a pointer. Copying will therefore create an invalid object. + // Private and unimplemented copy constructor. + Array(const Array &); + Array &operator=(const Array &); +}; + +// Specialization for Array[struct] with access using Offset pointer. +// This specialization used by idl_gen_text.cpp. +template class Array, length> { + static_assert(flatbuffers::is_same::value, "unexpected type T"); + + public: + typedef const void *return_type; + + const uint8_t *Data() const { return data_; } + + // Make idl_gen_text.cpp::PrintContainer happy. + return_type operator[](uoffset_t) const { + FLATBUFFERS_ASSERT(false); + return nullptr; + } + + private: + // This class is only used to access pre-existing data. + Array(); + Array(const Array &); + Array &operator=(const Array &); + + uint8_t data_[1]; +}; + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span(Array &arr) + FLATBUFFERS_NOEXCEPT { + static_assert( + Array::is_span_observable, + "wrong type U, only plain struct, LE-scalar, or byte types are allowed"); + return span(arr.data(), N); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span( + const Array &arr) FLATBUFFERS_NOEXCEPT { + static_assert( + Array::is_span_observable, + "wrong type U, only plain struct, LE-scalar, or byte types are allowed"); + return span(arr.data(), N); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span +make_bytes_span(Array &arr) FLATBUFFERS_NOEXCEPT { + static_assert(Array::is_span_observable, + "internal error, Array might hold only scalars or structs"); + return span(arr.Data(), sizeof(U) * N); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span +make_bytes_span(const Array &arr) FLATBUFFERS_NOEXCEPT { + static_assert(Array::is_span_observable, + "internal error, Array might hold only scalars or structs"); + return span(arr.Data(), sizeof(U) * N); +} + +// Cast a raw T[length] to a raw flatbuffers::Array +// without endian conversion. Use with care. +// TODO: move these Cast-methods to `internal` namespace. +template +Array &CastToArray(T (&arr)[length]) { + return *reinterpret_cast *>(arr); +} + +template +const Array &CastToArray(const T (&arr)[length]) { + return *reinterpret_cast *>(arr); +} + +template +Array &CastToArrayOfEnum(T (&arr)[length]) { + static_assert(sizeof(E) == sizeof(T), "invalid enum type E"); + return *reinterpret_cast *>(arr); +} + +template +const Array &CastToArrayOfEnum(const T (&arr)[length]) { + static_assert(sizeof(E) == sizeof(T), "invalid enum type E"); + return *reinterpret_cast *>(arr); +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_ARRAY_H_ diff --git a/new_ai_tools/include/flatbuffers/base.h b/new_ai_tools/include/flatbuffers/base.h new file mode 100644 index 00000000..458ac3f7 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/base.h @@ -0,0 +1,474 @@ +#ifndef FLATBUFFERS_BASE_H_ +#define FLATBUFFERS_BASE_H_ + +// clang-format off + +// If activate should be declared and included first. +#if defined(FLATBUFFERS_MEMORY_LEAK_TRACKING) && \ + defined(_MSC_VER) && defined(_DEBUG) + // The _CRTDBG_MAP_ALLOC inside will replace + // calloc/free (etc) to its debug version using #define directives. + #define _CRTDBG_MAP_ALLOC + #include + #include + // Replace operator new by trace-enabled version. + #define DEBUG_NEW new(_NORMAL_BLOCK, __FILE__, __LINE__) + #define new DEBUG_NEW +#endif + +#if !defined(FLATBUFFERS_ASSERT) +#include +#define FLATBUFFERS_ASSERT assert +#elif defined(FLATBUFFERS_ASSERT_INCLUDE) +// Include file with forward declaration +#include FLATBUFFERS_ASSERT_INCLUDE +#endif + +#ifndef ARDUINO +#include +#endif + +#include +#include +#include + +#if defined(ARDUINO) && !defined(ARDUINOSTL_M_H) + #include +#else + #include +#endif + +#include +#include +#include +#include +#include +#include +#include + +#if defined(__unix__) && !defined(FLATBUFFERS_LOCALE_INDEPENDENT) + #include +#endif + +#ifdef __ANDROID__ + #include +#endif + +#if defined(__ICCARM__) +#include +#endif + +// Note the __clang__ check is needed, because clang presents itself +// as an older GNUC compiler (4.2). +// Clang 3.3 and later implement all of the ISO C++ 2011 standard. +// Clang 3.4 and later implement all of the ISO C++ 2014 standard. +// http://clang.llvm.org/cxx_status.html + +// Note the MSVC value '__cplusplus' may be incorrect: +// The '__cplusplus' predefined macro in the MSVC stuck at the value 199711L, +// indicating (erroneously!) that the compiler conformed to the C++98 Standard. +// This value should be correct starting from MSVC2017-15.7-Preview-3. +// The '__cplusplus' will be valid only if MSVC2017-15.7-P3 and the `/Zc:__cplusplus` switch is set. +// Workaround (for details see MSDN): +// Use the _MSC_VER and _MSVC_LANG definition instead of the __cplusplus for compatibility. +// The _MSVC_LANG macro reports the Standard version regardless of the '/Zc:__cplusplus' switch. + +#if defined(__GNUC__) && !defined(__clang__) + #define FLATBUFFERS_GCC (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) +#else + #define FLATBUFFERS_GCC 0 +#endif + +#if defined(__clang__) + #define FLATBUFFERS_CLANG (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) +#else + #define FLATBUFFERS_CLANG 0 +#endif + +/// @cond FLATBUFFERS_INTERNAL +#if __cplusplus <= 199711L && \ + (!defined(_MSC_VER) || _MSC_VER < 1600) && \ + (!defined(__GNUC__) || \ + (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40400)) + #error A C++11 compatible compiler with support for the auto typing is \ + required for FlatBuffers. + #error __cplusplus _MSC_VER __GNUC__ __GNUC_MINOR__ __GNUC_PATCHLEVEL__ +#endif + +#if !defined(__clang__) && \ + defined(__GNUC__) && \ + (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40600) + // Backwards compatibility for g++ 4.4, and 4.5 which don't have the nullptr + // and constexpr keywords. Note the __clang__ check is needed, because clang + // presents itself as an older GNUC compiler. + #ifndef nullptr_t + const class nullptr_t { + public: + template inline operator T*() const { return 0; } + private: + void operator&() const; + } nullptr = {}; + #endif + #ifndef constexpr + #define constexpr const + #endif +#endif + +// The wire format uses a little endian encoding (since that's efficient for +// the common platforms). +#if defined(__s390x__) + #define FLATBUFFERS_LITTLEENDIAN 0 +#endif // __s390x__ +#if !defined(FLATBUFFERS_LITTLEENDIAN) + #if defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__) + #if (defined(__BIG_ENDIAN__) || \ + (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__)) + #define FLATBUFFERS_LITTLEENDIAN 0 + #else + #define FLATBUFFERS_LITTLEENDIAN 1 + #endif // __BIG_ENDIAN__ + #elif defined(_MSC_VER) + #if defined(_M_PPC) + #define FLATBUFFERS_LITTLEENDIAN 0 + #else + #define FLATBUFFERS_LITTLEENDIAN 1 + #endif + #else + #error Unable to determine endianness, define FLATBUFFERS_LITTLEENDIAN. + #endif +#endif // !defined(FLATBUFFERS_LITTLEENDIAN) + +#define FLATBUFFERS_VERSION_MAJOR 2 +#define FLATBUFFERS_VERSION_MINOR 0 +#define FLATBUFFERS_VERSION_REVISION 6 +#define FLATBUFFERS_STRING_EXPAND(X) #X +#define FLATBUFFERS_STRING(X) FLATBUFFERS_STRING_EXPAND(X) +namespace flatbuffers { + // Returns version as string "MAJOR.MINOR.REVISION". + const char* FLATBUFFERS_VERSION(); +} + +#if (!defined(_MSC_VER) || _MSC_VER > 1600) && \ + (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 407)) || \ + defined(__clang__) + #define FLATBUFFERS_FINAL_CLASS final + #define FLATBUFFERS_OVERRIDE override + #define FLATBUFFERS_EXPLICIT_CPP11 explicit + #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE : flatbuffers::voffset_t +#else + #define FLATBUFFERS_FINAL_CLASS + #define FLATBUFFERS_OVERRIDE + #define FLATBUFFERS_EXPLICIT_CPP11 + #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE +#endif + +#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \ + (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \ + (defined(__cpp_constexpr) && __cpp_constexpr >= 200704) + #define FLATBUFFERS_CONSTEXPR constexpr + #define FLATBUFFERS_CONSTEXPR_CPP11 constexpr + #define FLATBUFFERS_CONSTEXPR_DEFINED +#else + #define FLATBUFFERS_CONSTEXPR const + #define FLATBUFFERS_CONSTEXPR_CPP11 +#endif + +#if (defined(__cplusplus) && __cplusplus >= 201402L) || \ + (defined(__cpp_constexpr) && __cpp_constexpr >= 201304) + #define FLATBUFFERS_CONSTEXPR_CPP14 FLATBUFFERS_CONSTEXPR_CPP11 +#else + #define FLATBUFFERS_CONSTEXPR_CPP14 +#endif + +#if (defined(__GXX_EXPERIMENTAL_CXX0X__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \ + (defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 190023026)) || \ + defined(__clang__) + #define FLATBUFFERS_NOEXCEPT noexcept +#else + #define FLATBUFFERS_NOEXCEPT +#endif + +// NOTE: the FLATBUFFERS_DELETE_FUNC macro may change the access mode to +// private, so be sure to put it at the end or reset access mode explicitly. +#if (!defined(_MSC_VER) || _MSC_FULL_VER >= 180020827) && \ + (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 404)) || \ + defined(__clang__) + #define FLATBUFFERS_DELETE_FUNC(func) func = delete +#else + #define FLATBUFFERS_DELETE_FUNC(func) private: func +#endif + +#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \ + (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \ + defined(__clang__) + #define FLATBUFFERS_DEFAULT_DECLARATION +#endif + +// Check if we can use template aliases +// Not possible if Microsoft Compiler before 2012 +// Possible is the language feature __cpp_alias_templates is defined well +// Or possible if the C++ std is C+11 or newer +#if (defined(_MSC_VER) && _MSC_VER > 1700 /* MSVC2012 */) \ + || (defined(__cpp_alias_templates) && __cpp_alias_templates >= 200704) \ + || (defined(__cplusplus) && __cplusplus >= 201103L) + #define FLATBUFFERS_TEMPLATES_ALIASES +#endif + +#ifndef FLATBUFFERS_HAS_STRING_VIEW + // Only provide flatbuffers::string_view if __has_include can be used + // to detect a header that provides an implementation + #if defined(__has_include) + // Check for std::string_view (in c++17) + #if __has_include() && (__cplusplus >= 201606 || (defined(_HAS_CXX17) && _HAS_CXX17)) + #include + namespace flatbuffers { + typedef std::string_view string_view; + } + #define FLATBUFFERS_HAS_STRING_VIEW 1 + // Check for std::experimental::string_view (in c++14, compiler-dependent) + #elif __has_include() && (__cplusplus >= 201411) + #include + namespace flatbuffers { + typedef std::experimental::string_view string_view; + } + #define FLATBUFFERS_HAS_STRING_VIEW 1 + // Check for absl::string_view + #elif __has_include("absl/strings/string_view.h") + #include "absl/strings/string_view.h" + namespace flatbuffers { + typedef absl::string_view string_view; + } + #define FLATBUFFERS_HAS_STRING_VIEW 1 + #endif + #endif // __has_include +#endif // !FLATBUFFERS_HAS_STRING_VIEW + +#ifndef FLATBUFFERS_GENERAL_HEAP_ALLOC_OK + // Allow heap allocations to be used + #define FLATBUFFERS_GENERAL_HEAP_ALLOC_OK 1 +#endif // !FLATBUFFERS_GENERAL_HEAP_ALLOC_OK + +#ifndef FLATBUFFERS_HAS_NEW_STRTOD + // Modern (C++11) strtod and strtof functions are available for use. + // 1) nan/inf strings as argument of strtod; + // 2) hex-float as argument of strtod/strtof. + #if (defined(_MSC_VER) && _MSC_VER >= 1900) || \ + (defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \ + (defined(__clang__)) + #define FLATBUFFERS_HAS_NEW_STRTOD 1 + #endif +#endif // !FLATBUFFERS_HAS_NEW_STRTOD + +#ifndef FLATBUFFERS_LOCALE_INDEPENDENT + // Enable locale independent functions {strtof_l, strtod_l,strtoll_l, strtoull_l}. + #if ((defined(_MSC_VER) && _MSC_VER >= 1800) || \ + (defined(_XOPEN_VERSION) && (_XOPEN_VERSION>=700)) && (!defined(__ANDROID_API__) || (defined(__ANDROID_API__) && (__ANDROID_API__>=21)))) + #define FLATBUFFERS_LOCALE_INDEPENDENT 1 + #else + #define FLATBUFFERS_LOCALE_INDEPENDENT 0 + #endif +#endif // !FLATBUFFERS_LOCALE_INDEPENDENT + +// Suppress Undefined Behavior Sanitizer (recoverable only). Usage: +// - __supress_ubsan__("undefined") +// - __supress_ubsan__("signed-integer-overflow") +#if defined(__clang__) && (__clang_major__ > 3 || (__clang_major__ == 3 && __clang_minor__ >=7)) + #define __supress_ubsan__(type) __attribute__((no_sanitize(type))) +#elif defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409) + #define __supress_ubsan__(type) __attribute__((no_sanitize_undefined)) +#else + #define __supress_ubsan__(type) +#endif + +// This is constexpr function used for checking compile-time constants. +// Avoid `#pragma warning(disable: 4127) // C4127: expression is constant`. +template FLATBUFFERS_CONSTEXPR inline bool IsConstTrue(T t) { + return !!t; +} + +// Enable C++ attribute [[]] if std:c++17 or higher. +#if ((__cplusplus >= 201703L) \ + || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L))) + // All attributes unknown to an implementation are ignored without causing an error. + #define FLATBUFFERS_ATTRIBUTE(attr) attr + + #define FLATBUFFERS_FALLTHROUGH() [[fallthrough]] +#else + #define FLATBUFFERS_ATTRIBUTE(attr) + + #if FLATBUFFERS_CLANG >= 30800 + #define FLATBUFFERS_FALLTHROUGH() [[clang::fallthrough]] + #elif FLATBUFFERS_GCC >= 70300 + #define FLATBUFFERS_FALLTHROUGH() [[gnu::fallthrough]] + #else + #define FLATBUFFERS_FALLTHROUGH() + #endif +#endif + +/// @endcond + +/// @file +namespace flatbuffers { + +/// @cond FLATBUFFERS_INTERNAL +// Our default offset / size type, 32bit on purpose on 64bit systems. +// Also, using a consistent offset type maintains compatibility of serialized +// offset values between 32bit and 64bit systems. +typedef uint32_t uoffset_t; + +// Signed offsets for references that can go in both directions. +typedef int32_t soffset_t; + +// Offset/index used in v-tables, can be changed to uint8_t in +// format forks to save a bit of space if desired. +typedef uint16_t voffset_t; + +typedef uintmax_t largest_scalar_t; + +// In 32bits, this evaluates to 2GB - 1 +#define FLATBUFFERS_MAX_BUFFER_SIZE ((1ULL << (sizeof(::flatbuffers::soffset_t) * 8 - 1)) - 1) + +// We support aligning the contents of buffers up to this size. +#define FLATBUFFERS_MAX_ALIGNMENT 16 + +/// @brief The length of a FlatBuffer file header. +static const size_t kFileIdentifierLength = 4; + +inline bool VerifyAlignmentRequirements(size_t align, size_t min_align = 1) { + return (min_align <= align) && (align <= (FLATBUFFERS_MAX_ALIGNMENT)) && + (align & (align - 1)) == 0; // must be power of 2 +} + +#if defined(_MSC_VER) + #pragma warning(disable: 4351) // C4351: new behavior: elements of array ... will be default initialized + #pragma warning(push) + #pragma warning(disable: 4127) // C4127: conditional expression is constant +#endif + +template T EndianSwap(T t) { + #if defined(_MSC_VER) + #define FLATBUFFERS_BYTESWAP16 _byteswap_ushort + #define FLATBUFFERS_BYTESWAP32 _byteswap_ulong + #define FLATBUFFERS_BYTESWAP64 _byteswap_uint64 + #elif defined(__ICCARM__) + #define FLATBUFFERS_BYTESWAP16 __REV16 + #define FLATBUFFERS_BYTESWAP32 __REV + #define FLATBUFFERS_BYTESWAP64(x) \ + ((__REV(static_cast(x >> 32U))) | (static_cast(__REV(static_cast(x)))) << 32U) + #else + #if defined(__GNUC__) && __GNUC__ * 100 + __GNUC_MINOR__ < 408 && !defined(__clang__) + // __builtin_bswap16 was missing prior to GCC 4.8. + #define FLATBUFFERS_BYTESWAP16(x) \ + static_cast(__builtin_bswap32(static_cast(x) << 16)) + #else + #define FLATBUFFERS_BYTESWAP16 __builtin_bswap16 + #endif + #define FLATBUFFERS_BYTESWAP32 __builtin_bswap32 + #define FLATBUFFERS_BYTESWAP64 __builtin_bswap64 + #endif + if (sizeof(T) == 1) { // Compile-time if-then's. + return t; + } else if (sizeof(T) == 2) { + union { T t; uint16_t i; } u = { t }; + u.i = FLATBUFFERS_BYTESWAP16(u.i); + return u.t; + } else if (sizeof(T) == 4) { + union { T t; uint32_t i; } u = { t }; + u.i = FLATBUFFERS_BYTESWAP32(u.i); + return u.t; + } else if (sizeof(T) == 8) { + union { T t; uint64_t i; } u = { t }; + u.i = FLATBUFFERS_BYTESWAP64(u.i); + return u.t; + } else { + FLATBUFFERS_ASSERT(0); + return t; + } +} + +#if defined(_MSC_VER) + #pragma warning(pop) +#endif + + +template T EndianScalar(T t) { + #if FLATBUFFERS_LITTLEENDIAN + return t; + #else + return EndianSwap(t); + #endif +} + +template +// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details. +__supress_ubsan__("alignment") +T ReadScalar(const void *p) { + return EndianScalar(*reinterpret_cast(p)); +} + +// See https://github.com/google/flatbuffers/issues/5950 + +#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000) + #pragma GCC diagnostic push + #pragma GCC diagnostic ignored "-Wstringop-overflow" +#endif + +template +// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details. +__supress_ubsan__("alignment") +void WriteScalar(void *p, T t) { + *reinterpret_cast(p) = EndianScalar(t); +} + +template struct Offset; +template __supress_ubsan__("alignment") void WriteScalar(void *p, Offset t) { + *reinterpret_cast(p) = EndianScalar(t.o); +} + +#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000) + #pragma GCC diagnostic pop +#endif + +// Computes how many bytes you'd have to pad to be able to write an +// "scalar_size" scalar if the buffer had grown to "buf_size" (downwards in +// memory). +__supress_ubsan__("unsigned-integer-overflow") +inline size_t PaddingBytes(size_t buf_size, size_t scalar_size) { + return ((~buf_size) + 1) & (scalar_size - 1); +} + +// Generic 'operator==' with conditional specialisations. +// T e - new value of a scalar field. +// T def - default of scalar (is known at compile-time). +template inline bool IsTheSameAs(T e, T def) { return e == def; } + +#if defined(FLATBUFFERS_NAN_DEFAULTS) && \ + defined(FLATBUFFERS_HAS_NEW_STRTOD) && (FLATBUFFERS_HAS_NEW_STRTOD > 0) +// Like `operator==(e, def)` with weak NaN if T=(float|double). +template inline bool IsFloatTheSameAs(T e, T def) { + return (e == def) || ((def != def) && (e != e)); +} +template<> inline bool IsTheSameAs(float e, float def) { + return IsFloatTheSameAs(e, def); +} +template<> inline bool IsTheSameAs(double e, double def) { + return IsFloatTheSameAs(e, def); +} +#endif + +// Check 'v' is out of closed range [low; high]. +// Workaround for GCC warning [-Werror=type-limits]: +// comparison is always true due to limited range of data type. +template +inline bool IsOutRange(const T &v, const T &low, const T &high) { + return (v < low) || (high < v); +} + +// Check 'v' is in closed range [low; high]. +template +inline bool IsInRange(const T &v, const T &low, const T &high) { + return !IsOutRange(v, low, high); +} + +} // namespace flatbuffers +#endif // FLATBUFFERS_BASE_H_ diff --git a/new_ai_tools/include/flatbuffers/bfbs_generator.h b/new_ai_tools/include/flatbuffers/bfbs_generator.h new file mode 100644 index 00000000..08faeb3e --- /dev/null +++ b/new_ai_tools/include/flatbuffers/bfbs_generator.h @@ -0,0 +1,43 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_BFBS_GENERATOR_H_ +#define FLATBUFFERS_BFBS_GENERATOR_H_ + +#include + +namespace flatbuffers { + +enum GeneratorStatus { + OK, + FAILED, + FAILED_VERIFICATION, +}; + +// A Flatbuffer Code Generator that receives a binary serialized reflection.fbs +// and generates code from it. +class BfbsGenerator { + public: + virtual ~BfbsGenerator() {} + + // Generate code from the provided `buffer` of given `length`. The buffer is + // a serialized reflection.fbs. + virtual GeneratorStatus Generate(const uint8_t *buffer, int64_t length) = 0; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_BFBS_GENERATOR_H_ diff --git a/new_ai_tools/include/flatbuffers/buffer.h b/new_ai_tools/include/flatbuffers/buffer.h new file mode 100644 index 00000000..e8d2ce9c --- /dev/null +++ b/new_ai_tools/include/flatbuffers/buffer.h @@ -0,0 +1,142 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_BUFFER_H_ +#define FLATBUFFERS_BUFFER_H_ + +#include "flatbuffers/base.h" + +namespace flatbuffers { + +// Wrapper for uoffset_t to allow safe template specialization. +// Value is allowed to be 0 to indicate a null object (see e.g. AddOffset). +template struct Offset { + uoffset_t o; + Offset() : o(0) {} + Offset(uoffset_t _o) : o(_o) {} + Offset Union() const { return Offset(o); } + bool IsNull() const { return !o; } +}; + +inline void EndianCheck() { + int endiantest = 1; + // If this fails, see FLATBUFFERS_LITTLEENDIAN above. + FLATBUFFERS_ASSERT(*reinterpret_cast(&endiantest) == + FLATBUFFERS_LITTLEENDIAN); + (void)endiantest; +} + +template FLATBUFFERS_CONSTEXPR size_t AlignOf() { + // clang-format off + #ifdef _MSC_VER + return __alignof(T); + #else + #ifndef alignof + return __alignof__(T); + #else + return alignof(T); + #endif + #endif + // clang-format on +} + +// Lexicographically compare two strings (possibly containing nulls), and +// return true if the first is less than the second. +static inline bool StringLessThan(const char *a_data, uoffset_t a_size, + const char *b_data, uoffset_t b_size) { + const auto cmp = memcmp(a_data, b_data, (std::min)(a_size, b_size)); + return cmp == 0 ? a_size < b_size : cmp < 0; +} + +// When we read serialized data from memory, in the case of most scalars, +// we want to just read T, but in the case of Offset, we want to actually +// perform the indirection and return a pointer. +// The template specialization below does just that. +// It is wrapped in a struct since function templates can't overload on the +// return type like this. +// The typedef is for the convenience of callers of this function +// (avoiding the need for a trailing return decltype) +template struct IndirectHelper { + typedef T return_type; + typedef T mutable_return_type; + static const size_t element_stride = sizeof(T); + static return_type Read(const uint8_t *p, uoffset_t i) { + return EndianScalar((reinterpret_cast(p))[i]); + } +}; +template struct IndirectHelper> { + typedef const T *return_type; + typedef T *mutable_return_type; + static const size_t element_stride = sizeof(uoffset_t); + static return_type Read(const uint8_t *p, uoffset_t i) { + p += i * sizeof(uoffset_t); + return reinterpret_cast(p + ReadScalar(p)); + } +}; +template struct IndirectHelper { + typedef const T *return_type; + typedef T *mutable_return_type; + static const size_t element_stride = sizeof(T); + static return_type Read(const uint8_t *p, uoffset_t i) { + return reinterpret_cast(p + i * sizeof(T)); + } +}; + +/// @brief Get a pointer to the the file_identifier section of the buffer. +/// @return Returns a const char pointer to the start of the file_identifier +/// characters in the buffer. The returned char * has length +/// 'flatbuffers::FlatBufferBuilder::kFileIdentifierLength'. +/// This function is UNDEFINED for FlatBuffers whose schema does not include +/// a file_identifier (likely points at padding or the start of a the root +/// vtable). +inline const char *GetBufferIdentifier(const void *buf, + bool size_prefixed = false) { + return reinterpret_cast(buf) + + ((size_prefixed) ? 2 * sizeof(uoffset_t) : sizeof(uoffset_t)); +} + +// Helper to see if the identifier in a buffer has the expected value. +inline bool BufferHasIdentifier(const void *buf, const char *identifier, + bool size_prefixed = false) { + return strncmp(GetBufferIdentifier(buf, size_prefixed), identifier, + flatbuffers::kFileIdentifierLength) == 0; +} + +/// @cond FLATBUFFERS_INTERNAL +// Helpers to get a typed pointer to the root object contained in the buffer. +template T *GetMutableRoot(void *buf) { + EndianCheck(); + return reinterpret_cast( + reinterpret_cast(buf) + + EndianScalar(*reinterpret_cast(buf))); +} + +template T *GetMutableSizePrefixedRoot(void *buf) { + return GetMutableRoot(reinterpret_cast(buf) + + sizeof(uoffset_t)); +} + +template const T *GetRoot(const void *buf) { + return GetMutableRoot(const_cast(buf)); +} + +template const T *GetSizePrefixedRoot(const void *buf) { + return GetRoot(reinterpret_cast(buf) + sizeof(uoffset_t)); +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_BUFFER_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/buffer_ref.h b/new_ai_tools/include/flatbuffers/buffer_ref.h new file mode 100644 index 00000000..ce302073 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/buffer_ref.h @@ -0,0 +1,53 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_BUFFER_REF_H_ +#define FLATBUFFERS_BUFFER_REF_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/verifier.h" + +namespace flatbuffers { + +// Convenient way to bundle a buffer and its length, to pass it around +// typed by its root. +// A BufferRef does not own its buffer. +struct BufferRefBase {}; // for std::is_base_of + +template struct BufferRef : BufferRefBase { + BufferRef() : buf(nullptr), len(0), must_free(false) {} + BufferRef(uint8_t *_buf, uoffset_t _len) + : buf(_buf), len(_len), must_free(false) {} + + ~BufferRef() { + if (must_free) free(buf); + } + + const T *GetRoot() const { return flatbuffers::GetRoot(buf); } + + bool Verify() { + Verifier verifier(buf, len); + return verifier.VerifyBuffer(nullptr); + } + + uint8_t *buf; + uoffset_t len; + bool must_free; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_BUFFER_REF_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/code_generators.h b/new_ai_tools/include/flatbuffers/code_generators.h new file mode 100644 index 00000000..d1f7b5a4 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/code_generators.h @@ -0,0 +1,235 @@ +/* + * Copyright 2014 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_CODE_GENERATORS_H_ +#define FLATBUFFERS_CODE_GENERATORS_H_ + +#include +#include + +#include "flatbuffers/idl.h" + +namespace flatbuffers { + +// Utility class to assist in generating code through use of text templates. +// +// Example code: +// CodeWriter code("\t"); +// code.SetValue("NAME", "Foo"); +// code += "void {{NAME}}() { printf("%s", "{{NAME}}"); }"; +// code.SetValue("NAME", "Bar"); +// code += "void {{NAME}}() { printf("%s", "{{NAME}}"); }"; +// std::cout << code.ToString() << std::endl; +// +// Output: +// void Foo() { printf("%s", "Foo"); } +// void Bar() { printf("%s", "Bar"); } +class CodeWriter { + public: + CodeWriter(std::string pad = std::string()) + : pad_(pad), cur_ident_lvl_(0), ignore_ident_(false) {} + + // Clears the current "written" code. + void Clear() { + stream_.str(""); + stream_.clear(); + } + + // Associates a key with a value. All subsequent calls to operator+=, where + // the specified key is contained in {{ and }} delimiters will be replaced by + // the given value. + void SetValue(const std::string &key, const std::string &value) { + value_map_[key] = value; + } + + std::string GetValue(const std::string &key) const { + const auto it = value_map_.find(key); + return it == value_map_.end() ? "" : it->second; + } + + // Appends the given text to the generated code as well as a newline + // character. Any text within {{ and }} delimiters is replaced by values + // previously stored in the CodeWriter by calling SetValue above. The newline + // will be suppressed if the text ends with the \\ character. + void operator+=(std::string text); + + // Returns the current contents of the CodeWriter as a std::string. + std::string ToString() const { return stream_.str(); } + + // Increase ident level for writing code + void IncrementIdentLevel() { cur_ident_lvl_++; } + // Decrease ident level for writing code + void DecrementIdentLevel() { + if (cur_ident_lvl_) cur_ident_lvl_--; + } + + void SetPadding(const std::string &padding) { pad_ = padding; } + + private: + std::map value_map_; + std::stringstream stream_; + std::string pad_; + int cur_ident_lvl_; + bool ignore_ident_; + + // Add ident padding (tab or space) based on ident level + void AppendIdent(std::stringstream &stream); +}; + +class BaseGenerator { + public: + virtual bool generate() = 0; + + static std::string NamespaceDir(const Parser &parser, const std::string &path, + const Namespace &ns, + const bool dasherize = false); + + static std::string ToDasherizedCase(const std::string pascal_case); + + std::string GeneratedFileName(const std::string &path, + const std::string &file_name, + const IDLOptions &options) const; + + protected: + BaseGenerator(const Parser &parser, const std::string &path, + const std::string &file_name, std::string qualifying_start, + std::string qualifying_separator, std::string default_extension) + : parser_(parser), + path_(path), + file_name_(file_name), + qualifying_start_(qualifying_start), + qualifying_separator_(qualifying_separator), + default_extension_(default_extension) {} + virtual ~BaseGenerator() {} + + // No copy/assign. + BaseGenerator &operator=(const BaseGenerator &); + BaseGenerator(const BaseGenerator &); + + std::string NamespaceDir(const Namespace &ns, + const bool dasherize = false) const; + + static const char *FlatBuffersGeneratedWarning(); + + static std::string FullNamespace(const char *separator, const Namespace &ns); + + static std::string LastNamespacePart(const Namespace &ns); + + // tracks the current namespace for early exit in WrapInNameSpace + // c++, java and csharp returns a different namespace from + // the following default (no early exit, always fully qualify), + // which works for js and php + virtual const Namespace *CurrentNameSpace() const { return nullptr; } + + // Ensure that a type is prefixed with its namespace even within + // its own namespace to avoid conflict between generated method + // names and similarly named classes or structs + std::string WrapInNameSpace(const Namespace *ns, + const std::string &name) const; + + std::string WrapInNameSpace(const Definition &def) const; + + std::string GetNameSpace(const Definition &def) const; + + const Parser &parser_; + const std::string &path_; + const std::string &file_name_; + const std::string qualifying_start_; + const std::string qualifying_separator_; + const std::string default_extension_; +}; + +struct CommentConfig { + const char *first_line; + const char *content_line_prefix; + const char *last_line; +}; + +extern void GenComment(const std::vector &dc, + std::string *code_ptr, const CommentConfig *config, + const char *prefix = ""); + +class FloatConstantGenerator { + public: + virtual ~FloatConstantGenerator() {} + std::string GenFloatConstant(const FieldDef &field) const; + + private: + virtual std::string Value(double v, const std::string &src) const = 0; + virtual std::string Inf(double v) const = 0; + virtual std::string NaN(double v) const = 0; + + virtual std::string Value(float v, const std::string &src) const = 0; + virtual std::string Inf(float v) const = 0; + virtual std::string NaN(float v) const = 0; + + template + std::string GenFloatConstantImpl(const FieldDef &field) const; +}; + +class SimpleFloatConstantGenerator : public FloatConstantGenerator { + public: + SimpleFloatConstantGenerator(const char *nan_number, + const char *pos_inf_number, + const char *neg_inf_number); + + private: + std::string Value(double v, + const std::string &src) const FLATBUFFERS_OVERRIDE; + std::string Inf(double v) const FLATBUFFERS_OVERRIDE; + std::string NaN(double v) const FLATBUFFERS_OVERRIDE; + + std::string Value(float v, const std::string &src) const FLATBUFFERS_OVERRIDE; + std::string Inf(float v) const FLATBUFFERS_OVERRIDE; + std::string NaN(float v) const FLATBUFFERS_OVERRIDE; + + const std::string nan_number_; + const std::string pos_inf_number_; + const std::string neg_inf_number_; +}; + +// C++, C#, Java like generator. +class TypedFloatConstantGenerator : public FloatConstantGenerator { + public: + TypedFloatConstantGenerator(const char *double_prefix, + const char *single_prefix, const char *nan_number, + const char *pos_inf_number, + const char *neg_inf_number = ""); + + private: + std::string Value(double v, + const std::string &src) const FLATBUFFERS_OVERRIDE; + std::string Inf(double v) const FLATBUFFERS_OVERRIDE; + + std::string NaN(double v) const FLATBUFFERS_OVERRIDE; + + std::string Value(float v, const std::string &src) const FLATBUFFERS_OVERRIDE; + std::string Inf(float v) const FLATBUFFERS_OVERRIDE; + std::string NaN(float v) const FLATBUFFERS_OVERRIDE; + + std::string MakeNaN(const std::string &prefix) const; + std::string MakeInf(bool neg, const std::string &prefix) const; + + const std::string double_prefix_; + const std::string single_prefix_; + const std::string nan_number_; + const std::string pos_inf_number_; + const std::string neg_inf_number_; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_CODE_GENERATORS_H_ diff --git a/new_ai_tools/include/flatbuffers/default_allocator.h b/new_ai_tools/include/flatbuffers/default_allocator.h new file mode 100644 index 00000000..8b173af1 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/default_allocator.h @@ -0,0 +1,64 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_DEFAULT_ALLOCATOR_H_ +#define FLATBUFFERS_DEFAULT_ALLOCATOR_H_ + +#include "flatbuffers/allocator.h" +#include "flatbuffers/base.h" + +namespace flatbuffers { + +// DefaultAllocator uses new/delete to allocate memory regions +class DefaultAllocator : public Allocator { + public: + uint8_t *allocate(size_t size) FLATBUFFERS_OVERRIDE { + return new uint8_t[size]; + } + + void deallocate(uint8_t *p, size_t) FLATBUFFERS_OVERRIDE { delete[] p; } + + static void dealloc(void *p, size_t) { delete[] static_cast(p); } +}; + +// These functions allow for a null allocator to mean use the default allocator, +// as used by DetachedBuffer and vector_downward below. +// This is to avoid having a statically or dynamically allocated default +// allocator, or having to move it between the classes that may own it. +inline uint8_t *Allocate(Allocator *allocator, size_t size) { + return allocator ? allocator->allocate(size) + : DefaultAllocator().allocate(size); +} + +inline void Deallocate(Allocator *allocator, uint8_t *p, size_t size) { + if (allocator) + allocator->deallocate(p, size); + else + DefaultAllocator().deallocate(p, size); +} + +inline uint8_t *ReallocateDownward(Allocator *allocator, uint8_t *old_p, + size_t old_size, size_t new_size, + size_t in_use_back, size_t in_use_front) { + return allocator ? allocator->reallocate_downward(old_p, old_size, new_size, + in_use_back, in_use_front) + : DefaultAllocator().reallocate_downward( + old_p, old_size, new_size, in_use_back, in_use_front); +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_DEFAULT_ALLOCATOR_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/detached_buffer.h b/new_ai_tools/include/flatbuffers/detached_buffer.h new file mode 100644 index 00000000..760a0884 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/detached_buffer.h @@ -0,0 +1,114 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_DETACHED_BUFFER_H_ +#define FLATBUFFERS_DETACHED_BUFFER_H_ + +#include "flatbuffers/allocator.h" +#include "flatbuffers/base.h" +#include "flatbuffers/default_allocator.h" + +namespace flatbuffers { + +// DetachedBuffer is a finished flatbuffer memory region, detached from its +// builder. The original memory region and allocator are also stored so that +// the DetachedBuffer can manage the memory lifetime. +class DetachedBuffer { + public: + DetachedBuffer() + : allocator_(nullptr), + own_allocator_(false), + buf_(nullptr), + reserved_(0), + cur_(nullptr), + size_(0) {} + + DetachedBuffer(Allocator *allocator, bool own_allocator, uint8_t *buf, + size_t reserved, uint8_t *cur, size_t sz) + : allocator_(allocator), + own_allocator_(own_allocator), + buf_(buf), + reserved_(reserved), + cur_(cur), + size_(sz) {} + + DetachedBuffer(DetachedBuffer &&other) + : allocator_(other.allocator_), + own_allocator_(other.own_allocator_), + buf_(other.buf_), + reserved_(other.reserved_), + cur_(other.cur_), + size_(other.size_) { + other.reset(); + } + + DetachedBuffer &operator=(DetachedBuffer &&other) { + if (this == &other) return *this; + + destroy(); + + allocator_ = other.allocator_; + own_allocator_ = other.own_allocator_; + buf_ = other.buf_; + reserved_ = other.reserved_; + cur_ = other.cur_; + size_ = other.size_; + + other.reset(); + + return *this; + } + + ~DetachedBuffer() { destroy(); } + + const uint8_t *data() const { return cur_; } + + uint8_t *data() { return cur_; } + + size_t size() const { return size_; } + + // These may change access mode, leave these at end of public section + FLATBUFFERS_DELETE_FUNC(DetachedBuffer(const DetachedBuffer &other)); + FLATBUFFERS_DELETE_FUNC( + DetachedBuffer &operator=(const DetachedBuffer &other)); + + protected: + Allocator *allocator_; + bool own_allocator_; + uint8_t *buf_; + size_t reserved_; + uint8_t *cur_; + size_t size_; + + inline void destroy() { + if (buf_) Deallocate(allocator_, buf_, reserved_); + if (own_allocator_ && allocator_) { delete allocator_; } + reset(); + } + + inline void reset() { + allocator_ = nullptr; + own_allocator_ = false; + buf_ = nullptr; + reserved_ = 0; + cur_ = nullptr; + size_ = 0; + } +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_DETACHED_BUFFER_H_ diff --git a/new_ai_tools/include/flatbuffers/flatbuffer_builder.h b/new_ai_tools/include/flatbuffers/flatbuffer_builder.h new file mode 100644 index 00000000..99941938 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/flatbuffer_builder.h @@ -0,0 +1,1197 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_FLATBUFFER_BUILDER_H_ +#define FLATBUFFERS_FLATBUFFER_BUILDER_H_ + +#include + +#include "flatbuffers/allocator.h" +#include "flatbuffers/array.h" +#include "flatbuffers/base.h" +#include "flatbuffers/buffer_ref.h" +#include "flatbuffers/default_allocator.h" +#include "flatbuffers/detached_buffer.h" +#include "flatbuffers/stl_emulation.h" +#include "flatbuffers/string.h" +#include "flatbuffers/struct.h" +#include "flatbuffers/table.h" +#include "flatbuffers/vector.h" +#include "flatbuffers/vector_downward.h" +#include "flatbuffers/verifier.h" + +namespace flatbuffers { + +// Converts a Field ID to a virtual table offset. +inline voffset_t FieldIndexToOffset(voffset_t field_id) { + // Should correspond to what EndTable() below builds up. + const int fixed_fields = 2; // Vtable size and Object Size. + return static_cast((field_id + fixed_fields) * sizeof(voffset_t)); +} + +template> +const T *data(const std::vector &v) { + // Eventually the returned pointer gets passed down to memcpy, so + // we need it to be non-null to avoid undefined behavior. + static uint8_t t; + return v.empty() ? reinterpret_cast(&t) : &v.front(); +} +template> +T *data(std::vector &v) { + // Eventually the returned pointer gets passed down to memcpy, so + // we need it to be non-null to avoid undefined behavior. + static uint8_t t; + return v.empty() ? reinterpret_cast(&t) : &v.front(); +} + +/// @addtogroup flatbuffers_cpp_api +/// @{ +/// @class FlatBufferBuilder +/// @brief Helper class to hold data needed in creation of a FlatBuffer. +/// To serialize data, you typically call one of the `Create*()` functions in +/// the generated code, which in turn call a sequence of `StartTable`/ +/// `PushElement`/`AddElement`/`EndTable`, or the builtin `CreateString`/ +/// `CreateVector` functions. Do this is depth-first order to build up a tree to +/// the root. `Finish()` wraps up the buffer ready for transport. +class FlatBufferBuilder { + public: + /// @brief Default constructor for FlatBufferBuilder. + /// @param[in] initial_size The initial size of the buffer, in bytes. Defaults + /// to `1024`. + /// @param[in] allocator An `Allocator` to use. If null will use + /// `DefaultAllocator`. + /// @param[in] own_allocator Whether the builder/vector should own the + /// allocator. Defaults to / `false`. + /// @param[in] buffer_minalign Force the buffer to be aligned to the given + /// minimum alignment upon reallocation. Only needed if you intend to store + /// types with custom alignment AND you wish to read the buffer in-place + /// directly after creation. + explicit FlatBufferBuilder( + size_t initial_size = 1024, Allocator *allocator = nullptr, + bool own_allocator = false, + size_t buffer_minalign = AlignOf()) + : buf_(initial_size, allocator, own_allocator, buffer_minalign), + num_field_loc(0), + max_voffset_(0), + nested(false), + finished(false), + minalign_(1), + force_defaults_(false), + dedup_vtables_(true), + string_pool(nullptr) { + EndianCheck(); + } + + /// @brief Move constructor for FlatBufferBuilder. + FlatBufferBuilder(FlatBufferBuilder &&other) + : buf_(1024, nullptr, false, AlignOf()), + num_field_loc(0), + max_voffset_(0), + nested(false), + finished(false), + minalign_(1), + force_defaults_(false), + dedup_vtables_(true), + string_pool(nullptr) { + EndianCheck(); + // Default construct and swap idiom. + // Lack of delegating constructors in vs2010 makes it more verbose than + // needed. + Swap(other); + } + + /// @brief Move assignment operator for FlatBufferBuilder. + FlatBufferBuilder &operator=(FlatBufferBuilder &&other) { + // Move construct a temporary and swap idiom + FlatBufferBuilder temp(std::move(other)); + Swap(temp); + return *this; + } + + void Swap(FlatBufferBuilder &other) { + using std::swap; + buf_.swap(other.buf_); + swap(num_field_loc, other.num_field_loc); + swap(max_voffset_, other.max_voffset_); + swap(nested, other.nested); + swap(finished, other.finished); + swap(minalign_, other.minalign_); + swap(force_defaults_, other.force_defaults_); + swap(dedup_vtables_, other.dedup_vtables_); + swap(string_pool, other.string_pool); + } + + ~FlatBufferBuilder() { + if (string_pool) delete string_pool; + } + + void Reset() { + Clear(); // clear builder state + buf_.reset(); // deallocate buffer + } + + /// @brief Reset all the state in this FlatBufferBuilder so it can be reused + /// to construct another buffer. + void Clear() { + ClearOffsets(); + buf_.clear(); + nested = false; + finished = false; + minalign_ = 1; + if (string_pool) string_pool->clear(); + } + + /// @brief The current size of the serialized buffer, counting from the end. + /// @return Returns an `uoffset_t` with the current size of the buffer. + uoffset_t GetSize() const { return buf_.size(); } + + /// @brief Get the serialized buffer (after you call `Finish()`). + /// @return Returns an `uint8_t` pointer to the FlatBuffer data inside the + /// buffer. + uint8_t *GetBufferPointer() const { + Finished(); + return buf_.data(); + } + + /// @brief Get the serialized buffer (after you call `Finish()`) as a span. + /// @return Returns a constructed flatbuffers::span that is a view over the + /// FlatBuffer data inside the buffer. + flatbuffers::span GetBufferSpan() const { + Finished(); + return flatbuffers::span(buf_.data(), buf_.size()); + } + + /// @brief Get a pointer to an unfinished buffer. + /// @return Returns a `uint8_t` pointer to the unfinished buffer. + uint8_t *GetCurrentBufferPointer() const { return buf_.data(); } + + /// @brief Get the released pointer to the serialized buffer. + /// @warning Do NOT attempt to use this FlatBufferBuilder afterwards! + /// @return A `FlatBuffer` that owns the buffer and its allocator and + /// behaves similar to a `unique_ptr` with a deleter. + FLATBUFFERS_ATTRIBUTE([[deprecated("use Release() instead")]]) + DetachedBuffer ReleaseBufferPointer() { + Finished(); + return buf_.release(); + } + + /// @brief Get the released DetachedBuffer. + /// @return A `DetachedBuffer` that owns the buffer and its allocator. + DetachedBuffer Release() { + Finished(); + return buf_.release(); + } + + /// @brief Get the released pointer to the serialized buffer. + /// @param size The size of the memory block containing + /// the serialized `FlatBuffer`. + /// @param offset The offset from the released pointer where the finished + /// `FlatBuffer` starts. + /// @return A raw pointer to the start of the memory block containing + /// the serialized `FlatBuffer`. + /// @remark If the allocator is owned, it gets deleted when the destructor is + /// called.. + uint8_t *ReleaseRaw(size_t &size, size_t &offset) { + Finished(); + return buf_.release_raw(size, offset); + } + + /// @brief get the minimum alignment this buffer needs to be accessed + /// properly. This is only known once all elements have been written (after + /// you call Finish()). You can use this information if you need to embed + /// a FlatBuffer in some other buffer, such that you can later read it + /// without first having to copy it into its own buffer. + size_t GetBufferMinAlignment() const { + Finished(); + return minalign_; + } + + /// @cond FLATBUFFERS_INTERNAL + void Finished() const { + // If you get this assert, you're attempting to get access a buffer + // which hasn't been finished yet. Be sure to call + // FlatBufferBuilder::Finish with your root table. + // If you really need to access an unfinished buffer, call + // GetCurrentBufferPointer instead. + FLATBUFFERS_ASSERT(finished); + } + /// @endcond + + /// @brief In order to save space, fields that are set to their default value + /// don't get serialized into the buffer. + /// @param[in] fd When set to `true`, always serializes default values that + /// are set. Optional fields which are not set explicitly, will still not be + /// serialized. + void ForceDefaults(bool fd) { force_defaults_ = fd; } + + /// @brief By default vtables are deduped in order to save space. + /// @param[in] dedup When set to `true`, dedup vtables. + void DedupVtables(bool dedup) { dedup_vtables_ = dedup; } + + /// @cond FLATBUFFERS_INTERNAL + void Pad(size_t num_bytes) { buf_.fill(num_bytes); } + + void TrackMinAlign(size_t elem_size) { + if (elem_size > minalign_) minalign_ = elem_size; + } + + void Align(size_t elem_size) { + TrackMinAlign(elem_size); + buf_.fill(PaddingBytes(buf_.size(), elem_size)); + } + + void PushFlatBuffer(const uint8_t *bytes, size_t size) { + PushBytes(bytes, size); + finished = true; + } + + void PushBytes(const uint8_t *bytes, size_t size) { buf_.push(bytes, size); } + + void PopBytes(size_t amount) { buf_.pop(amount); } + + template void AssertScalarT() { + // The code assumes power of 2 sizes and endian-swap-ability. + static_assert(flatbuffers::is_scalar::value, "T must be a scalar type"); + } + + // Write a single aligned scalar to the buffer + template uoffset_t PushElement(T element) { + AssertScalarT(); + Align(sizeof(T)); + buf_.push_small(EndianScalar(element)); + return GetSize(); + } + + template uoffset_t PushElement(Offset off) { + // Special case for offsets: see ReferTo below. + return PushElement(ReferTo(off.o)); + } + + // When writing fields, we track where they are, so we can create correct + // vtables later. + void TrackField(voffset_t field, uoffset_t off) { + FieldLoc fl = { off, field }; + buf_.scratch_push_small(fl); + num_field_loc++; + if (field > max_voffset_) { max_voffset_ = field; } + } + + // Like PushElement, but additionally tracks the field this represents. + template void AddElement(voffset_t field, T e, T def) { + // We don't serialize values equal to the default. + if (IsTheSameAs(e, def) && !force_defaults_) return; + TrackField(field, PushElement(e)); + } + + template void AddElement(voffset_t field, T e) { + TrackField(field, PushElement(e)); + } + + template void AddOffset(voffset_t field, Offset off) { + if (off.IsNull()) return; // Don't store. + AddElement(field, ReferTo(off.o), static_cast(0)); + } + + template void AddStruct(voffset_t field, const T *structptr) { + if (!structptr) return; // Default, don't store. + Align(AlignOf()); + buf_.push_small(*structptr); + TrackField(field, GetSize()); + } + + void AddStructOffset(voffset_t field, uoffset_t off) { + TrackField(field, off); + } + + // Offsets initially are relative to the end of the buffer (downwards). + // This function converts them to be relative to the current location + // in the buffer (when stored here), pointing upwards. + uoffset_t ReferTo(uoffset_t off) { + // Align to ensure GetSize() below is correct. + Align(sizeof(uoffset_t)); + // Offset must refer to something already in buffer. + const uoffset_t size = GetSize(); + FLATBUFFERS_ASSERT(off && off <= size); + return size - off + static_cast(sizeof(uoffset_t)); + } + + void NotNested() { + // If you hit this, you're trying to construct a Table/Vector/String + // during the construction of its parent table (between the MyTableBuilder + // and table.Finish(). + // Move the creation of these sub-objects to above the MyTableBuilder to + // not get this assert. + // Ignoring this assert may appear to work in simple cases, but the reason + // it is here is that storing objects in-line may cause vtable offsets + // to not fit anymore. It also leads to vtable duplication. + FLATBUFFERS_ASSERT(!nested); + // If you hit this, fields were added outside the scope of a table. + FLATBUFFERS_ASSERT(!num_field_loc); + } + + // From generated code (or from the parser), we call StartTable/EndTable + // with a sequence of AddElement calls in between. + uoffset_t StartTable() { + NotNested(); + nested = true; + return GetSize(); + } + + // This finishes one serialized object by generating the vtable if it's a + // table, comparing it against existing vtables, and writing the + // resulting vtable offset. + uoffset_t EndTable(uoffset_t start) { + // If you get this assert, a corresponding StartTable wasn't called. + FLATBUFFERS_ASSERT(nested); + // Write the vtable offset, which is the start of any Table. + // We fill it's value later. + auto vtableoffsetloc = PushElement(0); + // Write a vtable, which consists entirely of voffset_t elements. + // It starts with the number of offsets, followed by a type id, followed + // by the offsets themselves. In reverse: + // Include space for the last offset and ensure empty tables have a + // minimum size. + max_voffset_ = + (std::max)(static_cast(max_voffset_ + sizeof(voffset_t)), + FieldIndexToOffset(0)); + buf_.fill_big(max_voffset_); + auto table_object_size = vtableoffsetloc - start; + // Vtable use 16bit offsets. + FLATBUFFERS_ASSERT(table_object_size < 0x10000); + WriteScalar(buf_.data() + sizeof(voffset_t), + static_cast(table_object_size)); + WriteScalar(buf_.data(), max_voffset_); + // Write the offsets into the table + for (auto it = buf_.scratch_end() - num_field_loc * sizeof(FieldLoc); + it < buf_.scratch_end(); it += sizeof(FieldLoc)) { + auto field_location = reinterpret_cast(it); + auto pos = static_cast(vtableoffsetloc - field_location->off); + // If this asserts, it means you've set a field twice. + FLATBUFFERS_ASSERT( + !ReadScalar(buf_.data() + field_location->id)); + WriteScalar(buf_.data() + field_location->id, pos); + } + ClearOffsets(); + auto vt1 = reinterpret_cast(buf_.data()); + auto vt1_size = ReadScalar(vt1); + auto vt_use = GetSize(); + // See if we already have generated a vtable with this exact same + // layout before. If so, make it point to the old one, remove this one. + if (dedup_vtables_) { + for (auto it = buf_.scratch_data(); it < buf_.scratch_end(); + it += sizeof(uoffset_t)) { + auto vt_offset_ptr = reinterpret_cast(it); + auto vt2 = reinterpret_cast(buf_.data_at(*vt_offset_ptr)); + auto vt2_size = ReadScalar(vt2); + if (vt1_size != vt2_size || 0 != memcmp(vt2, vt1, vt1_size)) continue; + vt_use = *vt_offset_ptr; + buf_.pop(GetSize() - vtableoffsetloc); + break; + } + } + // If this is a new vtable, remember it. + if (vt_use == GetSize()) { buf_.scratch_push_small(vt_use); } + // Fill the vtable offset we created above. + // The offset points from the beginning of the object to where the + // vtable is stored. + // Offsets default direction is downward in memory for future format + // flexibility (storing all vtables at the start of the file). + WriteScalar(buf_.data_at(vtableoffsetloc), + static_cast(vt_use) - + static_cast(vtableoffsetloc)); + + nested = false; + return vtableoffsetloc; + } + + FLATBUFFERS_ATTRIBUTE([[deprecated("call the version above instead")]]) + uoffset_t EndTable(uoffset_t start, voffset_t /*numfields*/) { + return EndTable(start); + } + + // This checks a required field has been set in a given table that has + // just been constructed. + template void Required(Offset table, voffset_t field); + + uoffset_t StartStruct(size_t alignment) { + Align(alignment); + return GetSize(); + } + + uoffset_t EndStruct() { return GetSize(); } + + void ClearOffsets() { + buf_.scratch_pop(num_field_loc * sizeof(FieldLoc)); + num_field_loc = 0; + max_voffset_ = 0; + } + + // Aligns such that when "len" bytes are written, an object can be written + // after it with "alignment" without padding. + void PreAlign(size_t len, size_t alignment) { + TrackMinAlign(alignment); + buf_.fill(PaddingBytes(GetSize() + len, alignment)); + } + template void PreAlign(size_t len) { + AssertScalarT(); + PreAlign(len, sizeof(T)); + } + /// @endcond + + /// @brief Store a string in the buffer, which can contain any binary data. + /// @param[in] str A const char pointer to the data to be stored as a string. + /// @param[in] len The number of bytes that should be stored from `str`. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateString(const char *str, size_t len) { + NotNested(); + PreAlign(len + 1); // Always 0-terminated. + buf_.fill(1); + PushBytes(reinterpret_cast(str), len); + PushElement(static_cast(len)); + return Offset(GetSize()); + } + + /// @brief Store a string in the buffer, which is null-terminated. + /// @param[in] str A const char pointer to a C-string to add to the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateString(const char *str) { + return CreateString(str, strlen(str)); + } + + /// @brief Store a string in the buffer, which is null-terminated. + /// @param[in] str A char pointer to a C-string to add to the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateString(char *str) { + return CreateString(str, strlen(str)); + } + + /// @brief Store a string in the buffer, which can contain any binary data. + /// @param[in] str A const reference to a std::string to store in the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateString(const std::string &str) { + return CreateString(str.c_str(), str.length()); + } + + // clang-format off + #ifdef FLATBUFFERS_HAS_STRING_VIEW + /// @brief Store a string in the buffer, which can contain any binary data. + /// @param[in] str A const string_view to copy in to the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateString(flatbuffers::string_view str) { + return CreateString(str.data(), str.size()); + } + #endif // FLATBUFFERS_HAS_STRING_VIEW + // clang-format on + + /// @brief Store a string in the buffer, which can contain any binary data. + /// @param[in] str A const pointer to a `String` struct to add to the buffer. + /// @return Returns the offset in the buffer where the string starts + Offset CreateString(const String *str) { + return str ? CreateString(str->c_str(), str->size()) : 0; + } + + /// @brief Store a string in the buffer, which can contain any binary data. + /// @param[in] str A const reference to a std::string like type with support + /// of T::c_str() and T::length() to store in the buffer. + /// @return Returns the offset in the buffer where the string starts. + template Offset CreateString(const T &str) { + return CreateString(str.c_str(), str.length()); + } + + /// @brief Store a string in the buffer, which can contain any binary data. + /// If a string with this exact contents has already been serialized before, + /// instead simply returns the offset of the existing string. This uses a map + /// stored on the heap, but only stores the numerical offsets. + /// @param[in] str A const char pointer to the data to be stored as a string. + /// @param[in] len The number of bytes that should be stored from `str`. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateSharedString(const char *str, size_t len) { + FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); + if (!string_pool) + string_pool = new StringOffsetMap(StringOffsetCompare(buf_)); + auto size_before_string = buf_.size(); + // Must first serialize the string, since the set is all offsets into + // buffer. + auto off = CreateString(str, len); + auto it = string_pool->find(off); + // If it exists we reuse existing serialized data! + if (it != string_pool->end()) { + // We can remove the string we serialized. + buf_.pop(buf_.size() - size_before_string); + return *it; + } + // Record this string for future use. + string_pool->insert(off); + return off; + } + +#ifdef FLATBUFFERS_HAS_STRING_VIEW + /// @brief Store a string in the buffer, which can contain any binary data. + /// If a string with this exact contents has already been serialized before, + /// instead simply returns the offset of the existing string. This uses a map + /// stored on the heap, but only stores the numerical offsets. + /// @param[in] str A const std::string_view to store in the buffer. + /// @return Returns the offset in the buffer where the string starts + Offset CreateSharedString(const flatbuffers::string_view str) { + return CreateSharedString(str.data(), str.size()); + } +#else + /// @brief Store a string in the buffer, which null-terminated. + /// If a string with this exact contents has already been serialized before, + /// instead simply returns the offset of the existing string. This uses a map + /// stored on the heap, but only stores the numerical offsets. + /// @param[in] str A const char pointer to a C-string to add to the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateSharedString(const char *str) { + return CreateSharedString(str, strlen(str)); + } + + /// @brief Store a string in the buffer, which can contain any binary data. + /// If a string with this exact contents has already been serialized before, + /// instead simply returns the offset of the existing string. This uses a map + /// stored on the heap, but only stores the numerical offsets. + /// @param[in] str A const reference to a std::string to store in the buffer. + /// @return Returns the offset in the buffer where the string starts. + Offset CreateSharedString(const std::string &str) { + return CreateSharedString(str.c_str(), str.length()); + } +#endif + + /// @brief Store a string in the buffer, which can contain any binary data. + /// If a string with this exact contents has already been serialized before, + /// instead simply returns the offset of the existing string. This uses a map + /// stored on the heap, but only stores the numerical offsets. + /// @param[in] str A const pointer to a `String` struct to add to the buffer. + /// @return Returns the offset in the buffer where the string starts + Offset CreateSharedString(const String *str) { + return CreateSharedString(str->c_str(), str->size()); + } + + /// @cond FLATBUFFERS_INTERNAL + uoffset_t EndVector(size_t len) { + FLATBUFFERS_ASSERT(nested); // Hit if no corresponding StartVector. + nested = false; + return PushElement(static_cast(len)); + } + + void StartVector(size_t len, size_t elemsize) { + NotNested(); + nested = true; + PreAlign(len * elemsize); + PreAlign(len * elemsize, elemsize); // Just in case elemsize > uoffset_t. + } + + // Call this right before StartVector/CreateVector if you want to force the + // alignment to be something different than what the element size would + // normally dictate. + // This is useful when storing a nested_flatbuffer in a vector of bytes, + // or when storing SIMD floats, etc. + void ForceVectorAlignment(size_t len, size_t elemsize, size_t alignment) { + FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment)); + PreAlign(len * elemsize, alignment); + } + + // Similar to ForceVectorAlignment but for String fields. + void ForceStringAlignment(size_t len, size_t alignment) { + FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment)); + PreAlign((len + 1) * sizeof(char), alignment); + } + + /// @endcond + + /// @brief Serialize an array into a FlatBuffer `vector`. + /// @tparam T The data type of the array elements. + /// @param[in] v A pointer to the array of type `T` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template Offset> CreateVector(const T *v, size_t len) { + // If this assert hits, you're specifying a template argument that is + // causing the wrong overload to be selected, remove it. + AssertScalarT(); + StartVector(len, sizeof(T)); + if (len == 0) { return Offset>(EndVector(len)); } + // clang-format off + #if FLATBUFFERS_LITTLEENDIAN + PushBytes(reinterpret_cast(v), len * sizeof(T)); + #else + if (sizeof(T) == 1) { + PushBytes(reinterpret_cast(v), len); + } else { + for (auto i = len; i > 0; ) { + PushElement(v[--i]); + } + } + #endif + // clang-format on + return Offset>(EndVector(len)); + } + + /// @brief Serialize an array like object into a FlatBuffer `vector`. + /// @tparam T The data type of the array elements. + /// @tparam C The type of the array. + /// @param[in] array A reference to an array like object of type `T` to + /// serialize into the buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template Offset> CreateVector(const C &array) { + return CreateVector(array.data(), array.size()); + } + + template + Offset>> CreateVector(const Offset *v, size_t len) { + StartVector(len, sizeof(Offset)); + for (auto i = len; i > 0;) { PushElement(v[--i]); } + return Offset>>(EndVector(len)); + } + + /// @brief Serialize a `std::vector` into a FlatBuffer `vector`. + /// @tparam T The data type of the `std::vector` elements. + /// @param v A const reference to the `std::vector` to serialize into the + /// buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVector(const std::vector &v) { + return CreateVector(data(v), v.size()); + } + + // vector may be implemented using a bit-set, so we can't access it as + // an array. Instead, read elements manually. + // Background: https://isocpp.org/blog/2012/11/on-vectorbool + Offset> CreateVector(const std::vector &v) { + StartVector(v.size(), sizeof(uint8_t)); + for (auto i = v.size(); i > 0;) { + PushElement(static_cast(v[--i])); + } + return Offset>(EndVector(v.size())); + } + + /// @brief Serialize values returned by a function into a FlatBuffer `vector`. + /// This is a convenience function that takes care of iteration for you. + /// @tparam T The data type of the `std::vector` elements. + /// @param f A function that takes the current iteration 0..vector_size-1 and + /// returns any type that you can construct a FlatBuffers vector out of. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVector(size_t vector_size, + const std::function &f) { + FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); + std::vector elems(vector_size); + for (size_t i = 0; i < vector_size; i++) elems[i] = f(i); + return CreateVector(elems); + } + + /// @brief Serialize values returned by a function into a FlatBuffer `vector`. + /// This is a convenience function that takes care of iteration for you. This + /// uses a vector stored on the heap to store the intermediate results of the + /// iteration. + /// @tparam T The data type of the `std::vector` elements. + /// @param f A function that takes the current iteration 0..vector_size-1, + /// and the state parameter returning any type that you can construct a + /// FlatBuffers vector out of. + /// @param state State passed to f. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVector(size_t vector_size, F f, S *state) { + FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); + std::vector elems(vector_size); + for (size_t i = 0; i < vector_size; i++) elems[i] = f(i, state); + return CreateVector(elems); + } + + /// @brief Serialize a `std::vector` into a FlatBuffer `vector`. + /// This is a convenience function for a common case. + /// @param v A const reference to the `std::vector` to serialize into the + /// buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset>> CreateVectorOfStrings( + const std::vector &v) { + return CreateVectorOfStrings(v.cbegin(), v.cend()); + } + + /// @brief Serialize a collection of Strings into a FlatBuffer `vector`. + /// This is a convenience function for a common case. + /// @param begin The begining iterator of the collection + /// @param end The ending iterator of the collection + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset>> CreateVectorOfStrings(It begin, It end) { + auto size = std::distance(begin, end); + auto scratch_buffer_usage = size * sizeof(Offset); + // If there is not enough space to store the offsets, there definitely won't + // be enough space to store all the strings. So ensuring space for the + // scratch region is OK, for it it fails, it would have failed later. + buf_.ensure_space(scratch_buffer_usage); + for (auto it = begin; it != end; ++it) { + buf_.scratch_push_small(CreateString(*it)); + } + StartVector(size, sizeof(Offset)); + for (auto i = 1; i <= size; i++) { + // Note we re-evaluate the buf location each iteration to account for any + // underlying buffer resizing that may occur. + PushElement(*reinterpret_cast *>( + buf_.scratch_end() - i * sizeof(Offset))); + } + buf_.scratch_pop(scratch_buffer_usage); + return Offset>>(EndVector(size)); + } + + /// @brief Serialize an array of structs into a FlatBuffer `vector`. + /// @tparam T The data type of the struct array elements. + /// @param[in] v A pointer to the array of type `T` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVectorOfStructs(const T *v, size_t len) { + StartVector(len * sizeof(T) / AlignOf(), AlignOf()); + if (len > 0) { + PushBytes(reinterpret_cast(v), sizeof(T) * len); + } + return Offset>(EndVector(len)); + } + + /// @brief Serialize an array of native structs into a FlatBuffer `vector`. + /// @tparam T The data type of the struct array elements. + /// @tparam S The data type of the native struct array elements. + /// @param[in] v A pointer to the array of type `S` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @param[in] pack_func Pointer to a function to convert the native struct + /// to the FlatBuffer struct. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVectorOfNativeStructs( + const S *v, size_t len, T (*const pack_func)(const S &)) { + FLATBUFFERS_ASSERT(pack_func); + auto structs = StartVectorOfStructs(len); + for (size_t i = 0; i < len; i++) { structs[i] = pack_func(v[i]); } + return EndVectorOfStructs(len); + } + + /// @brief Serialize an array of native structs into a FlatBuffer `vector`. + /// @tparam T The data type of the struct array elements. + /// @tparam S The data type of the native struct array elements. + /// @param[in] v A pointer to the array of type `S` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVectorOfNativeStructs(const S *v, + size_t len) { + extern T Pack(const S &); + return CreateVectorOfNativeStructs(v, len, Pack); + } + + /// @brief Serialize an array of structs into a FlatBuffer `vector`. + /// @tparam T The data type of the struct array elements. + /// @param[in] filler A function that takes the current iteration + /// 0..vector_size-1 and a pointer to the struct that must be filled. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + /// This is mostly useful when flatbuffers are generated with mutation + /// accessors. + template + Offset> CreateVectorOfStructs( + size_t vector_size, const std::function &filler) { + T *structs = StartVectorOfStructs(vector_size); + for (size_t i = 0; i < vector_size; i++) { + filler(i, structs); + structs++; + } + return EndVectorOfStructs(vector_size); + } + + /// @brief Serialize an array of structs into a FlatBuffer `vector`. + /// @tparam T The data type of the struct array elements. + /// @param[in] f A function that takes the current iteration 0..vector_size-1, + /// a pointer to the struct that must be filled and the state argument. + /// @param[in] state Arbitrary state to pass to f. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + /// This is mostly useful when flatbuffers are generated with mutation + /// accessors. + template + Offset> CreateVectorOfStructs(size_t vector_size, F f, + S *state) { + T *structs = StartVectorOfStructs(vector_size); + for (size_t i = 0; i < vector_size; i++) { + f(i, structs, state); + structs++; + } + return EndVectorOfStructs(vector_size); + } + + /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector`. + /// @tparam T The data type of the `std::vector` struct elements. + /// @param[in] v A const reference to the `std::vector` of structs to + /// serialize into the buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVectorOfStructs( + const std::vector &v) { + return CreateVectorOfStructs(data(v), v.size()); + } + + /// @brief Serialize a `std::vector` of native structs into a FlatBuffer + /// `vector`. + /// @tparam T The data type of the `std::vector` struct elements. + /// @tparam S The data type of the `std::vector` native struct elements. + /// @param[in] v A const reference to the `std::vector` of structs to + /// serialize into the buffer as a `vector`. + /// @param[in] pack_func Pointer to a function to convert the native struct + /// to the FlatBuffer struct. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVectorOfNativeStructs( + const std::vector &v, T (*const pack_func)(const S &)) { + return CreateVectorOfNativeStructs(data(v), v.size(), pack_func); + } + + /// @brief Serialize a `std::vector` of native structs into a FlatBuffer + /// `vector`. + /// @tparam T The data type of the `std::vector` struct elements. + /// @tparam S The data type of the `std::vector` native struct elements. + /// @param[in] v A const reference to the `std::vector` of structs to + /// serialize into the buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVectorOfNativeStructs( + const std::vector &v) { + return CreateVectorOfNativeStructs(data(v), v.size()); + } + + /// @cond FLATBUFFERS_INTERNAL + template struct StructKeyComparator { + bool operator()(const T &a, const T &b) const { + return a.KeyCompareLessThan(&b); + } + }; + /// @endcond + + /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector` + /// in sorted order. + /// @tparam T The data type of the `std::vector` struct elements. + /// @param[in] v A const reference to the `std::vector` of structs to + /// serialize into the buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVectorOfSortedStructs( + std::vector *v) { + return CreateVectorOfSortedStructs(data(*v), v->size()); + } + + /// @brief Serialize a `std::vector` of native structs into a FlatBuffer + /// `vector` in sorted order. + /// @tparam T The data type of the `std::vector` struct elements. + /// @tparam S The data type of the `std::vector` native struct elements. + /// @param[in] v A const reference to the `std::vector` of structs to + /// serialize into the buffer as a `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset> CreateVectorOfSortedNativeStructs( + std::vector *v) { + return CreateVectorOfSortedNativeStructs(data(*v), v->size()); + } + + /// @brief Serialize an array of structs into a FlatBuffer `vector` in sorted + /// order. + /// @tparam T The data type of the struct array elements. + /// @param[in] v A pointer to the array of type `T` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVectorOfSortedStructs(T *v, size_t len) { + std::stable_sort(v, v + len, StructKeyComparator()); + return CreateVectorOfStructs(v, len); + } + + /// @brief Serialize an array of native structs into a FlatBuffer `vector` in + /// sorted order. + /// @tparam T The data type of the struct array elements. + /// @tparam S The data type of the native struct array elements. + /// @param[in] v A pointer to the array of type `S` to serialize into the + /// buffer as a `vector`. + /// @param[in] len The number of elements to serialize. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset> CreateVectorOfSortedNativeStructs(S *v, + size_t len) { + extern T Pack(const S &); + auto structs = StartVectorOfStructs(len); + for (size_t i = 0; i < len; i++) { structs[i] = Pack(v[i]); } + std::stable_sort(structs, structs + len, StructKeyComparator()); + return EndVectorOfStructs(len); + } + + /// @cond FLATBUFFERS_INTERNAL + template struct TableKeyComparator { + TableKeyComparator(vector_downward &buf) : buf_(buf) {} + TableKeyComparator(const TableKeyComparator &other) : buf_(other.buf_) {} + bool operator()(const Offset &a, const Offset &b) const { + auto table_a = reinterpret_cast(buf_.data_at(a.o)); + auto table_b = reinterpret_cast(buf_.data_at(b.o)); + return table_a->KeyCompareLessThan(table_b); + } + vector_downward &buf_; + + private: + FLATBUFFERS_DELETE_FUNC( + TableKeyComparator &operator=(const TableKeyComparator &other)); + }; + /// @endcond + + /// @brief Serialize an array of `table` offsets as a `vector` in the buffer + /// in sorted order. + /// @tparam T The data type that the offset refers to. + /// @param[in] v An array of type `Offset` that contains the `table` + /// offsets to store in the buffer in sorted order. + /// @param[in] len The number of elements to store in the `vector`. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template + Offset>> CreateVectorOfSortedTables(Offset *v, + size_t len) { + std::stable_sort(v, v + len, TableKeyComparator(buf_)); + return CreateVector(v, len); + } + + /// @brief Serialize an array of `table` offsets as a `vector` in the buffer + /// in sorted order. + /// @tparam T The data type that the offset refers to. + /// @param[in] v An array of type `Offset` that contains the `table` + /// offsets to store in the buffer in sorted order. + /// @return Returns a typed `Offset` into the serialized data indicating + /// where the vector is stored. + template> + Offset>> CreateVectorOfSortedTables( + std::vector, Alloc> *v) { + return CreateVectorOfSortedTables(data(*v), v->size()); + } + + /// @brief Specialized version of `CreateVector` for non-copying use cases. + /// Write the data any time later to the returned buffer pointer `buf`. + /// @param[in] len The number of elements to store in the `vector`. + /// @param[in] elemsize The size of each element in the `vector`. + /// @param[out] buf A pointer to a `uint8_t` pointer that can be + /// written to at a later time to serialize the data into a `vector` + /// in the buffer. + uoffset_t CreateUninitializedVector(size_t len, size_t elemsize, + uint8_t **buf) { + NotNested(); + StartVector(len, elemsize); + buf_.make_space(len * elemsize); + auto vec_start = GetSize(); + auto vec_end = EndVector(len); + *buf = buf_.data_at(vec_start); + return vec_end; + } + + /// @brief Specialized version of `CreateVector` for non-copying use cases. + /// Write the data any time later to the returned buffer pointer `buf`. + /// @tparam T The data type of the data that will be stored in the buffer + /// as a `vector`. + /// @param[in] len The number of elements to store in the `vector`. + /// @param[out] buf A pointer to a pointer of type `T` that can be + /// written to at a later time to serialize the data into a `vector` + /// in the buffer. + template + Offset> CreateUninitializedVector(size_t len, T **buf) { + AssertScalarT(); + return CreateUninitializedVector(len, sizeof(T), + reinterpret_cast(buf)); + } + + template + Offset> CreateUninitializedVectorOfStructs(size_t len, + T **buf) { + return CreateUninitializedVector(len, sizeof(T), + reinterpret_cast(buf)); + } + + // @brief Create a vector of scalar type T given as input a vector of scalar + // type U, useful with e.g. pre "enum class" enums, or any existing scalar + // data of the wrong type. + template + Offset> CreateVectorScalarCast(const U *v, size_t len) { + AssertScalarT(); + AssertScalarT(); + StartVector(len, sizeof(T)); + for (auto i = len; i > 0;) { PushElement(static_cast(v[--i])); } + return Offset>(EndVector(len)); + } + + /// @brief Write a struct by itself, typically to be part of a union. + template Offset CreateStruct(const T &structobj) { + NotNested(); + Align(AlignOf()); + buf_.push_small(structobj); + return Offset(GetSize()); + } + + /// @brief Finish serializing a buffer by writing the root offset. + /// @param[in] file_identifier If a `file_identifier` is given, the buffer + /// will be prefixed with a standard FlatBuffers file header. + template + void Finish(Offset root, const char *file_identifier = nullptr) { + Finish(root.o, file_identifier, false); + } + + /// @brief Finish a buffer with a 32 bit size field pre-fixed (size of the + /// buffer following the size field). These buffers are NOT compatible + /// with standard buffers created by Finish, i.e. you can't call GetRoot + /// on them, you have to use GetSizePrefixedRoot instead. + /// All >32 bit quantities in this buffer will be aligned when the whole + /// size pre-fixed buffer is aligned. + /// These kinds of buffers are useful for creating a stream of FlatBuffers. + template + void FinishSizePrefixed(Offset root, + const char *file_identifier = nullptr) { + Finish(root.o, file_identifier, true); + } + + void SwapBufAllocator(FlatBufferBuilder &other) { + buf_.swap_allocator(other.buf_); + } + + /// @brief The length of a FlatBuffer file header. + static const size_t kFileIdentifierLength = + ::flatbuffers::kFileIdentifierLength; + + protected: + // You shouldn't really be copying instances of this class. + FlatBufferBuilder(const FlatBufferBuilder &); + FlatBufferBuilder &operator=(const FlatBufferBuilder &); + + void Finish(uoffset_t root, const char *file_identifier, bool size_prefix) { + NotNested(); + buf_.clear_scratch(); + // This will cause the whole buffer to be aligned. + PreAlign((size_prefix ? sizeof(uoffset_t) : 0) + sizeof(uoffset_t) + + (file_identifier ? kFileIdentifierLength : 0), + minalign_); + if (file_identifier) { + FLATBUFFERS_ASSERT(strlen(file_identifier) == kFileIdentifierLength); + PushBytes(reinterpret_cast(file_identifier), + kFileIdentifierLength); + } + PushElement(ReferTo(root)); // Location of root. + if (size_prefix) { PushElement(GetSize()); } + finished = true; + } + + struct FieldLoc { + uoffset_t off; + voffset_t id; + }; + + vector_downward buf_; + + // Accumulating offsets of table members while it is being built. + // We store these in the scratch pad of buf_, after the vtable offsets. + uoffset_t num_field_loc; + // Track how much of the vtable is in use, so we can output the most compact + // possible vtable. + voffset_t max_voffset_; + + // Ensure objects are not nested. + bool nested; + + // Ensure the buffer is finished before it is being accessed. + bool finished; + + size_t minalign_; + + bool force_defaults_; // Serialize values equal to their defaults anyway. + + bool dedup_vtables_; + + struct StringOffsetCompare { + StringOffsetCompare(const vector_downward &buf) : buf_(&buf) {} + bool operator()(const Offset &a, const Offset &b) const { + auto stra = reinterpret_cast(buf_->data_at(a.o)); + auto strb = reinterpret_cast(buf_->data_at(b.o)); + return StringLessThan(stra->data(), stra->size(), strb->data(), + strb->size()); + } + const vector_downward *buf_; + }; + + // For use with CreateSharedString. Instantiated on first use only. + typedef std::set, StringOffsetCompare> StringOffsetMap; + StringOffsetMap *string_pool; + + private: + // Allocates space for a vector of structures. + // Must be completed with EndVectorOfStructs(). + template T *StartVectorOfStructs(size_t vector_size) { + StartVector(vector_size * sizeof(T) / AlignOf(), AlignOf()); + return reinterpret_cast(buf_.make_space(vector_size * sizeof(T))); + } + + // End the vector of structures in the flatbuffers. + // Vector should have previously be started with StartVectorOfStructs(). + template + Offset> EndVectorOfStructs(size_t vector_size) { + return Offset>(EndVector(vector_size)); + } +}; +/// @} + +/// Helpers to get a typed pointer to objects that are currently being built. +/// @warning Creating new objects will lead to reallocations and invalidates +/// the pointer! +template +T *GetMutableTemporaryPointer(FlatBufferBuilder &fbb, Offset offset) { + return reinterpret_cast(fbb.GetCurrentBufferPointer() + fbb.GetSize() - + offset.o); +} + +template +const T *GetTemporaryPointer(FlatBufferBuilder &fbb, Offset offset) { + return GetMutableTemporaryPointer(fbb, offset); +} + +template +void FlatBufferBuilder::Required(Offset table, voffset_t field) { + auto table_ptr = reinterpret_cast(buf_.data_at(table.o)); + bool ok = table_ptr->GetOptionalFieldOffset(field) != 0; + // If this fails, the caller will show what field needs to be set. + FLATBUFFERS_ASSERT(ok); + (void)ok; +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_VECTOR_DOWNWARD_H_ diff --git a/new_ai_tools/include/flatbuffers/flatbuffers.h b/new_ai_tools/include/flatbuffers/flatbuffers.h new file mode 100644 index 00000000..64217889 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/flatbuffers.h @@ -0,0 +1,270 @@ +/* + * Copyright 2014 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_H_ +#define FLATBUFFERS_H_ + +// TODO: These includes are for mitigating the pains of users editing their +// source because they relied on flatbuffers.h to include everything for them. +#include "flatbuffers/array.h" +#include "flatbuffers/base.h" +#include "flatbuffers/buffer.h" +#include "flatbuffers/buffer_ref.h" +#include "flatbuffers/detached_buffer.h" +#include "flatbuffers/flatbuffer_builder.h" +#include "flatbuffers/stl_emulation.h" +#include "flatbuffers/string.h" +#include "flatbuffers/struct.h" +#include "flatbuffers/table.h" +#include "flatbuffers/vector.h" +#include "flatbuffers/vector_downward.h" +#include "flatbuffers/verifier.h" + +namespace flatbuffers { + +/// @brief This can compute the start of a FlatBuffer from a root pointer, i.e. +/// it is the opposite transformation of GetRoot(). +/// This may be useful if you want to pass on a root and have the recipient +/// delete the buffer afterwards. +inline const uint8_t *GetBufferStartFromRootPointer(const void *root) { + auto table = reinterpret_cast(root); + auto vtable = table->GetVTable(); + // Either the vtable is before the root or after the root. + auto start = (std::min)(vtable, reinterpret_cast(root)); + // Align to at least sizeof(uoffset_t). + start = reinterpret_cast(reinterpret_cast(start) & + ~(sizeof(uoffset_t) - 1)); + // Additionally, there may be a file_identifier in the buffer, and the root + // offset. The buffer may have been aligned to any size between + // sizeof(uoffset_t) and FLATBUFFERS_MAX_ALIGNMENT (see "force_align"). + // Sadly, the exact alignment is only known when constructing the buffer, + // since it depends on the presence of values with said alignment properties. + // So instead, we simply look at the next uoffset_t values (root, + // file_identifier, and alignment padding) to see which points to the root. + // None of the other values can "impersonate" the root since they will either + // be 0 or four ASCII characters. + static_assert(flatbuffers::kFileIdentifierLength == sizeof(uoffset_t), + "file_identifier is assumed to be the same size as uoffset_t"); + for (auto possible_roots = FLATBUFFERS_MAX_ALIGNMENT / sizeof(uoffset_t) + 1; + possible_roots; possible_roots--) { + start -= sizeof(uoffset_t); + if (ReadScalar(start) + start == + reinterpret_cast(root)) + return start; + } + // We didn't find the root, either the "root" passed isn't really a root, + // or the buffer is corrupt. + // Assert, because calling this function with bad data may cause reads + // outside of buffer boundaries. + FLATBUFFERS_ASSERT(false); + return nullptr; +} + +/// @brief This return the prefixed size of a FlatBuffer. +inline uoffset_t GetPrefixedSize(const uint8_t *buf) { + return ReadScalar(buf); +} + +// Base class for native objects (FlatBuffer data de-serialized into native +// C++ data structures). +// Contains no functionality, purely documentative. +struct NativeTable {}; + +/// @brief Function types to be used with resolving hashes into objects and +/// back again. The resolver gets a pointer to a field inside an object API +/// object that is of the type specified in the schema using the attribute +/// `cpp_type` (it is thus important whatever you write to this address +/// matches that type). The value of this field is initially null, so you +/// may choose to implement a delayed binding lookup using this function +/// if you wish. The resolver does the opposite lookup, for when the object +/// is being serialized again. +typedef uint64_t hash_value_t; +typedef std::function + resolver_function_t; +typedef std::function rehasher_function_t; + +// Helper function to test if a field is present, using any of the field +// enums in the generated code. +// `table` must be a generated table type. Since this is a template parameter, +// this is not typechecked to be a subclass of Table, so beware! +// Note: this function will return false for fields equal to the default +// value, since they're not stored in the buffer (unless force_defaults was +// used). +template +bool IsFieldPresent(const T *table, typename T::FlatBuffersVTableOffset field) { + // Cast, since Table is a private baseclass of any table types. + return reinterpret_cast(table)->CheckField( + static_cast(field)); +} + +// Utility function for reverse lookups on the EnumNames*() functions +// (in the generated C++ code) +// names must be NULL terminated. +inline int LookupEnum(const char **names, const char *name) { + for (const char **p = names; *p; p++) + if (!strcmp(*p, name)) return static_cast(p - names); + return -1; +} + +// These macros allow us to layout a struct with a guarantee that they'll end +// up looking the same on different compilers and platforms. +// It does this by disallowing the compiler to do any padding, and then +// does padding itself by inserting extra padding fields that make every +// element aligned to its own size. +// Additionally, it manually sets the alignment of the struct as a whole, +// which is typically its largest element, or a custom size set in the schema +// by the force_align attribute. +// These are used in the generated code only. + +// clang-format off +#if defined(_MSC_VER) + #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \ + __pragma(pack(1)) \ + struct __declspec(align(alignment)) + #define FLATBUFFERS_STRUCT_END(name, size) \ + __pragma(pack()) \ + static_assert(sizeof(name) == size, "compiler breaks packing rules") +#elif defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__) + #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \ + _Pragma("pack(1)") \ + struct __attribute__((aligned(alignment))) + #define FLATBUFFERS_STRUCT_END(name, size) \ + _Pragma("pack()") \ + static_assert(sizeof(name) == size, "compiler breaks packing rules") +#else + #error Unknown compiler, please define structure alignment macros +#endif +// clang-format on + +// Minimal reflection via code generation. +// Besides full-fat reflection (see reflection.h) and parsing/printing by +// loading schemas (see idl.h), we can also have code generation for minimal +// reflection data which allows pretty-printing and other uses without needing +// a schema or a parser. +// Generate code with --reflect-types (types only) or --reflect-names (names +// also) to enable. +// See minireflect.h for utilities using this functionality. + +// These types are organized slightly differently as the ones in idl.h. +enum SequenceType { ST_TABLE, ST_STRUCT, ST_UNION, ST_ENUM }; + +// Scalars have the same order as in idl.h +// clang-format off +#define FLATBUFFERS_GEN_ELEMENTARY_TYPES(ET) \ + ET(ET_UTYPE) \ + ET(ET_BOOL) \ + ET(ET_CHAR) \ + ET(ET_UCHAR) \ + ET(ET_SHORT) \ + ET(ET_USHORT) \ + ET(ET_INT) \ + ET(ET_UINT) \ + ET(ET_LONG) \ + ET(ET_ULONG) \ + ET(ET_FLOAT) \ + ET(ET_DOUBLE) \ + ET(ET_STRING) \ + ET(ET_SEQUENCE) // See SequenceType. + +enum ElementaryType { + #define FLATBUFFERS_ET(E) E, + FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET) + #undef FLATBUFFERS_ET +}; + +inline const char * const *ElementaryTypeNames() { + static const char * const names[] = { + #define FLATBUFFERS_ET(E) #E, + FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET) + #undef FLATBUFFERS_ET + }; + return names; +} +// clang-format on + +// Basic type info cost just 16bits per field! +// We're explicitly defining the signedness since the signedness of integer +// bitfields is otherwise implementation-defined and causes warnings on older +// GCC compilers. +struct TypeCode { + // ElementaryType + unsigned short base_type : 4; + // Either vector (in table) or array (in struct) + unsigned short is_repeating : 1; + // Index into type_refs below, or -1 for none. + signed short sequence_ref : 11; +}; + +static_assert(sizeof(TypeCode) == 2, "TypeCode"); + +struct TypeTable; + +// Signature of the static method present in each type. +typedef const TypeTable *(*TypeFunction)(); + +struct TypeTable { + SequenceType st; + size_t num_elems; // of type_codes, values, names (but not type_refs). + const TypeCode *type_codes; // num_elems count + const TypeFunction *type_refs; // less than num_elems entries (see TypeCode). + const int16_t *array_sizes; // less than num_elems entries (see TypeCode). + const int64_t *values; // Only set for non-consecutive enum/union or structs. + const char *const *names; // Only set if compiled with --reflect-names. +}; + +// String which identifies the current version of FlatBuffers. +inline const char *flatbuffers_version_string() { + return "FlatBuffers " FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MAJOR) "." + FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MINOR) "." + FLATBUFFERS_STRING(FLATBUFFERS_VERSION_REVISION); +} + +// clang-format off +#define FLATBUFFERS_DEFINE_BITMASK_OPERATORS(E, T)\ + inline E operator | (E lhs, E rhs){\ + return E(T(lhs) | T(rhs));\ + }\ + inline E operator & (E lhs, E rhs){\ + return E(T(lhs) & T(rhs));\ + }\ + inline E operator ^ (E lhs, E rhs){\ + return E(T(lhs) ^ T(rhs));\ + }\ + inline E operator ~ (E lhs){\ + return E(~T(lhs));\ + }\ + inline E operator |= (E &lhs, E rhs){\ + lhs = lhs | rhs;\ + return lhs;\ + }\ + inline E operator &= (E &lhs, E rhs){\ + lhs = lhs & rhs;\ + return lhs;\ + }\ + inline E operator ^= (E &lhs, E rhs){\ + lhs = lhs ^ rhs;\ + return lhs;\ + }\ + inline bool operator !(E rhs) \ + {\ + return !bool(T(rhs)); \ + } +/// @endcond +} // namespace flatbuffers + +// clang-format on + +#endif // FLATBUFFERS_H_ diff --git a/new_ai_tools/include/flatbuffers/flatc.h b/new_ai_tools/include/flatbuffers/flatc.h new file mode 100644 index 00000000..3dba5e14 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/flatc.h @@ -0,0 +1,111 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_FLATC_H_ +#define FLATBUFFERS_FLATC_H_ + +#include +#include +#include + +#include "flatbuffers/bfbs_generator.h" +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/idl.h" +#include "flatbuffers/util.h" + +namespace flatbuffers { + +extern void LogCompilerWarn(const std::string &warn); +extern void LogCompilerError(const std::string &err); + +struct FlatCOption { + std::string short_opt; + std::string long_opt; + std::string parameter; + std::string description; +}; + +class FlatCompiler { + public: + // Output generator for the various programming languages and formats we + // support. + struct Generator { + typedef bool (*GenerateFn)(const flatbuffers::Parser &parser, + const std::string &path, + const std::string &file_name); + typedef std::string (*MakeRuleFn)(const flatbuffers::Parser &parser, + const std::string &path, + const std::string &file_name); + typedef bool (*ParsingCompletedFn)(const flatbuffers::Parser &parser, + const std::string &output_path); + + GenerateFn generate; + const char *lang_name; + bool schema_only; + GenerateFn generateGRPC; + flatbuffers::IDLOptions::Language lang; + FlatCOption option; + MakeRuleFn make_rule; + BfbsGenerator *bfbs_generator; + ParsingCompletedFn parsing_completed; + }; + + typedef void (*WarnFn)(const FlatCompiler *flatc, const std::string &warn, + bool show_exe_name); + + typedef void (*ErrorFn)(const FlatCompiler *flatc, const std::string &err, + bool usage, bool show_exe_name); + + // Parameters required to initialize the FlatCompiler. + struct InitParams { + InitParams() + : generators(nullptr), + num_generators(0), + warn_fn(nullptr), + error_fn(nullptr) {} + + const Generator *generators; + size_t num_generators; + WarnFn warn_fn; + ErrorFn error_fn; + }; + + explicit FlatCompiler(const InitParams ¶ms) : params_(params) {} + + int Compile(int argc, const char **argv); + + std::string GetShortUsageString(const char *program_name) const; + std::string GetUsageString(const char *program_name) const; + + private: + void ParseFile(flatbuffers::Parser &parser, const std::string &filename, + const std::string &contents, + std::vector &include_directories) const; + + void LoadBinarySchema(Parser &parser, const std::string &filename, + const std::string &contents); + + void Warn(const std::string &warn, bool show_exe_name = true) const; + + void Error(const std::string &err, bool usage = true, + bool show_exe_name = true) const; + + InitParams params_; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_FLATC_H_ diff --git a/new_ai_tools/include/flatbuffers/flexbuffers.h b/new_ai_tools/include/flatbuffers/flexbuffers.h new file mode 100644 index 00000000..b4b0332b --- /dev/null +++ b/new_ai_tools/include/flatbuffers/flexbuffers.h @@ -0,0 +1,1897 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_FLEXBUFFERS_H_ +#define FLATBUFFERS_FLEXBUFFERS_H_ + +#include +// Used to select STL variant. +#include "flatbuffers/base.h" +// We use the basic binary writing functions from the regular FlatBuffers. +#include "flatbuffers/util.h" + +#ifdef _MSC_VER +# include +#endif + +#if defined(_MSC_VER) +# pragma warning(push) +# pragma warning(disable : 4127) // C4127: conditional expression is constant +#endif + +namespace flexbuffers { + +class Reference; +class Map; + +// These are used in the lower 2 bits of a type field to determine the size of +// the elements (and or size field) of the item pointed to (e.g. vector). +enum BitWidth { + BIT_WIDTH_8 = 0, + BIT_WIDTH_16 = 1, + BIT_WIDTH_32 = 2, + BIT_WIDTH_64 = 3, +}; + +// These are used as the upper 6 bits of a type field to indicate the actual +// type. +enum Type { + FBT_NULL = 0, + FBT_INT = 1, + FBT_UINT = 2, + FBT_FLOAT = 3, + // Types above stored inline, types below (except FBT_BOOL) store an offset. + FBT_KEY = 4, + FBT_STRING = 5, + FBT_INDIRECT_INT = 6, + FBT_INDIRECT_UINT = 7, + FBT_INDIRECT_FLOAT = 8, + FBT_MAP = 9, + FBT_VECTOR = 10, // Untyped. + FBT_VECTOR_INT = 11, // Typed any size (stores no type table). + FBT_VECTOR_UINT = 12, + FBT_VECTOR_FLOAT = 13, + FBT_VECTOR_KEY = 14, + // DEPRECATED, use FBT_VECTOR or FBT_VECTOR_KEY instead. + // Read test.cpp/FlexBuffersDeprecatedTest() for details on why. + FBT_VECTOR_STRING_DEPRECATED = 15, + FBT_VECTOR_INT2 = 16, // Typed tuple (no type table, no size field). + FBT_VECTOR_UINT2 = 17, + FBT_VECTOR_FLOAT2 = 18, + FBT_VECTOR_INT3 = 19, // Typed triple (no type table, no size field). + FBT_VECTOR_UINT3 = 20, + FBT_VECTOR_FLOAT3 = 21, + FBT_VECTOR_INT4 = 22, // Typed quad (no type table, no size field). + FBT_VECTOR_UINT4 = 23, + FBT_VECTOR_FLOAT4 = 24, + FBT_BLOB = 25, + FBT_BOOL = 26, + FBT_VECTOR_BOOL = + 36, // To Allow the same type of conversion of type to vector type + + FBT_MAX_TYPE = 37 +}; + +inline bool IsInline(Type t) { return t <= FBT_FLOAT || t == FBT_BOOL; } + +inline bool IsTypedVectorElementType(Type t) { + return (t >= FBT_INT && t <= FBT_STRING) || t == FBT_BOOL; +} + +inline bool IsTypedVector(Type t) { + return (t >= FBT_VECTOR_INT && t <= FBT_VECTOR_STRING_DEPRECATED) || + t == FBT_VECTOR_BOOL; +} + +inline bool IsFixedTypedVector(Type t) { + return t >= FBT_VECTOR_INT2 && t <= FBT_VECTOR_FLOAT4; +} + +inline Type ToTypedVector(Type t, size_t fixed_len = 0) { + FLATBUFFERS_ASSERT(IsTypedVectorElementType(t)); + switch (fixed_len) { + case 0: return static_cast(t - FBT_INT + FBT_VECTOR_INT); + case 2: return static_cast(t - FBT_INT + FBT_VECTOR_INT2); + case 3: return static_cast(t - FBT_INT + FBT_VECTOR_INT3); + case 4: return static_cast(t - FBT_INT + FBT_VECTOR_INT4); + default: FLATBUFFERS_ASSERT(0); return FBT_NULL; + } +} + +inline Type ToTypedVectorElementType(Type t) { + FLATBUFFERS_ASSERT(IsTypedVector(t)); + return static_cast(t - FBT_VECTOR_INT + FBT_INT); +} + +inline Type ToFixedTypedVectorElementType(Type t, uint8_t *len) { + FLATBUFFERS_ASSERT(IsFixedTypedVector(t)); + auto fixed_type = t - FBT_VECTOR_INT2; + *len = static_cast(fixed_type / 3 + + 2); // 3 types each, starting from length 2. + return static_cast(fixed_type % 3 + FBT_INT); +} + +// TODO: implement proper support for 8/16bit floats, or decide not to +// support them. +typedef int16_t half; +typedef int8_t quarter; + +// TODO: can we do this without conditionals using intrinsics or inline asm +// on some platforms? Given branch prediction the method below should be +// decently quick, but it is the most frequently executed function. +// We could do an (unaligned) 64-bit read if we ifdef out the platforms for +// which that doesn't work (or where we'd read into un-owned memory). +template +R ReadSizedScalar(const uint8_t *data, uint8_t byte_width) { + return byte_width < 4 + ? (byte_width < 2 + ? static_cast(flatbuffers::ReadScalar(data)) + : static_cast(flatbuffers::ReadScalar(data))) + : (byte_width < 8 + ? static_cast(flatbuffers::ReadScalar(data)) + : static_cast(flatbuffers::ReadScalar(data))); +} + +inline int64_t ReadInt64(const uint8_t *data, uint8_t byte_width) { + return ReadSizedScalar( + data, byte_width); +} + +inline uint64_t ReadUInt64(const uint8_t *data, uint8_t byte_width) { + // This is the "hottest" function (all offset lookups use this), so worth + // optimizing if possible. + // TODO: GCC apparently replaces memcpy by a rep movsb, but only if count is a + // constant, which here it isn't. Test if memcpy is still faster than + // the conditionals in ReadSizedScalar. Can also use inline asm. + // clang-format off + #if defined(_MSC_VER) && defined(_M_X64) && !defined(_M_ARM64EC) + // This is 64-bit Windows only, __movsb does not work on 32-bit Windows. + uint64_t u = 0; + __movsb(reinterpret_cast(&u), + reinterpret_cast(data), byte_width); + return flatbuffers::EndianScalar(u); + #else + return ReadSizedScalar( + data, byte_width); + #endif + // clang-format on +} + +inline double ReadDouble(const uint8_t *data, uint8_t byte_width) { + return ReadSizedScalar(data, + byte_width); +} + +inline const uint8_t *Indirect(const uint8_t *offset, uint8_t byte_width) { + return offset - ReadUInt64(offset, byte_width); +} + +template const uint8_t *Indirect(const uint8_t *offset) { + return offset - flatbuffers::ReadScalar(offset); +} + +inline BitWidth WidthU(uint64_t u) { +#define FLATBUFFERS_GET_FIELD_BIT_WIDTH(value, width) \ + { \ + if (!((u) & ~((1ULL << (width)) - 1ULL))) return BIT_WIDTH_##width; \ + } + FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 8); + FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 16); + FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 32); +#undef FLATBUFFERS_GET_FIELD_BIT_WIDTH + return BIT_WIDTH_64; +} + +inline BitWidth WidthI(int64_t i) { + auto u = static_cast(i) << 1; + return WidthU(i >= 0 ? u : ~u); +} + +inline BitWidth WidthF(double f) { + return static_cast(static_cast(f)) == f ? BIT_WIDTH_32 + : BIT_WIDTH_64; +} + +// Base class of all types below. +// Points into the data buffer and allows access to one type. +class Object { + public: + Object(const uint8_t *data, uint8_t byte_width) + : data_(data), byte_width_(byte_width) {} + + protected: + const uint8_t *data_; + uint8_t byte_width_; +}; + +// Object that has a size, obtained either from size prefix, or elsewhere. +class Sized : public Object { + public: + // Size prefix. + Sized(const uint8_t *data, uint8_t byte_width) + : Object(data, byte_width), size_(read_size()) {} + // Manual size. + Sized(const uint8_t *data, uint8_t byte_width, size_t sz) + : Object(data, byte_width), size_(sz) {} + size_t size() const { return size_; } + // Access size stored in `byte_width_` bytes before data_ pointer. + size_t read_size() const { + return static_cast(ReadUInt64(data_ - byte_width_, byte_width_)); + } + + protected: + size_t size_; +}; + +class String : public Sized { + public: + // Size prefix. + String(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {} + // Manual size. + String(const uint8_t *data, uint8_t byte_width, size_t sz) + : Sized(data, byte_width, sz) {} + + size_t length() const { return size(); } + const char *c_str() const { return reinterpret_cast(data_); } + std::string str() const { return std::string(c_str(), size()); } + + static String EmptyString() { + static const char *empty_string = ""; + return String(reinterpret_cast(empty_string), 1, 0); + } + bool IsTheEmptyString() const { return data_ == EmptyString().data_; } +}; + +class Blob : public Sized { + public: + Blob(const uint8_t *data_buf, uint8_t byte_width) + : Sized(data_buf, byte_width) {} + + static Blob EmptyBlob() { + static const uint8_t empty_blob[] = { 0 /*len*/ }; + return Blob(empty_blob + 1, 1); + } + bool IsTheEmptyBlob() const { return data_ == EmptyBlob().data_; } + const uint8_t *data() const { return data_; } +}; + +class Vector : public Sized { + public: + Vector(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {} + + Reference operator[](size_t i) const; + + static Vector EmptyVector() { + static const uint8_t empty_vector[] = { 0 /*len*/ }; + return Vector(empty_vector + 1, 1); + } + bool IsTheEmptyVector() const { return data_ == EmptyVector().data_; } +}; + +class TypedVector : public Sized { + public: + TypedVector(const uint8_t *data, uint8_t byte_width, Type element_type) + : Sized(data, byte_width), type_(element_type) {} + + Reference operator[](size_t i) const; + + static TypedVector EmptyTypedVector() { + static const uint8_t empty_typed_vector[] = { 0 /*len*/ }; + return TypedVector(empty_typed_vector + 1, 1, FBT_INT); + } + bool IsTheEmptyVector() const { + return data_ == TypedVector::EmptyTypedVector().data_; + } + + Type ElementType() { return type_; } + + friend Reference; + + private: + Type type_; + + friend Map; +}; + +class FixedTypedVector : public Object { + public: + FixedTypedVector(const uint8_t *data, uint8_t byte_width, Type element_type, + uint8_t len) + : Object(data, byte_width), type_(element_type), len_(len) {} + + Reference operator[](size_t i) const; + + static FixedTypedVector EmptyFixedTypedVector() { + static const uint8_t fixed_empty_vector[] = { 0 /* unused */ }; + return FixedTypedVector(fixed_empty_vector, 1, FBT_INT, 0); + } + bool IsTheEmptyFixedTypedVector() const { + return data_ == FixedTypedVector::EmptyFixedTypedVector().data_; + } + + Type ElementType() const { return type_; } + uint8_t size() const { return len_; } + + private: + Type type_; + uint8_t len_; +}; + +class Map : public Vector { + public: + Map(const uint8_t *data, uint8_t byte_width) : Vector(data, byte_width) {} + + Reference operator[](const char *key) const; + Reference operator[](const std::string &key) const; + + Vector Values() const { return Vector(data_, byte_width_); } + + TypedVector Keys() const { + const size_t num_prefixed_fields = 3; + auto keys_offset = data_ - byte_width_ * num_prefixed_fields; + return TypedVector(Indirect(keys_offset, byte_width_), + static_cast( + ReadUInt64(keys_offset + byte_width_, byte_width_)), + FBT_KEY); + } + + static Map EmptyMap() { + static const uint8_t empty_map[] = { + 0 /*keys_len*/, 0 /*keys_offset*/, 1 /*keys_width*/, 0 /*len*/ + }; + return Map(empty_map + 4, 1); + } + + bool IsTheEmptyMap() const { return data_ == EmptyMap().data_; } +}; + +template +void AppendToString(std::string &s, T &&v, bool keys_quoted) { + s += "[ "; + for (size_t i = 0; i < v.size(); i++) { + if (i) s += ", "; + v[i].ToString(true, keys_quoted, s); + } + s += " ]"; +} + +class Reference { + public: + Reference() + : data_(nullptr), parent_width_(0), byte_width_(0), type_(FBT_NULL) {} + + Reference(const uint8_t *data, uint8_t parent_width, uint8_t byte_width, + Type type) + : data_(data), + parent_width_(parent_width), + byte_width_(byte_width), + type_(type) {} + + Reference(const uint8_t *data, uint8_t parent_width, uint8_t packed_type) + : data_(data), parent_width_(parent_width) { + byte_width_ = 1U << static_cast(packed_type & 3); + type_ = static_cast(packed_type >> 2); + } + + Type GetType() const { return type_; } + + bool IsNull() const { return type_ == FBT_NULL; } + bool IsBool() const { return type_ == FBT_BOOL; } + bool IsInt() const { return type_ == FBT_INT || type_ == FBT_INDIRECT_INT; } + bool IsUInt() const { + return type_ == FBT_UINT || type_ == FBT_INDIRECT_UINT; + } + bool IsIntOrUint() const { return IsInt() || IsUInt(); } + bool IsFloat() const { + return type_ == FBT_FLOAT || type_ == FBT_INDIRECT_FLOAT; + } + bool IsNumeric() const { return IsIntOrUint() || IsFloat(); } + bool IsString() const { return type_ == FBT_STRING; } + bool IsKey() const { return type_ == FBT_KEY; } + bool IsVector() const { return type_ == FBT_VECTOR || type_ == FBT_MAP; } + bool IsUntypedVector() const { return type_ == FBT_VECTOR; } + bool IsTypedVector() const { return flexbuffers::IsTypedVector(type_); } + bool IsFixedTypedVector() const { + return flexbuffers::IsFixedTypedVector(type_); + } + bool IsAnyVector() const { + return (IsTypedVector() || IsFixedTypedVector() || IsVector()); + } + bool IsMap() const { return type_ == FBT_MAP; } + bool IsBlob() const { return type_ == FBT_BLOB; } + bool AsBool() const { + return (type_ == FBT_BOOL ? ReadUInt64(data_, parent_width_) + : AsUInt64()) != 0; + } + + // Reads any type as a int64_t. Never fails, does most sensible conversion. + // Truncates floats, strings are attempted to be parsed for a number, + // vectors/maps return their size. Returns 0 if all else fails. + int64_t AsInt64() const { + if (type_ == FBT_INT) { + // A fast path for the common case. + return ReadInt64(data_, parent_width_); + } else + switch (type_) { + case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_); + case FBT_UINT: return ReadUInt64(data_, parent_width_); + case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_); + case FBT_FLOAT: + return static_cast(ReadDouble(data_, parent_width_)); + case FBT_INDIRECT_FLOAT: + return static_cast(ReadDouble(Indirect(), byte_width_)); + case FBT_NULL: return 0; + case FBT_STRING: return flatbuffers::StringToInt(AsString().c_str()); + case FBT_VECTOR: return static_cast(AsVector().size()); + case FBT_BOOL: return ReadInt64(data_, parent_width_); + default: + // Convert other things to int. + return 0; + } + } + + // TODO: could specialize these to not use AsInt64() if that saves + // extension ops in generated code, and use a faster op than ReadInt64. + int32_t AsInt32() const { return static_cast(AsInt64()); } + int16_t AsInt16() const { return static_cast(AsInt64()); } + int8_t AsInt8() const { return static_cast(AsInt64()); } + + uint64_t AsUInt64() const { + if (type_ == FBT_UINT) { + // A fast path for the common case. + return ReadUInt64(data_, parent_width_); + } else + switch (type_) { + case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_); + case FBT_INT: return ReadInt64(data_, parent_width_); + case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_); + case FBT_FLOAT: + return static_cast(ReadDouble(data_, parent_width_)); + case FBT_INDIRECT_FLOAT: + return static_cast(ReadDouble(Indirect(), byte_width_)); + case FBT_NULL: return 0; + case FBT_STRING: return flatbuffers::StringToUInt(AsString().c_str()); + case FBT_VECTOR: return static_cast(AsVector().size()); + case FBT_BOOL: return ReadUInt64(data_, parent_width_); + default: + // Convert other things to uint. + return 0; + } + } + + uint32_t AsUInt32() const { return static_cast(AsUInt64()); } + uint16_t AsUInt16() const { return static_cast(AsUInt64()); } + uint8_t AsUInt8() const { return static_cast(AsUInt64()); } + + double AsDouble() const { + if (type_ == FBT_FLOAT) { + // A fast path for the common case. + return ReadDouble(data_, parent_width_); + } else + switch (type_) { + case FBT_INDIRECT_FLOAT: return ReadDouble(Indirect(), byte_width_); + case FBT_INT: + return static_cast(ReadInt64(data_, parent_width_)); + case FBT_UINT: + return static_cast(ReadUInt64(data_, parent_width_)); + case FBT_INDIRECT_INT: + return static_cast(ReadInt64(Indirect(), byte_width_)); + case FBT_INDIRECT_UINT: + return static_cast(ReadUInt64(Indirect(), byte_width_)); + case FBT_NULL: return 0.0; + case FBT_STRING: { + double d; + flatbuffers::StringToNumber(AsString().c_str(), &d); + return d; + } + case FBT_VECTOR: return static_cast(AsVector().size()); + case FBT_BOOL: + return static_cast(ReadUInt64(data_, parent_width_)); + default: + // Convert strings and other things to float. + return 0; + } + } + + float AsFloat() const { return static_cast(AsDouble()); } + + const char *AsKey() const { + if (type_ == FBT_KEY || type_ == FBT_STRING) { + return reinterpret_cast(Indirect()); + } else { + return ""; + } + } + + // This function returns the empty string if you try to read something that + // is not a string or key. + String AsString() const { + if (type_ == FBT_STRING) { + return String(Indirect(), byte_width_); + } else if (type_ == FBT_KEY) { + auto key = Indirect(); + return String(key, byte_width_, + strlen(reinterpret_cast(key))); + } else { + return String::EmptyString(); + } + } + + // Unlike AsString(), this will convert any type to a std::string. + std::string ToString() const { + std::string s; + ToString(false, false, s); + return s; + } + + // Convert any type to a JSON-like string. strings_quoted determines if + // string values at the top level receive "" quotes (inside other values + // they always do). keys_quoted determines if keys are quoted, at any level. + // TODO(wvo): add further options to have indentation/newlines. + void ToString(bool strings_quoted, bool keys_quoted, std::string &s) const { + if (type_ == FBT_STRING) { + String str(Indirect(), byte_width_); + if (strings_quoted) { + flatbuffers::EscapeString(str.c_str(), str.length(), &s, true, false); + } else { + s.append(str.c_str(), str.length()); + } + } else if (IsKey()) { + auto str = AsKey(); + if (keys_quoted) { + flatbuffers::EscapeString(str, strlen(str), &s, true, false); + } else { + s += str; + } + } else if (IsInt()) { + s += flatbuffers::NumToString(AsInt64()); + } else if (IsUInt()) { + s += flatbuffers::NumToString(AsUInt64()); + } else if (IsFloat()) { + s += flatbuffers::NumToString(AsDouble()); + } else if (IsNull()) { + s += "null"; + } else if (IsBool()) { + s += AsBool() ? "true" : "false"; + } else if (IsMap()) { + s += "{ "; + auto m = AsMap(); + auto keys = m.Keys(); + auto vals = m.Values(); + for (size_t i = 0; i < keys.size(); i++) { + bool kq = keys_quoted; + if (!kq) { + // FlexBuffers keys may contain arbitrary characters, only allow + // unquoted if it looks like an "identifier": + const char *p = keys[i].AsKey(); + if (!flatbuffers::is_alpha(*p) && *p != '_') { + kq = true; + } else { + while (*++p) { + if (!flatbuffers::is_alnum(*p) && *p != '_') { + kq = true; + break; + } + } + } + } + keys[i].ToString(true, kq, s); + s += ": "; + vals[i].ToString(true, keys_quoted, s); + if (i < keys.size() - 1) s += ", "; + } + s += " }"; + } else if (IsVector()) { + AppendToString(s, AsVector(), keys_quoted); + } else if (IsTypedVector()) { + AppendToString(s, AsTypedVector(), keys_quoted); + } else if (IsFixedTypedVector()) { + AppendToString(s, AsFixedTypedVector(), keys_quoted); + } else if (IsBlob()) { + auto blob = AsBlob(); + flatbuffers::EscapeString(reinterpret_cast(blob.data()), + blob.size(), &s, true, false); + } else { + s += "(?)"; + } + } + + // This function returns the empty blob if you try to read a not-blob. + // Strings can be viewed as blobs too. + Blob AsBlob() const { + if (type_ == FBT_BLOB || type_ == FBT_STRING) { + return Blob(Indirect(), byte_width_); + } else { + return Blob::EmptyBlob(); + } + } + + // This function returns the empty vector if you try to read a not-vector. + // Maps can be viewed as vectors too. + Vector AsVector() const { + if (type_ == FBT_VECTOR || type_ == FBT_MAP) { + return Vector(Indirect(), byte_width_); + } else { + return Vector::EmptyVector(); + } + } + + TypedVector AsTypedVector() const { + if (IsTypedVector()) { + auto tv = + TypedVector(Indirect(), byte_width_, ToTypedVectorElementType(type_)); + if (tv.type_ == FBT_STRING) { + // These can't be accessed as strings, since we don't know the bit-width + // of the size field, see the declaration of + // FBT_VECTOR_STRING_DEPRECATED above for details. + // We change the type here to be keys, which are a subtype of strings, + // and will ignore the size field. This will truncate strings with + // embedded nulls. + tv.type_ = FBT_KEY; + } + return tv; + } else { + return TypedVector::EmptyTypedVector(); + } + } + + FixedTypedVector AsFixedTypedVector() const { + if (IsFixedTypedVector()) { + uint8_t len = 0; + auto vtype = ToFixedTypedVectorElementType(type_, &len); + return FixedTypedVector(Indirect(), byte_width_, vtype, len); + } else { + return FixedTypedVector::EmptyFixedTypedVector(); + } + } + + Map AsMap() const { + if (type_ == FBT_MAP) { + return Map(Indirect(), byte_width_); + } else { + return Map::EmptyMap(); + } + } + + template T As() const; + + // Experimental: Mutation functions. + // These allow scalars in an already created buffer to be updated in-place. + // Since by default scalars are stored in the smallest possible space, + // the new value may not fit, in which case these functions return false. + // To avoid this, you can construct the values you intend to mutate using + // Builder::ForceMinimumBitWidth. + bool MutateInt(int64_t i) { + if (type_ == FBT_INT) { + return Mutate(data_, i, parent_width_, WidthI(i)); + } else if (type_ == FBT_INDIRECT_INT) { + return Mutate(Indirect(), i, byte_width_, WidthI(i)); + } else if (type_ == FBT_UINT) { + auto u = static_cast(i); + return Mutate(data_, u, parent_width_, WidthU(u)); + } else if (type_ == FBT_INDIRECT_UINT) { + auto u = static_cast(i); + return Mutate(Indirect(), u, byte_width_, WidthU(u)); + } else { + return false; + } + } + + bool MutateBool(bool b) { + return type_ == FBT_BOOL && Mutate(data_, b, parent_width_, BIT_WIDTH_8); + } + + bool MutateUInt(uint64_t u) { + if (type_ == FBT_UINT) { + return Mutate(data_, u, parent_width_, WidthU(u)); + } else if (type_ == FBT_INDIRECT_UINT) { + return Mutate(Indirect(), u, byte_width_, WidthU(u)); + } else if (type_ == FBT_INT) { + auto i = static_cast(u); + return Mutate(data_, i, parent_width_, WidthI(i)); + } else if (type_ == FBT_INDIRECT_INT) { + auto i = static_cast(u); + return Mutate(Indirect(), i, byte_width_, WidthI(i)); + } else { + return false; + } + } + + bool MutateFloat(float f) { + if (type_ == FBT_FLOAT) { + return MutateF(data_, f, parent_width_, BIT_WIDTH_32); + } else if (type_ == FBT_INDIRECT_FLOAT) { + return MutateF(Indirect(), f, byte_width_, BIT_WIDTH_32); + } else { + return false; + } + } + + bool MutateFloat(double d) { + if (type_ == FBT_FLOAT) { + return MutateF(data_, d, parent_width_, WidthF(d)); + } else if (type_ == FBT_INDIRECT_FLOAT) { + return MutateF(Indirect(), d, byte_width_, WidthF(d)); + } else { + return false; + } + } + + bool MutateString(const char *str, size_t len) { + auto s = AsString(); + if (s.IsTheEmptyString()) return false; + // This is very strict, could allow shorter strings, but that creates + // garbage. + if (s.length() != len) return false; + memcpy(const_cast(s.c_str()), str, len); + return true; + } + bool MutateString(const char *str) { return MutateString(str, strlen(str)); } + bool MutateString(const std::string &str) { + return MutateString(str.data(), str.length()); + } + + private: + const uint8_t *Indirect() const { + return flexbuffers::Indirect(data_, parent_width_); + } + + template + bool Mutate(const uint8_t *dest, T t, size_t byte_width, + BitWidth value_width) { + auto fits = static_cast(static_cast(1U) << value_width) <= + byte_width; + if (fits) { + t = flatbuffers::EndianScalar(t); + memcpy(const_cast(dest), &t, byte_width); + } + return fits; + } + + template + bool MutateF(const uint8_t *dest, T t, size_t byte_width, + BitWidth value_width) { + if (byte_width == sizeof(double)) + return Mutate(dest, static_cast(t), byte_width, value_width); + if (byte_width == sizeof(float)) + return Mutate(dest, static_cast(t), byte_width, value_width); + FLATBUFFERS_ASSERT(false); + return false; + } + + friend class Verifier; + + const uint8_t *data_; + uint8_t parent_width_; + uint8_t byte_width_; + Type type_; +}; + +// Template specialization for As(). +template<> inline bool Reference::As() const { return AsBool(); } + +template<> inline int8_t Reference::As() const { return AsInt8(); } +template<> inline int16_t Reference::As() const { return AsInt16(); } +template<> inline int32_t Reference::As() const { return AsInt32(); } +template<> inline int64_t Reference::As() const { return AsInt64(); } + +template<> inline uint8_t Reference::As() const { return AsUInt8(); } +template<> inline uint16_t Reference::As() const { + return AsUInt16(); +} +template<> inline uint32_t Reference::As() const { + return AsUInt32(); +} +template<> inline uint64_t Reference::As() const { + return AsUInt64(); +} + +template<> inline double Reference::As() const { return AsDouble(); } +template<> inline float Reference::As() const { return AsFloat(); } + +template<> inline String Reference::As() const { return AsString(); } +template<> inline std::string Reference::As() const { + return AsString().str(); +} + +template<> inline Blob Reference::As() const { return AsBlob(); } +template<> inline Vector Reference::As() const { return AsVector(); } +template<> inline TypedVector Reference::As() const { + return AsTypedVector(); +} +template<> inline FixedTypedVector Reference::As() const { + return AsFixedTypedVector(); +} +template<> inline Map Reference::As() const { return AsMap(); } + +inline uint8_t PackedType(BitWidth bit_width, Type type) { + return static_cast(bit_width | (type << 2)); +} + +inline uint8_t NullPackedType() { return PackedType(BIT_WIDTH_8, FBT_NULL); } + +// Vector accessors. +// Note: if you try to access outside of bounds, you get a Null value back +// instead. Normally this would be an assert, but since this is "dynamically +// typed" data, you may not want that (someone sends you a 2d vector and you +// wanted 3d). +// The Null converts seamlessly into a default value for any other type. +// TODO(wvo): Could introduce an #ifdef that makes this into an assert? +inline Reference Vector::operator[](size_t i) const { + auto len = size(); + if (i >= len) return Reference(nullptr, 1, NullPackedType()); + auto packed_type = (data_ + len * byte_width_)[i]; + auto elem = data_ + i * byte_width_; + return Reference(elem, byte_width_, packed_type); +} + +inline Reference TypedVector::operator[](size_t i) const { + auto len = size(); + if (i >= len) return Reference(nullptr, 1, NullPackedType()); + auto elem = data_ + i * byte_width_; + return Reference(elem, byte_width_, 1, type_); +} + +inline Reference FixedTypedVector::operator[](size_t i) const { + if (i >= len_) return Reference(nullptr, 1, NullPackedType()); + auto elem = data_ + i * byte_width_; + return Reference(elem, byte_width_, 1, type_); +} + +template int KeyCompare(const void *key, const void *elem) { + auto str_elem = reinterpret_cast( + Indirect(reinterpret_cast(elem))); + auto skey = reinterpret_cast(key); + return strcmp(skey, str_elem); +} + +inline Reference Map::operator[](const char *key) const { + auto keys = Keys(); + // We can't pass keys.byte_width_ to the comparison function, so we have + // to pick the right one ahead of time. + int (*comp)(const void *, const void *) = nullptr; + switch (keys.byte_width_) { + case 1: comp = KeyCompare; break; + case 2: comp = KeyCompare; break; + case 4: comp = KeyCompare; break; + case 8: comp = KeyCompare; break; + default: FLATBUFFERS_ASSERT(false); return Reference(); + } + auto res = std::bsearch(key, keys.data_, keys.size(), keys.byte_width_, comp); + if (!res) return Reference(nullptr, 1, NullPackedType()); + auto i = (reinterpret_cast(res) - keys.data_) / keys.byte_width_; + return (*static_cast(this))[i]; +} + +inline Reference Map::operator[](const std::string &key) const { + return (*this)[key.c_str()]; +} + +inline Reference GetRoot(const uint8_t *buffer, size_t size) { + // See Finish() below for the serialization counterpart of this. + // The root starts at the end of the buffer, so we parse backwards from there. + auto end = buffer + size; + auto byte_width = *--end; + auto packed_type = *--end; + end -= byte_width; // The root data item. + return Reference(end, byte_width, packed_type); +} + +inline Reference GetRoot(const std::vector &buffer) { + return GetRoot(buffer.data(), buffer.size()); +} + +// Flags that configure how the Builder behaves. +// The "Share" flags determine if the Builder automatically tries to pool +// this type. Pooling can reduce the size of serialized data if there are +// multiple maps of the same kind, at the expense of slightly slower +// serialization (the cost of lookups) and more memory use (std::set). +// By default this is on for keys, but off for strings. +// Turn keys off if you have e.g. only one map. +// Turn strings on if you expect many non-unique string values. +// Additionally, sharing key vectors can save space if you have maps with +// identical field populations. +enum BuilderFlag { + BUILDER_FLAG_NONE = 0, + BUILDER_FLAG_SHARE_KEYS = 1, + BUILDER_FLAG_SHARE_STRINGS = 2, + BUILDER_FLAG_SHARE_KEYS_AND_STRINGS = 3, + BUILDER_FLAG_SHARE_KEY_VECTORS = 4, + BUILDER_FLAG_SHARE_ALL = 7, +}; + +class Builder FLATBUFFERS_FINAL_CLASS { + public: + Builder(size_t initial_size = 256, + BuilderFlag flags = BUILDER_FLAG_SHARE_KEYS) + : buf_(initial_size), + finished_(false), + has_duplicate_keys_(false), + flags_(flags), + force_min_bit_width_(BIT_WIDTH_8), + key_pool(KeyOffsetCompare(buf_)), + string_pool(StringOffsetCompare(buf_)) { + buf_.clear(); + } + +#ifdef FLATBUFFERS_DEFAULT_DECLARATION + Builder(Builder &&) = default; + Builder &operator=(Builder &&) = default; +#endif + + /// @brief Get the serialized buffer (after you call `Finish()`). + /// @return Returns a vector owned by this class. + const std::vector &GetBuffer() const { + Finished(); + return buf_; + } + + // Size of the buffer. Does not include unfinished values. + size_t GetSize() const { return buf_.size(); } + + // Reset all state so we can re-use the buffer. + void Clear() { + buf_.clear(); + stack_.clear(); + finished_ = false; + // flags_ remains as-is; + force_min_bit_width_ = BIT_WIDTH_8; + key_pool.clear(); + string_pool.clear(); + } + + // All value constructing functions below have two versions: one that + // takes a key (for placement inside a map) and one that doesn't (for inside + // vectors and elsewhere). + + void Null() { stack_.push_back(Value()); } + void Null(const char *key) { + Key(key); + Null(); + } + + void Int(int64_t i) { stack_.push_back(Value(i, FBT_INT, WidthI(i))); } + void Int(const char *key, int64_t i) { + Key(key); + Int(i); + } + + void UInt(uint64_t u) { stack_.push_back(Value(u, FBT_UINT, WidthU(u))); } + void UInt(const char *key, uint64_t u) { + Key(key); + UInt(u); + } + + void Float(float f) { stack_.push_back(Value(f)); } + void Float(const char *key, float f) { + Key(key); + Float(f); + } + + void Double(double f) { stack_.push_back(Value(f)); } + void Double(const char *key, double d) { + Key(key); + Double(d); + } + + void Bool(bool b) { stack_.push_back(Value(b)); } + void Bool(const char *key, bool b) { + Key(key); + Bool(b); + } + + void IndirectInt(int64_t i) { PushIndirect(i, FBT_INDIRECT_INT, WidthI(i)); } + void IndirectInt(const char *key, int64_t i) { + Key(key); + IndirectInt(i); + } + + void IndirectUInt(uint64_t u) { + PushIndirect(u, FBT_INDIRECT_UINT, WidthU(u)); + } + void IndirectUInt(const char *key, uint64_t u) { + Key(key); + IndirectUInt(u); + } + + void IndirectFloat(float f) { + PushIndirect(f, FBT_INDIRECT_FLOAT, BIT_WIDTH_32); + } + void IndirectFloat(const char *key, float f) { + Key(key); + IndirectFloat(f); + } + + void IndirectDouble(double f) { + PushIndirect(f, FBT_INDIRECT_FLOAT, WidthF(f)); + } + void IndirectDouble(const char *key, double d) { + Key(key); + IndirectDouble(d); + } + + size_t Key(const char *str, size_t len) { + auto sloc = buf_.size(); + WriteBytes(str, len + 1); + if (flags_ & BUILDER_FLAG_SHARE_KEYS) { + auto it = key_pool.find(sloc); + if (it != key_pool.end()) { + // Already in the buffer. Remove key we just serialized, and use + // existing offset instead. + buf_.resize(sloc); + sloc = *it; + } else { + key_pool.insert(sloc); + } + } + stack_.push_back(Value(static_cast(sloc), FBT_KEY, BIT_WIDTH_8)); + return sloc; + } + + size_t Key(const char *str) { return Key(str, strlen(str)); } + size_t Key(const std::string &str) { return Key(str.c_str(), str.size()); } + + size_t String(const char *str, size_t len) { + auto reset_to = buf_.size(); + auto sloc = CreateBlob(str, len, 1, FBT_STRING); + if (flags_ & BUILDER_FLAG_SHARE_STRINGS) { + StringOffset so(sloc, len); + auto it = string_pool.find(so); + if (it != string_pool.end()) { + // Already in the buffer. Remove string we just serialized, and use + // existing offset instead. + buf_.resize(reset_to); + sloc = it->first; + stack_.back().u_ = sloc; + } else { + string_pool.insert(so); + } + } + return sloc; + } + size_t String(const char *str) { return String(str, strlen(str)); } + size_t String(const std::string &str) { + return String(str.c_str(), str.size()); + } + void String(const flexbuffers::String &str) { + String(str.c_str(), str.length()); + } + + void String(const char *key, const char *str) { + Key(key); + String(str); + } + void String(const char *key, const std::string &str) { + Key(key); + String(str); + } + void String(const char *key, const flexbuffers::String &str) { + Key(key); + String(str); + } + + size_t Blob(const void *data, size_t len) { + return CreateBlob(data, len, 0, FBT_BLOB); + } + size_t Blob(const std::vector &v) { + return CreateBlob(v.data(), v.size(), 0, FBT_BLOB); + } + + void Blob(const char *key, const void *data, size_t len) { + Key(key); + Blob(data, len); + } + void Blob(const char *key, const std::vector &v) { + Key(key); + Blob(v); + } + + // TODO(wvo): support all the FlexBuffer types (like flexbuffers::String), + // e.g. Vector etc. Also in overloaded versions. + // Also some FlatBuffers types? + + size_t StartVector() { return stack_.size(); } + size_t StartVector(const char *key) { + Key(key); + return stack_.size(); + } + size_t StartMap() { return stack_.size(); } + size_t StartMap(const char *key) { + Key(key); + return stack_.size(); + } + + // TODO(wvo): allow this to specify an alignment greater than the natural + // alignment. + size_t EndVector(size_t start, bool typed, bool fixed) { + auto vec = CreateVector(start, stack_.size() - start, 1, typed, fixed); + // Remove temp elements and return vector. + stack_.resize(start); + stack_.push_back(vec); + return static_cast(vec.u_); + } + + size_t EndMap(size_t start) { + // We should have interleaved keys and values on the stack. + // Make sure it is an even number: + auto len = stack_.size() - start; + FLATBUFFERS_ASSERT(!(len & 1)); + len /= 2; + // Make sure keys are all strings: + for (auto key = start; key < stack_.size(); key += 2) { + FLATBUFFERS_ASSERT(stack_[key].type_ == FBT_KEY); + } + // Now sort values, so later we can do a binary search lookup. + // We want to sort 2 array elements at a time. + struct TwoValue { + Value key; + Value val; + }; + // TODO(wvo): strict aliasing? + // TODO(wvo): allow the caller to indicate the data is already sorted + // for maximum efficiency? With an assert to check sortedness to make sure + // we're not breaking binary search. + // Or, we can track if the map is sorted as keys are added which would be + // be quite cheap (cheaper than checking it here), so we can skip this + // step automatically when appliccable, and encourage people to write in + // sorted fashion. + // std::sort is typically already a lot faster on sorted data though. + auto dict = reinterpret_cast(stack_.data() + start); + std::sort( + dict, dict + len, [&](const TwoValue &a, const TwoValue &b) -> bool { + auto as = reinterpret_cast(buf_.data() + a.key.u_); + auto bs = reinterpret_cast(buf_.data() + b.key.u_); + auto comp = strcmp(as, bs); + // We want to disallow duplicate keys, since this results in a + // map where values cannot be found. + // But we can't assert here (since we don't want to fail on + // random JSON input) or have an error mechanism. + // Instead, we set has_duplicate_keys_ in the builder to + // signal this. + // TODO: Have to check for pointer equality, as some sort + // implementation apparently call this function with the same + // element?? Why? + if (!comp && &a != &b) has_duplicate_keys_ = true; + return comp < 0; + }); + // First create a vector out of all keys. + // TODO(wvo): if kBuilderFlagShareKeyVectors is true, see if we can share + // the first vector. + auto keys = CreateVector(start, len, 2, true, false); + auto vec = CreateVector(start + 1, len, 2, false, false, &keys); + // Remove temp elements and return map. + stack_.resize(start); + stack_.push_back(vec); + return static_cast(vec.u_); + } + + // Call this after EndMap to see if the map had any duplicate keys. + // Any map with such keys won't be able to retrieve all values. + bool HasDuplicateKeys() const { return has_duplicate_keys_; } + + template size_t Vector(F f) { + auto start = StartVector(); + f(); + return EndVector(start, false, false); + } + template size_t Vector(F f, T &state) { + auto start = StartVector(); + f(state); + return EndVector(start, false, false); + } + template size_t Vector(const char *key, F f) { + auto start = StartVector(key); + f(); + return EndVector(start, false, false); + } + template + size_t Vector(const char *key, F f, T &state) { + auto start = StartVector(key); + f(state); + return EndVector(start, false, false); + } + + template void Vector(const T *elems, size_t len) { + if (flatbuffers::is_scalar::value) { + // This path should be a lot quicker and use less space. + ScalarVector(elems, len, false); + } else { + auto start = StartVector(); + for (size_t i = 0; i < len; i++) Add(elems[i]); + EndVector(start, false, false); + } + } + template + void Vector(const char *key, const T *elems, size_t len) { + Key(key); + Vector(elems, len); + } + template void Vector(const std::vector &vec) { + Vector(vec.data(), vec.size()); + } + + template size_t TypedVector(F f) { + auto start = StartVector(); + f(); + return EndVector(start, true, false); + } + template size_t TypedVector(F f, T &state) { + auto start = StartVector(); + f(state); + return EndVector(start, true, false); + } + template size_t TypedVector(const char *key, F f) { + auto start = StartVector(key); + f(); + return EndVector(start, true, false); + } + template + size_t TypedVector(const char *key, F f, T &state) { + auto start = StartVector(key); + f(state); + return EndVector(start, true, false); + } + + template size_t FixedTypedVector(const T *elems, size_t len) { + // We only support a few fixed vector lengths. Anything bigger use a + // regular typed vector. + FLATBUFFERS_ASSERT(len >= 2 && len <= 4); + // And only scalar values. + static_assert(flatbuffers::is_scalar::value, "Unrelated types"); + return ScalarVector(elems, len, true); + } + + template + size_t FixedTypedVector(const char *key, const T *elems, size_t len) { + Key(key); + return FixedTypedVector(elems, len); + } + + template size_t Map(F f) { + auto start = StartMap(); + f(); + return EndMap(start); + } + template size_t Map(F f, T &state) { + auto start = StartMap(); + f(state); + return EndMap(start); + } + template size_t Map(const char *key, F f) { + auto start = StartMap(key); + f(); + return EndMap(start); + } + template size_t Map(const char *key, F f, T &state) { + auto start = StartMap(key); + f(state); + return EndMap(start); + } + template void Map(const std::map &map) { + auto start = StartMap(); + for (auto it = map.begin(); it != map.end(); ++it) + Add(it->first.c_str(), it->second); + EndMap(start); + } + + // If you wish to share a value explicitly (a value not shared automatically + // through one of the BUILDER_FLAG_SHARE_* flags) you can do so with these + // functions. Or if you wish to turn those flags off for performance reasons + // and still do some explicit sharing. For example: + // builder.IndirectDouble(M_PI); + // auto id = builder.LastValue(); // Remember where we stored it. + // .. more code goes here .. + // builder.ReuseValue(id); // Refers to same double by offset. + // LastValue works regardless of whether the value has a key or not. + // Works on any data type. + struct Value; + Value LastValue() { return stack_.back(); } + void ReuseValue(Value v) { stack_.push_back(v); } + void ReuseValue(const char *key, Value v) { + Key(key); + ReuseValue(v); + } + + // Overloaded Add that tries to call the correct function above. + void Add(int8_t i) { Int(i); } + void Add(int16_t i) { Int(i); } + void Add(int32_t i) { Int(i); } + void Add(int64_t i) { Int(i); } + void Add(uint8_t u) { UInt(u); } + void Add(uint16_t u) { UInt(u); } + void Add(uint32_t u) { UInt(u); } + void Add(uint64_t u) { UInt(u); } + void Add(float f) { Float(f); } + void Add(double d) { Double(d); } + void Add(bool b) { Bool(b); } + void Add(const char *str) { String(str); } + void Add(const std::string &str) { String(str); } + void Add(const flexbuffers::String &str) { String(str); } + + template void Add(const std::vector &vec) { Vector(vec); } + + template void Add(const char *key, const T &t) { + Key(key); + Add(t); + } + + template void Add(const std::map &map) { + Map(map); + } + + template void operator+=(const T &t) { Add(t); } + + // This function is useful in combination with the Mutate* functions above. + // It forces elements of vectors and maps to have a minimum size, such that + // they can later be updated without failing. + // Call with no arguments to reset. + void ForceMinimumBitWidth(BitWidth bw = BIT_WIDTH_8) { + force_min_bit_width_ = bw; + } + + void Finish() { + // If you hit this assert, you likely have objects that were never included + // in a parent. You need to have exactly one root to finish a buffer. + // Check your Start/End calls are matched, and all objects are inside + // some other object. + FLATBUFFERS_ASSERT(stack_.size() == 1); + + // Write root value. + auto byte_width = Align(stack_[0].ElemWidth(buf_.size(), 0)); + WriteAny(stack_[0], byte_width); + // Write root type. + Write(stack_[0].StoredPackedType(), 1); + // Write root size. Normally determined by parent, but root has no parent :) + Write(byte_width, 1); + + finished_ = true; + } + + private: + void Finished() const { + // If you get this assert, you're attempting to get access a buffer + // which hasn't been finished yet. Be sure to call + // Builder::Finish with your root object. + FLATBUFFERS_ASSERT(finished_); + } + + // Align to prepare for writing a scalar with a certain size. + uint8_t Align(BitWidth alignment) { + auto byte_width = 1U << alignment; + buf_.insert(buf_.end(), flatbuffers::PaddingBytes(buf_.size(), byte_width), + 0); + return static_cast(byte_width); + } + + void WriteBytes(const void *val, size_t size) { + buf_.insert(buf_.end(), reinterpret_cast(val), + reinterpret_cast(val) + size); + } + + template void Write(T val, size_t byte_width) { + FLATBUFFERS_ASSERT(sizeof(T) >= byte_width); + val = flatbuffers::EndianScalar(val); + WriteBytes(&val, byte_width); + } + + void WriteDouble(double f, uint8_t byte_width) { + switch (byte_width) { + case 8: Write(f, byte_width); break; + case 4: Write(static_cast(f), byte_width); break; + // case 2: Write(static_cast(f), byte_width); break; + // case 1: Write(static_cast(f), byte_width); break; + default: FLATBUFFERS_ASSERT(0); + } + } + + void WriteOffset(uint64_t o, uint8_t byte_width) { + auto reloff = buf_.size() - o; + FLATBUFFERS_ASSERT(byte_width == 8 || reloff < 1ULL << (byte_width * 8)); + Write(reloff, byte_width); + } + + template void PushIndirect(T val, Type type, BitWidth bit_width) { + auto byte_width = Align(bit_width); + auto iloc = buf_.size(); + Write(val, byte_width); + stack_.push_back(Value(static_cast(iloc), type, bit_width)); + } + + static BitWidth WidthB(size_t byte_width) { + switch (byte_width) { + case 1: return BIT_WIDTH_8; + case 2: return BIT_WIDTH_16; + case 4: return BIT_WIDTH_32; + case 8: return BIT_WIDTH_64; + default: FLATBUFFERS_ASSERT(false); return BIT_WIDTH_64; + } + } + + template static Type GetScalarType() { + static_assert(flatbuffers::is_scalar::value, "Unrelated types"); + return flatbuffers::is_floating_point::value ? FBT_FLOAT + : flatbuffers::is_same::value + ? FBT_BOOL + : (flatbuffers::is_unsigned::value ? FBT_UINT : FBT_INT); + } + + public: + // This was really intended to be private, except for LastValue/ReuseValue. + struct Value { + union { + int64_t i_; + uint64_t u_; + double f_; + }; + + Type type_; + + // For scalars: of itself, for vector: of its elements, for string: length. + BitWidth min_bit_width_; + + Value() : i_(0), type_(FBT_NULL), min_bit_width_(BIT_WIDTH_8) {} + + Value(bool b) + : u_(static_cast(b)), + type_(FBT_BOOL), + min_bit_width_(BIT_WIDTH_8) {} + + Value(int64_t i, Type t, BitWidth bw) + : i_(i), type_(t), min_bit_width_(bw) {} + Value(uint64_t u, Type t, BitWidth bw) + : u_(u), type_(t), min_bit_width_(bw) {} + + Value(float f) + : f_(static_cast(f)), + type_(FBT_FLOAT), + min_bit_width_(BIT_WIDTH_32) {} + Value(double f) : f_(f), type_(FBT_FLOAT), min_bit_width_(WidthF(f)) {} + + uint8_t StoredPackedType(BitWidth parent_bit_width_ = BIT_WIDTH_8) const { + return PackedType(StoredWidth(parent_bit_width_), type_); + } + + BitWidth ElemWidth(size_t buf_size, size_t elem_index) const { + if (IsInline(type_)) { + return min_bit_width_; + } else { + // We have an absolute offset, but want to store a relative offset + // elem_index elements beyond the current buffer end. Since whether + // the relative offset fits in a certain byte_width depends on + // the size of the elements before it (and their alignment), we have + // to test for each size in turn. + for (size_t byte_width = 1; + byte_width <= sizeof(flatbuffers::largest_scalar_t); + byte_width *= 2) { + // Where are we going to write this offset? + auto offset_loc = buf_size + + flatbuffers::PaddingBytes(buf_size, byte_width) + + elem_index * byte_width; + // Compute relative offset. + auto offset = offset_loc - u_; + // Does it fit? + auto bit_width = WidthU(offset); + if (static_cast(static_cast(1U) << bit_width) == + byte_width) + return bit_width; + } + FLATBUFFERS_ASSERT(false); // Must match one of the sizes above. + return BIT_WIDTH_64; + } + } + + BitWidth StoredWidth(BitWidth parent_bit_width_ = BIT_WIDTH_8) const { + if (IsInline(type_)) { + return (std::max)(min_bit_width_, parent_bit_width_); + } else { + return min_bit_width_; + } + } + }; + + private: + void WriteAny(const Value &val, uint8_t byte_width) { + switch (val.type_) { + case FBT_NULL: + case FBT_INT: Write(val.i_, byte_width); break; + case FBT_BOOL: + case FBT_UINT: Write(val.u_, byte_width); break; + case FBT_FLOAT: WriteDouble(val.f_, byte_width); break; + default: WriteOffset(val.u_, byte_width); break; + } + } + + size_t CreateBlob(const void *data, size_t len, size_t trailing, Type type) { + auto bit_width = WidthU(len); + auto byte_width = Align(bit_width); + Write(len, byte_width); + auto sloc = buf_.size(); + WriteBytes(data, len + trailing); + stack_.push_back(Value(static_cast(sloc), type, bit_width)); + return sloc; + } + + template + size_t ScalarVector(const T *elems, size_t len, bool fixed) { + auto vector_type = GetScalarType(); + auto byte_width = sizeof(T); + auto bit_width = WidthB(byte_width); + // If you get this assert, you're trying to write a vector with a size + // field that is bigger than the scalars you're trying to write (e.g. a + // byte vector > 255 elements). For such types, write a "blob" instead. + // TODO: instead of asserting, could write vector with larger elements + // instead, though that would be wasteful. + FLATBUFFERS_ASSERT(WidthU(len) <= bit_width); + Align(bit_width); + if (!fixed) Write(len, byte_width); + auto vloc = buf_.size(); + for (size_t i = 0; i < len; i++) Write(elems[i], byte_width); + stack_.push_back(Value(static_cast(vloc), + ToTypedVector(vector_type, fixed ? len : 0), + bit_width)); + return vloc; + } + + Value CreateVector(size_t start, size_t vec_len, size_t step, bool typed, + bool fixed, const Value *keys = nullptr) { + FLATBUFFERS_ASSERT( + !fixed || + typed); // typed=false, fixed=true combination is not supported. + // Figure out smallest bit width we can store this vector with. + auto bit_width = (std::max)(force_min_bit_width_, WidthU(vec_len)); + auto prefix_elems = 1; + if (keys) { + // If this vector is part of a map, we will pre-fix an offset to the keys + // to this vector. + bit_width = (std::max)(bit_width, keys->ElemWidth(buf_.size(), 0)); + prefix_elems += 2; + } + Type vector_type = FBT_KEY; + // Check bit widths and types for all elements. + for (size_t i = start; i < stack_.size(); i += step) { + auto elem_width = + stack_[i].ElemWidth(buf_.size(), i - start + prefix_elems); + bit_width = (std::max)(bit_width, elem_width); + if (typed) { + if (i == start) { + vector_type = stack_[i].type_; + } else { + // If you get this assert, you are writing a typed vector with + // elements that are not all the same type. + FLATBUFFERS_ASSERT(vector_type == stack_[i].type_); + } + } + } + // If you get this assert, your typed types are not one of: + // Int / UInt / Float / Key. + FLATBUFFERS_ASSERT(!typed || IsTypedVectorElementType(vector_type)); + auto byte_width = Align(bit_width); + // Write vector. First the keys width/offset if available, and size. + if (keys) { + WriteOffset(keys->u_, byte_width); + Write(1ULL << keys->min_bit_width_, byte_width); + } + if (!fixed) Write(vec_len, byte_width); + // Then the actual data. + auto vloc = buf_.size(); + for (size_t i = start; i < stack_.size(); i += step) { + WriteAny(stack_[i], byte_width); + } + // Then the types. + if (!typed) { + for (size_t i = start; i < stack_.size(); i += step) { + buf_.push_back(stack_[i].StoredPackedType(bit_width)); + } + } + return Value(static_cast(vloc), + keys ? FBT_MAP + : (typed ? ToTypedVector(vector_type, fixed ? vec_len : 0) + : FBT_VECTOR), + bit_width); + } + + // You shouldn't really be copying instances of this class. + Builder(const Builder &); + Builder &operator=(const Builder &); + + std::vector buf_; + std::vector stack_; + + bool finished_; + bool has_duplicate_keys_; + + BuilderFlag flags_; + + BitWidth force_min_bit_width_; + + struct KeyOffsetCompare { + explicit KeyOffsetCompare(const std::vector &buf) : buf_(&buf) {} + bool operator()(size_t a, size_t b) const { + auto stra = reinterpret_cast(buf_->data() + a); + auto strb = reinterpret_cast(buf_->data() + b); + return strcmp(stra, strb) < 0; + } + const std::vector *buf_; + }; + + typedef std::pair StringOffset; + struct StringOffsetCompare { + explicit StringOffsetCompare(const std::vector &buf) + : buf_(&buf) {} + bool operator()(const StringOffset &a, const StringOffset &b) const { + auto stra = buf_->data() + a.first; + auto strb = buf_->data() + b.first; + auto cr = memcmp(stra, strb, (std::min)(a.second, b.second) + 1); + return cr < 0 || (cr == 0 && a.second < b.second); + } + const std::vector *buf_; + }; + + typedef std::set KeyOffsetMap; + typedef std::set StringOffsetMap; + + KeyOffsetMap key_pool; + StringOffsetMap string_pool; + + friend class Verifier; +}; + +// Helper class to verify the integrity of a FlexBuffer +class Verifier FLATBUFFERS_FINAL_CLASS { + public: + Verifier(const uint8_t *buf, size_t buf_len, + // Supplying this vector likely results in faster verification + // of larger buffers with many shared keys/strings, but + // comes at the cost of using additional memory the same size of + // the buffer being verified, so it is by default off. + std::vector *reuse_tracker = nullptr, + bool _check_alignment = true, size_t max_depth = 64) + : buf_(buf), + size_(buf_len), + depth_(0), + max_depth_(max_depth), + num_vectors_(0), + max_vectors_(buf_len), + check_alignment_(_check_alignment), + reuse_tracker_(reuse_tracker) { + FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE); + if (reuse_tracker_) { + reuse_tracker_->clear(); + reuse_tracker_->resize(size_, PackedType(BIT_WIDTH_8, FBT_NULL)); + } + } + + private: + // Central location where any verification failures register. + bool Check(bool ok) const { + // clang-format off + #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE + FLATBUFFERS_ASSERT(ok); + #endif + // clang-format on + return ok; + } + + // Verify any range within the buffer. + bool VerifyFrom(size_t elem, size_t elem_len) const { + return Check(elem_len < size_ && elem <= size_ - elem_len); + } + bool VerifyBefore(size_t elem, size_t elem_len) const { + return Check(elem_len <= elem); + } + + bool VerifyFromPointer(const uint8_t *p, size_t len) { + auto o = static_cast(p - buf_); + return VerifyFrom(o, len); + } + bool VerifyBeforePointer(const uint8_t *p, size_t len) { + auto o = static_cast(p - buf_); + return VerifyBefore(o, len); + } + + bool VerifyByteWidth(size_t width) { + return Check(width == 1 || width == 2 || width == 4 || width == 8); + } + + bool VerifyType(int type) { return Check(type >= 0 && type < FBT_MAX_TYPE); } + + bool VerifyOffset(uint64_t off, const uint8_t *p) { + return Check(off <= static_cast(size_)) && + off <= static_cast(p - buf_); + } + + bool VerifyAlignment(const uint8_t *p, size_t size) const { + auto o = static_cast(p - buf_); + return Check((o & (size - 1)) == 0 || !check_alignment_); + } + +// Macro, since we want to escape from parent function & use lazy args. +#define FLEX_CHECK_VERIFIED(P, PACKED_TYPE) \ + if (reuse_tracker_) { \ + auto packed_type = PACKED_TYPE; \ + auto existing = (*reuse_tracker_)[P - buf_]; \ + if (existing == packed_type) return true; \ + /* Fail verification if already set with different type! */ \ + if (!Check(existing == 0)) return false; \ + (*reuse_tracker_)[P - buf_] = packed_type; \ + } + + bool VerifyVector(Reference r, const uint8_t *p, Type elem_type) { + // Any kind of nesting goes thru this function, so guard against that + // here, both with simple nesting checks, and the reuse tracker if on. + depth_++; + num_vectors_++; + if (!Check(depth_ <= max_depth_ && num_vectors_ <= max_vectors_)) + return false; + auto size_byte_width = r.byte_width_; + FLEX_CHECK_VERIFIED(p, + PackedType(Builder::WidthB(size_byte_width), r.type_)); + if (!VerifyBeforePointer(p, size_byte_width)) return false; + auto sized = Sized(p, size_byte_width); + auto num_elems = sized.size(); + auto elem_byte_width = r.type_ == FBT_STRING || r.type_ == FBT_BLOB + ? uint8_t(1) + : r.byte_width_; + auto max_elems = SIZE_MAX / elem_byte_width; + if (!Check(num_elems < max_elems)) + return false; // Protect against byte_size overflowing. + auto byte_size = num_elems * elem_byte_width; + if (!VerifyFromPointer(p, byte_size)) return false; + if (elem_type == FBT_NULL) { + // Verify type bytes after the vector. + if (!VerifyFromPointer(p + byte_size, num_elems)) return false; + auto v = Vector(p, size_byte_width); + for (size_t i = 0; i < num_elems; i++) + if (!VerifyRef(v[i])) return false; + } else if (elem_type == FBT_KEY) { + auto v = TypedVector(p, elem_byte_width, FBT_KEY); + for (size_t i = 0; i < num_elems; i++) + if (!VerifyRef(v[i])) return false; + } else { + FLATBUFFERS_ASSERT(IsInline(elem_type)); + } + depth_--; + return true; + } + + bool VerifyKeys(const uint8_t *p, uint8_t byte_width) { + // The vector part of the map has already been verified. + const size_t num_prefixed_fields = 3; + if (!VerifyBeforePointer(p, byte_width * num_prefixed_fields)) return false; + p -= byte_width * num_prefixed_fields; + auto off = ReadUInt64(p, byte_width); + if (!VerifyOffset(off, p)) return false; + auto key_byte_with = + static_cast(ReadUInt64(p + byte_width, byte_width)); + if (!VerifyByteWidth(key_byte_with)) return false; + return VerifyVector(Reference(p, byte_width, key_byte_with, FBT_VECTOR_KEY), + p - off, FBT_KEY); + } + + bool VerifyKey(const uint8_t *p) { + FLEX_CHECK_VERIFIED(p, PackedType(BIT_WIDTH_8, FBT_KEY)); + while (p < buf_ + size_) + if (*p++) return true; + return false; + } + +#undef FLEX_CHECK_VERIFIED + + bool VerifyTerminator(const String &s) { + return VerifyFromPointer(reinterpret_cast(s.c_str()), + s.size() + 1); + } + + bool VerifyRef(Reference r) { + // r.parent_width_ and r.data_ already verified. + if (!VerifyByteWidth(r.byte_width_) || !VerifyType(r.type_)) { + return false; + } + if (IsInline(r.type_)) { + // Inline scalars, don't require further verification. + return true; + } + // All remaining types are an offset. + auto off = ReadUInt64(r.data_, r.parent_width_); + if (!VerifyOffset(off, r.data_)) return false; + auto p = r.Indirect(); + if (!VerifyAlignment(p, r.byte_width_)) return false; + switch (r.type_) { + case FBT_INDIRECT_INT: + case FBT_INDIRECT_UINT: + case FBT_INDIRECT_FLOAT: return VerifyFromPointer(p, r.byte_width_); + case FBT_KEY: return VerifyKey(p); + case FBT_MAP: + return VerifyVector(r, p, FBT_NULL) && VerifyKeys(p, r.byte_width_); + case FBT_VECTOR: return VerifyVector(r, p, FBT_NULL); + case FBT_VECTOR_INT: return VerifyVector(r, p, FBT_INT); + case FBT_VECTOR_BOOL: + case FBT_VECTOR_UINT: return VerifyVector(r, p, FBT_UINT); + case FBT_VECTOR_FLOAT: return VerifyVector(r, p, FBT_FLOAT); + case FBT_VECTOR_KEY: return VerifyVector(r, p, FBT_KEY); + case FBT_VECTOR_STRING_DEPRECATED: + // Use of FBT_KEY here intentional, see elsewhere. + return VerifyVector(r, p, FBT_KEY); + case FBT_BLOB: return VerifyVector(r, p, FBT_UINT); + case FBT_STRING: + return VerifyVector(r, p, FBT_UINT) && + VerifyTerminator(String(p, r.byte_width_)); + case FBT_VECTOR_INT2: + case FBT_VECTOR_UINT2: + case FBT_VECTOR_FLOAT2: + case FBT_VECTOR_INT3: + case FBT_VECTOR_UINT3: + case FBT_VECTOR_FLOAT3: + case FBT_VECTOR_INT4: + case FBT_VECTOR_UINT4: + case FBT_VECTOR_FLOAT4: { + uint8_t len = 0; + auto vtype = ToFixedTypedVectorElementType(r.type_, &len); + if (!VerifyType(vtype)) return false; + return VerifyFromPointer(p, r.byte_width_ * len); + } + default: return false; + } + } + + public: + bool VerifyBuffer() { + if (!Check(size_ >= 3)) return false; + auto end = buf_ + size_; + auto byte_width = *--end; + auto packed_type = *--end; + return VerifyByteWidth(byte_width) && Check(end - buf_ >= byte_width) && + VerifyRef(Reference(end - byte_width, byte_width, packed_type)); + } + + private: + const uint8_t *buf_; + size_t size_; + size_t depth_; + const size_t max_depth_; + size_t num_vectors_; + const size_t max_vectors_; + bool check_alignment_; + std::vector *reuse_tracker_; +}; + +// Utility function that contructs the Verifier for you, see above for +// parameters. +inline bool VerifyBuffer(const uint8_t *buf, size_t buf_len, + std::vector *reuse_tracker = nullptr) { + Verifier verifier(buf, buf_len, reuse_tracker); + return verifier.VerifyBuffer(); +} + +#ifdef FLATBUFFERS_H_ +// This is a verifier utility function that works together with the +// FlatBuffers verifier, which should only be present if flatbuffer.h +// has been included (which it typically is in generated code). +inline bool VerifyNestedFlexBuffer(const flatbuffers::Vector *nv, + flatbuffers::Verifier &verifier) { + if (!nv) return true; + return verifier.Check(flexbuffers::VerifyBuffer( + nv->data(), nv->size(), verifier.GetFlexReuseTracker())); +} +#endif + +} // namespace flexbuffers + +#if defined(_MSC_VER) +# pragma warning(pop) +#endif + +#endif // FLATBUFFERS_FLEXBUFFERS_H_ diff --git a/new_ai_tools/include/flatbuffers/grpc.h b/new_ai_tools/include/flatbuffers/grpc.h new file mode 100644 index 00000000..5d15bc51 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/grpc.h @@ -0,0 +1,300 @@ +/* + * Copyright 2014 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_GRPC_H_ +#define FLATBUFFERS_GRPC_H_ + +// Helper functionality to glue FlatBuffers and GRPC. + +#include "flatbuffers/flatbuffers.h" +#include "grpc/byte_buffer_reader.h" +#include "grpcpp/support/byte_buffer.h" +#include "grpcpp/support/slice.h" + +namespace flatbuffers { +namespace grpc { + +// Message is a typed wrapper around a buffer that manages the underlying +// `grpc_slice` and also provides flatbuffers-specific helpers such as `Verify` +// and `GetRoot`. Since it is backed by a `grpc_slice`, the underlying buffer +// is refcounted and ownership is be managed automatically. +template class Message { + public: + Message() {} + + Message(::grpc::Slice slice) : slice_(slice) {} + + Message &operator=(const Message &other) = delete; + + Message(Message &&other) = default; + + Message(const Message &other) = delete; + + Message &operator=(Message &&other) = default; + + const uint8_t *mutable_data() const { return slice_.begin(); } + + const uint8_t *data() const { return slice_.begin(); } + + size_t size() const { return slice_.size(); } + + bool Verify() const { + Verifier verifier(data(), size()); + return verifier.VerifyBuffer(nullptr); + } + + T *GetMutableRoot() { return flatbuffers::GetMutableRoot(mutable_data()); } + + const T *GetRoot() const { return flatbuffers::GetRoot(data()); } + + // This is only intended for serializer use, or if you know what you're doing + const ::grpc::Slice &BorrowSlice() const { return slice_; } + + private: + ::grpc::Slice slice_; +}; + +class MessageBuilder; + +// SliceAllocator is a gRPC-specific allocator that uses the `grpc_slice` +// refcounted slices to manage memory ownership. This makes it easy and +// efficient to transfer buffers to gRPC. +class SliceAllocator : public Allocator { + public: + SliceAllocator() {} + + SliceAllocator(const SliceAllocator &other) = delete; + SliceAllocator &operator=(const SliceAllocator &other) = delete; + + SliceAllocator(SliceAllocator &&other) { + // default-construct and swap idiom + swap(other); + } + + SliceAllocator &operator=(SliceAllocator &&other) { + // move-construct and swap idiom + SliceAllocator temp(std::move(other)); + swap(temp); + return *this; + } + + void swap(SliceAllocator &other) { + using std::swap; + swap(slice_, other.slice_); + } + + virtual ~SliceAllocator() {} + + virtual uint8_t *allocate(size_t size) override { + FLATBUFFERS_ASSERT(slice_.size() == 0); + slice_ = ::grpc::Slice(size); + return const_cast(slice_.begin()); + } + + virtual void deallocate(uint8_t *p, size_t size) override { + FLATBUFFERS_ASSERT(p == slice_.begin()); + FLATBUFFERS_ASSERT(size == slice_.size()); + slice_ = ::grpc::Slice(); + } + + virtual uint8_t *reallocate_downward(uint8_t *old_p, size_t old_size, + size_t new_size, size_t in_use_back, + size_t in_use_front) override { + FLATBUFFERS_ASSERT(old_p == slice_.begin()); + FLATBUFFERS_ASSERT(old_size == slice_.size()); + FLATBUFFERS_ASSERT(new_size > old_size); + ::grpc::Slice old_slice = slice_; + ::grpc::Slice new_slice = ::grpc::Slice(new_size); + uint8_t *new_p = const_cast(new_slice.begin()); + memcpy_downward(old_p, old_size, new_p, new_size, in_use_back, + in_use_front); + slice_ = new_slice; + return new_p; + } + + private: + ::grpc::Slice &get_slice(uint8_t *p, size_t size) { + FLATBUFFERS_ASSERT(p == slice_.begin()); + FLATBUFFERS_ASSERT(size == slice_.size()); + return slice_; + } + + ::grpc::Slice slice_; + + friend class MessageBuilder; +}; + +// SliceAllocatorMember is a hack to ensure that the MessageBuilder's +// slice_allocator_ member is constructed before the FlatBufferBuilder, since +// the allocator is used in the FlatBufferBuilder ctor. +namespace detail { +struct SliceAllocatorMember { + SliceAllocator slice_allocator_; +}; +} // namespace detail + +// MessageBuilder is a gRPC-specific FlatBufferBuilder that uses SliceAllocator +// to allocate gRPC buffers. +class MessageBuilder : private detail::SliceAllocatorMember, + public FlatBufferBuilder { + public: + explicit MessageBuilder(uoffset_t initial_size = 1024) + : FlatBufferBuilder(initial_size, &slice_allocator_, false) {} + + MessageBuilder(const MessageBuilder &other) = delete; + MessageBuilder &operator=(const MessageBuilder &other) = delete; + + MessageBuilder(MessageBuilder &&other) + : FlatBufferBuilder(1024, &slice_allocator_, false) { + // Default construct and swap idiom. + Swap(other); + } + + /// Create a MessageBuilder from a FlatBufferBuilder. + explicit MessageBuilder(FlatBufferBuilder &&src, + void (*dealloc)(void *, + size_t) = &DefaultAllocator::dealloc) + : FlatBufferBuilder(1024, &slice_allocator_, false) { + src.Swap(*this); + src.SwapBufAllocator(*this); + if (buf_.capacity()) { + uint8_t *buf = buf_.scratch_data(); // pointer to memory + size_t capacity = buf_.capacity(); // size of memory + slice_allocator_.slice_ = ::grpc::Slice(buf, capacity, dealloc); + } else { + slice_allocator_.slice_ = ::grpc::Slice(); + } + } + + /// Move-assign a FlatBufferBuilder to a MessageBuilder. + /// Only FlatBufferBuilder with default allocator (basically, nullptr) is + /// supported. + MessageBuilder &operator=(FlatBufferBuilder &&src) { + // Move construct a temporary and swap + MessageBuilder temp(std::move(src)); + Swap(temp); + return *this; + } + + MessageBuilder &operator=(MessageBuilder &&other) { + // Move construct a temporary and swap + MessageBuilder temp(std::move(other)); + Swap(temp); + return *this; + } + + void Swap(MessageBuilder &other) { + slice_allocator_.swap(other.slice_allocator_); + FlatBufferBuilder::Swap(other); + // After swapping the FlatBufferBuilder, we swap back the allocator, which + // restores the original allocator back in place. This is necessary because + // MessageBuilder's allocator is its own member (SliceAllocatorMember). The + // allocator passed to FlatBufferBuilder::vector_downward must point to this + // member. + buf_.swap_allocator(other.buf_); + } + + // Releases the ownership of the buffer pointer. + // Returns the size, offset, and the original grpc_slice that + // allocated the buffer. Also see grpc_slice_unref(). + uint8_t *ReleaseRaw(size_t &size, size_t &offset, ::grpc::Slice &slice) { + uint8_t *buf = FlatBufferBuilder::ReleaseRaw(size, offset); + slice = slice_allocator_.slice_; + slice_allocator_.slice_ = ::grpc::Slice(); + return buf; + } + + ~MessageBuilder() {} + + // GetMessage extracts the subslice of the buffer corresponding to the + // flatbuffers-encoded region and wraps it in a `Message` to handle buffer + // ownership. + template Message GetMessage() { + auto buf_data = buf_.scratch_data(); // pointer to memory + auto buf_size = buf_.capacity(); // size of memory + auto msg_data = buf_.data(); // pointer to msg + auto msg_size = buf_.size(); // size of msg + // Do some sanity checks on data/size + FLATBUFFERS_ASSERT(msg_data); + FLATBUFFERS_ASSERT(msg_size); + FLATBUFFERS_ASSERT(msg_data >= buf_data); + FLATBUFFERS_ASSERT(msg_data + msg_size <= buf_data + buf_size); + // Calculate offsets from the buffer start + auto begin = msg_data - buf_data; + auto end = begin + msg_size; + // Get the slice we are working with (no refcount change) + ::grpc::Slice slice = slice_allocator_.get_slice(buf_data, buf_size); + // Extract a subslice of the existing slice (increment refcount) + ::grpc::Slice subslice = slice.sub(begin, end); + // Wrap the subslice in a `Message`, but don't increment refcount + Message msg(subslice); + return msg; + } + + template Message ReleaseMessage() { + Message msg = GetMessage(); + Reset(); + return msg; + } + + private: + // SliceAllocator slice_allocator_; // part of SliceAllocatorMember +}; + +} // namespace grpc +} // namespace flatbuffers + +namespace grpc { + +template class SerializationTraits> { + public: + static grpc::Status Serialize(const flatbuffers::grpc::Message &msg, + ByteBuffer *buffer, bool *own_buffer) { + // Package the single slice into a `ByteBuffer`, + // incrementing the refcount in the process. + *buffer = ByteBuffer(&msg.BorrowSlice(), 1); + *own_buffer = true; + return grpc::Status::OK; + } + + // Deserialize by pulling the + static grpc::Status Deserialize(ByteBuffer *buf, + flatbuffers::grpc::Message *msg) { + Slice slice; + if (!buf->TrySingleSlice(&slice).ok()) { + if (!buf->DumpToSingleSlice(&slice).ok()) { + buf->Clear(); + return ::grpc::Status(::grpc::StatusCode::INTERNAL, "No payload"); + } + } + *msg = flatbuffers::grpc::Message(slice); + buf->Clear(); +#if FLATBUFFERS_GRPC_DISABLE_AUTO_VERIFICATION + return ::grpc::Status::OK; +#else + if (msg->Verify()) { + return ::grpc::Status::OK; + } else { + return ::grpc::Status(::grpc::StatusCode::INTERNAL, + "Message verification failed"); + } +#endif + } +}; + +} // namespace grpc + +#endif // FLATBUFFERS_GRPC_H_ diff --git a/new_ai_tools/include/flatbuffers/hash.h b/new_ai_tools/include/flatbuffers/hash.h new file mode 100644 index 00000000..aebf0713 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/hash.h @@ -0,0 +1,127 @@ +/* + * Copyright 2015 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_HASH_H_ +#define FLATBUFFERS_HASH_H_ + +#include +#include + +#include "flatbuffers/flatbuffers.h" + +namespace flatbuffers { + +template struct FnvTraits { + static const T kFnvPrime; + static const T kOffsetBasis; +}; + +template<> struct FnvTraits { + static const uint32_t kFnvPrime = 0x01000193; + static const uint32_t kOffsetBasis = 0x811C9DC5; +}; + +template<> struct FnvTraits { + static const uint64_t kFnvPrime = 0x00000100000001b3ULL; + static const uint64_t kOffsetBasis = 0xcbf29ce484222645ULL; +}; + +template T HashFnv1(const char *input) { + T hash = FnvTraits::kOffsetBasis; + for (const char *c = input; *c; ++c) { + hash *= FnvTraits::kFnvPrime; + hash ^= static_cast(*c); + } + return hash; +} + +template T HashFnv1a(const char *input) { + T hash = FnvTraits::kOffsetBasis; + for (const char *c = input; *c; ++c) { + hash ^= static_cast(*c); + hash *= FnvTraits::kFnvPrime; + } + return hash; +} + +template<> inline uint16_t HashFnv1(const char *input) { + uint32_t hash = HashFnv1(input); + return (hash >> 16) ^ (hash & 0xffff); +} + +template<> inline uint16_t HashFnv1a(const char *input) { + uint32_t hash = HashFnv1a(input); + return (hash >> 16) ^ (hash & 0xffff); +} + +template struct NamedHashFunction { + const char *name; + + typedef T (*HashFunction)(const char *); + HashFunction function; +}; + +const NamedHashFunction kHashFunctions16[] = { + { "fnv1_16", HashFnv1 }, + { "fnv1a_16", HashFnv1a }, +}; + +const NamedHashFunction kHashFunctions32[] = { + { "fnv1_32", HashFnv1 }, + { "fnv1a_32", HashFnv1a }, +}; + +const NamedHashFunction kHashFunctions64[] = { + { "fnv1_64", HashFnv1 }, + { "fnv1a_64", HashFnv1a }, +}; + +inline NamedHashFunction::HashFunction FindHashFunction16( + const char *name) { + std::size_t size = sizeof(kHashFunctions16) / sizeof(kHashFunctions16[0]); + for (std::size_t i = 0; i < size; ++i) { + if (std::strcmp(name, kHashFunctions16[i].name) == 0) { + return kHashFunctions16[i].function; + } + } + return nullptr; +} + +inline NamedHashFunction::HashFunction FindHashFunction32( + const char *name) { + std::size_t size = sizeof(kHashFunctions32) / sizeof(kHashFunctions32[0]); + for (std::size_t i = 0; i < size; ++i) { + if (std::strcmp(name, kHashFunctions32[i].name) == 0) { + return kHashFunctions32[i].function; + } + } + return nullptr; +} + +inline NamedHashFunction::HashFunction FindHashFunction64( + const char *name) { + std::size_t size = sizeof(kHashFunctions64) / sizeof(kHashFunctions64[0]); + for (std::size_t i = 0; i < size; ++i) { + if (std::strcmp(name, kHashFunctions64[i].name) == 0) { + return kHashFunctions64[i].function; + } + } + return nullptr; +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_HASH_H_ diff --git a/new_ai_tools/include/flatbuffers/idl.h b/new_ai_tools/include/flatbuffers/idl.h new file mode 100644 index 00000000..7b04d0b5 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/idl.h @@ -0,0 +1,1232 @@ +/* + * Copyright 2014 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_IDL_H_ +#define FLATBUFFERS_IDL_H_ + +#include +#include +#include +#include + +#include "flatbuffers/base.h" +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/flexbuffers.h" +#include "flatbuffers/hash.h" +#include "flatbuffers/reflection.h" + +// This file defines the data types representing a parsed IDL (Interface +// Definition Language) / schema file. + +// Limits maximum depth of nested objects. +// Prevents stack overflow while parse scheme, or json, or flexbuffer. +#if !defined(FLATBUFFERS_MAX_PARSING_DEPTH) +# define FLATBUFFERS_MAX_PARSING_DEPTH 64 +#endif + +namespace flatbuffers { + +// The order of these matters for Is*() functions below. +// Additionally, Parser::ParseType assumes bool..string is a contiguous range +// of type tokens. +// clang-format off +#define FLATBUFFERS_GEN_TYPES_SCALAR(TD) \ + TD(NONE, "", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) \ + TD(UTYPE, "", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) /* begin scalar/int */ \ + TD(BOOL, "bool", uint8_t, boolean,bool, bool, bool, bool, Boolean, Bool) \ + TD(CHAR, "byte", int8_t, byte, int8, sbyte, int8, i8, Byte, Int8) \ + TD(UCHAR, "ubyte", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) \ + TD(SHORT, "short", int16_t, short, int16, short, int16, i16, Short, Int16) \ + TD(USHORT, "ushort", uint16_t, short, uint16, ushort, uint16, u16, UShort, UInt16) \ + TD(INT, "int", int32_t, int, int32, int, int32, i32, Int, Int32) \ + TD(UINT, "uint", uint32_t, int, uint32, uint, uint32, u32, UInt, UInt32) \ + TD(LONG, "long", int64_t, long, int64, long, int64, i64, Long, Int64) \ + TD(ULONG, "ulong", uint64_t, long, uint64, ulong, uint64, u64, ULong, UInt64) /* end int */ \ + TD(FLOAT, "float", float, float, float32, float, float32, f32, Float, Float32) /* begin float */ \ + TD(DOUBLE, "double", double, double, float64, double, float64, f64, Double, Double) /* end float/scalar */ +#define FLATBUFFERS_GEN_TYPES_POINTER(TD) \ + TD(STRING, "string", Offset, int, int, StringOffset, int, unused, Int, Offset) \ + TD(VECTOR, "", Offset, int, int, VectorOffset, int, unused, Int, Offset) \ + TD(STRUCT, "", Offset, int, int, int, int, unused, Int, Offset) \ + TD(UNION, "", Offset, int, int, int, int, unused, Int, Offset) +#define FLATBUFFERS_GEN_TYPE_ARRAY(TD) \ + TD(ARRAY, "", int, int, int, int, int, unused, Int, Offset) +// The fields are: +// - enum +// - FlatBuffers schema type. +// - C++ type. +// - Java type. +// - Go type. +// - C# / .Net type. +// - Python type. +// - Kotlin type. +// - Rust type. + +// using these macros, we can now write code dealing with types just once, e.g. + +/* +switch (type) { + #define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, JTYPE, GTYPE, NTYPE, PTYPE, \ + RTYPE, KTYPE) \ + case BASE_TYPE_ ## ENUM: \ + // do something specific to CTYPE here + FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) + #undef FLATBUFFERS_TD +} +*/ + +// If not all FLATBUFFERS_GEN_() arguments are necessary for implementation +// of FLATBUFFERS_TD, you can use a variadic macro (with __VA_ARGS__ if needed). +// In the above example, only CTYPE is used to generate the code, it can be rewritten: + +/* +switch (type) { + #define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, ...) \ + case BASE_TYPE_ ## ENUM: \ + // do something specific to CTYPE here + FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) + #undef FLATBUFFERS_TD +} +*/ + +#define FLATBUFFERS_GEN_TYPES(TD) \ + FLATBUFFERS_GEN_TYPES_SCALAR(TD) \ + FLATBUFFERS_GEN_TYPES_POINTER(TD) \ + FLATBUFFERS_GEN_TYPE_ARRAY(TD) + +// Create an enum for all the types above. +#ifdef __GNUC__ +__extension__ // Stop GCC complaining about trailing comma with -Wpendantic. +#endif +enum BaseType { + #define FLATBUFFERS_TD(ENUM, ...) \ + BASE_TYPE_ ## ENUM, + FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) + #undef FLATBUFFERS_TD +}; + +#define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, ...) \ + static_assert(sizeof(CTYPE) <= sizeof(largest_scalar_t), \ + "define largest_scalar_t as " #CTYPE); + FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) +#undef FLATBUFFERS_TD + +inline bool IsScalar (BaseType t) { return t >= BASE_TYPE_UTYPE && + t <= BASE_TYPE_DOUBLE; } +inline bool IsInteger(BaseType t) { return t >= BASE_TYPE_UTYPE && + t <= BASE_TYPE_ULONG; } +inline bool IsFloat (BaseType t) { return t == BASE_TYPE_FLOAT || + t == BASE_TYPE_DOUBLE; } +inline bool IsLong (BaseType t) { return t == BASE_TYPE_LONG || + t == BASE_TYPE_ULONG; } +inline bool IsBool (BaseType t) { return t == BASE_TYPE_BOOL; } +inline bool IsOneByte(BaseType t) { return t >= BASE_TYPE_UTYPE && + t <= BASE_TYPE_UCHAR; } + +inline bool IsUnsigned(BaseType t) { + return (t == BASE_TYPE_UTYPE) || (t == BASE_TYPE_UCHAR) || + (t == BASE_TYPE_USHORT) || (t == BASE_TYPE_UINT) || + (t == BASE_TYPE_ULONG); +} + +// clang-format on + +extern const char *const kTypeNames[]; +extern const char kTypeSizes[]; + +inline size_t SizeOf(BaseType t) { return kTypeSizes[t]; } + +struct StructDef; +struct EnumDef; +class Parser; + +// Represents any type in the IDL, which is a combination of the BaseType +// and additional information for vectors/structs_. +struct Type { + explicit Type(BaseType _base_type = BASE_TYPE_NONE, StructDef *_sd = nullptr, + EnumDef *_ed = nullptr, uint16_t _fixed_length = 0) + : base_type(_base_type), + element(BASE_TYPE_NONE), + struct_def(_sd), + enum_def(_ed), + fixed_length(_fixed_length) {} + + bool operator==(const Type &o) { + return base_type == o.base_type && element == o.element && + struct_def == o.struct_def && enum_def == o.enum_def; + } + + Type VectorType() const { + return Type(element, struct_def, enum_def, fixed_length); + } + + Offset Serialize(FlatBufferBuilder *builder) const; + + bool Deserialize(const Parser &parser, const reflection::Type *type); + + BaseType base_type; + BaseType element; // only set if t == BASE_TYPE_VECTOR + StructDef *struct_def; // only set if t or element == BASE_TYPE_STRUCT + EnumDef *enum_def; // set if t == BASE_TYPE_UNION / BASE_TYPE_UTYPE, + // or for an integral type derived from an enum. + uint16_t fixed_length; // only set if t == BASE_TYPE_ARRAY +}; + +// Represents a parsed scalar value, it's type, and field offset. +struct Value { + Value() + : constant("0"), + offset(static_cast(~(static_cast(0U)))) {} + Type type; + std::string constant; + voffset_t offset; +}; + +// Helper class that retains the original order of a set of identifiers and +// also provides quick lookup. +template class SymbolTable { + public: + ~SymbolTable() { + for (auto it = vec.begin(); it != vec.end(); ++it) { delete *it; } + } + + bool Add(const std::string &name, T *e) { + vec.emplace_back(e); + auto it = dict.find(name); + if (it != dict.end()) return true; + dict[name] = e; + return false; + } + + void Move(const std::string &oldname, const std::string &newname) { + auto it = dict.find(oldname); + if (it != dict.end()) { + auto obj = it->second; + dict.erase(it); + dict[newname] = obj; + } else { + FLATBUFFERS_ASSERT(false); + } + } + + T *Lookup(const std::string &name) const { + auto it = dict.find(name); + return it == dict.end() ? nullptr : it->second; + } + + public: + std::map dict; // quick lookup + std::vector vec; // Used to iterate in order of insertion +}; + +// A name space, as set in the schema. +struct Namespace { + Namespace() : from_table(0) {} + + // Given a (potentially unqualified) name, return the "fully qualified" name + // which has a full namespaced descriptor. + // With max_components you can request less than the number of components + // the current namespace has. + std::string GetFullyQualifiedName(const std::string &name, + size_t max_components = 1000) const; + + std::vector components; + size_t from_table; // Part of the namespace corresponds to a message/table. +}; + +inline bool operator<(const Namespace &a, const Namespace &b) { + size_t min_size = std::min(a.components.size(), b.components.size()); + for (size_t i = 0; i < min_size; ++i) { + if (a.components[i] != b.components[i]) + return a.components[i] < b.components[i]; + } + return a.components.size() < b.components.size(); +} + +// Base class for all definition types (fields, structs_, enums_). +struct Definition { + Definition() + : generated(false), + defined_namespace(nullptr), + serialized_location(0), + index(-1), + refcount(1), + declaration_file(nullptr) {} + + flatbuffers::Offset< + flatbuffers::Vector>> + SerializeAttributes(FlatBufferBuilder *builder, const Parser &parser) const; + + bool DeserializeAttributes(Parser &parser, + const Vector> *attrs); + + std::string name; + std::string file; + std::vector doc_comment; + SymbolTable attributes; + bool generated; // did we already output code for this definition? + Namespace *defined_namespace; // Where it was defined. + + // For use with Serialize() + uoffset_t serialized_location; + int index; // Inside the vector it is stored. + int refcount; + const std::string *declaration_file; +}; + +struct FieldDef : public Definition { + FieldDef() + : deprecated(false), + key(false), + shared(false), + native_inline(false), + flexbuffer(false), + presence(kDefault), + nested_flatbuffer(NULL), + padding(0) {} + + Offset Serialize(FlatBufferBuilder *builder, uint16_t id, + const Parser &parser) const; + + bool Deserialize(Parser &parser, const reflection::Field *field); + + bool IsScalarOptional() const { + return IsScalar(value.type.base_type) && IsOptional(); + } + bool IsOptional() const { return presence == kOptional; } + bool IsRequired() const { return presence == kRequired; } + bool IsDefault() const { return presence == kDefault; } + + Value value; + bool deprecated; // Field is allowed to be present in old data, but can't be. + // written in new data nor accessed in new code. + bool key; // Field functions as a key for creating sorted vectors. + bool shared; // Field will be using string pooling (i.e. CreateSharedString) + // as default serialization behavior if field is a string. + bool native_inline; // Field will be defined inline (instead of as a pointer) + // for native tables if field is a struct. + bool flexbuffer; // This field contains FlexBuffer data. + + enum Presence { + // Field must always be present. + kRequired, + // Non-presence should be signalled to and controlled by users. + kOptional, + // Non-presence is hidden from users. + // Implementations may omit writing default values. + kDefault, + }; + Presence static MakeFieldPresence(bool optional, bool required) { + FLATBUFFERS_ASSERT(!(required && optional)); + // clang-format off + return required ? FieldDef::kRequired + : optional ? FieldDef::kOptional + : FieldDef::kDefault; + // clang-format on + } + Presence presence; + + StructDef *nested_flatbuffer; // This field contains nested FlatBuffer data. + size_t padding; // Bytes to always pad after this field. +}; + +struct StructDef : public Definition { + StructDef() + : fixed(false), + predecl(true), + sortbysize(true), + has_key(false), + minalign(1), + bytesize(0) {} + + void PadLastField(size_t min_align) { + auto padding = PaddingBytes(bytesize, min_align); + bytesize += padding; + if (fields.vec.size()) fields.vec.back()->padding = padding; + } + + Offset Serialize(FlatBufferBuilder *builder, + const Parser &parser) const; + + bool Deserialize(Parser &parser, const reflection::Object *object); + + SymbolTable fields; + + bool fixed; // If it's struct, not a table. + bool predecl; // If it's used before it was defined. + bool sortbysize; // Whether fields come in the declaration or size order. + bool has_key; // It has a key field. + size_t minalign; // What the whole object needs to be aligned to. + size_t bytesize; // Size if fixed. + + flatbuffers::unique_ptr original_location; +}; + +struct EnumDef; +struct EnumValBuilder; + +struct EnumVal { + Offset Serialize(FlatBufferBuilder *builder, + const Parser &parser) const; + + bool Deserialize(const Parser &parser, const reflection::EnumVal *val); + + uint64_t GetAsUInt64() const { return static_cast(value); } + int64_t GetAsInt64() const { return value; } + bool IsZero() const { return 0 == value; } + bool IsNonZero() const { return !IsZero(); } + + std::string name; + std::vector doc_comment; + Type union_type; + + private: + friend EnumDef; + friend EnumValBuilder; + friend bool operator==(const EnumVal &lhs, const EnumVal &rhs); + + EnumVal(const std::string &_name, int64_t _val) : name(_name), value(_val) {} + EnumVal() : value(0) {} + + int64_t value; +}; + +struct EnumDef : public Definition { + EnumDef() : is_union(false), uses_multiple_type_instances(false) {} + + Offset Serialize(FlatBufferBuilder *builder, + const Parser &parser) const; + + bool Deserialize(Parser &parser, const reflection::Enum *values); + + template void ChangeEnumValue(EnumVal *ev, T new_val); + void SortByValue(); + void RemoveDuplicates(); + + std::string AllFlags() const; + const EnumVal *MinValue() const; + const EnumVal *MaxValue() const; + // Returns the number of integer steps from v1 to v2. + uint64_t Distance(const EnumVal *v1, const EnumVal *v2) const; + // Returns the number of integer steps from Min to Max. + uint64_t Distance() const { return Distance(MinValue(), MaxValue()); } + + EnumVal *ReverseLookup(int64_t enum_idx, + bool skip_union_default = false) const; + EnumVal *FindByValue(const std::string &constant) const; + + std::string ToString(const EnumVal &ev) const { + return IsUInt64() ? NumToString(ev.GetAsUInt64()) + : NumToString(ev.GetAsInt64()); + } + + size_t size() const { return vals.vec.size(); } + + const std::vector &Vals() const { return vals.vec; } + + const EnumVal *Lookup(const std::string &enum_name) const { + return vals.Lookup(enum_name); + } + + bool is_union; + // Type is a union which uses type aliases where at least one type is + // available under two different names. + bool uses_multiple_type_instances; + Type underlying_type; + + private: + bool IsUInt64() const { + return (BASE_TYPE_ULONG == underlying_type.base_type); + } + + friend EnumValBuilder; + SymbolTable vals; +}; + +inline bool IsString(const Type &type) { + return type.base_type == BASE_TYPE_STRING; +} + +inline bool IsStruct(const Type &type) { + return type.base_type == BASE_TYPE_STRUCT && type.struct_def->fixed; +} + +inline bool IsUnion(const Type &type) { + return type.enum_def != nullptr && type.enum_def->is_union; +} + +inline bool IsUnionType(const Type &type) { + return IsUnion(type) && IsInteger(type.base_type); +} + +inline bool IsVector(const Type &type) { + return type.base_type == BASE_TYPE_VECTOR; +} + +inline bool IsArray(const Type &type) { + return type.base_type == BASE_TYPE_ARRAY; +} + +inline bool IsSeries(const Type &type) { + return IsVector(type) || IsArray(type); +} + +inline bool IsEnum(const Type &type) { + return type.enum_def != nullptr && IsInteger(type.base_type); +} + +inline size_t InlineSize(const Type &type) { + return IsStruct(type) + ? type.struct_def->bytesize + : (IsArray(type) + ? InlineSize(type.VectorType()) * type.fixed_length + : SizeOf(type.base_type)); +} + +inline size_t InlineAlignment(const Type &type) { + if (IsStruct(type)) { + return type.struct_def->minalign; + } else if (IsArray(type)) { + return IsStruct(type.VectorType()) ? type.struct_def->minalign + : SizeOf(type.element); + } else { + return SizeOf(type.base_type); + } +} +inline bool operator==(const EnumVal &lhs, const EnumVal &rhs) { + return lhs.value == rhs.value; +} +inline bool operator!=(const EnumVal &lhs, const EnumVal &rhs) { + return !(lhs == rhs); +} + +inline bool EqualByName(const Type &a, const Type &b) { + return a.base_type == b.base_type && a.element == b.element && + (a.struct_def == b.struct_def || + a.struct_def->name == b.struct_def->name) && + (a.enum_def == b.enum_def || a.enum_def->name == b.enum_def->name); +} + +struct RPCCall : public Definition { + Offset Serialize(FlatBufferBuilder *builder, + const Parser &parser) const; + + bool Deserialize(Parser &parser, const reflection::RPCCall *call); + + StructDef *request, *response; +}; + +struct ServiceDef : public Definition { + Offset Serialize(FlatBufferBuilder *builder, + const Parser &parser) const; + bool Deserialize(Parser &parser, const reflection::Service *service); + + SymbolTable calls; +}; + +// Container of options that may apply to any of the source/text generators. +struct IDLOptions { + // field case style options for C++ + enum CaseStyle { CaseStyle_Unchanged = 0, CaseStyle_Upper, CaseStyle_Lower }; + + bool gen_jvmstatic; + // Use flexbuffers instead for binary and text generation + bool use_flexbuffers; + bool strict_json; + bool output_default_scalars_in_json; + int indent_step; + bool output_enum_identifiers; + bool prefixed_enums; + bool scoped_enums; + bool include_dependence_headers; + bool mutable_buffer; + bool one_file; + bool proto_mode; + bool proto_oneof_union; + bool generate_all; + bool skip_unexpected_fields_in_json; + bool generate_name_strings; + bool generate_object_based_api; + bool gen_compare; + std::string cpp_object_api_pointer_type; + std::string cpp_object_api_string_type; + bool cpp_object_api_string_flexible_constructor; + CaseStyle cpp_object_api_field_case_style; + bool cpp_direct_copy; + bool gen_nullable; + bool java_checkerframework; + bool gen_generated; + bool gen_json_coders; + std::string object_prefix; + std::string object_suffix; + bool union_value_namespacing; + bool allow_non_utf8; + bool natural_utf8; + std::string include_prefix; + bool keep_include_path; + bool binary_schema_comments; + bool binary_schema_builtins; + bool binary_schema_gen_embed; + std::string go_import; + std::string go_namespace; + bool protobuf_ascii_alike; + bool size_prefixed; + std::string root_type; + bool force_defaults; + bool java_primitive_has_method; + bool cs_gen_json_serializer; + std::vector cpp_includes; + std::string cpp_std; + bool cpp_static_reflection; + std::string proto_namespace_suffix; + std::string filename_suffix; + std::string filename_extension; + bool no_warnings; + bool warnings_as_errors; + std::string project_root; + bool cs_global_alias; + bool json_nested_flatbuffers; + bool json_nested_flexbuffers; + bool json_nested_legacy_flatbuffers; + + // Possible options for the more general generator below. + enum Language { + kJava = 1 << 0, + kCSharp = 1 << 1, + kGo = 1 << 2, + kCpp = 1 << 3, + kPython = 1 << 5, + kPhp = 1 << 6, + kJson = 1 << 7, + kBinary = 1 << 8, + kTs = 1 << 9, + kJsonSchema = 1 << 10, + kDart = 1 << 11, + kLua = 1 << 12, + kLobster = 1 << 13, + kRust = 1 << 14, + kKotlin = 1 << 15, + kSwift = 1 << 16, + kMAX + }; + + enum MiniReflect { kNone, kTypes, kTypesAndNames }; + + MiniReflect mini_reflect; + + // If set, require all fields in a table to be explicitly numbered. + bool require_explicit_ids; + + // If set, implement serde::Serialize for generated Rust types + bool rust_serialize; + + // If set, generate rust types in individual files with a root module file. + bool rust_module_root_file; + + // The corresponding language bit will be set if a language is included + // for code generation. + unsigned long lang_to_generate; + + // If set (default behavior), empty string fields will be set to nullptr to + // make the flatbuffer more compact. + bool set_empty_strings_to_null; + + // If set (default behavior), empty vector fields will be set to nullptr to + // make the flatbuffer more compact. + bool set_empty_vectors_to_null; + + IDLOptions() + : gen_jvmstatic(false), + use_flexbuffers(false), + strict_json(false), + output_default_scalars_in_json(false), + indent_step(2), + output_enum_identifiers(true), + prefixed_enums(true), + scoped_enums(false), + include_dependence_headers(true), + mutable_buffer(false), + one_file(false), + proto_mode(false), + proto_oneof_union(false), + generate_all(false), + skip_unexpected_fields_in_json(false), + generate_name_strings(false), + generate_object_based_api(false), + gen_compare(false), + cpp_object_api_pointer_type("std::unique_ptr"), + cpp_object_api_string_flexible_constructor(false), + cpp_object_api_field_case_style(CaseStyle_Unchanged), + cpp_direct_copy(true), + gen_nullable(false), + java_checkerframework(false), + gen_generated(false), + gen_json_coders(false), + object_suffix("T"), + union_value_namespacing(true), + allow_non_utf8(false), + natural_utf8(false), + keep_include_path(false), + binary_schema_comments(false), + binary_schema_builtins(false), + binary_schema_gen_embed(false), + protobuf_ascii_alike(false), + size_prefixed(false), + force_defaults(false), + java_primitive_has_method(false), + cs_gen_json_serializer(false), + cpp_static_reflection(false), + filename_suffix("_generated"), + filename_extension(), + no_warnings(false), + warnings_as_errors(false), + project_root(""), + cs_global_alias(false), + json_nested_flatbuffers(true), + json_nested_flexbuffers(true), + json_nested_legacy_flatbuffers(false), + mini_reflect(IDLOptions::kNone), + require_explicit_ids(false), + rust_serialize(false), + rust_module_root_file(false), + lang_to_generate(0), + set_empty_strings_to_null(true), + set_empty_vectors_to_null(true) {} +}; + +// This encapsulates where the parser is in the current source file. +struct ParserState { + ParserState() + : cursor_(nullptr), + line_start_(nullptr), + line_(0), + token_(-1), + attr_is_trivial_ascii_string_(true) {} + + protected: + void ResetState(const char *source) { + cursor_ = source; + line_ = 0; + MarkNewLine(); + } + + void MarkNewLine() { + line_start_ = cursor_; + line_ += 1; + } + + int64_t CursorPosition() const { + FLATBUFFERS_ASSERT(cursor_ && line_start_ && cursor_ >= line_start_); + return static_cast(cursor_ - line_start_); + } + + const char *cursor_; + const char *line_start_; + int line_; // the current line being parsed + int token_; + + // Flag: text in attribute_ is true ASCII string without escape + // sequences. Only printable ASCII (without [\t\r\n]). + // Used for number-in-string (and base64 string in future). + bool attr_is_trivial_ascii_string_; + std::string attribute_; + std::vector doc_comment_; +}; + +// A way to make error propagation less error prone by requiring values to be +// checked. +// Once you create a value of this type you must either: +// - Call Check() on it. +// - Copy or assign it to another value. +// Failure to do so leads to an assert. +// This guarantees that this as return value cannot be ignored. +class CheckedError { + public: + explicit CheckedError(bool error) + : is_error_(error), has_been_checked_(false) {} + + CheckedError &operator=(const CheckedError &other) { + is_error_ = other.is_error_; + has_been_checked_ = false; + other.has_been_checked_ = true; + return *this; + } + + CheckedError(const CheckedError &other) { + *this = other; // Use assignment operator. + } + + ~CheckedError() { FLATBUFFERS_ASSERT(has_been_checked_); } + + bool Check() { + has_been_checked_ = true; + return is_error_; + } + + private: + bool is_error_; + mutable bool has_been_checked_; +}; + +// Additionally, in GCC we can get these errors statically, for additional +// assurance: +// clang-format off +#ifdef __GNUC__ +#define FLATBUFFERS_CHECKED_ERROR CheckedError \ + __attribute__((warn_unused_result)) +#else +#define FLATBUFFERS_CHECKED_ERROR CheckedError +#endif +// clang-format on + +class Parser : public ParserState { + public: + explicit Parser(const IDLOptions &options = IDLOptions()) + : current_namespace_(nullptr), + empty_namespace_(nullptr), + flex_builder_(256, flexbuffers::BUILDER_FLAG_SHARE_ALL), + root_struct_def_(nullptr), + opts(options), + uses_flexbuffers_(false), + has_warning_(false), + advanced_features_(0), + source_(nullptr), + anonymous_counter_(0), + parse_depth_counter_(0) { + if (opts.force_defaults) { builder_.ForceDefaults(true); } + // Start out with the empty namespace being current. + empty_namespace_ = new Namespace(); + namespaces_.push_back(empty_namespace_); + current_namespace_ = empty_namespace_; + known_attributes_["deprecated"] = true; + known_attributes_["required"] = true; + known_attributes_["key"] = true; + known_attributes_["shared"] = true; + known_attributes_["hash"] = true; + known_attributes_["id"] = true; + known_attributes_["force_align"] = true; + known_attributes_["bit_flags"] = true; + known_attributes_["original_order"] = true; + known_attributes_["nested_flatbuffer"] = true; + known_attributes_["csharp_partial"] = true; + known_attributes_["streaming"] = true; + known_attributes_["idempotent"] = true; + known_attributes_["cpp_type"] = true; + known_attributes_["cpp_ptr_type"] = true; + known_attributes_["cpp_ptr_type_get"] = true; + known_attributes_["cpp_str_type"] = true; + known_attributes_["cpp_str_flex_ctor"] = true; + known_attributes_["native_inline"] = true; + known_attributes_["native_custom_alloc"] = true; + known_attributes_["native_type"] = true; + known_attributes_["native_type_pack_name"] = true; + known_attributes_["native_default"] = true; + known_attributes_["flexbuffer"] = true; + known_attributes_["private"] = true; + } + + ~Parser() { + for (auto it = namespaces_.begin(); it != namespaces_.end(); ++it) { + delete *it; + } + } + + // Parse the string containing either schema or JSON data, which will + // populate the SymbolTable's or the FlatBufferBuilder above. + // include_paths is used to resolve any include statements, and typically + // should at least include the project path (where you loaded source_ from). + // include_paths must be nullptr terminated if specified. + // If include_paths is nullptr, it will attempt to load from the current + // directory. + // If the source was loaded from a file and isn't an include file, + // supply its name in source_filename. + // All paths specified in this call must be in posix format, if you accept + // paths from user input, please call PosixPath on them first. + bool Parse(const char *_source, const char **include_paths = nullptr, + const char *source_filename = nullptr); + + bool ParseJson(const char *json, const char *json_filename = nullptr); + + // Set the root type. May override the one set in the schema. + bool SetRootType(const char *name); + + // Mark all definitions as already having code generated. + void MarkGenerated(); + + // Get the files recursively included by the given file. The returned + // container will have at least the given file. + std::set GetIncludedFilesRecursive( + const std::string &file_name) const; + + // Fills builder_ with a binary version of the schema parsed. + // See reflection/reflection.fbs + void Serialize(); + + // Deserialize a schema buffer + bool Deserialize(const uint8_t *buf, const size_t size); + + // Fills internal structure as if the schema passed had been loaded by parsing + // with Parse except that included filenames will not be populated. + bool Deserialize(const reflection::Schema *schema); + + Type *DeserializeType(const reflection::Type *type); + + // Checks that the schema represented by this parser is a safe evolution + // of the schema provided. Returns non-empty error on any problems. + std::string ConformTo(const Parser &base); + + // Similar to Parse(), but now only accepts JSON to be parsed into a + // FlexBuffer. + bool ParseFlexBuffer(const char *source, const char *source_filename, + flexbuffers::Builder *builder); + + StructDef *LookupStruct(const std::string &id) const; + StructDef *LookupStructThruParentNamespaces(const std::string &id) const; + + std::string UnqualifiedName(const std::string &fullQualifiedName); + + FLATBUFFERS_CHECKED_ERROR Error(const std::string &msg); + + // @brief Verify that any of 'opts.lang_to_generate' supports Optional scalars + // in a schema. + // @param opts Options used to parce a schema and generate code. + static bool SupportsOptionalScalars(const flatbuffers::IDLOptions &opts); + + private: + class ParseDepthGuard; + + void Message(const std::string &msg); + void Warning(const std::string &msg); + FLATBUFFERS_CHECKED_ERROR ParseHexNum(int nibbles, uint64_t *val); + FLATBUFFERS_CHECKED_ERROR Next(); + FLATBUFFERS_CHECKED_ERROR SkipByteOrderMark(); + bool Is(int t) const; + bool IsIdent(const char *id) const; + FLATBUFFERS_CHECKED_ERROR Expect(int t); + std::string TokenToStringId(int t) const; + EnumDef *LookupEnum(const std::string &id); + FLATBUFFERS_CHECKED_ERROR ParseNamespacing(std::string *id, + std::string *last); + FLATBUFFERS_CHECKED_ERROR ParseTypeIdent(Type &type); + FLATBUFFERS_CHECKED_ERROR ParseType(Type &type); + FLATBUFFERS_CHECKED_ERROR AddField(StructDef &struct_def, + const std::string &name, const Type &type, + FieldDef **dest); + FLATBUFFERS_CHECKED_ERROR ParseField(StructDef &struct_def); + FLATBUFFERS_CHECKED_ERROR ParseString(Value &val, bool use_string_pooling); + FLATBUFFERS_CHECKED_ERROR ParseComma(); + FLATBUFFERS_CHECKED_ERROR ParseAnyValue(Value &val, FieldDef *field, + size_t parent_fieldn, + const StructDef *parent_struct_def, + uoffset_t count, + bool inside_vector = false); + template + FLATBUFFERS_CHECKED_ERROR ParseTableDelimiters(size_t &fieldn, + const StructDef *struct_def, + F body); + FLATBUFFERS_CHECKED_ERROR ParseTable(const StructDef &struct_def, + std::string *value, uoffset_t *ovalue); + void SerializeStruct(const StructDef &struct_def, const Value &val); + void SerializeStruct(FlatBufferBuilder &builder, const StructDef &struct_def, + const Value &val); + template + FLATBUFFERS_CHECKED_ERROR ParseVectorDelimiters(uoffset_t &count, F body); + FLATBUFFERS_CHECKED_ERROR ParseVector(const Type &type, uoffset_t *ovalue, + FieldDef *field, size_t fieldn); + FLATBUFFERS_CHECKED_ERROR ParseArray(Value &array); + FLATBUFFERS_CHECKED_ERROR ParseNestedFlatbuffer( + Value &val, FieldDef *field, size_t fieldn, + const StructDef *parent_struct_def); + FLATBUFFERS_CHECKED_ERROR ParseMetaData(SymbolTable *attributes); + FLATBUFFERS_CHECKED_ERROR TryTypedValue(const std::string *name, int dtoken, + bool check, Value &e, BaseType req, + bool *destmatch); + FLATBUFFERS_CHECKED_ERROR ParseHash(Value &e, FieldDef *field); + FLATBUFFERS_CHECKED_ERROR TokenError(); + FLATBUFFERS_CHECKED_ERROR ParseSingleValue(const std::string *name, Value &e, + bool check_now); + FLATBUFFERS_CHECKED_ERROR ParseFunction(const std::string *name, Value &e); + FLATBUFFERS_CHECKED_ERROR ParseEnumFromString(const Type &type, + std::string *result); + StructDef *LookupCreateStruct(const std::string &name, + bool create_if_new = true, + bool definition = false); + FLATBUFFERS_CHECKED_ERROR ParseEnum(bool is_union, EnumDef **dest, + const char *filename); + FLATBUFFERS_CHECKED_ERROR ParseNamespace(); + FLATBUFFERS_CHECKED_ERROR StartStruct(const std::string &name, + StructDef **dest); + FLATBUFFERS_CHECKED_ERROR StartEnum(const std::string &name, bool is_union, + EnumDef **dest); + FLATBUFFERS_CHECKED_ERROR ParseDecl(const char *filename); + FLATBUFFERS_CHECKED_ERROR ParseService(const char *filename); + FLATBUFFERS_CHECKED_ERROR ParseProtoFields(StructDef *struct_def, + bool isextend, bool inside_oneof); + FLATBUFFERS_CHECKED_ERROR ParseProtoOption(); + FLATBUFFERS_CHECKED_ERROR ParseProtoKey(); + FLATBUFFERS_CHECKED_ERROR ParseProtoDecl(); + FLATBUFFERS_CHECKED_ERROR ParseProtoCurliesOrIdent(); + FLATBUFFERS_CHECKED_ERROR ParseTypeFromProtoType(Type *type); + FLATBUFFERS_CHECKED_ERROR SkipAnyJsonValue(); + FLATBUFFERS_CHECKED_ERROR ParseFlexBufferNumericConstant( + flexbuffers::Builder *builder); + FLATBUFFERS_CHECKED_ERROR ParseFlexBufferValue(flexbuffers::Builder *builder); + FLATBUFFERS_CHECKED_ERROR StartParseFile(const char *source, + const char *source_filename); + FLATBUFFERS_CHECKED_ERROR ParseRoot(const char *_source, + const char **include_paths, + const char *source_filename); + FLATBUFFERS_CHECKED_ERROR DoParse(const char *_source, + const char **include_paths, + const char *source_filename, + const char *include_filename); + FLATBUFFERS_CHECKED_ERROR DoParseJson(); + FLATBUFFERS_CHECKED_ERROR CheckClash(std::vector &fields, + StructDef *struct_def, + const char *suffix, BaseType baseType); + FLATBUFFERS_CHECKED_ERROR ParseAlignAttribute( + const std::string &align_constant, size_t min_align, size_t *align); + + bool SupportsAdvancedUnionFeatures() const; + bool SupportsAdvancedArrayFeatures() const; + bool SupportsOptionalScalars() const; + bool SupportsDefaultVectorsAndStrings() const; + Namespace *UniqueNamespace(Namespace *ns); + + FLATBUFFERS_CHECKED_ERROR RecurseError(); + template CheckedError Recurse(F f); + + const std::string &GetPooledString(const std::string &s) const; + + public: + SymbolTable types_; + SymbolTable structs_; + SymbolTable enums_; + SymbolTable services_; + std::vector namespaces_; + Namespace *current_namespace_; + Namespace *empty_namespace_; + std::string error_; // User readable error_ if Parse() == false + + FlatBufferBuilder builder_; // any data contained in the file + flexbuffers::Builder flex_builder_; + flexbuffers::Reference flex_root_; + StructDef *root_struct_def_; + std::string file_identifier_; + std::string file_extension_; + + std::map included_files_; + std::map> files_included_per_file_; + std::vector native_included_files_; + + std::map known_attributes_; + + IDLOptions opts; + bool uses_flexbuffers_; + bool has_warning_; + + uint64_t advanced_features_; + + private: + const char *source_; + + std::string file_being_parsed_; + + std::vector> field_stack_; + + // TODO(cneo): Refactor parser to use string_cache more often to save + // on memory usage. + mutable std::set string_cache_; + + int anonymous_counter_; + int parse_depth_counter_; // stack-overflow guard +}; + +// Utility functions for multiple generators: + +extern std::string MakeCamel(const std::string &in, bool first = true); + +extern std::string MakeScreamingCamel(const std::string &in); + +// Generate text (JSON) from a given FlatBuffer, and a given Parser +// object that has been populated with the corresponding schema. +// If ident_step is 0, no indentation will be generated. Additionally, +// if it is less than 0, no linefeeds will be generated either. +// See idl_gen_text.cpp. +// strict_json adds "quotes" around field names if true. +// If the flatbuffer cannot be encoded in JSON (e.g., it contains non-UTF-8 +// byte arrays in String values), returns false. +extern bool GenerateTextFromTable(const Parser &parser, const void *table, + const std::string &tablename, + std::string *text); +extern bool GenerateText(const Parser &parser, const void *flatbuffer, + std::string *text); +extern bool GenerateTextFile(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Json schema to string +// See idl_gen_json_schema.cpp. +extern bool GenerateJsonSchema(const Parser &parser, std::string *json); + +// Generate binary files from a given FlatBuffer, and a given Parser +// object that has been populated with the corresponding schema. +// See code_generators.cpp. +extern bool GenerateBinary(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a C++ header from the definitions in the Parser object. +// See idl_gen_cpp. +extern bool GenerateCPP(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate C# files from the definitions in the Parser object. +// See idl_gen_csharp.cpp. +extern bool GenerateCSharp(const Parser &parser, const std::string &path, + const std::string &file_name); + +extern bool GenerateDart(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Java files from the definitions in the Parser object. +// See idl_gen_java.cpp. +extern bool GenerateJava(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate JavaScript or TypeScript code from the definitions in the Parser +// object. See idl_gen_js. +extern bool GenerateTS(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Go files from the definitions in the Parser object. +// See idl_gen_go.cpp. +extern bool GenerateGo(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Php code from the definitions in the Parser object. +// See idl_gen_php. +extern bool GeneratePhp(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Python files from the definitions in the Parser object. +// See idl_gen_python.cpp. +extern bool GeneratePython(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Lobster files from the definitions in the Parser object. +// See idl_gen_lobster.cpp. +extern bool GenerateLobster(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Lua files from the definitions in the Parser object. +// See idl_gen_lua.cpp. +extern bool GenerateLua(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Rust files from the definitions in the Parser object. +// See idl_gen_rust.cpp. +extern bool GenerateRust(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Json schema file +// See idl_gen_json_schema.cpp. +extern bool GenerateJsonSchema(const Parser &parser, const std::string &path, + const std::string &file_name); + +extern bool GenerateKotlin(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate Swift classes. +// See idl_gen_swift.cpp +extern bool GenerateSwift(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a schema file from the internal representation, useful after +// parsing a .proto schema. +extern std::string GenerateFBS(const Parser &parser, + const std::string &file_name); +extern bool GenerateFBS(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for the generated TypeScript code. +// See idl_gen_ts.cpp. +extern std::string TSMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for the generated C++ header. +// See idl_gen_cpp.cpp. +extern std::string CPPMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for the generated Dart code +// see idl_gen_dart.cpp +extern std::string DartMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for the generated Rust code. +// See idl_gen_rust.cpp. +extern std::string RustMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for generated Java or C# files. +// See code_generators.cpp. +extern std::string CSharpMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); +extern std::string JavaMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate a make rule for the generated text (JSON) files. +// See idl_gen_text.cpp. +extern std::string TextMakeRule(const Parser &parser, const std::string &path, + const std::string &file_names); + +// Generate a make rule for the generated binary files. +// See code_generators.cpp. +extern std::string BinaryMakeRule(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate GRPC Cpp interfaces. +// See idl_gen_grpc.cpp. +bool GenerateCppGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate GRPC Go interfaces. +// See idl_gen_grpc.cpp. +bool GenerateGoGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate GRPC Java classes. +// See idl_gen_grpc.cpp +bool GenerateJavaGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate GRPC Python interfaces. +// See idl_gen_grpc.cpp. +bool GeneratePythonGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +// Generate GRPC Swift interfaces. +// See idl_gen_grpc.cpp. +extern bool GenerateSwiftGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +extern bool GenerateTSGRPC(const Parser &parser, const std::string &path, + const std::string &file_name); + +extern bool GenerateRustModuleRootFile(const Parser &parser, + const std::string &path); +} // namespace flatbuffers + +#endif // FLATBUFFERS_IDL_H_ diff --git a/new_ai_tools/include/flatbuffers/minireflect.h b/new_ai_tools/include/flatbuffers/minireflect.h new file mode 100644 index 00000000..26fd86c9 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/minireflect.h @@ -0,0 +1,419 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_MINIREFLECT_H_ +#define FLATBUFFERS_MINIREFLECT_H_ + +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/util.h" + +namespace flatbuffers { + +// Utilities that can be used with the "mini reflection" tables present +// in generated code with --reflect-types (only types) or --reflect-names +// (also names). +// This allows basic reflection functionality such as pretty-printing +// that does not require the use of the schema parser or loading of binary +// schema files at runtime (reflection.h). + +// For any of the functions below that take `const TypeTable *`, you pass +// `FooTypeTable()` if the type of the root is `Foo`. + +// First, a generic iterator that can be used by multiple algorithms. + +struct IterationVisitor { + // These mark the scope of a table or struct. + virtual void StartSequence() {} + virtual void EndSequence() {} + // Called for each field regardless of whether it is present or not. + // If not present, val == nullptr. set_idx is the index of all set fields. + virtual void Field(size_t /*field_idx*/, size_t /*set_idx*/, + ElementaryType /*type*/, bool /*is_vector*/, + const TypeTable * /*type_table*/, const char * /*name*/, + const uint8_t * /*val*/) {} + // Called for a value that is actually present, after a field, or as part + // of a vector. + virtual void UType(uint8_t, const char *) {} + virtual void Bool(bool) {} + virtual void Char(int8_t, const char *) {} + virtual void UChar(uint8_t, const char *) {} + virtual void Short(int16_t, const char *) {} + virtual void UShort(uint16_t, const char *) {} + virtual void Int(int32_t, const char *) {} + virtual void UInt(uint32_t, const char *) {} + virtual void Long(int64_t) {} + virtual void ULong(uint64_t) {} + virtual void Float(float) {} + virtual void Double(double) {} + virtual void String(const String *) {} + virtual void Unknown(const uint8_t *) {} // From a future version. + // These mark the scope of a vector. + virtual void StartVector() {} + virtual void EndVector() {} + virtual void Element(size_t /*i*/, ElementaryType /*type*/, + const TypeTable * /*type_table*/, + const uint8_t * /*val*/) {} + virtual ~IterationVisitor() {} +}; + +inline size_t InlineSize(ElementaryType type, const TypeTable *type_table) { + switch (type) { + case ET_UTYPE: + case ET_BOOL: + case ET_CHAR: + case ET_UCHAR: return 1; + case ET_SHORT: + case ET_USHORT: return 2; + case ET_INT: + case ET_UINT: + case ET_FLOAT: + case ET_STRING: return 4; + case ET_LONG: + case ET_ULONG: + case ET_DOUBLE: return 8; + case ET_SEQUENCE: + switch (type_table->st) { + case ST_TABLE: + case ST_UNION: return 4; + case ST_STRUCT: + return static_cast(type_table->values[type_table->num_elems]); + default: FLATBUFFERS_ASSERT(false); return 1; + } + default: FLATBUFFERS_ASSERT(false); return 1; + } +} + +inline int64_t LookupEnum(int64_t enum_val, const int64_t *values, + size_t num_values) { + if (!values) return enum_val; + for (size_t i = 0; i < num_values; i++) { + if (enum_val == values[i]) return static_cast(i); + } + return -1; // Unknown enum value. +} + +template const char *EnumName(T tval, const TypeTable *type_table) { + if (!type_table || !type_table->names) return nullptr; + auto i = LookupEnum(static_cast(tval), type_table->values, + type_table->num_elems); + if (i >= 0 && i < static_cast(type_table->num_elems)) { + return type_table->names[i]; + } + return nullptr; +} + +void IterateObject(const uint8_t *obj, const TypeTable *type_table, + IterationVisitor *visitor); + +inline void IterateValue(ElementaryType type, const uint8_t *val, + const TypeTable *type_table, const uint8_t *prev_val, + soffset_t vector_index, IterationVisitor *visitor) { + switch (type) { + case ET_UTYPE: { + auto tval = ReadScalar(val); + visitor->UType(tval, EnumName(tval, type_table)); + break; + } + case ET_BOOL: { + visitor->Bool(ReadScalar(val) != 0); + break; + } + case ET_CHAR: { + auto tval = ReadScalar(val); + visitor->Char(tval, EnumName(tval, type_table)); + break; + } + case ET_UCHAR: { + auto tval = ReadScalar(val); + visitor->UChar(tval, EnumName(tval, type_table)); + break; + } + case ET_SHORT: { + auto tval = ReadScalar(val); + visitor->Short(tval, EnumName(tval, type_table)); + break; + } + case ET_USHORT: { + auto tval = ReadScalar(val); + visitor->UShort(tval, EnumName(tval, type_table)); + break; + } + case ET_INT: { + auto tval = ReadScalar(val); + visitor->Int(tval, EnumName(tval, type_table)); + break; + } + case ET_UINT: { + auto tval = ReadScalar(val); + visitor->UInt(tval, EnumName(tval, type_table)); + break; + } + case ET_LONG: { + visitor->Long(ReadScalar(val)); + break; + } + case ET_ULONG: { + visitor->ULong(ReadScalar(val)); + break; + } + case ET_FLOAT: { + visitor->Float(ReadScalar(val)); + break; + } + case ET_DOUBLE: { + visitor->Double(ReadScalar(val)); + break; + } + case ET_STRING: { + val += ReadScalar(val); + visitor->String(reinterpret_cast(val)); + break; + } + case ET_SEQUENCE: { + switch (type_table->st) { + case ST_TABLE: + val += ReadScalar(val); + IterateObject(val, type_table, visitor); + break; + case ST_STRUCT: IterateObject(val, type_table, visitor); break; + case ST_UNION: { + val += ReadScalar(val); + FLATBUFFERS_ASSERT(prev_val); + auto union_type = *prev_val; // Always a uint8_t. + if (vector_index >= 0) { + auto type_vec = reinterpret_cast *>(prev_val); + union_type = type_vec->Get(static_cast(vector_index)); + } + auto type_code_idx = + LookupEnum(union_type, type_table->values, type_table->num_elems); + if (type_code_idx >= 0 && + type_code_idx < static_cast(type_table->num_elems)) { + auto type_code = type_table->type_codes[type_code_idx]; + switch (type_code.base_type) { + case ET_SEQUENCE: { + auto ref = type_table->type_refs[type_code.sequence_ref](); + IterateObject(val, ref, visitor); + break; + } + case ET_STRING: + visitor->String(reinterpret_cast(val)); + break; + default: visitor->Unknown(val); + } + } else { + visitor->Unknown(val); + } + break; + } + case ST_ENUM: FLATBUFFERS_ASSERT(false); break; + } + break; + } + default: { + visitor->Unknown(val); + break; + } + } +} + +inline void IterateObject(const uint8_t *obj, const TypeTable *type_table, + IterationVisitor *visitor) { + visitor->StartSequence(); + const uint8_t *prev_val = nullptr; + size_t set_idx = 0; + size_t array_idx = 0; + for (size_t i = 0; i < type_table->num_elems; i++) { + auto type_code = type_table->type_codes[i]; + auto type = static_cast(type_code.base_type); + auto is_repeating = type_code.is_repeating != 0; + auto ref_idx = type_code.sequence_ref; + const TypeTable *ref = nullptr; + if (ref_idx >= 0) { ref = type_table->type_refs[ref_idx](); } + auto name = type_table->names ? type_table->names[i] : nullptr; + const uint8_t *val = nullptr; + if (type_table->st == ST_TABLE) { + val = reinterpret_cast(obj)->GetAddressOf( + FieldIndexToOffset(static_cast(i))); + } else { + val = obj + type_table->values[i]; + } + visitor->Field(i, set_idx, type, is_repeating, ref, name, val); + if (val) { + set_idx++; + if (is_repeating) { + auto elem_ptr = val; + size_t size = 0; + if (type_table->st == ST_TABLE) { + // variable length vector + val += ReadScalar(val); + auto vec = reinterpret_cast *>(val); + elem_ptr = vec->Data(); + size = vec->size(); + } else { + // otherwise fixed size array + size = type_table->array_sizes[array_idx]; + ++array_idx; + } + visitor->StartVector(); + for (size_t j = 0; j < size; j++) { + visitor->Element(j, type, ref, elem_ptr); + IterateValue(type, elem_ptr, ref, prev_val, static_cast(j), + visitor); + elem_ptr += InlineSize(type, ref); + } + visitor->EndVector(); + } else { + IterateValue(type, val, ref, prev_val, -1, visitor); + } + } + prev_val = val; + } + visitor->EndSequence(); +} + +inline void IterateFlatBuffer(const uint8_t *buffer, + const TypeTable *type_table, + IterationVisitor *callback) { + IterateObject(GetRoot(buffer), type_table, callback); +} + +// Outputting a Flatbuffer to a string. Tries to conform as close to JSON / +// the output generated by idl_gen_text.cpp. + +struct ToStringVisitor : public IterationVisitor { + std::string s; + std::string d; + bool q; + std::string in; + size_t indent_level; + bool vector_delimited; + ToStringVisitor(std::string delimiter, bool quotes, std::string indent, + bool vdelimited = true) + : d(delimiter), + q(quotes), + in(indent), + indent_level(0), + vector_delimited(vdelimited) {} + ToStringVisitor(std::string delimiter) + : d(delimiter), + q(false), + in(""), + indent_level(0), + vector_delimited(true) {} + + void append_indent() { + for (size_t i = 0; i < indent_level; i++) { s += in; } + } + + void StartSequence() { + s += "{"; + s += d; + indent_level++; + } + void EndSequence() { + s += d; + indent_level--; + append_indent(); + s += "}"; + } + void Field(size_t /*field_idx*/, size_t set_idx, ElementaryType /*type*/, + bool /*is_vector*/, const TypeTable * /*type_table*/, + const char *name, const uint8_t *val) { + if (!val) return; + if (set_idx) { + s += ","; + s += d; + } + append_indent(); + if (name) { + if (q) s += "\""; + s += name; + if (q) s += "\""; + s += ": "; + } + } + template void Named(T x, const char *name) { + if (name) { + if (q) s += "\""; + s += name; + if (q) s += "\""; + } else { + s += NumToString(x); + } + } + void UType(uint8_t x, const char *name) { Named(x, name); } + void Bool(bool x) { s += x ? "true" : "false"; } + void Char(int8_t x, const char *name) { Named(x, name); } + void UChar(uint8_t x, const char *name) { Named(x, name); } + void Short(int16_t x, const char *name) { Named(x, name); } + void UShort(uint16_t x, const char *name) { Named(x, name); } + void Int(int32_t x, const char *name) { Named(x, name); } + void UInt(uint32_t x, const char *name) { Named(x, name); } + void Long(int64_t x) { s += NumToString(x); } + void ULong(uint64_t x) { s += NumToString(x); } + void Float(float x) { s += NumToString(x); } + void Double(double x) { s += NumToString(x); } + void String(const struct String *str) { + EscapeString(str->c_str(), str->size(), &s, true, false); + } + void Unknown(const uint8_t *) { s += "(?)"; } + void StartVector() { + s += "["; + if (vector_delimited) { + s += d; + indent_level++; + append_indent(); + } else { + s += " "; + } + } + void EndVector() { + if (vector_delimited) { + s += d; + indent_level--; + append_indent(); + } else { + s += " "; + } + s += "]"; + } + void Element(size_t i, ElementaryType /*type*/, + const TypeTable * /*type_table*/, const uint8_t * /*val*/) { + if (i) { + s += ","; + if (vector_delimited) { + s += d; + append_indent(); + } else { + s += " "; + } + } + } +}; + +inline std::string FlatBufferToString(const uint8_t *buffer, + const TypeTable *type_table, + bool multi_line = false, + bool vector_delimited = true) { + ToStringVisitor tostring_visitor(multi_line ? "\n" : " ", false, "", + vector_delimited); + IterateFlatBuffer(buffer, type_table, &tostring_visitor); + return tostring_visitor.s; +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_MINIREFLECT_H_ diff --git a/new_ai_tools/include/flatbuffers/pch/flatc_pch.h b/new_ai_tools/include/flatbuffers/pch/flatc_pch.h new file mode 100644 index 00000000..77132790 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/pch/flatc_pch.h @@ -0,0 +1,39 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_FLATC_PCH_H_ +#define FLATBUFFERS_FLATC_PCH_H_ + +// stl +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// flatbuffers +#include "flatbuffers/pch/pch.h" +#include "flatbuffers/code_generators.h" +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/flexbuffers.h" +#include "flatbuffers/idl.h" + +#endif // FLATBUFFERS_FLATC_PCH_H_ diff --git a/new_ai_tools/include/flatbuffers/pch/pch.h b/new_ai_tools/include/flatbuffers/pch/pch.h new file mode 100644 index 00000000..804e99ed --- /dev/null +++ b/new_ai_tools/include/flatbuffers/pch/pch.h @@ -0,0 +1,38 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_PCH_H_ +#define FLATBUFFERS_PCH_H_ + +// stl +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// flatbuffers +#include "flatbuffers/util.h" + +#endif // FLATBUFFERS_PCH_H_ diff --git a/new_ai_tools/include/flatbuffers/reflection.h b/new_ai_tools/include/flatbuffers/reflection.h new file mode 100644 index 00000000..8e700f0b --- /dev/null +++ b/new_ai_tools/include/flatbuffers/reflection.h @@ -0,0 +1,502 @@ +/* + * Copyright 2015 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_REFLECTION_H_ +#define FLATBUFFERS_REFLECTION_H_ + +// This is somewhat of a circular dependency because flatc (and thus this +// file) is needed to generate this header in the first place. +// Should normally not be a problem since it can be generated by the +// previous version of flatc whenever this code needs to change. +// See scripts/generate_code.py for generation. +#include "flatbuffers/reflection_generated.h" + +// Helper functionality for reflection. + +namespace flatbuffers { + +// ------------------------- GETTERS ------------------------- + +inline bool IsScalar(reflection::BaseType t) { + return t >= reflection::UType && t <= reflection::Double; +} +inline bool IsInteger(reflection::BaseType t) { + return t >= reflection::UType && t <= reflection::ULong; +} +inline bool IsFloat(reflection::BaseType t) { + return t == reflection::Float || t == reflection::Double; +} +inline bool IsLong(reflection::BaseType t) { + return t == reflection::Long || t == reflection::ULong; +} + +// Size of a basic type, don't use with structs. +inline size_t GetTypeSize(reflection::BaseType base_type) { + // This needs to correspond to the BaseType enum. + static size_t sizes[] = { + 0, // None + 1, // UType + 1, // Bool + 1, // Byte + 1, // UByte + 2, // Short + 2, // UShort + 4, // Int + 4, // UInt + 8, // Long + 8, // ULong + 4, // Float + 8, // Double + 4, // String + 4, // Vector + 4, // Obj + 4, // Union + 0, // Array. Only used in structs. 0 was chosen to prevent out-of-bounds + // errors. + + 0 // MaxBaseType. This must be kept the last entry in this array. + }; + static_assert(sizeof(sizes) / sizeof(size_t) == reflection::MaxBaseType + 1, + "Size of sizes[] array does not match the count of BaseType " + "enum values."); + return sizes[base_type]; +} + +// Same as above, but now correctly returns the size of a struct if +// the field (or vector element) is a struct. +inline size_t GetTypeSizeInline(reflection::BaseType base_type, int type_index, + const reflection::Schema &schema) { + if (base_type == reflection::Obj && + schema.objects()->Get(type_index)->is_struct()) { + return schema.objects()->Get(type_index)->bytesize(); + } else { + return GetTypeSize(base_type); + } +} + +// Get the root, regardless of what type it is. +inline Table *GetAnyRoot(uint8_t *flatbuf) { + return GetMutableRoot(flatbuf); +} +inline const Table *GetAnyRoot(const uint8_t *flatbuf) { + return GetRoot
(flatbuf); +} + +// Get a field's default, if you know it's an integer, and its exact type. +template T GetFieldDefaultI(const reflection::Field &field) { + FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); + return static_cast(field.default_integer()); +} + +// Get a field's default, if you know it's floating point and its exact type. +template T GetFieldDefaultF(const reflection::Field &field) { + FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); + return static_cast(field.default_real()); +} + +// Get a field, if you know it's an integer, and its exact type. +template +T GetFieldI(const Table &table, const reflection::Field &field) { + FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); + return table.GetField(field.offset(), + static_cast(field.default_integer())); +} + +// Get a field, if you know it's floating point and its exact type. +template +T GetFieldF(const Table &table, const reflection::Field &field) { + FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); + return table.GetField(field.offset(), + static_cast(field.default_real())); +} + +// Get a field, if you know it's a string. +inline const String *GetFieldS(const Table &table, + const reflection::Field &field) { + FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::String); + return table.GetPointer(field.offset()); +} + +// Get a field, if you know it's a vector. +template +Vector *GetFieldV(const Table &table, const reflection::Field &field) { + FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Vector && + sizeof(T) == GetTypeSize(field.type()->element())); + return table.GetPointer *>(field.offset()); +} + +// Get a field, if you know it's a vector, generically. +// To actually access elements, use the return value together with +// field.type()->element() in any of GetAnyVectorElemI below etc. +inline VectorOfAny *GetFieldAnyV(const Table &table, + const reflection::Field &field) { + return table.GetPointer(field.offset()); +} + +// Get a field, if you know it's a table. +inline Table *GetFieldT(const Table &table, const reflection::Field &field) { + FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj || + field.type()->base_type() == reflection::Union); + return table.GetPointer
(field.offset()); +} + +// Get a field, if you know it's a struct. +inline const Struct *GetFieldStruct(const Table &table, + const reflection::Field &field) { + // TODO: This does NOT check if the field is a table or struct, but we'd need + // access to the schema to check the is_struct flag. + FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj); + return table.GetStruct(field.offset()); +} + +// Get a structure's field, if you know it's a struct. +inline const Struct *GetFieldStruct(const Struct &structure, + const reflection::Field &field) { + FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj); + return structure.GetStruct(field.offset()); +} + +// Raw helper functions used below: get any value in memory as a 64bit int, a +// double or a string. +// All scalars get static_cast to an int64_t, strings use strtoull, every other +// data type returns 0. +int64_t GetAnyValueI(reflection::BaseType type, const uint8_t *data); +// All scalars static cast to double, strings use strtod, every other data +// type is 0.0. +double GetAnyValueF(reflection::BaseType type, const uint8_t *data); +// All scalars converted using stringstream, strings as-is, and all other +// data types provide some level of debug-pretty-printing. +std::string GetAnyValueS(reflection::BaseType type, const uint8_t *data, + const reflection::Schema *schema, int type_index); + +// Get any table field as a 64bit int, regardless of what type it is. +inline int64_t GetAnyFieldI(const Table &table, + const reflection::Field &field) { + auto field_ptr = table.GetAddressOf(field.offset()); + return field_ptr ? GetAnyValueI(field.type()->base_type(), field_ptr) + : field.default_integer(); +} + +// Get any table field as a double, regardless of what type it is. +inline double GetAnyFieldF(const Table &table, const reflection::Field &field) { + auto field_ptr = table.GetAddressOf(field.offset()); + return field_ptr ? GetAnyValueF(field.type()->base_type(), field_ptr) + : field.default_real(); +} + +// Get any table field as a string, regardless of what type it is. +// You may pass nullptr for the schema if you don't care to have fields that +// are of table type pretty-printed. +inline std::string GetAnyFieldS(const Table &table, + const reflection::Field &field, + const reflection::Schema *schema) { + auto field_ptr = table.GetAddressOf(field.offset()); + return field_ptr ? GetAnyValueS(field.type()->base_type(), field_ptr, schema, + field.type()->index()) + : ""; +} + +// Get any struct field as a 64bit int, regardless of what type it is. +inline int64_t GetAnyFieldI(const Struct &st, const reflection::Field &field) { + return GetAnyValueI(field.type()->base_type(), + st.GetAddressOf(field.offset())); +} + +// Get any struct field as a double, regardless of what type it is. +inline double GetAnyFieldF(const Struct &st, const reflection::Field &field) { + return GetAnyValueF(field.type()->base_type(), + st.GetAddressOf(field.offset())); +} + +// Get any struct field as a string, regardless of what type it is. +inline std::string GetAnyFieldS(const Struct &st, + const reflection::Field &field) { + return GetAnyValueS(field.type()->base_type(), + st.GetAddressOf(field.offset()), nullptr, -1); +} + +// Get any vector element as a 64bit int, regardless of what type it is. +inline int64_t GetAnyVectorElemI(const VectorOfAny *vec, + reflection::BaseType elem_type, size_t i) { + return GetAnyValueI(elem_type, vec->Data() + GetTypeSize(elem_type) * i); +} + +// Get any vector element as a double, regardless of what type it is. +inline double GetAnyVectorElemF(const VectorOfAny *vec, + reflection::BaseType elem_type, size_t i) { + return GetAnyValueF(elem_type, vec->Data() + GetTypeSize(elem_type) * i); +} + +// Get any vector element as a string, regardless of what type it is. +inline std::string GetAnyVectorElemS(const VectorOfAny *vec, + reflection::BaseType elem_type, size_t i) { + return GetAnyValueS(elem_type, vec->Data() + GetTypeSize(elem_type) * i, + nullptr, -1); +} + +// Get a vector element that's a table/string/vector from a generic vector. +// Pass Table/String/VectorOfAny as template parameter. +// Warning: does no typechecking. +template +T *GetAnyVectorElemPointer(const VectorOfAny *vec, size_t i) { + auto elem_ptr = vec->Data() + sizeof(uoffset_t) * i; + return reinterpret_cast(elem_ptr + ReadScalar(elem_ptr)); +} + +// Get the inline-address of a vector element. Useful for Structs (pass Struct +// as template arg), or being able to address a range of scalars in-line. +// Get elem_size from GetTypeSizeInline(). +// Note: little-endian data on all platforms, use EndianScalar() instead of +// raw pointer access with scalars). +template +T *GetAnyVectorElemAddressOf(const VectorOfAny *vec, size_t i, + size_t elem_size) { + return reinterpret_cast(vec->Data() + elem_size * i); +} + +// Similarly, for elements of tables. +template +T *GetAnyFieldAddressOf(const Table &table, const reflection::Field &field) { + return reinterpret_cast(table.GetAddressOf(field.offset())); +} + +// Similarly, for elements of structs. +template +T *GetAnyFieldAddressOf(const Struct &st, const reflection::Field &field) { + return reinterpret_cast(st.GetAddressOf(field.offset())); +} + +// ------------------------- SETTERS ------------------------- + +// Set any scalar field, if you know its exact type. +template +bool SetField(Table *table, const reflection::Field &field, T val) { + reflection::BaseType type = field.type()->base_type(); + if (!IsScalar(type)) { return false; } + FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(type)); + T def; + if (IsInteger(type)) { + def = GetFieldDefaultI(field); + } else { + FLATBUFFERS_ASSERT(IsFloat(type)); + def = GetFieldDefaultF(field); + } + return table->SetField(field.offset(), val, def); +} + +// Raw helper functions used below: set any value in memory as a 64bit int, a +// double or a string. +// These work for all scalar values, but do nothing for other data types. +// To set a string, see SetString below. +void SetAnyValueI(reflection::BaseType type, uint8_t *data, int64_t val); +void SetAnyValueF(reflection::BaseType type, uint8_t *data, double val); +void SetAnyValueS(reflection::BaseType type, uint8_t *data, const char *val); + +// Set any table field as a 64bit int, regardless of type what it is. +inline bool SetAnyFieldI(Table *table, const reflection::Field &field, + int64_t val) { + auto field_ptr = table->GetAddressOf(field.offset()); + if (!field_ptr) return val == GetFieldDefaultI(field); + SetAnyValueI(field.type()->base_type(), field_ptr, val); + return true; +} + +// Set any table field as a double, regardless of what type it is. +inline bool SetAnyFieldF(Table *table, const reflection::Field &field, + double val) { + auto field_ptr = table->GetAddressOf(field.offset()); + if (!field_ptr) return val == GetFieldDefaultF(field); + SetAnyValueF(field.type()->base_type(), field_ptr, val); + return true; +} + +// Set any table field as a string, regardless of what type it is. +inline bool SetAnyFieldS(Table *table, const reflection::Field &field, + const char *val) { + auto field_ptr = table->GetAddressOf(field.offset()); + if (!field_ptr) return false; + SetAnyValueS(field.type()->base_type(), field_ptr, val); + return true; +} + +// Set any struct field as a 64bit int, regardless of type what it is. +inline void SetAnyFieldI(Struct *st, const reflection::Field &field, + int64_t val) { + SetAnyValueI(field.type()->base_type(), st->GetAddressOf(field.offset()), + val); +} + +// Set any struct field as a double, regardless of type what it is. +inline void SetAnyFieldF(Struct *st, const reflection::Field &field, + double val) { + SetAnyValueF(field.type()->base_type(), st->GetAddressOf(field.offset()), + val); +} + +// Set any struct field as a string, regardless of type what it is. +inline void SetAnyFieldS(Struct *st, const reflection::Field &field, + const char *val) { + SetAnyValueS(field.type()->base_type(), st->GetAddressOf(field.offset()), + val); +} + +// Set any vector element as a 64bit int, regardless of type what it is. +inline void SetAnyVectorElemI(VectorOfAny *vec, reflection::BaseType elem_type, + size_t i, int64_t val) { + SetAnyValueI(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); +} + +// Set any vector element as a double, regardless of type what it is. +inline void SetAnyVectorElemF(VectorOfAny *vec, reflection::BaseType elem_type, + size_t i, double val) { + SetAnyValueF(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); +} + +// Set any vector element as a string, regardless of type what it is. +inline void SetAnyVectorElemS(VectorOfAny *vec, reflection::BaseType elem_type, + size_t i, const char *val) { + SetAnyValueS(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); +} + +// ------------------------- RESIZING SETTERS ------------------------- + +// "smart" pointer for use with resizing vectors: turns a pointer inside +// a vector into a relative offset, such that it is not affected by resizes. +template class pointer_inside_vector { + public: + pointer_inside_vector(T *ptr, std::vector &vec) + : offset_(reinterpret_cast(ptr) - + reinterpret_cast(vec.data())), + vec_(vec) {} + + T *operator*() const { + return reinterpret_cast(reinterpret_cast(vec_.data()) + + offset_); + } + T *operator->() const { return operator*(); } + + private: + size_t offset_; + std::vector &vec_; +}; + +// Helper to create the above easily without specifying template args. +template +pointer_inside_vector piv(T *ptr, std::vector &vec) { + return pointer_inside_vector(ptr, vec); +} + +inline const char *UnionTypeFieldSuffix() { return "_type"; } + +// Helper to figure out the actual table type a union refers to. +inline const reflection::Object &GetUnionType( + const reflection::Schema &schema, const reflection::Object &parent, + const reflection::Field &unionfield, const Table &table) { + auto enumdef = schema.enums()->Get(unionfield.type()->index()); + // TODO: this is clumsy and slow, but no other way to find it? + auto type_field = parent.fields()->LookupByKey( + (unionfield.name()->str() + UnionTypeFieldSuffix()).c_str()); + FLATBUFFERS_ASSERT(type_field); + auto union_type = GetFieldI(table, *type_field); + auto enumval = enumdef->values()->LookupByKey(union_type); + return *schema.objects()->Get(enumval->union_type()->index()); +} + +// Changes the contents of a string inside a FlatBuffer. FlatBuffer must +// live inside a std::vector so we can resize the buffer if needed. +// "str" must live inside "flatbuf" and may be invalidated after this call. +// If your FlatBuffer's root table is not the schema's root table, you should +// pass in your root_table type as well. +void SetString(const reflection::Schema &schema, const std::string &val, + const String *str, std::vector *flatbuf, + const reflection::Object *root_table = nullptr); + +// Resizes a flatbuffers::Vector inside a FlatBuffer. FlatBuffer must +// live inside a std::vector so we can resize the buffer if needed. +// "vec" must live inside "flatbuf" and may be invalidated after this call. +// If your FlatBuffer's root table is not the schema's root table, you should +// pass in your root_table type as well. +uint8_t *ResizeAnyVector(const reflection::Schema &schema, uoffset_t newsize, + const VectorOfAny *vec, uoffset_t num_elems, + uoffset_t elem_size, std::vector *flatbuf, + const reflection::Object *root_table = nullptr); + +template +void ResizeVector(const reflection::Schema &schema, uoffset_t newsize, T val, + const Vector *vec, std::vector *flatbuf, + const reflection::Object *root_table = nullptr) { + auto delta_elem = static_cast(newsize) - static_cast(vec->size()); + auto newelems = ResizeAnyVector( + schema, newsize, reinterpret_cast(vec), vec->size(), + static_cast(sizeof(T)), flatbuf, root_table); + // Set new elements to "val". + for (int i = 0; i < delta_elem; i++) { + auto loc = newelems + i * sizeof(T); + auto is_scalar = flatbuffers::is_scalar::value; + if (is_scalar) { + WriteScalar(loc, val); + } else { // struct + *reinterpret_cast(loc) = val; + } + } +} + +// Adds any new data (in the form of a new FlatBuffer) to an existing +// FlatBuffer. This can be used when any of the above methods are not +// sufficient, in particular for adding new tables and new fields. +// This is potentially slightly less efficient than a FlatBuffer constructed +// in one piece, since the new FlatBuffer doesn't share any vtables with the +// existing one. +// The return value can now be set using Vector::MutateOffset or SetFieldT +// below. +const uint8_t *AddFlatBuffer(std::vector &flatbuf, + const uint8_t *newbuf, size_t newlen); + +inline bool SetFieldT(Table *table, const reflection::Field &field, + const uint8_t *val) { + FLATBUFFERS_ASSERT(sizeof(uoffset_t) == + GetTypeSize(field.type()->base_type())); + return table->SetPointer(field.offset(), val); +} + +// ------------------------- COPYING ------------------------- + +// Generic copying of tables from a FlatBuffer into a FlatBuffer builder. +// Can be used to do any kind of merging/selecting you may want to do out +// of existing buffers. Also useful to reconstruct a whole buffer if the +// above resizing functionality has introduced garbage in a buffer you want +// to remove. +// Note: this does not deal with DAGs correctly. If the table passed forms a +// DAG, the copy will be a tree instead (with duplicates). Strings can be +// shared however, by passing true for use_string_pooling. + +Offset CopyTable(FlatBufferBuilder &fbb, + const reflection::Schema &schema, + const reflection::Object &objectdef, + const Table &table, + bool use_string_pooling = false); + +// Verifies the provided flatbuffer using reflection. +// root should point to the root type for this flatbuffer. +// buf should point to the start of flatbuffer data. +// length specifies the size of the flatbuffer data. +bool Verify(const reflection::Schema &schema, const reflection::Object &root, + const uint8_t *buf, size_t length, uoffset_t max_depth = 64, + uoffset_t max_tables = 1000000); + +} // namespace flatbuffers + +#endif // FLATBUFFERS_REFLECTION_H_ diff --git a/new_ai_tools/include/flatbuffers/reflection_generated.h b/new_ai_tools/include/flatbuffers/reflection_generated.h new file mode 100644 index 00000000..dcb0f7e0 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/reflection_generated.h @@ -0,0 +1,1449 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ +#define FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ + +#include "flatbuffers/flatbuffers.h" + +namespace reflection { + +struct Type; +struct TypeBuilder; + +struct KeyValue; +struct KeyValueBuilder; + +struct EnumVal; +struct EnumValBuilder; + +struct Enum; +struct EnumBuilder; + +struct Field; +struct FieldBuilder; + +struct Object; +struct ObjectBuilder; + +struct RPCCall; +struct RPCCallBuilder; + +struct Service; +struct ServiceBuilder; + +struct SchemaFile; +struct SchemaFileBuilder; + +struct Schema; +struct SchemaBuilder; + +enum BaseType { + None = 0, + UType = 1, + Bool = 2, + Byte = 3, + UByte = 4, + Short = 5, + UShort = 6, + Int = 7, + UInt = 8, + Long = 9, + ULong = 10, + Float = 11, + Double = 12, + String = 13, + Vector = 14, + Obj = 15, + Union = 16, + Array = 17, + MaxBaseType = 18 +}; + +inline const BaseType (&EnumValuesBaseType())[19] { + static const BaseType values[] = { + None, + UType, + Bool, + Byte, + UByte, + Short, + UShort, + Int, + UInt, + Long, + ULong, + Float, + Double, + String, + Vector, + Obj, + Union, + Array, + MaxBaseType + }; + return values; +} + +inline const char * const *EnumNamesBaseType() { + static const char * const names[20] = { + "None", + "UType", + "Bool", + "Byte", + "UByte", + "Short", + "UShort", + "Int", + "UInt", + "Long", + "ULong", + "Float", + "Double", + "String", + "Vector", + "Obj", + "Union", + "Array", + "MaxBaseType", + nullptr + }; + return names; +} + +inline const char *EnumNameBaseType(BaseType e) { + if (flatbuffers::IsOutRange(e, None, MaxBaseType)) return ""; + const size_t index = static_cast(e); + return EnumNamesBaseType()[index]; +} + +/// New schema language features that are not supported by old code generators. +enum AdvancedFeatures { + AdvancedArrayFeatures = 1ULL, + AdvancedUnionFeatures = 2ULL, + OptionalScalars = 4ULL, + DefaultVectorsAndStrings = 8ULL +}; + +inline const AdvancedFeatures (&EnumValuesAdvancedFeatures())[4] { + static const AdvancedFeatures values[] = { + AdvancedArrayFeatures, + AdvancedUnionFeatures, + OptionalScalars, + DefaultVectorsAndStrings + }; + return values; +} + +inline const char * const *EnumNamesAdvancedFeatures() { + static const char * const names[9] = { + "AdvancedArrayFeatures", + "AdvancedUnionFeatures", + "", + "OptionalScalars", + "", + "", + "", + "DefaultVectorsAndStrings", + nullptr + }; + return names; +} + +inline const char *EnumNameAdvancedFeatures(AdvancedFeatures e) { + if (flatbuffers::IsOutRange(e, AdvancedArrayFeatures, DefaultVectorsAndStrings)) return ""; + const size_t index = static_cast(e) - static_cast(AdvancedArrayFeatures); + return EnumNamesAdvancedFeatures()[index]; +} + +struct Type FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TypeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BASE_TYPE = 4, + VT_ELEMENT = 6, + VT_INDEX = 8, + VT_FIXED_LENGTH = 10, + VT_BASE_SIZE = 12, + VT_ELEMENT_SIZE = 14 + }; + reflection::BaseType base_type() const { + return static_cast(GetField(VT_BASE_TYPE, 0)); + } + reflection::BaseType element() const { + return static_cast(GetField(VT_ELEMENT, 0)); + } + int32_t index() const { + return GetField(VT_INDEX, -1); + } + uint16_t fixed_length() const { + return GetField(VT_FIXED_LENGTH, 0); + } + /// The size (octets) of the `base_type` field. + uint32_t base_size() const { + return GetField(VT_BASE_SIZE, 4); + } + /// The size (octets) of the `element` field, if present. + uint32_t element_size() const { + return GetField(VT_ELEMENT_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BASE_TYPE, 1) && + VerifyField(verifier, VT_ELEMENT, 1) && + VerifyField(verifier, VT_INDEX, 4) && + VerifyField(verifier, VT_FIXED_LENGTH, 2) && + VerifyField(verifier, VT_BASE_SIZE, 4) && + VerifyField(verifier, VT_ELEMENT_SIZE, 4) && + verifier.EndTable(); + } +}; + +struct TypeBuilder { + typedef Type Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_base_type(reflection::BaseType base_type) { + fbb_.AddElement(Type::VT_BASE_TYPE, static_cast(base_type), 0); + } + void add_element(reflection::BaseType element) { + fbb_.AddElement(Type::VT_ELEMENT, static_cast(element), 0); + } + void add_index(int32_t index) { + fbb_.AddElement(Type::VT_INDEX, index, -1); + } + void add_fixed_length(uint16_t fixed_length) { + fbb_.AddElement(Type::VT_FIXED_LENGTH, fixed_length, 0); + } + void add_base_size(uint32_t base_size) { + fbb_.AddElement(Type::VT_BASE_SIZE, base_size, 4); + } + void add_element_size(uint32_t element_size) { + fbb_.AddElement(Type::VT_ELEMENT_SIZE, element_size, 0); + } + explicit TypeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateType( + flatbuffers::FlatBufferBuilder &_fbb, + reflection::BaseType base_type = reflection::None, + reflection::BaseType element = reflection::None, + int32_t index = -1, + uint16_t fixed_length = 0, + uint32_t base_size = 4, + uint32_t element_size = 0) { + TypeBuilder builder_(_fbb); + builder_.add_element_size(element_size); + builder_.add_base_size(base_size); + builder_.add_index(index); + builder_.add_fixed_length(fixed_length); + builder_.add_element(element); + builder_.add_base_type(base_type); + return builder_.Finish(); +} + +struct KeyValue FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef KeyValueBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEY = 4, + VT_VALUE = 6 + }; + const flatbuffers::String *key() const { + return GetPointer(VT_KEY); + } + bool KeyCompareLessThan(const KeyValue *o) const { + return *key() < *o->key(); + } + int KeyCompareWithValue(const char *_key) const { + return strcmp(key()->c_str(), _key); + } + const flatbuffers::String *value() const { + return GetPointer(VT_VALUE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_KEY) && + verifier.VerifyString(key()) && + VerifyOffset(verifier, VT_VALUE) && + verifier.VerifyString(value()) && + verifier.EndTable(); + } +}; + +struct KeyValueBuilder { + typedef KeyValue Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_key(flatbuffers::Offset key) { + fbb_.AddOffset(KeyValue::VT_KEY, key); + } + void add_value(flatbuffers::Offset value) { + fbb_.AddOffset(KeyValue::VT_VALUE, value); + } + explicit KeyValueBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, KeyValue::VT_KEY); + return o; + } +}; + +inline flatbuffers::Offset CreateKeyValue( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset key = 0, + flatbuffers::Offset value = 0) { + KeyValueBuilder builder_(_fbb); + builder_.add_value(value); + builder_.add_key(key); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateKeyValueDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *key = nullptr, + const char *value = nullptr) { + auto key__ = key ? _fbb.CreateString(key) : 0; + auto value__ = value ? _fbb.CreateString(value) : 0; + return reflection::CreateKeyValue( + _fbb, + key__, + value__); +} + +struct EnumVal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EnumValBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_VALUE = 6, + VT_UNION_TYPE = 10, + VT_DOCUMENTATION = 12 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + int64_t value() const { + return GetField(VT_VALUE, 0); + } + bool KeyCompareLessThan(const EnumVal *o) const { + return value() < o->value(); + } + int KeyCompareWithValue(int64_t _value) const { + return static_cast(value() > _value) - static_cast(value() < _value); + } + const reflection::Type *union_type() const { + return GetPointer(VT_UNION_TYPE); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyField(verifier, VT_VALUE, 8) && + VerifyOffset(verifier, VT_UNION_TYPE) && + verifier.VerifyTable(union_type()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + verifier.EndTable(); + } +}; + +struct EnumValBuilder { + typedef EnumVal Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(EnumVal::VT_NAME, name); + } + void add_value(int64_t value) { + fbb_.AddElement(EnumVal::VT_VALUE, value, 0); + } + void add_union_type(flatbuffers::Offset union_type) { + fbb_.AddOffset(EnumVal::VT_UNION_TYPE, union_type); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(EnumVal::VT_DOCUMENTATION, documentation); + } + explicit EnumValBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, EnumVal::VT_NAME); + return o; + } +}; + +inline flatbuffers::Offset CreateEnumVal( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + int64_t value = 0, + flatbuffers::Offset union_type = 0, + flatbuffers::Offset>> documentation = 0) { + EnumValBuilder builder_(_fbb); + builder_.add_value(value); + builder_.add_documentation(documentation); + builder_.add_union_type(union_type); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateEnumValDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + int64_t value = 0, + flatbuffers::Offset union_type = 0, + const std::vector> *documentation = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + return reflection::CreateEnumVal( + _fbb, + name__, + value, + union_type, + documentation__); +} + +struct Enum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EnumBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_VALUES = 6, + VT_IS_UNION = 8, + VT_UNDERLYING_TYPE = 10, + VT_ATTRIBUTES = 12, + VT_DOCUMENTATION = 14, + VT_DECLARATION_FILE = 16 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool KeyCompareLessThan(const Enum *o) const { + return *name() < *o->name(); + } + int KeyCompareWithValue(const char *_name) const { + return strcmp(name()->c_str(), _name); + } + const flatbuffers::Vector> *values() const { + return GetPointer> *>(VT_VALUES); + } + bool is_union() const { + return GetField(VT_IS_UNION, 0) != 0; + } + const reflection::Type *underlying_type() const { + return GetPointer(VT_UNDERLYING_TYPE); + } + const flatbuffers::Vector> *attributes() const { + return GetPointer> *>(VT_ATTRIBUTES); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + /// File that this Enum is declared in. + const flatbuffers::String *declaration_file() const { + return GetPointer(VT_DECLARATION_FILE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffsetRequired(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.VerifyVectorOfTables(values()) && + VerifyField(verifier, VT_IS_UNION, 1) && + VerifyOffsetRequired(verifier, VT_UNDERLYING_TYPE) && + verifier.VerifyTable(underlying_type()) && + VerifyOffset(verifier, VT_ATTRIBUTES) && + verifier.VerifyVector(attributes()) && + verifier.VerifyVectorOfTables(attributes()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + VerifyOffset(verifier, VT_DECLARATION_FILE) && + verifier.VerifyString(declaration_file()) && + verifier.EndTable(); + } +}; + +struct EnumBuilder { + typedef Enum Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Enum::VT_NAME, name); + } + void add_values(flatbuffers::Offset>> values) { + fbb_.AddOffset(Enum::VT_VALUES, values); + } + void add_is_union(bool is_union) { + fbb_.AddElement(Enum::VT_IS_UNION, static_cast(is_union), 0); + } + void add_underlying_type(flatbuffers::Offset underlying_type) { + fbb_.AddOffset(Enum::VT_UNDERLYING_TYPE, underlying_type); + } + void add_attributes(flatbuffers::Offset>> attributes) { + fbb_.AddOffset(Enum::VT_ATTRIBUTES, attributes); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(Enum::VT_DOCUMENTATION, documentation); + } + void add_declaration_file(flatbuffers::Offset declaration_file) { + fbb_.AddOffset(Enum::VT_DECLARATION_FILE, declaration_file); + } + explicit EnumBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, Enum::VT_NAME); + fbb_.Required(o, Enum::VT_VALUES); + fbb_.Required(o, Enum::VT_UNDERLYING_TYPE); + return o; + } +}; + +inline flatbuffers::Offset CreateEnum( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset>> values = 0, + bool is_union = false, + flatbuffers::Offset underlying_type = 0, + flatbuffers::Offset>> attributes = 0, + flatbuffers::Offset>> documentation = 0, + flatbuffers::Offset declaration_file = 0) { + EnumBuilder builder_(_fbb); + builder_.add_declaration_file(declaration_file); + builder_.add_documentation(documentation); + builder_.add_attributes(attributes); + builder_.add_underlying_type(underlying_type); + builder_.add_values(values); + builder_.add_name(name); + builder_.add_is_union(is_union); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateEnumDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + std::vector> *values = nullptr, + bool is_union = false, + flatbuffers::Offset underlying_type = 0, + std::vector> *attributes = nullptr, + const std::vector> *documentation = nullptr, + const char *declaration_file = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto values__ = values ? _fbb.CreateVectorOfSortedTables(values) : 0; + auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; + return reflection::CreateEnum( + _fbb, + name__, + values__, + is_union, + underlying_type, + attributes__, + documentation__, + declaration_file__); +} + +struct Field FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FieldBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_TYPE = 6, + VT_ID = 8, + VT_OFFSET = 10, + VT_DEFAULT_INTEGER = 12, + VT_DEFAULT_REAL = 14, + VT_DEPRECATED = 16, + VT_REQUIRED = 18, + VT_KEY = 20, + VT_ATTRIBUTES = 22, + VT_DOCUMENTATION = 24, + VT_OPTIONAL = 26, + VT_PADDING = 28 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool KeyCompareLessThan(const Field *o) const { + return *name() < *o->name(); + } + int KeyCompareWithValue(const char *_name) const { + return strcmp(name()->c_str(), _name); + } + const reflection::Type *type() const { + return GetPointer(VT_TYPE); + } + uint16_t id() const { + return GetField(VT_ID, 0); + } + uint16_t offset() const { + return GetField(VT_OFFSET, 0); + } + int64_t default_integer() const { + return GetField(VT_DEFAULT_INTEGER, 0); + } + double default_real() const { + return GetField(VT_DEFAULT_REAL, 0.0); + } + bool deprecated() const { + return GetField(VT_DEPRECATED, 0) != 0; + } + bool required() const { + return GetField(VT_REQUIRED, 0) != 0; + } + bool key() const { + return GetField(VT_KEY, 0) != 0; + } + const flatbuffers::Vector> *attributes() const { + return GetPointer> *>(VT_ATTRIBUTES); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + bool optional() const { + return GetField(VT_OPTIONAL, 0) != 0; + } + /// Number of padding octets to always add after this field. Structs only. + uint16_t padding() const { + return GetField(VT_PADDING, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffsetRequired(verifier, VT_TYPE) && + verifier.VerifyTable(type()) && + VerifyField(verifier, VT_ID, 2) && + VerifyField(verifier, VT_OFFSET, 2) && + VerifyField(verifier, VT_DEFAULT_INTEGER, 8) && + VerifyField(verifier, VT_DEFAULT_REAL, 8) && + VerifyField(verifier, VT_DEPRECATED, 1) && + VerifyField(verifier, VT_REQUIRED, 1) && + VerifyField(verifier, VT_KEY, 1) && + VerifyOffset(verifier, VT_ATTRIBUTES) && + verifier.VerifyVector(attributes()) && + verifier.VerifyVectorOfTables(attributes()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + VerifyField(verifier, VT_OPTIONAL, 1) && + VerifyField(verifier, VT_PADDING, 2) && + verifier.EndTable(); + } +}; + +struct FieldBuilder { + typedef Field Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Field::VT_NAME, name); + } + void add_type(flatbuffers::Offset type) { + fbb_.AddOffset(Field::VT_TYPE, type); + } + void add_id(uint16_t id) { + fbb_.AddElement(Field::VT_ID, id, 0); + } + void add_offset(uint16_t offset) { + fbb_.AddElement(Field::VT_OFFSET, offset, 0); + } + void add_default_integer(int64_t default_integer) { + fbb_.AddElement(Field::VT_DEFAULT_INTEGER, default_integer, 0); + } + void add_default_real(double default_real) { + fbb_.AddElement(Field::VT_DEFAULT_REAL, default_real, 0.0); + } + void add_deprecated(bool deprecated) { + fbb_.AddElement(Field::VT_DEPRECATED, static_cast(deprecated), 0); + } + void add_required(bool required) { + fbb_.AddElement(Field::VT_REQUIRED, static_cast(required), 0); + } + void add_key(bool key) { + fbb_.AddElement(Field::VT_KEY, static_cast(key), 0); + } + void add_attributes(flatbuffers::Offset>> attributes) { + fbb_.AddOffset(Field::VT_ATTRIBUTES, attributes); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(Field::VT_DOCUMENTATION, documentation); + } + void add_optional(bool optional) { + fbb_.AddElement(Field::VT_OPTIONAL, static_cast(optional), 0); + } + void add_padding(uint16_t padding) { + fbb_.AddElement(Field::VT_PADDING, padding, 0); + } + explicit FieldBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, Field::VT_NAME); + fbb_.Required(o, Field::VT_TYPE); + return o; + } +}; + +inline flatbuffers::Offset CreateField( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset type = 0, + uint16_t id = 0, + uint16_t offset = 0, + int64_t default_integer = 0, + double default_real = 0.0, + bool deprecated = false, + bool required = false, + bool key = false, + flatbuffers::Offset>> attributes = 0, + flatbuffers::Offset>> documentation = 0, + bool optional = false, + uint16_t padding = 0) { + FieldBuilder builder_(_fbb); + builder_.add_default_real(default_real); + builder_.add_default_integer(default_integer); + builder_.add_documentation(documentation); + builder_.add_attributes(attributes); + builder_.add_type(type); + builder_.add_name(name); + builder_.add_padding(padding); + builder_.add_offset(offset); + builder_.add_id(id); + builder_.add_optional(optional); + builder_.add_key(key); + builder_.add_required(required); + builder_.add_deprecated(deprecated); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateFieldDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + flatbuffers::Offset type = 0, + uint16_t id = 0, + uint16_t offset = 0, + int64_t default_integer = 0, + double default_real = 0.0, + bool deprecated = false, + bool required = false, + bool key = false, + std::vector> *attributes = nullptr, + const std::vector> *documentation = nullptr, + bool optional = false, + uint16_t padding = 0) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + return reflection::CreateField( + _fbb, + name__, + type, + id, + offset, + default_integer, + default_real, + deprecated, + required, + key, + attributes__, + documentation__, + optional, + padding); +} + +struct Object FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ObjectBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_FIELDS = 6, + VT_IS_STRUCT = 8, + VT_MINALIGN = 10, + VT_BYTESIZE = 12, + VT_ATTRIBUTES = 14, + VT_DOCUMENTATION = 16, + VT_DECLARATION_FILE = 18 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool KeyCompareLessThan(const Object *o) const { + return *name() < *o->name(); + } + int KeyCompareWithValue(const char *_name) const { + return strcmp(name()->c_str(), _name); + } + const flatbuffers::Vector> *fields() const { + return GetPointer> *>(VT_FIELDS); + } + bool is_struct() const { + return GetField(VT_IS_STRUCT, 0) != 0; + } + int32_t minalign() const { + return GetField(VT_MINALIGN, 0); + } + int32_t bytesize() const { + return GetField(VT_BYTESIZE, 0); + } + const flatbuffers::Vector> *attributes() const { + return GetPointer> *>(VT_ATTRIBUTES); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + /// File that this Object is declared in. + const flatbuffers::String *declaration_file() const { + return GetPointer(VT_DECLARATION_FILE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffsetRequired(verifier, VT_FIELDS) && + verifier.VerifyVector(fields()) && + verifier.VerifyVectorOfTables(fields()) && + VerifyField(verifier, VT_IS_STRUCT, 1) && + VerifyField(verifier, VT_MINALIGN, 4) && + VerifyField(verifier, VT_BYTESIZE, 4) && + VerifyOffset(verifier, VT_ATTRIBUTES) && + verifier.VerifyVector(attributes()) && + verifier.VerifyVectorOfTables(attributes()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + VerifyOffset(verifier, VT_DECLARATION_FILE) && + verifier.VerifyString(declaration_file()) && + verifier.EndTable(); + } +}; + +struct ObjectBuilder { + typedef Object Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Object::VT_NAME, name); + } + void add_fields(flatbuffers::Offset>> fields) { + fbb_.AddOffset(Object::VT_FIELDS, fields); + } + void add_is_struct(bool is_struct) { + fbb_.AddElement(Object::VT_IS_STRUCT, static_cast(is_struct), 0); + } + void add_minalign(int32_t minalign) { + fbb_.AddElement(Object::VT_MINALIGN, minalign, 0); + } + void add_bytesize(int32_t bytesize) { + fbb_.AddElement(Object::VT_BYTESIZE, bytesize, 0); + } + void add_attributes(flatbuffers::Offset>> attributes) { + fbb_.AddOffset(Object::VT_ATTRIBUTES, attributes); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(Object::VT_DOCUMENTATION, documentation); + } + void add_declaration_file(flatbuffers::Offset declaration_file) { + fbb_.AddOffset(Object::VT_DECLARATION_FILE, declaration_file); + } + explicit ObjectBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, Object::VT_NAME); + fbb_.Required(o, Object::VT_FIELDS); + return o; + } +}; + +inline flatbuffers::Offset CreateObject( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset>> fields = 0, + bool is_struct = false, + int32_t minalign = 0, + int32_t bytesize = 0, + flatbuffers::Offset>> attributes = 0, + flatbuffers::Offset>> documentation = 0, + flatbuffers::Offset declaration_file = 0) { + ObjectBuilder builder_(_fbb); + builder_.add_declaration_file(declaration_file); + builder_.add_documentation(documentation); + builder_.add_attributes(attributes); + builder_.add_bytesize(bytesize); + builder_.add_minalign(minalign); + builder_.add_fields(fields); + builder_.add_name(name); + builder_.add_is_struct(is_struct); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateObjectDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + std::vector> *fields = nullptr, + bool is_struct = false, + int32_t minalign = 0, + int32_t bytesize = 0, + std::vector> *attributes = nullptr, + const std::vector> *documentation = nullptr, + const char *declaration_file = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto fields__ = fields ? _fbb.CreateVectorOfSortedTables(fields) : 0; + auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; + return reflection::CreateObject( + _fbb, + name__, + fields__, + is_struct, + minalign, + bytesize, + attributes__, + documentation__, + declaration_file__); +} + +struct RPCCall FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RPCCallBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_REQUEST = 6, + VT_RESPONSE = 8, + VT_ATTRIBUTES = 10, + VT_DOCUMENTATION = 12 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool KeyCompareLessThan(const RPCCall *o) const { + return *name() < *o->name(); + } + int KeyCompareWithValue(const char *_name) const { + return strcmp(name()->c_str(), _name); + } + const reflection::Object *request() const { + return GetPointer(VT_REQUEST); + } + const reflection::Object *response() const { + return GetPointer(VT_RESPONSE); + } + const flatbuffers::Vector> *attributes() const { + return GetPointer> *>(VT_ATTRIBUTES); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffsetRequired(verifier, VT_REQUEST) && + verifier.VerifyTable(request()) && + VerifyOffsetRequired(verifier, VT_RESPONSE) && + verifier.VerifyTable(response()) && + VerifyOffset(verifier, VT_ATTRIBUTES) && + verifier.VerifyVector(attributes()) && + verifier.VerifyVectorOfTables(attributes()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + verifier.EndTable(); + } +}; + +struct RPCCallBuilder { + typedef RPCCall Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(RPCCall::VT_NAME, name); + } + void add_request(flatbuffers::Offset request) { + fbb_.AddOffset(RPCCall::VT_REQUEST, request); + } + void add_response(flatbuffers::Offset response) { + fbb_.AddOffset(RPCCall::VT_RESPONSE, response); + } + void add_attributes(flatbuffers::Offset>> attributes) { + fbb_.AddOffset(RPCCall::VT_ATTRIBUTES, attributes); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(RPCCall::VT_DOCUMENTATION, documentation); + } + explicit RPCCallBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, RPCCall::VT_NAME); + fbb_.Required(o, RPCCall::VT_REQUEST); + fbb_.Required(o, RPCCall::VT_RESPONSE); + return o; + } +}; + +inline flatbuffers::Offset CreateRPCCall( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset request = 0, + flatbuffers::Offset response = 0, + flatbuffers::Offset>> attributes = 0, + flatbuffers::Offset>> documentation = 0) { + RPCCallBuilder builder_(_fbb); + builder_.add_documentation(documentation); + builder_.add_attributes(attributes); + builder_.add_response(response); + builder_.add_request(request); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateRPCCallDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + flatbuffers::Offset request = 0, + flatbuffers::Offset response = 0, + std::vector> *attributes = nullptr, + const std::vector> *documentation = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + return reflection::CreateRPCCall( + _fbb, + name__, + request, + response, + attributes__, + documentation__); +} + +struct Service FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ServiceBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_CALLS = 6, + VT_ATTRIBUTES = 8, + VT_DOCUMENTATION = 10, + VT_DECLARATION_FILE = 12 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool KeyCompareLessThan(const Service *o) const { + return *name() < *o->name(); + } + int KeyCompareWithValue(const char *_name) const { + return strcmp(name()->c_str(), _name); + } + const flatbuffers::Vector> *calls() const { + return GetPointer> *>(VT_CALLS); + } + const flatbuffers::Vector> *attributes() const { + return GetPointer> *>(VT_ATTRIBUTES); + } + const flatbuffers::Vector> *documentation() const { + return GetPointer> *>(VT_DOCUMENTATION); + } + /// File that this Service is declared in. + const flatbuffers::String *declaration_file() const { + return GetPointer(VT_DECLARATION_FILE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_CALLS) && + verifier.VerifyVector(calls()) && + verifier.VerifyVectorOfTables(calls()) && + VerifyOffset(verifier, VT_ATTRIBUTES) && + verifier.VerifyVector(attributes()) && + verifier.VerifyVectorOfTables(attributes()) && + VerifyOffset(verifier, VT_DOCUMENTATION) && + verifier.VerifyVector(documentation()) && + verifier.VerifyVectorOfStrings(documentation()) && + VerifyOffset(verifier, VT_DECLARATION_FILE) && + verifier.VerifyString(declaration_file()) && + verifier.EndTable(); + } +}; + +struct ServiceBuilder { + typedef Service Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Service::VT_NAME, name); + } + void add_calls(flatbuffers::Offset>> calls) { + fbb_.AddOffset(Service::VT_CALLS, calls); + } + void add_attributes(flatbuffers::Offset>> attributes) { + fbb_.AddOffset(Service::VT_ATTRIBUTES, attributes); + } + void add_documentation(flatbuffers::Offset>> documentation) { + fbb_.AddOffset(Service::VT_DOCUMENTATION, documentation); + } + void add_declaration_file(flatbuffers::Offset declaration_file) { + fbb_.AddOffset(Service::VT_DECLARATION_FILE, declaration_file); + } + explicit ServiceBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, Service::VT_NAME); + return o; + } +}; + +inline flatbuffers::Offset CreateService( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + flatbuffers::Offset>> calls = 0, + flatbuffers::Offset>> attributes = 0, + flatbuffers::Offset>> documentation = 0, + flatbuffers::Offset declaration_file = 0) { + ServiceBuilder builder_(_fbb); + builder_.add_declaration_file(declaration_file); + builder_.add_documentation(documentation); + builder_.add_attributes(attributes); + builder_.add_calls(calls); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateServiceDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + std::vector> *calls = nullptr, + std::vector> *attributes = nullptr, + const std::vector> *documentation = nullptr, + const char *declaration_file = nullptr) { + auto name__ = name ? _fbb.CreateString(name) : 0; + auto calls__ = calls ? _fbb.CreateVectorOfSortedTables(calls) : 0; + auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; + auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; + auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; + return reflection::CreateService( + _fbb, + name__, + calls__, + attributes__, + documentation__, + declaration_file__); +} + +/// File specific information. +/// Symbols declared within a file may be recovered by iterating over all +/// symbols and examining the `declaration_file` field. +struct SchemaFile FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SchemaFileBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FILENAME = 4, + VT_INCLUDED_FILENAMES = 6 + }; + /// Filename, relative to project root. + const flatbuffers::String *filename() const { + return GetPointer(VT_FILENAME); + } + bool KeyCompareLessThan(const SchemaFile *o) const { + return *filename() < *o->filename(); + } + int KeyCompareWithValue(const char *_filename) const { + return strcmp(filename()->c_str(), _filename); + } + /// Names of included files, relative to project root. + const flatbuffers::Vector> *included_filenames() const { + return GetPointer> *>(VT_INCLUDED_FILENAMES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_FILENAME) && + verifier.VerifyString(filename()) && + VerifyOffset(verifier, VT_INCLUDED_FILENAMES) && + verifier.VerifyVector(included_filenames()) && + verifier.VerifyVectorOfStrings(included_filenames()) && + verifier.EndTable(); + } +}; + +struct SchemaFileBuilder { + typedef SchemaFile Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_filename(flatbuffers::Offset filename) { + fbb_.AddOffset(SchemaFile::VT_FILENAME, filename); + } + void add_included_filenames(flatbuffers::Offset>> included_filenames) { + fbb_.AddOffset(SchemaFile::VT_INCLUDED_FILENAMES, included_filenames); + } + explicit SchemaFileBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, SchemaFile::VT_FILENAME); + return o; + } +}; + +inline flatbuffers::Offset CreateSchemaFile( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset filename = 0, + flatbuffers::Offset>> included_filenames = 0) { + SchemaFileBuilder builder_(_fbb); + builder_.add_included_filenames(included_filenames); + builder_.add_filename(filename); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSchemaFileDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *filename = nullptr, + const std::vector> *included_filenames = nullptr) { + auto filename__ = filename ? _fbb.CreateString(filename) : 0; + auto included_filenames__ = included_filenames ? _fbb.CreateVector>(*included_filenames) : 0; + return reflection::CreateSchemaFile( + _fbb, + filename__, + included_filenames__); +} + +struct Schema FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SchemaBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OBJECTS = 4, + VT_ENUMS = 6, + VT_FILE_IDENT = 8, + VT_FILE_EXT = 10, + VT_ROOT_TABLE = 12, + VT_SERVICES = 14, + VT_ADVANCED_FEATURES = 16, + VT_FBS_FILES = 18 + }; + const flatbuffers::Vector> *objects() const { + return GetPointer> *>(VT_OBJECTS); + } + const flatbuffers::Vector> *enums() const { + return GetPointer> *>(VT_ENUMS); + } + const flatbuffers::String *file_ident() const { + return GetPointer(VT_FILE_IDENT); + } + const flatbuffers::String *file_ext() const { + return GetPointer(VT_FILE_EXT); + } + const reflection::Object *root_table() const { + return GetPointer(VT_ROOT_TABLE); + } + const flatbuffers::Vector> *services() const { + return GetPointer> *>(VT_SERVICES); + } + reflection::AdvancedFeatures advanced_features() const { + return static_cast(GetField(VT_ADVANCED_FEATURES, 0)); + } + /// All the files used in this compilation. Files are relative to where + /// flatc was invoked. + const flatbuffers::Vector> *fbs_files() const { + return GetPointer> *>(VT_FBS_FILES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffsetRequired(verifier, VT_OBJECTS) && + verifier.VerifyVector(objects()) && + verifier.VerifyVectorOfTables(objects()) && + VerifyOffsetRequired(verifier, VT_ENUMS) && + verifier.VerifyVector(enums()) && + verifier.VerifyVectorOfTables(enums()) && + VerifyOffset(verifier, VT_FILE_IDENT) && + verifier.VerifyString(file_ident()) && + VerifyOffset(verifier, VT_FILE_EXT) && + verifier.VerifyString(file_ext()) && + VerifyOffset(verifier, VT_ROOT_TABLE) && + verifier.VerifyTable(root_table()) && + VerifyOffset(verifier, VT_SERVICES) && + verifier.VerifyVector(services()) && + verifier.VerifyVectorOfTables(services()) && + VerifyField(verifier, VT_ADVANCED_FEATURES, 8) && + VerifyOffset(verifier, VT_FBS_FILES) && + verifier.VerifyVector(fbs_files()) && + verifier.VerifyVectorOfTables(fbs_files()) && + verifier.EndTable(); + } +}; + +struct SchemaBuilder { + typedef Schema Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_objects(flatbuffers::Offset>> objects) { + fbb_.AddOffset(Schema::VT_OBJECTS, objects); + } + void add_enums(flatbuffers::Offset>> enums) { + fbb_.AddOffset(Schema::VT_ENUMS, enums); + } + void add_file_ident(flatbuffers::Offset file_ident) { + fbb_.AddOffset(Schema::VT_FILE_IDENT, file_ident); + } + void add_file_ext(flatbuffers::Offset file_ext) { + fbb_.AddOffset(Schema::VT_FILE_EXT, file_ext); + } + void add_root_table(flatbuffers::Offset root_table) { + fbb_.AddOffset(Schema::VT_ROOT_TABLE, root_table); + } + void add_services(flatbuffers::Offset>> services) { + fbb_.AddOffset(Schema::VT_SERVICES, services); + } + void add_advanced_features(reflection::AdvancedFeatures advanced_features) { + fbb_.AddElement(Schema::VT_ADVANCED_FEATURES, static_cast(advanced_features), 0); + } + void add_fbs_files(flatbuffers::Offset>> fbs_files) { + fbb_.AddOffset(Schema::VT_FBS_FILES, fbs_files); + } + explicit SchemaBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + fbb_.Required(o, Schema::VT_OBJECTS); + fbb_.Required(o, Schema::VT_ENUMS); + return o; + } +}; + +inline flatbuffers::Offset CreateSchema( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> objects = 0, + flatbuffers::Offset>> enums = 0, + flatbuffers::Offset file_ident = 0, + flatbuffers::Offset file_ext = 0, + flatbuffers::Offset root_table = 0, + flatbuffers::Offset>> services = 0, + reflection::AdvancedFeatures advanced_features = static_cast(0), + flatbuffers::Offset>> fbs_files = 0) { + SchemaBuilder builder_(_fbb); + builder_.add_advanced_features(advanced_features); + builder_.add_fbs_files(fbs_files); + builder_.add_services(services); + builder_.add_root_table(root_table); + builder_.add_file_ext(file_ext); + builder_.add_file_ident(file_ident); + builder_.add_enums(enums); + builder_.add_objects(objects); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSchemaDirect( + flatbuffers::FlatBufferBuilder &_fbb, + std::vector> *objects = nullptr, + std::vector> *enums = nullptr, + const char *file_ident = nullptr, + const char *file_ext = nullptr, + flatbuffers::Offset root_table = 0, + std::vector> *services = nullptr, + reflection::AdvancedFeatures advanced_features = static_cast(0), + std::vector> *fbs_files = nullptr) { + auto objects__ = objects ? _fbb.CreateVectorOfSortedTables(objects) : 0; + auto enums__ = enums ? _fbb.CreateVectorOfSortedTables(enums) : 0; + auto file_ident__ = file_ident ? _fbb.CreateString(file_ident) : 0; + auto file_ext__ = file_ext ? _fbb.CreateString(file_ext) : 0; + auto services__ = services ? _fbb.CreateVectorOfSortedTables(services) : 0; + auto fbs_files__ = fbs_files ? _fbb.CreateVectorOfSortedTables(fbs_files) : 0; + return reflection::CreateSchema( + _fbb, + objects__, + enums__, + file_ident__, + file_ext__, + root_table, + services__, + advanced_features, + fbs_files__); +} + +inline const reflection::Schema *GetSchema(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const reflection::Schema *GetSizePrefixedSchema(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline const char *SchemaIdentifier() { + return "BFBS"; +} + +inline bool SchemaBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, SchemaIdentifier()); +} + +inline bool SizePrefixedSchemaBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, SchemaIdentifier(), true); +} + +inline bool VerifySchemaBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(SchemaIdentifier()); +} + +inline bool VerifySizePrefixedSchemaBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(SchemaIdentifier()); +} + +inline const char *SchemaExtension() { + return "bfbs"; +} + +inline void FinishSchemaBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root, SchemaIdentifier()); +} + +inline void FinishSizePrefixedSchemaBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root, SchemaIdentifier()); +} + +} // namespace reflection + +#endif // FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ diff --git a/new_ai_tools/include/flatbuffers/registry.h b/new_ai_tools/include/flatbuffers/registry.h new file mode 100644 index 00000000..e8bb8f5e --- /dev/null +++ b/new_ai_tools/include/flatbuffers/registry.h @@ -0,0 +1,128 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_REGISTRY_H_ +#define FLATBUFFERS_REGISTRY_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/idl.h" + +namespace flatbuffers { + +// Convenience class to easily parse or generate text for arbitrary FlatBuffers. +// Simply pre-populate it with all schema filenames that may be in use, and +// This class will look them up using the file_identifier declared in the +// schema. +class Registry { + public: + // Call this for all schemas that may be in use. The identifier has + // a function in the generated code, e.g. MonsterIdentifier(). + void Register(const char *file_identifier, const char *schema_path) { + Schema schema; + schema.path_ = schema_path; + schemas_[file_identifier] = schema; + } + + // Generate text from an arbitrary FlatBuffer by looking up its + // file_identifier in the registry. + bool FlatBufferToText(const uint8_t *flatbuf, size_t len, std::string *dest) { + // Get the identifier out of the buffer. + // If the buffer is truncated, exit. + if (len < sizeof(uoffset_t) + kFileIdentifierLength) { + lasterror_ = "buffer truncated"; + return false; + } + std::string ident( + reinterpret_cast(flatbuf) + sizeof(uoffset_t), + kFileIdentifierLength); + // Load and parse the schema. + Parser parser; + if (!LoadSchema(ident, &parser)) return false; + // Now we're ready to generate text. + if (!GenerateText(parser, flatbuf, dest)) { + lasterror_ = "unable to generate text for FlatBuffer binary"; + return false; + } + return true; + } + + // Converts a binary buffer to text using one of the schemas in the registry, + // use the file_identifier to indicate which. + // If DetachedBuffer::data() is null then parsing failed. + DetachedBuffer TextToFlatBuffer(const char *text, + const char *file_identifier) { + // Load and parse the schema. + Parser parser; + if (!LoadSchema(file_identifier, &parser)) return DetachedBuffer(); + // Parse the text. + if (!parser.Parse(text)) { + lasterror_ = parser.error_; + return DetachedBuffer(); + } + // We have a valid FlatBuffer. Detach it from the builder and return. + return parser.builder_.Release(); + } + + // Modify any parsing / output options used by the other functions. + void SetOptions(const IDLOptions &opts) { opts_ = opts; } + + // If schemas used contain include statements, call this function for every + // directory the parser should search them for. + void AddIncludeDirectory(const char *path) { include_paths_.push_back(path); } + + // Returns a human readable error if any of the above functions fail. + const std::string &GetLastError() { return lasterror_; } + + private: + bool LoadSchema(const std::string &ident, Parser *parser) { + // Find the schema, if not, exit. + auto it = schemas_.find(ident); + if (it == schemas_.end()) { + // Don't attach the identifier, since it may not be human readable. + lasterror_ = "identifier for this buffer not in the registry"; + return false; + } + auto &schema = it->second; + // Load the schema from disk. If not, exit. + std::string schematext; + if (!LoadFile(schema.path_.c_str(), false, &schematext)) { + lasterror_ = "could not load schema: " + schema.path_; + return false; + } + // Parse schema. + parser->opts = opts_; + if (!parser->Parse(schematext.c_str(), include_paths_.data(), + schema.path_.c_str())) { + lasterror_ = parser->error_; + return false; + } + return true; + } + + struct Schema { + std::string path_; + // TODO(wvo) optionally cache schema file or parsed schema here. + }; + + std::string lasterror_; + IDLOptions opts_; + std::vector include_paths_; + std::map schemas_; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_REGISTRY_H_ diff --git a/new_ai_tools/include/flatbuffers/stl_emulation.h b/new_ai_tools/include/flatbuffers/stl_emulation.h new file mode 100644 index 00000000..851b1e11 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/stl_emulation.h @@ -0,0 +1,509 @@ +/* + * Copyright 2017 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_STL_EMULATION_H_ +#define FLATBUFFERS_STL_EMULATION_H_ + +// clang-format off +#include "flatbuffers/base.h" + +#include +#include +#include +#include +#include + +// Detect C++17 compatible compiler. +// __cplusplus >= 201703L - a compiler has support of 'static inline' variables. +#if defined(FLATBUFFERS_USE_STD_OPTIONAL) \ + || (defined(__cplusplus) && __cplusplus >= 201703L) \ + || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L)) + #include + #ifndef FLATBUFFERS_USE_STD_OPTIONAL + #define FLATBUFFERS_USE_STD_OPTIONAL + #endif +#endif // defined(FLATBUFFERS_USE_STD_OPTIONAL) ... + +// The __cpp_lib_span is the predefined feature macro. +#if defined(FLATBUFFERS_USE_STD_SPAN) + #include +#elif defined(__cpp_lib_span) && defined(__has_include) + #if __has_include() + #include + #define FLATBUFFERS_USE_STD_SPAN + #endif +#else + // Disable non-trivial ctors if FLATBUFFERS_SPAN_MINIMAL defined. + #if !defined(FLATBUFFERS_TEMPLATES_ALIASES) + #define FLATBUFFERS_SPAN_MINIMAL + #else + // Enable implicit construction of a span from a std::array. + #include + #endif +#endif // defined(FLATBUFFERS_USE_STD_SPAN) + +// This header provides backwards compatibility for older versions of the STL. +namespace flatbuffers { + +#if defined(FLATBUFFERS_TEMPLATES_ALIASES) + template + using numeric_limits = std::numeric_limits; +#else + template class numeric_limits : + public std::numeric_limits {}; +#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) + +#if defined(FLATBUFFERS_TEMPLATES_ALIASES) + template using is_scalar = std::is_scalar; + template using is_same = std::is_same; + template using is_floating_point = std::is_floating_point; + template using is_unsigned = std::is_unsigned; + template using is_enum = std::is_enum; + template using make_unsigned = std::make_unsigned; + template + using conditional = std::conditional; + template + using integral_constant = std::integral_constant; + template + using bool_constant = integral_constant; + using true_type = std::true_type; + using false_type = std::false_type; +#else + // MSVC 2010 doesn't support C++11 aliases. + template struct is_scalar : public std::is_scalar {}; + template struct is_same : public std::is_same {}; + template struct is_floating_point : + public std::is_floating_point {}; + template struct is_unsigned : public std::is_unsigned {}; + template struct is_enum : public std::is_enum {}; + template struct make_unsigned : public std::make_unsigned {}; + template + struct conditional : public std::conditional {}; + template + struct integral_constant : public std::integral_constant {}; + template + struct bool_constant : public integral_constant {}; + typedef bool_constant true_type; + typedef bool_constant false_type; +#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) + +#if defined(FLATBUFFERS_TEMPLATES_ALIASES) + template using unique_ptr = std::unique_ptr; +#else + // MSVC 2010 doesn't support C++11 aliases. + // We're manually "aliasing" the class here as we want to bring unique_ptr + // into the flatbuffers namespace. We have unique_ptr in the flatbuffers + // namespace we have a completely independent implementation (see below) + // for C++98 STL implementations. + template class unique_ptr : public std::unique_ptr { + public: + unique_ptr() {} + explicit unique_ptr(T* p) : std::unique_ptr(p) {} + unique_ptr(std::unique_ptr&& u) { *this = std::move(u); } + unique_ptr(unique_ptr&& u) { *this = std::move(u); } + unique_ptr& operator=(std::unique_ptr&& u) { + std::unique_ptr::reset(u.release()); + return *this; + } + unique_ptr& operator=(unique_ptr&& u) { + std::unique_ptr::reset(u.release()); + return *this; + } + unique_ptr& operator=(T* p) { + return std::unique_ptr::operator=(p); + } + }; +#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) + +#ifdef FLATBUFFERS_USE_STD_OPTIONAL +template +using Optional = std::optional; +using nullopt_t = std::nullopt_t; +inline constexpr nullopt_t nullopt = std::nullopt; + +#else +// Limited implementation of Optional type for a scalar T. +// This implementation limited by trivial types compatible with +// std::is_arithmetic or std::is_enum type traits. + +// A tag to indicate an empty flatbuffers::optional. +struct nullopt_t { + explicit FLATBUFFERS_CONSTEXPR_CPP11 nullopt_t(int) {} +}; + +#if defined(FLATBUFFERS_CONSTEXPR_DEFINED) + namespace internal { + template struct nullopt_holder { + static constexpr nullopt_t instance_ = nullopt_t(0); + }; + template + constexpr nullopt_t nullopt_holder::instance_; + } + static constexpr const nullopt_t &nullopt = internal::nullopt_holder::instance_; + +#else + namespace internal { + template struct nullopt_holder { + static const nullopt_t instance_; + }; + template + const nullopt_t nullopt_holder::instance_ = nullopt_t(0); + } + static const nullopt_t &nullopt = internal::nullopt_holder::instance_; + +#endif + +template +class Optional FLATBUFFERS_FINAL_CLASS { + // Non-scalar 'T' would extremely complicated Optional. + // Use is_scalar checking because flatbuffers flatbuffers::is_arithmetic + // isn't implemented. + static_assert(flatbuffers::is_scalar::value, "unexpected type T"); + + public: + ~Optional() {} + + FLATBUFFERS_CONSTEXPR_CPP11 Optional() FLATBUFFERS_NOEXCEPT + : value_(), has_value_(false) {} + + FLATBUFFERS_CONSTEXPR_CPP11 Optional(nullopt_t) FLATBUFFERS_NOEXCEPT + : value_(), has_value_(false) {} + + FLATBUFFERS_CONSTEXPR_CPP11 Optional(T val) FLATBUFFERS_NOEXCEPT + : value_(val), has_value_(true) {} + + FLATBUFFERS_CONSTEXPR_CPP11 Optional(const Optional &other) FLATBUFFERS_NOEXCEPT + : value_(other.value_), has_value_(other.has_value_) {} + + FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(const Optional &other) FLATBUFFERS_NOEXCEPT { + value_ = other.value_; + has_value_ = other.has_value_; + return *this; + } + + FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(nullopt_t) FLATBUFFERS_NOEXCEPT { + value_ = T(); + has_value_ = false; + return *this; + } + + FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(T val) FLATBUFFERS_NOEXCEPT { + value_ = val; + has_value_ = true; + return *this; + } + + void reset() FLATBUFFERS_NOEXCEPT { + *this = nullopt; + } + + void swap(Optional &other) FLATBUFFERS_NOEXCEPT { + std::swap(value_, other.value_); + std::swap(has_value_, other.has_value_); + } + + FLATBUFFERS_CONSTEXPR_CPP11 FLATBUFFERS_EXPLICIT_CPP11 operator bool() const FLATBUFFERS_NOEXCEPT { + return has_value_; + } + + FLATBUFFERS_CONSTEXPR_CPP11 bool has_value() const FLATBUFFERS_NOEXCEPT { + return has_value_; + } + + FLATBUFFERS_CONSTEXPR_CPP11 const T& operator*() const FLATBUFFERS_NOEXCEPT { + return value_; + } + + const T& value() const { + FLATBUFFERS_ASSERT(has_value()); + return value_; + } + + T value_or(T default_value) const FLATBUFFERS_NOEXCEPT { + return has_value() ? value_ : default_value; + } + + private: + T value_; + bool has_value_; +}; + +template +FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& opt, nullopt_t) FLATBUFFERS_NOEXCEPT { + return !opt; +} +template +FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(nullopt_t, const Optional& opt) FLATBUFFERS_NOEXCEPT { + return !opt; +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& lhs, const U& rhs) FLATBUFFERS_NOEXCEPT { + return static_cast(lhs) && (*lhs == rhs); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const T& lhs, const Optional& rhs) FLATBUFFERS_NOEXCEPT { + return static_cast(rhs) && (lhs == *rhs); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& lhs, const Optional& rhs) FLATBUFFERS_NOEXCEPT { + return static_cast(lhs) != static_cast(rhs) + ? false + : !static_cast(lhs) ? false : (*lhs == *rhs); +} +#endif // FLATBUFFERS_USE_STD_OPTIONAL + + +// Very limited and naive partial implementation of C++20 std::span. +#if defined(FLATBUFFERS_USE_STD_SPAN) + inline constexpr std::size_t dynamic_extent = std::dynamic_extent; + template + using span = std::span; + +#else // !defined(FLATBUFFERS_USE_STD_SPAN) +FLATBUFFERS_CONSTEXPR std::size_t dynamic_extent = static_cast(-1); + +// Exclude this code if MSVC2010 or non-STL Android is active. +// The non-STL Android doesn't have `std::is_convertible` required for SFINAE. +#if !defined(FLATBUFFERS_SPAN_MINIMAL) +namespace internal { + // This is SFINAE helper class for checking of a common condition: + // > This overload only participates in overload resolution + // > Check whether a pointer to an array of U can be converted + // > to a pointer to an array of E. + // This helper is used for checking of 'U -> const U'. + template + struct is_span_convertable { + using type = + typename std::conditional::value + && (Extent == dynamic_extent || N == Extent), + int, void>::type; + }; + + template + struct SpanIterator { + // TODO: upgrade to std::random_access_iterator_tag. + using iterator_category = std::forward_iterator_tag; + using difference_type = std::ptrdiff_t; + using value_type = typename std::remove_cv::type; + using reference = T&; + using pointer = T*; + + // Convince MSVC compiler that this iterator is trusted (it is verified). + #ifdef _MSC_VER + using _Unchecked_type = pointer; + #endif // _MSC_VER + + SpanIterator(pointer ptr) : ptr_(ptr) {} + reference operator*() const { return *ptr_; } + pointer operator->() { return ptr_; } + SpanIterator& operator++() { ptr_++; return *this; } + SpanIterator operator++(int) { auto tmp = *this; ++(*this); return tmp; } + + friend bool operator== (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ == rhs.ptr_; } + friend bool operator!= (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ != rhs.ptr_; } + + private: + pointer ptr_; + }; +} // namespace internal +#endif // !defined(FLATBUFFERS_SPAN_MINIMAL) + +// T - element type; must be a complete type that is not an abstract +// class type. +// Extent - the number of elements in the sequence, or dynamic. +template +class span FLATBUFFERS_FINAL_CLASS { + public: + typedef T element_type; + typedef T& reference; + typedef const T& const_reference; + typedef T* pointer; + typedef const T* const_pointer; + typedef std::size_t size_type; + + static FLATBUFFERS_CONSTEXPR size_type extent = Extent; + + // Returns the number of elements in the span. + FLATBUFFERS_CONSTEXPR_CPP11 size_type size() const FLATBUFFERS_NOEXCEPT { + return count_; + } + + // Returns the size of the sequence in bytes. + FLATBUFFERS_CONSTEXPR_CPP11 + size_type size_bytes() const FLATBUFFERS_NOEXCEPT { + return size() * sizeof(element_type); + } + + // Checks if the span is empty. + FLATBUFFERS_CONSTEXPR_CPP11 bool empty() const FLATBUFFERS_NOEXCEPT { + return size() == 0; + } + + // Returns a pointer to the beginning of the sequence. + FLATBUFFERS_CONSTEXPR_CPP11 pointer data() const FLATBUFFERS_NOEXCEPT { + return data_; + } + + #if !defined(FLATBUFFERS_SPAN_MINIMAL) + using Iterator = internal::SpanIterator; + using ConstIterator = internal::SpanIterator; + + Iterator begin() const { return Iterator(data()); } + Iterator end() const { return Iterator(data() + size()); } + + ConstIterator cbegin() const { return ConstIterator(data()); } + ConstIterator cend() const { return ConstIterator(data() + size()); } + #endif + + // Returns a reference to the idx-th element of the sequence. + // The behavior is undefined if the idx is greater than or equal to size(). + FLATBUFFERS_CONSTEXPR_CPP11 reference operator[](size_type idx) const { + return data()[idx]; + } + + FLATBUFFERS_CONSTEXPR_CPP11 span(const span &other) FLATBUFFERS_NOEXCEPT + : data_(other.data_), count_(other.count_) {} + + FLATBUFFERS_CONSTEXPR_CPP14 span &operator=(const span &other) + FLATBUFFERS_NOEXCEPT { + data_ = other.data_; + count_ = other.count_; + } + + // Limited implementation of + // `template constexpr std::span(It first, size_type count);`. + // + // Constructs a span that is a view over the range [first, first + count); + // the resulting span has: data() == first and size() == count. + // The behavior is undefined if [first, first + count) is not a valid range, + // or if (extent != flatbuffers::dynamic_extent && count != extent). + FLATBUFFERS_CONSTEXPR_CPP11 + explicit span(pointer first, size_type count) FLATBUFFERS_NOEXCEPT + : data_ (Extent == dynamic_extent ? first : (Extent == count ? first : nullptr)), + count_(Extent == dynamic_extent ? count : (Extent == count ? Extent : 0)) { + // Make span empty if the count argument is incompatible with span. + } + + // Exclude this code if MSVC2010 is active. The MSVC2010 isn't C++11 + // compliant, it doesn't support default template arguments for functions. + #if defined(FLATBUFFERS_SPAN_MINIMAL) + FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr), + count_(0) { + static_assert(extent == 0 || extent == dynamic_extent, "invalid span"); + } + + #else + // Constructs an empty span whose data() == nullptr and size() == 0. + // This overload only participates in overload resolution if + // extent == 0 || extent == flatbuffers::dynamic_extent. + // A dummy template argument N is need dependency for SFINAE. + template::type = 0> + FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr), + count_(0) { + static_assert(extent == 0 || extent == dynamic_extent, "invalid span"); + } + + // Constructs a span that is a view over the array arr; the resulting span + // has size() == N and data() == std::data(arr). These overloads only + // participate in overload resolution if + // extent == std::dynamic_extent || N == extent is true and + // std::remove_pointer_t(*)[] + // is convertible to element_type (*)[]. + template::type = 0> + FLATBUFFERS_CONSTEXPR_CPP11 span(element_type (&arr)[N]) FLATBUFFERS_NOEXCEPT + : data_(arr), count_(N) {} + + template::type = 0> + FLATBUFFERS_CONSTEXPR_CPP11 span(std::array &arr) FLATBUFFERS_NOEXCEPT + : data_(arr.data()), count_(N) {} + + //template + //FLATBUFFERS_CONSTEXPR_CPP11 span(std::array &arr) FLATBUFFERS_NOEXCEPT + // : data_(arr.data()), count_(N) {} + + template::type = 0> + FLATBUFFERS_CONSTEXPR_CPP11 span(const std::array &arr) FLATBUFFERS_NOEXCEPT + : data_(arr.data()), count_(N) {} + + // Converting constructor from another span s; + // the resulting span has size() == s.size() and data() == s.data(). + // This overload only participates in overload resolution + // if extent == std::dynamic_extent || N == extent is true and U (*)[] + // is convertible to element_type (*)[]. + template::type = 0> + FLATBUFFERS_CONSTEXPR_CPP11 span(const flatbuffers::span &s) FLATBUFFERS_NOEXCEPT + : span(s.data(), s.size()) { + } + + #endif // !defined(FLATBUFFERS_SPAN_MINIMAL) + + private: + // This is a naive implementation with 'count_' member even if (Extent != dynamic_extent). + pointer const data_; + size_type count_; +}; +#endif // defined(FLATBUFFERS_USE_STD_SPAN) + +#if !defined(FLATBUFFERS_SPAN_MINIMAL) +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(U(&arr)[N]) FLATBUFFERS_NOEXCEPT { + return span(arr); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(const U(&arr)[N]) FLATBUFFERS_NOEXCEPT { + return span(arr); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(std::array &arr) FLATBUFFERS_NOEXCEPT { + return span(arr); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(const std::array &arr) FLATBUFFERS_NOEXCEPT { + return span(arr); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(U *first, std::size_t count) FLATBUFFERS_NOEXCEPT { + return span(first, count); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 +flatbuffers::span make_span(const U *first, std::size_t count) FLATBUFFERS_NOEXCEPT { + return span(first, count); +} +#endif // !defined(FLATBUFFERS_SPAN_MINIMAL) + +} // namespace flatbuffers + +#endif // FLATBUFFERS_STL_EMULATION_H_ diff --git a/new_ai_tools/include/flatbuffers/string.h b/new_ai_tools/include/flatbuffers/string.h new file mode 100644 index 00000000..3db95fce --- /dev/null +++ b/new_ai_tools/include/flatbuffers/string.h @@ -0,0 +1,64 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_STRING_H_ +#define FLATBUFFERS_STRING_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/vector.h" + +namespace flatbuffers { + +struct String : public Vector { + const char *c_str() const { return reinterpret_cast(Data()); } + std::string str() const { return std::string(c_str(), size()); } + + // clang-format off + #ifdef FLATBUFFERS_HAS_STRING_VIEW + flatbuffers::string_view string_view() const { + return flatbuffers::string_view(c_str(), size()); + } + #endif // FLATBUFFERS_HAS_STRING_VIEW + // clang-format on + + bool operator<(const String &o) const { + return StringLessThan(this->data(), this->size(), o.data(), o.size()); + } +}; + +// Convenience function to get std::string from a String returning an empty +// string on null pointer. +static inline std::string GetString(const String *str) { + return str ? str->str() : ""; +} + +// Convenience function to get char* from a String returning an empty string on +// null pointer. +static inline const char *GetCstring(const String *str) { + return str ? str->c_str() : ""; +} + +#ifdef FLATBUFFERS_HAS_STRING_VIEW +// Convenience function to get string_view from a String returning an empty +// string_view on null pointer. +static inline flatbuffers::string_view GetStringView(const String *str) { + return str ? str->string_view() : flatbuffers::string_view(); +} +#endif // FLATBUFFERS_HAS_STRING_VIEW + +} // namespace flatbuffers + +#endif // FLATBUFFERS_STRING_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/struct.h b/new_ai_tools/include/flatbuffers/struct.h new file mode 100644 index 00000000..d8753c84 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/struct.h @@ -0,0 +1,53 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_STRUCT_H_ +#define FLATBUFFERS_STRUCT_H_ + +#include "flatbuffers/base.h" + +namespace flatbuffers { + +// "structs" are flat structures that do not have an offset table, thus +// always have all members present and do not support forwards/backwards +// compatible extensions. + +class Struct FLATBUFFERS_FINAL_CLASS { + public: + template T GetField(uoffset_t o) const { + return ReadScalar(&data_[o]); + } + + template T GetStruct(uoffset_t o) const { + return reinterpret_cast(&data_[o]); + } + + const uint8_t *GetAddressOf(uoffset_t o) const { return &data_[o]; } + uint8_t *GetAddressOf(uoffset_t o) { return &data_[o]; } + + private: + // private constructor & copy constructor: you obtain instances of this + // class by pointing to existing data only + Struct(); + Struct(const Struct &); + Struct &operator=(const Struct &); + + uint8_t data_[1]; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_STRUCT_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/table.h b/new_ai_tools/include/flatbuffers/table.h new file mode 100644 index 00000000..11b29247 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/table.h @@ -0,0 +1,168 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_TABLE_H_ +#define FLATBUFFERS_TABLE_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/verifier.h" + +namespace flatbuffers { + +// "tables" use an offset table (possibly shared) that allows fields to be +// omitted and added at will, but uses an extra indirection to read. +class Table { + public: + const uint8_t *GetVTable() const { + return data_ - ReadScalar(data_); + } + + // This gets the field offset for any of the functions below it, or 0 + // if the field was not present. + voffset_t GetOptionalFieldOffset(voffset_t field) const { + // The vtable offset is always at the start. + auto vtable = GetVTable(); + // The first element is the size of the vtable (fields + type id + itself). + auto vtsize = ReadScalar(vtable); + // If the field we're accessing is outside the vtable, we're reading older + // data, so it's the same as if the offset was 0 (not present). + return field < vtsize ? ReadScalar(vtable + field) : 0; + } + + template T GetField(voffset_t field, T defaultval) const { + auto field_offset = GetOptionalFieldOffset(field); + return field_offset ? ReadScalar(data_ + field_offset) : defaultval; + } + + template P GetPointer(voffset_t field) { + auto field_offset = GetOptionalFieldOffset(field); + auto p = data_ + field_offset; + return field_offset ? reinterpret_cast

(p + ReadScalar(p)) + : nullptr; + } + template P GetPointer(voffset_t field) const { + return const_cast

(this)->GetPointer

(field); + } + + template P GetStruct(voffset_t field) const { + auto field_offset = GetOptionalFieldOffset(field); + auto p = const_cast(data_ + field_offset); + return field_offset ? reinterpret_cast

(p) : nullptr; + } + + template + flatbuffers::Optional GetOptional(voffset_t field) const { + auto field_offset = GetOptionalFieldOffset(field); + auto p = data_ + field_offset; + return field_offset ? Optional(static_cast(ReadScalar(p))) + : Optional(); + } + + template bool SetField(voffset_t field, T val, T def) { + auto field_offset = GetOptionalFieldOffset(field); + if (!field_offset) return IsTheSameAs(val, def); + WriteScalar(data_ + field_offset, val); + return true; + } + template bool SetField(voffset_t field, T val) { + auto field_offset = GetOptionalFieldOffset(field); + if (!field_offset) return false; + WriteScalar(data_ + field_offset, val); + return true; + } + + bool SetPointer(voffset_t field, const uint8_t *val) { + auto field_offset = GetOptionalFieldOffset(field); + if (!field_offset) return false; + WriteScalar(data_ + field_offset, + static_cast(val - (data_ + field_offset))); + return true; + } + + uint8_t *GetAddressOf(voffset_t field) { + auto field_offset = GetOptionalFieldOffset(field); + return field_offset ? data_ + field_offset : nullptr; + } + const uint8_t *GetAddressOf(voffset_t field) const { + return const_cast

(this)->GetAddressOf(field); + } + + bool CheckField(voffset_t field) const { + return GetOptionalFieldOffset(field) != 0; + } + + // Verify the vtable of this table. + // Call this once per table, followed by VerifyField once per field. + bool VerifyTableStart(Verifier &verifier) const { + return verifier.VerifyTableStart(data_); + } + + // Verify a particular field. + template + bool VerifyField(const Verifier &verifier, voffset_t field, + size_t align) const { + // Calling GetOptionalFieldOffset should be safe now thanks to + // VerifyTable(). + auto field_offset = GetOptionalFieldOffset(field); + // Check the actual field. + return !field_offset || verifier.VerifyField(data_, field_offset, align); + } + + // VerifyField for required fields. + template + bool VerifyFieldRequired(const Verifier &verifier, voffset_t field, + size_t align) const { + auto field_offset = GetOptionalFieldOffset(field); + return verifier.Check(field_offset != 0) && + verifier.VerifyField(data_, field_offset, align); + } + + // Versions for offsets. + bool VerifyOffset(const Verifier &verifier, voffset_t field) const { + auto field_offset = GetOptionalFieldOffset(field); + return !field_offset || verifier.VerifyOffset(data_, field_offset); + } + + bool VerifyOffsetRequired(const Verifier &verifier, voffset_t field) const { + auto field_offset = GetOptionalFieldOffset(field); + return verifier.Check(field_offset != 0) && + verifier.VerifyOffset(data_, field_offset); + } + + private: + // private constructor & copy constructor: you obtain instances of this + // class by pointing to existing data only + Table(); + Table(const Table &other); + Table &operator=(const Table &); + + uint8_t data_[1]; +}; + +// This specialization allows avoiding warnings like: +// MSVC C4800: type: forcing value to bool 'true' or 'false'. +template<> +inline flatbuffers::Optional Table::GetOptional( + voffset_t field) const { + auto field_offset = GetOptionalFieldOffset(field); + auto p = data_ + field_offset; + return field_offset ? Optional(ReadScalar(p) != 0) + : Optional(); +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_TABLE_H_ diff --git a/new_ai_tools/include/flatbuffers/util.h b/new_ai_tools/include/flatbuffers/util.h new file mode 100644 index 00000000..690e63d3 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/util.h @@ -0,0 +1,690 @@ +/* + * Copyright 2014 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_UTIL_H_ +#define FLATBUFFERS_UTIL_H_ + +#include +#include + +#include "flatbuffers/base.h" +#include "flatbuffers/stl_emulation.h" + +#ifndef FLATBUFFERS_PREFER_PRINTF +# include +# include +#else // FLATBUFFERS_PREFER_PRINTF +# include +# include +#endif // FLATBUFFERS_PREFER_PRINTF + +#include + +namespace flatbuffers { + +// @locale-independent functions for ASCII characters set. + +// Fast checking that character lies in closed range: [a <= x <= b] +// using one compare (conditional branch) operator. +inline bool check_ascii_range(char x, char a, char b) { + FLATBUFFERS_ASSERT(a <= b); + // (Hacker's Delight): `a <= x <= b` <=> `(x-a) <={u} (b-a)`. + // The x, a, b will be promoted to int and subtracted without overflow. + return static_cast(x - a) <= static_cast(b - a); +} + +// Case-insensitive isalpha +inline bool is_alpha(char c) { + // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF). + return check_ascii_range(c & 0xDF, 'a' & 0xDF, 'z' & 0xDF); +} + +// Check for uppercase alpha +inline bool is_alpha_upper(char c) { return check_ascii_range(c, 'A', 'Z'); } + +// Check (case-insensitive) that `c` is equal to alpha. +inline bool is_alpha_char(char c, char alpha) { + FLATBUFFERS_ASSERT(is_alpha(alpha)); + // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF). + return ((c & 0xDF) == (alpha & 0xDF)); +} + +// https://en.cppreference.com/w/cpp/string/byte/isxdigit +// isdigit and isxdigit are the only standard narrow character classification +// functions that are not affected by the currently installed C locale. although +// some implementations (e.g. Microsoft in 1252 codepage) may classify +// additional single-byte characters as digits. +inline bool is_digit(char c) { return check_ascii_range(c, '0', '9'); } + +inline bool is_xdigit(char c) { + // Replace by look-up table. + return is_digit(c) || check_ascii_range(c & 0xDF, 'a' & 0xDF, 'f' & 0xDF); +} + +// Case-insensitive isalnum +inline bool is_alnum(char c) { return is_alpha(c) || is_digit(c); } + +inline char CharToUpper(char c) { + return static_cast(::toupper(static_cast(c))); +} + +inline char CharToLower(char c) { + return static_cast(::tolower(static_cast(c))); +} + +// @end-locale-independent functions for ASCII character set + +#ifdef FLATBUFFERS_PREFER_PRINTF +template size_t IntToDigitCount(T t) { + size_t digit_count = 0; + // Count the sign for negative numbers + if (t < 0) digit_count++; + // Count a single 0 left of the dot for fractional numbers + if (-1 < t && t < 1) digit_count++; + // Count digits until fractional part + T eps = std::numeric_limits::epsilon(); + while (t <= (-1 + eps) || (1 - eps) <= t) { + t /= 10; + digit_count++; + } + return digit_count; +} + +template size_t NumToStringWidth(T t, int precision = 0) { + size_t string_width = IntToDigitCount(t); + // Count the dot for floating point numbers + if (precision) string_width += (precision + 1); + return string_width; +} + +template +std::string NumToStringImplWrapper(T t, const char *fmt, int precision = 0) { + size_t string_width = NumToStringWidth(t, precision); + std::string s(string_width, 0x00); + // Allow snprintf to use std::string trailing null to detect buffer overflow + snprintf(const_cast(s.data()), (s.size() + 1), fmt, string_width, t); + return s; +} +#endif // FLATBUFFERS_PREFER_PRINTF + +// Convert an integer or floating point value to a string. +// In contrast to std::stringstream, "char" values are +// converted to a string of digits, and we don't use scientific notation. +template std::string NumToString(T t) { + // clang-format off + + #ifndef FLATBUFFERS_PREFER_PRINTF + std::stringstream ss; + ss << t; + return ss.str(); + #else // FLATBUFFERS_PREFER_PRINTF + auto v = static_cast(t); + return NumToStringImplWrapper(v, "%.*lld"); + #endif // FLATBUFFERS_PREFER_PRINTF + // clang-format on +} +// Avoid char types used as character data. +template<> inline std::string NumToString(signed char t) { + return NumToString(static_cast(t)); +} +template<> inline std::string NumToString(unsigned char t) { + return NumToString(static_cast(t)); +} +template<> inline std::string NumToString(char t) { + return NumToString(static_cast(t)); +} + +// Special versions for floats/doubles. +template std::string FloatToString(T t, int precision) { + // clang-format off + + #ifndef FLATBUFFERS_PREFER_PRINTF + // to_string() prints different numbers of digits for floats depending on + // platform and isn't available on Android, so we use stringstream + std::stringstream ss; + // Use std::fixed to suppress scientific notation. + ss << std::fixed; + // Default precision is 6, we want that to be higher for doubles. + ss << std::setprecision(precision); + ss << t; + auto s = ss.str(); + #else // FLATBUFFERS_PREFER_PRINTF + auto v = static_cast(t); + auto s = NumToStringImplWrapper(v, "%0.*f", precision); + #endif // FLATBUFFERS_PREFER_PRINTF + // clang-format on + // Sadly, std::fixed turns "1" into "1.00000", so here we undo that. + auto p = s.find_last_not_of('0'); + if (p != std::string::npos) { + // Strip trailing zeroes. If it is a whole number, keep one zero. + s.resize(p + (s[p] == '.' ? 2 : 1)); + } + return s; +} + +template<> inline std::string NumToString(double t) { + return FloatToString(t, 12); +} +template<> inline std::string NumToString(float t) { + return FloatToString(t, 6); +} + +// Convert an integer value to a hexadecimal string. +// The returned string length is always xdigits long, prefixed by 0 digits. +// For example, IntToStringHex(0x23, 8) returns the string "00000023". +inline std::string IntToStringHex(int i, int xdigits) { + FLATBUFFERS_ASSERT(i >= 0); + // clang-format off + + #ifndef FLATBUFFERS_PREFER_PRINTF + std::stringstream ss; + ss << std::setw(xdigits) << std::setfill('0') << std::hex << std::uppercase + << i; + return ss.str(); + #else // FLATBUFFERS_PREFER_PRINTF + return NumToStringImplWrapper(i, "%.*X", xdigits); + #endif // FLATBUFFERS_PREFER_PRINTF + // clang-format on +} + +// clang-format off +// Use locale independent functions {strtod_l, strtof_l, strtoll_l, strtoull_l}. +#if defined(FLATBUFFERS_LOCALE_INDEPENDENT) && (FLATBUFFERS_LOCALE_INDEPENDENT > 0) + class ClassicLocale { + #ifdef _MSC_VER + typedef _locale_t locale_type; + #else + typedef locale_t locale_type; // POSIX.1-2008 locale_t type + #endif + ClassicLocale(); + ~ClassicLocale(); + locale_type locale_; + static ClassicLocale instance_; + public: + static locale_type Get() { return instance_.locale_; } + }; + + #ifdef _MSC_VER + #define __strtoull_impl(s, pe, b) _strtoui64_l(s, pe, b, ClassicLocale::Get()) + #define __strtoll_impl(s, pe, b) _strtoi64_l(s, pe, b, ClassicLocale::Get()) + #define __strtod_impl(s, pe) _strtod_l(s, pe, ClassicLocale::Get()) + #define __strtof_impl(s, pe) _strtof_l(s, pe, ClassicLocale::Get()) + #else + #define __strtoull_impl(s, pe, b) strtoull_l(s, pe, b, ClassicLocale::Get()) + #define __strtoll_impl(s, pe, b) strtoll_l(s, pe, b, ClassicLocale::Get()) + #define __strtod_impl(s, pe) strtod_l(s, pe, ClassicLocale::Get()) + #define __strtof_impl(s, pe) strtof_l(s, pe, ClassicLocale::Get()) + #endif +#else + #define __strtod_impl(s, pe) strtod(s, pe) + #define __strtof_impl(s, pe) static_cast(strtod(s, pe)) + #ifdef _MSC_VER + #define __strtoull_impl(s, pe, b) _strtoui64(s, pe, b) + #define __strtoll_impl(s, pe, b) _strtoi64(s, pe, b) + #else + #define __strtoull_impl(s, pe, b) strtoull(s, pe, b) + #define __strtoll_impl(s, pe, b) strtoll(s, pe, b) + #endif +#endif + +inline void strtoval_impl(int64_t *val, const char *str, char **endptr, + int base) { + *val = __strtoll_impl(str, endptr, base); +} + +inline void strtoval_impl(uint64_t *val, const char *str, char **endptr, + int base) { + *val = __strtoull_impl(str, endptr, base); +} + +inline void strtoval_impl(double *val, const char *str, char **endptr) { + *val = __strtod_impl(str, endptr); +} + +// UBSAN: double to float is safe if numeric_limits::is_iec559 is true. +__supress_ubsan__("float-cast-overflow") +inline void strtoval_impl(float *val, const char *str, char **endptr) { + *val = __strtof_impl(str, endptr); +} +#undef __strtoull_impl +#undef __strtoll_impl +#undef __strtod_impl +#undef __strtof_impl +// clang-format on + +// Adaptor for strtoull()/strtoll(). +// Flatbuffers accepts numbers with any count of leading zeros (-009 is -9), +// while strtoll with base=0 interprets first leading zero as octal prefix. +// In future, it is possible to add prefixed 0b0101. +// 1) Checks errno code for overflow condition (out of range). +// 2) If base <= 0, function try to detect base of number by prefix. +// +// Return value (like strtoull and strtoll, but reject partial result): +// - If successful, an integer value corresponding to the str is returned. +// - If full string conversion can't be performed, 0 is returned. +// - If the converted value falls out of range of corresponding return type, a +// range error occurs. In this case value MAX(T)/MIN(T) is returned. +template +inline bool StringToIntegerImpl(T *val, const char *const str, + const int base = 0, + const bool check_errno = true) { + // T is int64_t or uint64_T + FLATBUFFERS_ASSERT(str); + if (base <= 0) { + auto s = str; + while (*s && !is_digit(*s)) s++; + if (s[0] == '0' && is_alpha_char(s[1], 'X')) + return StringToIntegerImpl(val, str, 16, check_errno); + // if a prefix not match, try base=10 + return StringToIntegerImpl(val, str, 10, check_errno); + } else { + if (check_errno) errno = 0; // clear thread-local errno + auto endptr = str; + strtoval_impl(val, str, const_cast(&endptr), base); + if ((*endptr != '\0') || (endptr == str)) { + *val = 0; // erase partial result + return false; // invalid string + } + // errno is out-of-range, return MAX/MIN + if (check_errno && errno) return false; + return true; + } +} + +template +inline bool StringToFloatImpl(T *val, const char *const str) { + // Type T must be either float or double. + FLATBUFFERS_ASSERT(str && val); + auto end = str; + strtoval_impl(val, str, const_cast(&end)); + auto done = (end != str) && (*end == '\0'); + if (!done) *val = 0; // erase partial result + return done; +} + +// Convert a string to an instance of T. +// Return value (matched with StringToInteger64Impl and strtod): +// - If successful, a numeric value corresponding to the str is returned. +// - If full string conversion can't be performed, 0 is returned. +// - If the converted value falls out of range of corresponding return type, a +// range error occurs. In this case value MAX(T)/MIN(T) is returned. +template inline bool StringToNumber(const char *s, T *val) { + // Assert on `unsigned long` and `signed long` on LP64. + // If it is necessary, it could be solved with flatbuffers::enable_if. + static_assert(sizeof(T) < sizeof(int64_t), "unexpected type T"); + FLATBUFFERS_ASSERT(s && val); + int64_t i64; + // The errno check isn't needed, will return MAX/MIN on overflow. + if (StringToIntegerImpl(&i64, s, 0, false)) { + const int64_t max = (flatbuffers::numeric_limits::max)(); + const int64_t min = flatbuffers::numeric_limits::lowest(); + if (i64 > max) { + *val = static_cast(max); + return false; + } + if (i64 < min) { + // For unsigned types return max to distinguish from + // "no conversion can be performed" when 0 is returned. + *val = static_cast(flatbuffers::is_unsigned::value ? max : min); + return false; + } + *val = static_cast(i64); + return true; + } + *val = 0; + return false; +} + +template<> inline bool StringToNumber(const char *str, int64_t *val) { + return StringToIntegerImpl(val, str); +} + +template<> +inline bool StringToNumber(const char *str, uint64_t *val) { + if (!StringToIntegerImpl(val, str)) return false; + // The strtoull accepts negative numbers: + // If the minus sign was part of the input sequence, the numeric value + // calculated from the sequence of digits is negated as if by unary minus + // in the result type, which applies unsigned integer wraparound rules. + // Fix this behaviour (except -0). + if (*val) { + auto s = str; + while (*s && !is_digit(*s)) s++; + s = (s > str) ? (s - 1) : s; // step back to one symbol + if (*s == '-') { + // For unsigned types return the max to distinguish from + // "no conversion can be performed". + *val = (flatbuffers::numeric_limits::max)(); + return false; + } + } + return true; +} + +template<> inline bool StringToNumber(const char *s, float *val) { + return StringToFloatImpl(val, s); +} + +template<> inline bool StringToNumber(const char *s, double *val) { + return StringToFloatImpl(val, s); +} + +inline int64_t StringToInt(const char *s, int base = 10) { + int64_t val; + return StringToIntegerImpl(&val, s, base) ? val : 0; +} + +inline uint64_t StringToUInt(const char *s, int base = 10) { + uint64_t val; + return StringToIntegerImpl(&val, s, base) ? val : 0; +} + +typedef bool (*LoadFileFunction)(const char *filename, bool binary, + std::string *dest); +typedef bool (*FileExistsFunction)(const char *filename); + +LoadFileFunction SetLoadFileFunction(LoadFileFunction load_file_function); + +FileExistsFunction SetFileExistsFunction( + FileExistsFunction file_exists_function); + +// Check if file "name" exists. +bool FileExists(const char *name); + +// Check if "name" exists and it is also a directory. +bool DirExists(const char *name); + +// Load file "name" into "buf" returning true if successful +// false otherwise. If "binary" is false data is read +// using ifstream's text mode, otherwise data is read with +// no transcoding. +bool LoadFile(const char *name, bool binary, std::string *buf); + +// Save data "buf" of length "len" bytes into a file +// "name" returning true if successful, false otherwise. +// If "binary" is false data is written using ifstream's +// text mode, otherwise data is written with no +// transcoding. +bool SaveFile(const char *name, const char *buf, size_t len, bool binary); + +// Save data "buf" into file "name" returning true if +// successful, false otherwise. If "binary" is false +// data is written using ifstream's text mode, otherwise +// data is written with no transcoding. +inline bool SaveFile(const char *name, const std::string &buf, bool binary) { + return SaveFile(name, buf.c_str(), buf.size(), binary); +} + +// Functionality for minimalistic portable path handling. + +// The functions below behave correctly regardless of whether posix ('/') or +// Windows ('/' or '\\') separators are used. + +// Any new separators inserted are always posix. +FLATBUFFERS_CONSTEXPR char kPathSeparator = '/'; + +// Returns the path with the extension, if any, removed. +std::string StripExtension(const std::string &filepath); + +// Returns the extension, if any. +std::string GetExtension(const std::string &filepath); + +// Return the last component of the path, after the last separator. +std::string StripPath(const std::string &filepath); + +// Strip the last component of the path + separator. +std::string StripFileName(const std::string &filepath); + +// Concatenates a path with a filename, regardless of whether the path +// ends in a separator or not. +std::string ConCatPathFileName(const std::string &path, + const std::string &filename); + +// Replaces any '\\' separators with '/' +std::string PosixPath(const char *path); +std::string PosixPath(const std::string &path); + +// This function ensure a directory exists, by recursively +// creating dirs for any parts of the path that don't exist yet. +void EnsureDirExists(const std::string &filepath); + +// Obtains the absolute path from any other path. +// Returns the input path if the absolute path couldn't be resolved. +std::string AbsolutePath(const std::string &filepath); + +// Returns files relative to the --project_root path, prefixed with `//`. +std::string RelativeToRootPath(const std::string &project, + const std::string &filepath); + +// To and from UTF-8 unicode conversion functions + +// Convert a unicode code point into a UTF-8 representation by appending it +// to a string. Returns the number of bytes generated. +inline int ToUTF8(uint32_t ucc, std::string *out) { + FLATBUFFERS_ASSERT(!(ucc & 0x80000000)); // Top bit can't be set. + // 6 possible encodings: http://en.wikipedia.org/wiki/UTF-8 + for (int i = 0; i < 6; i++) { + // Max bits this encoding can represent. + uint32_t max_bits = 6 + i * 5 + static_cast(!i); + if (ucc < (1u << max_bits)) { // does it fit? + // Remaining bits not encoded in the first byte, store 6 bits each + uint32_t remain_bits = i * 6; + // Store first byte: + (*out) += static_cast((0xFE << (max_bits - remain_bits)) | + (ucc >> remain_bits)); + // Store remaining bytes: + for (int j = i - 1; j >= 0; j--) { + (*out) += static_cast(((ucc >> (j * 6)) & 0x3F) | 0x80); + } + return i + 1; // Return the number of bytes added. + } + } + FLATBUFFERS_ASSERT(0); // Impossible to arrive here. + return -1; +} + +// Converts whatever prefix of the incoming string corresponds to a valid +// UTF-8 sequence into a unicode code. The incoming pointer will have been +// advanced past all bytes parsed. +// returns -1 upon corrupt UTF-8 encoding (ignore the incoming pointer in +// this case). +inline int FromUTF8(const char **in) { + int len = 0; + // Count leading 1 bits. + for (int mask = 0x80; mask >= 0x04; mask >>= 1) { + if (**in & mask) { + len++; + } else { + break; + } + } + if ((static_cast(**in) << len) & 0x80) + return -1; // Bit after leading 1's must be 0. + if (!len) return *(*in)++; + // UTF-8 encoded values with a length are between 2 and 4 bytes. + if (len < 2 || len > 4) { return -1; } + // Grab initial bits of the code. + int ucc = *(*in)++ & ((1 << (7 - len)) - 1); + for (int i = 0; i < len - 1; i++) { + if ((**in & 0xC0) != 0x80) return -1; // Upper bits must 1 0. + ucc <<= 6; + ucc |= *(*in)++ & 0x3F; // Grab 6 more bits of the code. + } + // UTF-8 cannot encode values between 0xD800 and 0xDFFF (reserved for + // UTF-16 surrogate pairs). + if (ucc >= 0xD800 && ucc <= 0xDFFF) { return -1; } + // UTF-8 must represent code points in their shortest possible encoding. + switch (len) { + case 2: + // Two bytes of UTF-8 can represent code points from U+0080 to U+07FF. + if (ucc < 0x0080 || ucc > 0x07FF) { return -1; } + break; + case 3: + // Three bytes of UTF-8 can represent code points from U+0800 to U+FFFF. + if (ucc < 0x0800 || ucc > 0xFFFF) { return -1; } + break; + case 4: + // Four bytes of UTF-8 can represent code points from U+10000 to U+10FFFF. + if (ucc < 0x10000 || ucc > 0x10FFFF) { return -1; } + break; + } + return ucc; +} + +#ifndef FLATBUFFERS_PREFER_PRINTF +// Wraps a string to a maximum length, inserting new lines where necessary. Any +// existing whitespace will be collapsed down to a single space. A prefix or +// suffix can be provided, which will be inserted before or after a wrapped +// line, respectively. +inline std::string WordWrap(const std::string in, size_t max_length, + const std::string wrapped_line_prefix, + const std::string wrapped_line_suffix) { + std::istringstream in_stream(in); + std::string wrapped, line, word; + + in_stream >> word; + line = word; + + while (in_stream >> word) { + if ((line.length() + 1 + word.length() + wrapped_line_suffix.length()) < + max_length) { + line += " " + word; + } else { + wrapped += line + wrapped_line_suffix + "\n"; + line = wrapped_line_prefix + word; + } + } + wrapped += line; + + return wrapped; +} +#endif // !FLATBUFFERS_PREFER_PRINTF + +inline bool EscapeString(const char *s, size_t length, std::string *_text, + bool allow_non_utf8, bool natural_utf8) { + std::string &text = *_text; + text += "\""; + for (uoffset_t i = 0; i < length; i++) { + char c = s[i]; + switch (c) { + case '\n': text += "\\n"; break; + case '\t': text += "\\t"; break; + case '\r': text += "\\r"; break; + case '\b': text += "\\b"; break; + case '\f': text += "\\f"; break; + case '\"': text += "\\\""; break; + case '\\': text += "\\\\"; break; + default: + if (c >= ' ' && c <= '~') { + text += c; + } else { + // Not printable ASCII data. Let's see if it's valid UTF-8 first: + const char *utf8 = s + i; + int ucc = FromUTF8(&utf8); + if (ucc < 0) { + if (allow_non_utf8) { + text += "\\x"; + text += IntToStringHex(static_cast(c), 2); + } else { + // There are two cases here: + // + // 1) We reached here by parsing an IDL file. In that case, + // we previously checked for non-UTF-8, so we shouldn't reach + // here. + // + // 2) We reached here by someone calling GenerateText() + // on a previously-serialized flatbuffer. The data might have + // non-UTF-8 Strings, or might be corrupt. + // + // In both cases, we have to give up and inform the caller + // they have no JSON. + return false; + } + } else { + if (natural_utf8) { + // utf8 points to past all utf-8 bytes parsed + text.append(s + i, static_cast(utf8 - s - i)); + } else if (ucc <= 0xFFFF) { + // Parses as Unicode within JSON's \uXXXX range, so use that. + text += "\\u"; + text += IntToStringHex(ucc, 4); + } else if (ucc <= 0x10FFFF) { + // Encode Unicode SMP values to a surrogate pair using two \u + // escapes. + uint32_t base = ucc - 0x10000; + auto high_surrogate = (base >> 10) + 0xD800; + auto low_surrogate = (base & 0x03FF) + 0xDC00; + text += "\\u"; + text += IntToStringHex(high_surrogate, 4); + text += "\\u"; + text += IntToStringHex(low_surrogate, 4); + } + // Skip past characters recognized. + i = static_cast(utf8 - s - 1); + } + } + break; + } + } + text += "\""; + return true; +} + +inline std::string BufferToHexText(const void *buffer, size_t buffer_size, + size_t max_length, + const std::string &wrapped_line_prefix, + const std::string &wrapped_line_suffix) { + std::string text = wrapped_line_prefix; + size_t start_offset = 0; + const char *s = reinterpret_cast(buffer); + for (size_t i = 0; s && i < buffer_size; i++) { + // Last iteration or do we have more? + bool have_more = i + 1 < buffer_size; + text += "0x"; + text += IntToStringHex(static_cast(s[i]), 2); + if (have_more) { text += ','; } + // If we have more to process and we reached max_length + if (have_more && + text.size() + wrapped_line_suffix.size() >= start_offset + max_length) { + text += wrapped_line_suffix; + text += '\n'; + start_offset = text.size(); + text += wrapped_line_prefix; + } + } + text += wrapped_line_suffix; + return text; +} + +// Remove paired quotes in a string: "text"|'text' -> text. +std::string RemoveStringQuotes(const std::string &s); + +// Change th global C-locale to locale with name . +// Returns an actual locale name in <_value>, useful if locale_name is "" or +// null. +bool SetGlobalTestLocale(const char *locale_name, + std::string *_value = nullptr); + +// Read (or test) a value of environment variable. +bool ReadEnvironmentVariable(const char *var_name, + std::string *_value = nullptr); + +// MSVC specific: Send all assert reports to STDOUT to prevent CI hangs. +void SetupDefaultCRTReportMode(); + +} // namespace flatbuffers + +#endif // FLATBUFFERS_UTIL_H_ diff --git a/new_ai_tools/include/flatbuffers/vector.h b/new_ai_tools/include/flatbuffers/vector.h new file mode 100644 index 00000000..f8a5d88e --- /dev/null +++ b/new_ai_tools/include/flatbuffers/vector.h @@ -0,0 +1,370 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_VECTOR_H_ +#define FLATBUFFERS_VECTOR_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/buffer.h" + +namespace flatbuffers { + +struct String; + +// An STL compatible iterator implementation for Vector below, effectively +// calling Get() for every element. +template struct VectorIterator { + typedef std::random_access_iterator_tag iterator_category; + typedef IT value_type; + typedef ptrdiff_t difference_type; + typedef IT *pointer; + typedef IT &reference; + + VectorIterator(const uint8_t *data, uoffset_t i) + : data_(data + IndirectHelper::element_stride * i) {} + VectorIterator(const VectorIterator &other) : data_(other.data_) {} + VectorIterator() : data_(nullptr) {} + + VectorIterator &operator=(const VectorIterator &other) { + data_ = other.data_; + return *this; + } + + VectorIterator &operator=(VectorIterator &&other) { + data_ = other.data_; + return *this; + } + + bool operator==(const VectorIterator &other) const { + return data_ == other.data_; + } + + bool operator<(const VectorIterator &other) const { + return data_ < other.data_; + } + + bool operator!=(const VectorIterator &other) const { + return data_ != other.data_; + } + + difference_type operator-(const VectorIterator &other) const { + return (data_ - other.data_) / IndirectHelper::element_stride; + } + + // Note: return type is incompatible with the standard + // `reference operator*()`. + IT operator*() const { return IndirectHelper::Read(data_, 0); } + + // Note: return type is incompatible with the standard + // `pointer operator->()`. + IT operator->() const { return IndirectHelper::Read(data_, 0); } + + VectorIterator &operator++() { + data_ += IndirectHelper::element_stride; + return *this; + } + + VectorIterator operator++(int) { + VectorIterator temp(data_, 0); + data_ += IndirectHelper::element_stride; + return temp; + } + + VectorIterator operator+(const uoffset_t &offset) const { + return VectorIterator(data_ + offset * IndirectHelper::element_stride, + 0); + } + + VectorIterator &operator+=(const uoffset_t &offset) { + data_ += offset * IndirectHelper::element_stride; + return *this; + } + + VectorIterator &operator--() { + data_ -= IndirectHelper::element_stride; + return *this; + } + + VectorIterator operator--(int) { + VectorIterator temp(data_, 0); + data_ -= IndirectHelper::element_stride; + return temp; + } + + VectorIterator operator-(const uoffset_t &offset) const { + return VectorIterator(data_ - offset * IndirectHelper::element_stride, + 0); + } + + VectorIterator &operator-=(const uoffset_t &offset) { + data_ -= offset * IndirectHelper::element_stride; + return *this; + } + + private: + const uint8_t *data_; +}; + +template +struct VectorReverseIterator : public std::reverse_iterator { + explicit VectorReverseIterator(Iterator iter) + : std::reverse_iterator(iter) {} + + // Note: return type is incompatible with the standard + // `reference operator*()`. + typename Iterator::value_type operator*() const { + auto tmp = std::reverse_iterator::current; + return *--tmp; + } + + // Note: return type is incompatible with the standard + // `pointer operator->()`. + typename Iterator::value_type operator->() const { + auto tmp = std::reverse_iterator::current; + return *--tmp; + } +}; + +// This is used as a helper type for accessing vectors. +// Vector::data() assumes the vector elements start after the length field. +template class Vector { + public: + typedef VectorIterator::mutable_return_type> + iterator; + typedef VectorIterator::return_type> + const_iterator; + typedef VectorReverseIterator reverse_iterator; + typedef VectorReverseIterator const_reverse_iterator; + + typedef typename flatbuffers::bool_constant::value> + scalar_tag; + + static FLATBUFFERS_CONSTEXPR bool is_span_observable = + scalar_tag::value && (FLATBUFFERS_LITTLEENDIAN || sizeof(T) == 1); + + uoffset_t size() const { return EndianScalar(length_); } + + // Deprecated: use size(). Here for backwards compatibility. + FLATBUFFERS_ATTRIBUTE([[deprecated("use size() instead")]]) + uoffset_t Length() const { return size(); } + + typedef typename IndirectHelper::return_type return_type; + typedef typename IndirectHelper::mutable_return_type mutable_return_type; + typedef return_type value_type; + + return_type Get(uoffset_t i) const { + FLATBUFFERS_ASSERT(i < size()); + return IndirectHelper::Read(Data(), i); + } + + return_type operator[](uoffset_t i) const { return Get(i); } + + // If this is a Vector of enums, T will be its storage type, not the enum + // type. This function makes it convenient to retrieve value with enum + // type E. + template E GetEnum(uoffset_t i) const { + return static_cast(Get(i)); + } + + // If this a vector of unions, this does the cast for you. There's no check + // to make sure this is the right type! + template const U *GetAs(uoffset_t i) const { + return reinterpret_cast(Get(i)); + } + + // If this a vector of unions, this does the cast for you. There's no check + // to make sure this is actually a string! + const String *GetAsString(uoffset_t i) const { + return reinterpret_cast(Get(i)); + } + + const void *GetStructFromOffset(size_t o) const { + return reinterpret_cast(Data() + o); + } + + iterator begin() { return iterator(Data(), 0); } + const_iterator begin() const { return const_iterator(Data(), 0); } + + iterator end() { return iterator(Data(), size()); } + const_iterator end() const { return const_iterator(Data(), size()); } + + reverse_iterator rbegin() { return reverse_iterator(end()); } + const_reverse_iterator rbegin() const { + return const_reverse_iterator(end()); + } + + reverse_iterator rend() { return reverse_iterator(begin()); } + const_reverse_iterator rend() const { + return const_reverse_iterator(begin()); + } + + const_iterator cbegin() const { return begin(); } + + const_iterator cend() const { return end(); } + + const_reverse_iterator crbegin() const { return rbegin(); } + + const_reverse_iterator crend() const { return rend(); } + + // Change elements if you have a non-const pointer to this object. + // Scalars only. See reflection.h, and the documentation. + void Mutate(uoffset_t i, const T &val) { + FLATBUFFERS_ASSERT(i < size()); + WriteScalar(data() + i, val); + } + + // Change an element of a vector of tables (or strings). + // "val" points to the new table/string, as you can obtain from + // e.g. reflection::AddFlatBuffer(). + void MutateOffset(uoffset_t i, const uint8_t *val) { + FLATBUFFERS_ASSERT(i < size()); + static_assert(sizeof(T) == sizeof(uoffset_t), "Unrelated types"); + WriteScalar(data() + i, + static_cast(val - (Data() + i * sizeof(uoffset_t)))); + } + + // Get a mutable pointer to tables/strings inside this vector. + mutable_return_type GetMutableObject(uoffset_t i) const { + FLATBUFFERS_ASSERT(i < size()); + return const_cast(IndirectHelper::Read(Data(), i)); + } + + // The raw data in little endian format. Use with care. + const uint8_t *Data() const { + return reinterpret_cast(&length_ + 1); + } + + uint8_t *Data() { return reinterpret_cast(&length_ + 1); } + + // Similarly, but typed, much like std::vector::data + const T *data() const { return reinterpret_cast(Data()); } + T *data() { return reinterpret_cast(Data()); } + + template return_type LookupByKey(K key) const { + void *search_result = std::bsearch( + &key, Data(), size(), IndirectHelper::element_stride, KeyCompare); + + if (!search_result) { + return nullptr; // Key not found. + } + + const uint8_t *element = reinterpret_cast(search_result); + + return IndirectHelper::Read(element, 0); + } + + template mutable_return_type MutableLookupByKey(K key) { + return const_cast(LookupByKey(key)); + } + + protected: + // This class is only used to access pre-existing data. Don't ever + // try to construct these manually. + Vector(); + + uoffset_t length_; + + private: + // This class is a pointer. Copying will therefore create an invalid object. + // Private and unimplemented copy constructor. + Vector(const Vector &); + Vector &operator=(const Vector &); + + template static int KeyCompare(const void *ap, const void *bp) { + const K *key = reinterpret_cast(ap); + const uint8_t *data = reinterpret_cast(bp); + auto table = IndirectHelper::Read(data, 0); + + // std::bsearch compares with the operands transposed, so we negate the + // result here. + return -table->KeyCompareWithValue(*key); + } +}; + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span(Vector &vec) + FLATBUFFERS_NOEXCEPT { + static_assert(Vector::is_span_observable, + "wrong type U, only LE-scalar, or byte types are allowed"); + return span(vec.data(), vec.size()); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span( + const Vector &vec) FLATBUFFERS_NOEXCEPT { + static_assert(Vector::is_span_observable, + "wrong type U, only LE-scalar, or byte types are allowed"); + return span(vec.data(), vec.size()); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_bytes_span( + Vector &vec) FLATBUFFERS_NOEXCEPT { + static_assert(Vector::scalar_tag::value, + "wrong type U, only LE-scalar, or byte types are allowed"); + return span(vec.Data(), vec.size() * sizeof(U)); +} + +template +FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_bytes_span( + const Vector &vec) FLATBUFFERS_NOEXCEPT { + static_assert(Vector::scalar_tag::value, + "wrong type U, only LE-scalar, or byte types are allowed"); + return span(vec.Data(), vec.size() * sizeof(U)); +} + +// Represent a vector much like the template above, but in this case we +// don't know what the element types are (used with reflection.h). +class VectorOfAny { + public: + uoffset_t size() const { return EndianScalar(length_); } + + const uint8_t *Data() const { + return reinterpret_cast(&length_ + 1); + } + uint8_t *Data() { return reinterpret_cast(&length_ + 1); } + + protected: + VectorOfAny(); + + uoffset_t length_; + + private: + VectorOfAny(const VectorOfAny &); + VectorOfAny &operator=(const VectorOfAny &); +}; + +template +Vector> *VectorCast(Vector> *ptr) { + static_assert(std::is_base_of::value, "Unrelated types"); + return reinterpret_cast> *>(ptr); +} + +template +const Vector> *VectorCast(const Vector> *ptr) { + static_assert(std::is_base_of::value, "Unrelated types"); + return reinterpret_cast> *>(ptr); +} + +// Convenient helper function to get the length of any vector, regardless +// of whether it is null or not (the field is not set). +template static inline size_t VectorLength(const Vector *v) { + return v ? v->size() : 0; +} + +} // namespace flatbuffers + +#endif // FLATBUFFERS_VERIFIER_H_ diff --git a/new_ai_tools/include/flatbuffers/vector_downward.h b/new_ai_tools/include/flatbuffers/vector_downward.h new file mode 100644 index 00000000..33913918 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/vector_downward.h @@ -0,0 +1,271 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_VECTOR_DOWNWARD_H_ +#define FLATBUFFERS_VECTOR_DOWNWARD_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/default_allocator.h" +#include "flatbuffers/detached_buffer.h" + +namespace flatbuffers { + +// This is a minimal replication of std::vector functionality, +// except growing from higher to lower addresses. i.e push_back() inserts data +// in the lowest address in the vector. +// Since this vector leaves the lower part unused, we support a "scratch-pad" +// that can be stored there for temporary data, to share the allocated space. +// Essentially, this supports 2 std::vectors in a single buffer. +class vector_downward { + public: + explicit vector_downward(size_t initial_size, Allocator *allocator, + bool own_allocator, size_t buffer_minalign) + : allocator_(allocator), + own_allocator_(own_allocator), + initial_size_(initial_size), + buffer_minalign_(buffer_minalign), + reserved_(0), + size_(0), + buf_(nullptr), + cur_(nullptr), + scratch_(nullptr) {} + + vector_downward(vector_downward &&other) + // clang-format on + : allocator_(other.allocator_), + own_allocator_(other.own_allocator_), + initial_size_(other.initial_size_), + buffer_minalign_(other.buffer_minalign_), + reserved_(other.reserved_), + size_(other.size_), + buf_(other.buf_), + cur_(other.cur_), + scratch_(other.scratch_) { + // No change in other.allocator_ + // No change in other.initial_size_ + // No change in other.buffer_minalign_ + other.own_allocator_ = false; + other.reserved_ = 0; + other.buf_ = nullptr; + other.cur_ = nullptr; + other.scratch_ = nullptr; + } + + vector_downward &operator=(vector_downward &&other) { + // Move construct a temporary and swap idiom + vector_downward temp(std::move(other)); + swap(temp); + return *this; + } + + ~vector_downward() { + clear_buffer(); + clear_allocator(); + } + + void reset() { + clear_buffer(); + clear(); + } + + void clear() { + if (buf_) { + cur_ = buf_ + reserved_; + } else { + reserved_ = 0; + cur_ = nullptr; + } + size_ = 0; + clear_scratch(); + } + + void clear_scratch() { scratch_ = buf_; } + + void clear_allocator() { + if (own_allocator_ && allocator_) { delete allocator_; } + allocator_ = nullptr; + own_allocator_ = false; + } + + void clear_buffer() { + if (buf_) Deallocate(allocator_, buf_, reserved_); + buf_ = nullptr; + } + + // Relinquish the pointer to the caller. + uint8_t *release_raw(size_t &allocated_bytes, size_t &offset) { + auto *buf = buf_; + allocated_bytes = reserved_; + offset = static_cast(cur_ - buf_); + + // release_raw only relinquishes the buffer ownership. + // Does not deallocate or reset the allocator. Destructor will do that. + buf_ = nullptr; + clear(); + return buf; + } + + // Relinquish the pointer to the caller. + DetachedBuffer release() { + // allocator ownership (if any) is transferred to DetachedBuffer. + DetachedBuffer fb(allocator_, own_allocator_, buf_, reserved_, cur_, + size()); + if (own_allocator_) { + allocator_ = nullptr; + own_allocator_ = false; + } + buf_ = nullptr; + clear(); + return fb; + } + + size_t ensure_space(size_t len) { + FLATBUFFERS_ASSERT(cur_ >= scratch_ && scratch_ >= buf_); + if (len > static_cast(cur_ - scratch_)) { reallocate(len); } + // Beyond this, signed offsets may not have enough range: + // (FlatBuffers > 2GB not supported). + FLATBUFFERS_ASSERT(size() < FLATBUFFERS_MAX_BUFFER_SIZE); + return len; + } + + inline uint8_t *make_space(size_t len) { + if (len) { + ensure_space(len); + cur_ -= len; + size_ += static_cast(len); + } + return cur_; + } + + // Returns nullptr if using the DefaultAllocator. + Allocator *get_custom_allocator() { return allocator_; } + + inline uoffset_t size() const { return size_; } + + uoffset_t scratch_size() const { + return static_cast(scratch_ - buf_); + } + + size_t capacity() const { return reserved_; } + + uint8_t *data() const { + FLATBUFFERS_ASSERT(cur_); + return cur_; + } + + uint8_t *scratch_data() const { + FLATBUFFERS_ASSERT(buf_); + return buf_; + } + + uint8_t *scratch_end() const { + FLATBUFFERS_ASSERT(scratch_); + return scratch_; + } + + uint8_t *data_at(size_t offset) const { return buf_ + reserved_ - offset; } + + void push(const uint8_t *bytes, size_t num) { + if (num > 0) { memcpy(make_space(num), bytes, num); } + } + + // Specialized version of push() that avoids memcpy call for small data. + template void push_small(const T &little_endian_t) { + make_space(sizeof(T)); + *reinterpret_cast(cur_) = little_endian_t; + } + + template void scratch_push_small(const T &t) { + ensure_space(sizeof(T)); + *reinterpret_cast(scratch_) = t; + scratch_ += sizeof(T); + } + + // fill() is most frequently called with small byte counts (<= 4), + // which is why we're using loops rather than calling memset. + void fill(size_t zero_pad_bytes) { + make_space(zero_pad_bytes); + for (size_t i = 0; i < zero_pad_bytes; i++) cur_[i] = 0; + } + + // Version for when we know the size is larger. + // Precondition: zero_pad_bytes > 0 + void fill_big(size_t zero_pad_bytes) { + memset(make_space(zero_pad_bytes), 0, zero_pad_bytes); + } + + void pop(size_t bytes_to_remove) { + cur_ += bytes_to_remove; + size_ -= static_cast(bytes_to_remove); + } + + void scratch_pop(size_t bytes_to_remove) { scratch_ -= bytes_to_remove; } + + void swap(vector_downward &other) { + using std::swap; + swap(allocator_, other.allocator_); + swap(own_allocator_, other.own_allocator_); + swap(initial_size_, other.initial_size_); + swap(buffer_minalign_, other.buffer_minalign_); + swap(reserved_, other.reserved_); + swap(size_, other.size_); + swap(buf_, other.buf_); + swap(cur_, other.cur_); + swap(scratch_, other.scratch_); + } + + void swap_allocator(vector_downward &other) { + using std::swap; + swap(allocator_, other.allocator_); + swap(own_allocator_, other.own_allocator_); + } + + private: + // You shouldn't really be copying instances of this class. + FLATBUFFERS_DELETE_FUNC(vector_downward(const vector_downward &)); + FLATBUFFERS_DELETE_FUNC(vector_downward &operator=(const vector_downward &)); + + Allocator *allocator_; + bool own_allocator_; + size_t initial_size_; + size_t buffer_minalign_; + size_t reserved_; + uoffset_t size_; + uint8_t *buf_; + uint8_t *cur_; // Points at location between empty (below) and used (above). + uint8_t *scratch_; // Points to the end of the scratchpad in use. + + void reallocate(size_t len) { + auto old_reserved = reserved_; + auto old_size = size(); + auto old_scratch_size = scratch_size(); + reserved_ += + (std::max)(len, old_reserved ? old_reserved / 2 : initial_size_); + reserved_ = (reserved_ + buffer_minalign_ - 1) & ~(buffer_minalign_ - 1); + if (buf_) { + buf_ = ReallocateDownward(allocator_, buf_, old_reserved, reserved_, + old_size, old_scratch_size); + } else { + buf_ = Allocate(allocator_, reserved_); + } + cur_ = buf_ + reserved_ - old_size; + scratch_ = buf_ + old_scratch_size; + } +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_VECTOR_DOWNWARD_H_ diff --git a/new_ai_tools/include/flatbuffers/verifier.h b/new_ai_tools/include/flatbuffers/verifier.h new file mode 100644 index 00000000..4512d451 --- /dev/null +++ b/new_ai_tools/include/flatbuffers/verifier.h @@ -0,0 +1,283 @@ +/* + * Copyright 2021 Google Inc. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef FLATBUFFERS_VERIFIER_H_ +#define FLATBUFFERS_VERIFIER_H_ + +#include "flatbuffers/base.h" +#include "flatbuffers/util.h" +#include "flatbuffers/vector.h" + +namespace flatbuffers { + +// Helper class to verify the integrity of a FlatBuffer +class Verifier FLATBUFFERS_FINAL_CLASS { + public: + Verifier(const uint8_t *buf, size_t buf_len, uoffset_t _max_depth = 64, + uoffset_t _max_tables = 1000000, bool _check_alignment = true) + : buf_(buf), + size_(buf_len), + depth_(0), + max_depth_(_max_depth), + num_tables_(0), + max_tables_(_max_tables), + upper_bound_(0), + check_alignment_(_check_alignment), + flex_reuse_tracker_(nullptr) { + FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE); + } + + // Central location where any verification failures register. + bool Check(bool ok) const { + // clang-format off + #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE + FLATBUFFERS_ASSERT(ok); + #endif + #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE + if (!ok) + upper_bound_ = 0; + #endif + // clang-format on + return ok; + } + + // Verify any range within the buffer. + bool Verify(size_t elem, size_t elem_len) const { + // clang-format off + #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE + auto upper_bound = elem + elem_len; + if (upper_bound_ < upper_bound) + upper_bound_ = upper_bound; + #endif + // clang-format on + return Check(elem_len < size_ && elem <= size_ - elem_len); + } + + bool VerifyAlignment(size_t elem, size_t align) const { + return Check((elem & (align - 1)) == 0 || !check_alignment_); + } + + // Verify a range indicated by sizeof(T). + template bool Verify(size_t elem) const { + return VerifyAlignment(elem, sizeof(T)) && Verify(elem, sizeof(T)); + } + + bool VerifyFromPointer(const uint8_t *p, size_t len) { + auto o = static_cast(p - buf_); + return Verify(o, len); + } + + // Verify relative to a known-good base pointer. + bool VerifyFieldStruct(const uint8_t *base, voffset_t elem_off, + size_t elem_len, size_t align) const { + auto f = static_cast(base - buf_) + elem_off; + return VerifyAlignment(f, align) && Verify(f, elem_len); + } + + template + bool VerifyField(const uint8_t *base, voffset_t elem_off, + size_t align) const { + auto f = static_cast(base - buf_) + elem_off; + return VerifyAlignment(f, align) && Verify(f, sizeof(T)); + } + + // Verify a pointer (may be NULL) of a table type. + template bool VerifyTable(const T *table) { + return !table || table->Verify(*this); + } + + // Verify a pointer (may be NULL) of any vector type. + template bool VerifyVector(const Vector *vec) const { + return !vec || VerifyVectorOrString(reinterpret_cast(vec), + sizeof(T)); + } + + // Verify a pointer (may be NULL) of a vector to struct. + template bool VerifyVector(const Vector *vec) const { + return VerifyVector(reinterpret_cast *>(vec)); + } + + // Verify a pointer (may be NULL) to string. + bool VerifyString(const String *str) const { + size_t end; + return !str || (VerifyVectorOrString(reinterpret_cast(str), + 1, &end) && + Verify(end, 1) && // Must have terminator + Check(buf_[end] == '\0')); // Terminating byte must be 0. + } + + // Common code between vectors and strings. + bool VerifyVectorOrString(const uint8_t *vec, size_t elem_size, + size_t *end = nullptr) const { + auto veco = static_cast(vec - buf_); + // Check we can read the size field. + if (!Verify(veco)) return false; + // Check the whole array. If this is a string, the byte past the array + // must be 0. + auto size = ReadScalar(vec); + auto max_elems = FLATBUFFERS_MAX_BUFFER_SIZE / elem_size; + if (!Check(size < max_elems)) + return false; // Protect against byte_size overflowing. + auto byte_size = sizeof(size) + elem_size * size; + if (end) *end = veco + byte_size; + return Verify(veco, byte_size); + } + + // Special case for string contents, after the above has been called. + bool VerifyVectorOfStrings(const Vector> *vec) const { + if (vec) { + for (uoffset_t i = 0; i < vec->size(); i++) { + if (!VerifyString(vec->Get(i))) return false; + } + } + return true; + } + + // Special case for table contents, after the above has been called. + template bool VerifyVectorOfTables(const Vector> *vec) { + if (vec) { + for (uoffset_t i = 0; i < vec->size(); i++) { + if (!vec->Get(i)->Verify(*this)) return false; + } + } + return true; + } + + __supress_ubsan__("unsigned-integer-overflow") bool VerifyTableStart( + const uint8_t *table) { + // Check the vtable offset. + auto tableo = static_cast(table - buf_); + if (!Verify(tableo)) return false; + // This offset may be signed, but doing the subtraction unsigned always + // gives the result we want. + auto vtableo = tableo - static_cast(ReadScalar(table)); + // Check the vtable size field, then check vtable fits in its entirety. + return VerifyComplexity() && Verify(vtableo) && + VerifyAlignment(ReadScalar(buf_ + vtableo), + sizeof(voffset_t)) && + Verify(vtableo, ReadScalar(buf_ + vtableo)); + } + + template + bool VerifyBufferFromStart(const char *identifier, size_t start) { + if (identifier && !Check((size_ >= 2 * sizeof(flatbuffers::uoffset_t) && + BufferHasIdentifier(buf_ + start, identifier)))) { + return false; + } + + // Call T::Verify, which must be in the generated code for this type. + auto o = VerifyOffset(start); + return o && reinterpret_cast(buf_ + start + o)->Verify(*this) + // clang-format off + #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE + && GetComputedSize() + #endif + ; + // clang-format on + } + + template + bool VerifyNestedFlatBuffer(const Vector *buf, + const char *identifier) { + if (!buf) return true; + Verifier nested_verifier(buf->data(), buf->size()); + return nested_verifier.VerifyBuffer(identifier); + } + + // Verify this whole buffer, starting with root type T. + template bool VerifyBuffer() { return VerifyBuffer(nullptr); } + + template bool VerifyBuffer(const char *identifier) { + return VerifyBufferFromStart(identifier, 0); + } + + template bool VerifySizePrefixedBuffer(const char *identifier) { + return Verify(0U) && + ReadScalar(buf_) == size_ - sizeof(uoffset_t) && + VerifyBufferFromStart(identifier, sizeof(uoffset_t)); + } + + uoffset_t VerifyOffset(size_t start) const { + if (!Verify(start)) return 0; + auto o = ReadScalar(buf_ + start); + // May not point to itself. + if (!Check(o != 0)) return 0; + // Can't wrap around / buffers are max 2GB. + if (!Check(static_cast(o) >= 0)) return 0; + // Must be inside the buffer to create a pointer from it (pointer outside + // buffer is UB). + if (!Verify(start + o, 1)) return 0; + return o; + } + + uoffset_t VerifyOffset(const uint8_t *base, voffset_t start) const { + return VerifyOffset(static_cast(base - buf_) + start); + } + + // Called at the start of a table to increase counters measuring data + // structure depth and amount, and possibly bails out with false if + // limits set by the constructor have been hit. Needs to be balanced + // with EndTable(). + bool VerifyComplexity() { + depth_++; + num_tables_++; + return Check(depth_ <= max_depth_ && num_tables_ <= max_tables_); + } + + // Called at the end of a table to pop the depth count. + bool EndTable() { + depth_--; + return true; + } + + // Returns the message size in bytes + size_t GetComputedSize() const { + // clang-format off + #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE + uintptr_t size = upper_bound_; + // Align the size to uoffset_t + size = (size - 1 + sizeof(uoffset_t)) & ~(sizeof(uoffset_t) - 1); + return (size > size_) ? 0 : size; + #else + // Must turn on FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE for this to work. + (void)upper_bound_; + FLATBUFFERS_ASSERT(false); + return 0; + #endif + // clang-format on + } + + std::vector *GetFlexReuseTracker() { return flex_reuse_tracker_; } + + void SetFlexReuseTracker(std::vector *rt) { + flex_reuse_tracker_ = rt; + } + + private: + const uint8_t *buf_; + size_t size_; + uoffset_t depth_; + uoffset_t max_depth_; + uoffset_t num_tables_; + uoffset_t max_tables_; + mutable size_t upper_bound_; + bool check_alignment_; + std::vector *flex_reuse_tracker_; +}; + +} // namespace flatbuffers + +#endif // FLATBUFFERS_VERIFIER_H_ diff --git a/new_ai_tools/include/ioserver.h b/new_ai_tools/include/ioserver.h new file mode 100644 index 00000000..9265416b --- /dev/null +++ b/new_ai_tools/include/ioserver.h @@ -0,0 +1,44 @@ +#ifndef _io_server_h_ +#define _io_server_h_ + +#ifdef __XC__ + +void ioserver(chanend c_model[], unsigned n_model); + +#else + +#include +#include + +#define CMD_LENGTH_BYTES (3) // CMD, Model, Tensor + +#define IOSERVER_INVOKE 1 +#define IOSERVER_TENSOR_SEND_OUTPUT 2 +#define IOSERVER_TENSOR_RECV_INPUT 3 +#define IOSERVER_ACK 5 +#define IOSERVER_NACK 6 +#define IOSERVER_RESET 7 +#define IOSERVER_EXIT 8 + +#define MAX_PACKET_SIZE (512) +#define MAX_PACKET_SIZE_WORDS (MAX_PACKET_SIZE / 4) + +#ifdef __cplusplus +extern "C" { +#endif +unsigned int ioserver_command_receive(chanend_t c_server, unsigned *tensor_num); +void ioserver_command_acknowledge(chanend_t c_server, unsigned int ack); +void ioserver_tensor_send_output(chanend_t c_server, unsigned int *data, + unsigned int n); + +void ioserver_tensor_recv_input(chanend_t c_server, unsigned int *data, + unsigned int n); + +void ioserver(chanend_t c_model[], unsigned n_model); +#ifdef __cplusplus +} +#endif + +#endif + +#endif diff --git a/new_ai_tools/include/lib_nn/api/TransposeConv.h b/new_ai_tools/include/lib_nn/api/TransposeConv.h new file mode 100644 index 00000000..644b7782 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/TransposeConv.h @@ -0,0 +1,24 @@ +// Copyright 2021 XMOS LIMITED. This Software is subject to the terms of the +// XMOS Public License: Version 1 +#include +#include +#include + +class ConvParams { + public: + ConvParams(std::array ks, std::vector weights, int h, int w) : + subH(h), subW(w), kernelShape(ks) , weights(weights){} + + public: + int subH; + int subW; + std::array kernelShape; + std::vector weights; +}; + +std::vector transpose_conv_reorder_kernel_weights( + int8_t *raw_weights, + std::array &shape, + int32_t stride_height, //vertical + int32_t stride_width //horizontal +) ; diff --git a/new_ai_tools/include/lib_nn/api/add_int16.h b/new_ai_tools/include/lib_nn/api/add_int16.h new file mode 100644 index 00000000..0d5539d8 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/add_int16.h @@ -0,0 +1,27 @@ +#ifndef _add_int16_h_ +#define _add_int16_h_ + +/** + * Function that implements a addition of two 16-bit tensors. + * The blob must have been created by a call to + * ``add_int16_tensor_blob()`` + * + * @param output Output tensor + * Must be word-aligned + * + * @param input1 Input tensor operand 1 + * Must be word-aligned + * + * @param input1 Input tensor operand 2 + * Must be word-aligned + * + * @param blob Transformed constant input tensor + * Must be word-aligned + * + * @param tensor_length Number of elements in the tensor (product of all dimensions) + * There are no constraints on this number. + */ +void add_int16_tensor(int16_t *output, int16_t *input1, int16_t *input2, + int tensor_length, void *blob); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/add_int16_transform.h b/new_ai_tools/include/lib_nn/api/add_int16_transform.h new file mode 100644 index 00000000..a18a8268 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/add_int16_transform.h @@ -0,0 +1,42 @@ +#ifndef _add_int16_transform_h_ +#define _add_int16_transform_h_ + +#include "nn_api.h" +#include + +/** + * Function that performs the compile time transformation of an int16 addition + * between two tensors. + * + * this function should be + * called at compile-time, and at run-time the output of this function shall be passed + * as the second input tensor of ``add_int16_tensor`` + * + * @param output Output of the function; a blob of + * ``ADD_INT16_TENSOR_BYTES()`` bytes. + * Must be word-aligned. + * + * @param input1_scaler Quantisation scaler for input1 + * Negate this to make add compute -A+B + * + * @param input2_scaler Quantisation scaler for input2 + * Negate this to make add compute A-B + * + * @param output_scaler Quantisation scaler for output + * + * @returns 1 on success, 0 on fail (fallback required) + */ +C_API int add_int16_tensor_blob(void *output, + float input1_scaler, + float input2_scaler, + float output_scaler, + char *err_msg); + +/** + * Macro that calculates the number of int16_t that should be allocated to + * store the output of ``add_int16_tensor_blob()`` + */ +#define ADD_INT16_TENSOR_BYTES() (2 * 16 * sizeof(int16_t)) + + +#endif diff --git a/new_ai_tools/include/lib_nn/api/dequantize_int16.h b/new_ai_tools/include/lib_nn/api/dequantize_int16.h new file mode 100644 index 00000000..df5612c6 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/dequantize_int16.h @@ -0,0 +1,22 @@ +#ifndef _dequantize_int16_h_ +#define _dequantize_int16_h_ + +/** + * Function that implements dequantization of a 16-bit tensor to a 32-bit tensor. + * The blob must have been created by a call to ``dequantize_int16_tensor_blob()`` + * + * @param output Output tensor + * Must be word-aligned + * + * @param input Input tensor + * + * @param blob Transformed constant input tensor + * Must be word-aligned + * + * @param tensor_length Number of elements in the tensor (product of all dimensions) + * There are no constraints on this number. + */ +void dequantize_int16_tensor(float *output, int16_t *input, + int tensor_length, void *blob); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h b/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h new file mode 100644 index 00000000..446ccdb0 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h @@ -0,0 +1,34 @@ +#ifndef _dequantize_int16_transform_h_ +#define _dequantize_int16_transform_h_ + +#include "nn_api.h" +#include + +/** + * Function that performs the compile time transformation of an int16 addition + * between two tensors. + * + * this function should be + * called at compile-time, and at run-time the output of this function shall be passed + * as the second input tensor of ``dequantize_int16_tensor`` + * + * @param output Output of the function; a blob of + * ``DEQUANTIZE_INT16_TENSOR_BYTES()`` bytes. + * Must be word-aligned. + * + * @param input_scaler Quantisation scaler for the input + * + * @returns 1 on success, 0 on fail (fallback required) + */ +C_API int dequantize_int16_tensor_blob(void *output, + float input_scaler, + char *err_msg); + +/** + * Macro that calculates the number of int16_t that should be allocated to + * store the output of ``dequantize_int16_tensor_blob()`` + */ +#define DEQUANTIZE_INT16_TENSOR_BYTES() (2 * sizeof(float)) + + +#endif diff --git a/new_ai_tools/include/lib_nn/api/expand_8_to_16.h b/new_ai_tools/include/lib_nn/api/expand_8_to_16.h new file mode 100644 index 00000000..7147df94 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/expand_8_to_16.h @@ -0,0 +1,8 @@ +#ifndef _expand_8_to_16_h_ +#define _expand_8_to_16_h_ + +#include + +extern void expand_8_to_16(int16_t *out, int8_t *in, int N); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/multiply_int16.h b/new_ai_tools/include/lib_nn/api/multiply_int16.h new file mode 100644 index 00000000..14a4cb75 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/multiply_int16.h @@ -0,0 +1,42 @@ +#ifndef _multiply_int16_h_ +#define _multiply_int16_h_ + +/** + * Function that implements a multiplication of two 16-bit tensors where + * both tensors are variable. The blob must have been created by a call to + * ``multiply_int16_tensor_blob()`` + * + * @param output Output tensor + * Must be word-aligned + * + * @param input Non constant input tensor + * Must be word-aligned + * + * @param blob Transformed constant input tensor + * Must be word-aligned + * + * @param tensor_length Number of elements in the tensor (product of all dimensions) + * There are no constraints on this number. + */ +void multiply_int16_tensor(int16_t *output, int16_t *input1, int16_t *input2, + int tensor_length, void *blob); + +/** + * Function that implements a requantifies a 16-bit tensor. The blob must have been + * created at compile time using ``requantise_int16_tensor_blob()`` + * + * @param output Output tensor + * Must be word-aligned + * + * @param input Non constant input tensor + * Must be word-aligned + * + * @param tensor_length Number of elements in the tensor (product of all dimensions) + * There are no constraints on this number. + * + * @param blob Transformed constant input tensor + * Must be word-aligned + */ +void requantize_int16_tensor(int16_t *output, int16_t *input1, int tensor_length, void *blob); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h b/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h new file mode 100644 index 00000000..87ac66e6 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h @@ -0,0 +1,71 @@ +#ifndef _multiply_int16_transform_h_ +#define _multiply_int16_transform_h_ + +#include "nn_api.h" +#include + + +/** + * Function that performs the compile time transformation of an int16 to int16 + * re-quantisation. + * + * For an int16 re-quantisation, this function should be + * called at compile-time, and at run-time the output of this function shall be passed + * as the second input tensor of ``requantise_int16_tensor`` + * + * @param output Output of the function; a blob of + * ``REQUANTISE_INT16_BYTES()`` bytes. + * Must be word-aligned. + * + * @param input1_scaler Quantisation scaler for input + * + * @param output_scaler Quantisation scaler for output + * + * @returns 1 on success, 0 on fail (fallback required) + */ +C_API int requantize_int16_tensor_blob(void *output, + float input_scaler, + float output_scaler, + char *err_msg); +/** + * Macro that calculates the number of int16_t that should be allocated to + * store the output of ``quantise_int16_tensor_blob()`` + */ +#define REQUANTIZE_INT16_TENSOR_BYTES() (16 * sizeof(int16_t)) + + +/** + * Function that performs the compile time transformation of an int16 multiplication + * between two tensors. + * + * this function should be + * called at compile-time, and at run-time the output of this function shall be passed + * as the second input tensor of ``multiply_int16_tensor`` + * + * @param output Output of the function; a blob of + * ``MULTIPLY_INT16_TENSOR_BYTES()`` bytes. + * Must be word-aligned. + * + * @param input1_scaler Quantisation scaler for input1 + * + * @param input2_scaler Quantisation scaler for input2 + * + * @param output_scaler Quantisation scaler for output + * + * @returns 1 on success, 0 on fail (fallback required) + */ +C_API int multiply_int16_tensor_blob(void *output, + float input1_scaler, + float input2_scaler, + float output_scaler, + char *err_msg); + +/** + * Macro that calculates the number of int16_t that should be allocated to + * store the output of ``multiply_int16_tensor_blob()`` + * TODO: this could be zero and stored in the pointer... + */ +#define MULTIPLY_INT16_TENSOR_BYTES() (2 * sizeof(int16_t)) + + +#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_api.h b/new_ai_tools/include/lib_nn/api/nn_api.h new file mode 100644 index 00000000..737b0759 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_api.h @@ -0,0 +1,15 @@ +#pragma once + +#ifdef __cplusplus +#define EXTERN_C extern "C" +#else +#define EXTERN_C +#endif + +#define C_API EXTERN_C + +#define ERR_MSG_DESCRIPTOR_FAIL_BYTES() (128) + +#ifndef __has_builtin +#define __has_builtin(x) 0 +#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_arch.h b/new_ai_tools/include/lib_nn/api/nn_arch.h new file mode 100644 index 00000000..b3ecf9c1 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_arch.h @@ -0,0 +1,17 @@ +#pragma once + +#include "nn_api.h" + +typedef enum { + TARGET_ARCH_XS3A = 0, + TARGET_ARCH_VX4A = 1, +} nn_target_arch_t; + +extern nn_target_arch_t NN_ARCH; + +C_API void SetNNTargetArch(nn_target_arch_t arch); + +typedef enum { + VLMUL_SHR_XS3A = 14, + VLMUL_SHR_VX4A = 15, +} nn_vlmul_shr_t; \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_bin_types.h b/new_ai_tools/include/lib_nn/api/nn_bin_types.h new file mode 100644 index 00000000..256daf0d --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_bin_types.h @@ -0,0 +1,14 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef NN_BNN_TYPES_H +#define NN_BNN_TYPES_H +#include + +typedef int8_t bnn_bool_t; +typedef int32_t bnn_b32_t; + +typedef struct bnn_b256_t { + bnn_b32_t d[8]; +} bnn_b256_t; + +#endif // NN_BNN_TYPES_H \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_config.h b/new_ai_tools/include/lib_nn/api/nn_config.h new file mode 100644 index 00000000..93b892aa --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_config.h @@ -0,0 +1,287 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#pragma once + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_GLOBAL + * @brief Configure whether (supported) operators use `-127` or `-128` as the + * their lower saturation bound. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is defined, it is used as the value + * for each config macro `CONFIG_SYMMETRIC_SATURATION_*` (e.g. + * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8`), unless that macro has been + * explicitly set. + * + * Bypassing the symmetric saturation bound requires additional logic, and so + * will generally make the operators slower, though this will be more or less + * significant, depending on the specific operators. + */ + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_deep + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `conv2d_deep()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `conv2d_deep()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` to be + * `1`. If it is defined to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` is undefined, then the value of + * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If + * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` defaults + * to 0, using a lower saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_deep +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_conv2d_deep \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_conv2d_deep (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `conv2d_shallowin()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `conv2d_shallowin()`, define + * `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` to be `1`. If it is defined to + * `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` is undefined, then the + * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is + * defined. If neither symbol is defined, + * `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` defaults to 0, using a lower + * saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_im2col + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `conv2d_im2col()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `conv2d_shallowin()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` + * to be `1`. If it is defined to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` is undefined, then the value + * of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. + * If neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` + * defaults to 0, using a lower saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_im2col +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_conv2d_im2col \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_conv2d_im2col (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `conv2d_depthwise()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `conv2d_depthwise()`, define + * `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` to be `1`. If it is defined to + * `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` is undefined, then the + * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is + * defined. If neither symbol is defined, + * `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` defaults to 0, using a lower + * saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `conv2d_1x1()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `conv2d_1x1()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` to be + * `1`. If it is defined to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` is undefined, then the value of + * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If + * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` defaults + * to 0, using a lower saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_avgpool2d + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `avgpool2d()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `avgpool2d()`, define `CONFIG_SYMMETRIC_SATURATION_avgpool2d` to be `1`. + * If it is defined to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_avgpool2d` is undefined, then the value of + * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If + * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_avgpool2d` defaults + * to 0, using a lower saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_avgpool2d +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_avgpool2d CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_avgpool2d (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_avgpool2d_global + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `avgpool2d_global()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `avgpool2d_global()`, define + * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` to be `1`. If it is defined to + * `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` is undefined, then the + * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is + * defined. If neither symbol is defined, + * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` defaults to 0, using a lower + * saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_avgpool2d_global +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_avgpool2d_global \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_avgpool2d_global (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `requantize_16_to_8()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `requantize_16_to_8()`, define + * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` to be `1`. If it is defined + * to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` is undefined, then the + * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is + * defined. If neither symbol is defined, + * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` defaults to 0, using a lower + * saturation bound of `-128`. + * + * Unfortunately, bypassing the symmetric saturation bounds requires significant + * additional logic, and so with the symmetric saturation bound, + * `requantize_16_to_8()` is approximately 2.5x faster. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 (0) +#endif +#endif + +/** + * @macro CONFIG_SYMMETRIC_SATURATION_fully_connected_8 + * @brief Configure whether `-127` or `-128` is used as the saturation limit for + * `fully_connected_8()`. + * + * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric + * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case + * (`-128`, `127`) can be used instead. + * + * To specify that the symmetric saturation lower bound (`-127`) should be used + * for `fully_connected_8()`, define + * `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` to be `1`. If it is defined + * to `0`, `-128` will be used instead. + * + * If `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` is undefined, then the + * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is + * defined. If neither symbol is defined, + * `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` defaults to 0, using a lower + * saturation bound of `-128`. + * + */ +#ifndef CONFIG_SYMMETRIC_SATURATION_fully_connected_8 +#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL +#define CONFIG_SYMMETRIC_SATURATION_fully_connected_8 \ + CONFIG_SYMMETRIC_SATURATION_GLOBAL +#else +#define CONFIG_SYMMETRIC_SATURATION_fully_connected_8 (0) +#endif +#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h b/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h new file mode 100644 index 00000000..a217b916 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h @@ -0,0 +1,72 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef CONV2D_STRUCTS_H_ +#define CONV2D_STRUCTS_H_ + +#include "nn_image.h" + +#define CONV2D_OUTPUT_LENGTH(input_length, filter_size, dilation, stride) \ + (((input_length - (filter_size + (filter_size - 1) * (dilation - 1)) + 1) + \ + stride - 1) / \ + stride) + +#define CONV2D_INPUT_LENGTH(output_length, filter_size, dilation, stride) \ + (output_length * stride - (stride - 1) - 1 + \ + (filter_size + (filter_size - 1) * (dilation - 1))) + +/** + * Describes the relationship between the convolution window and the + * input image. + */ +typedef struct { + /** The shape of the convolution window */ + struct { + /** Height of the convolution window in pixels */ + unsigned height; + /** Width of the convolution window in pixels */ + unsigned width; + } shape; + + /** + * The initial position of the convolution window, relative to the input + * image. + * + * The position given by this pair indicates where the top-left pixel of the + * convolution window begins relative to the top-left pixel of the input + * image. + * + * If this pair is, for example, `(0, 0)`, then the convolution window starts + * at the top left of the input image and involves no top or left padding. + */ + struct { + /** Row offset of convolution window inital position */ + int row; + /** Column offset of convolution window inital position */ + int column; + } start; + + /** + * The strides of the convolution window. These are the number of (input + * image) pixels that the convolution window moves down and right for each + * pixel moved down or right in the output image. + */ + struct { + /** Vertical stride of the convolution window. */ + int vertical; + /** Horizontal stride of the convolution window */ + int horizontal; + } stride; + + /** + * Note: Only supported where explicitly mentioned. + */ + struct { + /** Vertical dilation of the convolution window. */ + int vertical; + /** Horizontal dilation of the convolution window */ + int horizontal; + } dilation; + +} nn_window_params_t; + +#endif // CONV2D_STRUCTS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_image.h b/new_ai_tools/include/lib_nn/api/nn_image.h new file mode 100644 index 00000000..fcb7bb38 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_image.h @@ -0,0 +1,26 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef IMAGE_H_ +#define IMAGE_H_ + +#include "nn_types.h" + +/** + * This struct describes the basic parameters for an image tensor + */ +typedef struct { + /** + * Height of an image (in pixels) + */ + uint32_t height; + /** + * Width of the image (in pixels) + */ + uint32_t width; + /** + * Number of channels per pixel + */ + channel_count_t channels; +} nn_image_params_t; + +#endif // IMAGE_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_layers.h b/new_ai_tools/include/lib_nn/api/nn_layers.h new file mode 100644 index 00000000..ca3640e8 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_layers.h @@ -0,0 +1,311 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef LAYERS_H_ +#define LAYERS_H_ +#include "nn_api.h" +#include "nn_bin_types.h" +#include "nn_image.h" +#include + +/** + * Struct represents the parameters needed by each `bsign_8()` job. + * + * Values are set by `bsign_8_prepare()`. + * + * @note This struct is intended to be opaque. + */ +typedef struct { + mem_stride_t start; + int32_t length; +} nn_bsign_8_job_t; + +/** + * @brief Initialize an instance of the @oper{bsign_8} operator. + * + * See @oper_ref{bsign_8} for more details about the @oper{bsign_8} operator. To + * invoke a + * @oper{bsign_8} job, call bsign_8(). + * + * When bsign_8() is called, a job (`nn_bsign_8_job_t`) must be supplied to tell + * it how to do its work. This function initializes one or more jobs to be + * supplied in subsequent calls to bsign_8(). + * + * Each job computes a range of elements in the output vector (possibly the + * entire vector). + * + * `jobs` points to an array of `nn_bsign_8_t` to be initialized. Each element + * represents one job. There should be `job_count` elements in the array. + * + * `N` is the number of elements @math{N} in the input vector @tensor{x} and + * output vector @tensor{y}. + * + * `job_count` indicates the number of jobs to be initialized (and thus the + * number of elements in the `jobs` array). + * + * Unlike many other operators, @oper{bsign_8} will automatically divide the + * work to be done as evenly as possible between jobs. + * + * @param plan [out] The plan to be initialized. + * @param jobs [out] Array of jobs to be initialized. + * @param N [in] The number of elements in the input. + * @param[in] zero_point The value @math{z_0} to be used for padding (for all + * channels) + * @param job_count [in] The number of jobs to be initialized. + */ +void bsign_8_prepare(nn_bsign_8_job_t *jobs, int8_t *zero_point_vect, + const uint32_t N, const int8_t zero_point, + const int32_t job_count); + +/** + * @brief Execute @oper{bsign_8} job. + * + * See @oper_ref{bsign_8} for more details about the @oper{requantize_16_to_8} + * operator. + * + * An instance of the @oper{bsign_8} operator requires an job (but no plan is + * required). See bsign_8_prepare() for more details. + * + * `Y` points to the output vector @tensor{y} with length @math{N}. The address + * supplied for `Y` should be the start address of the output vector (for any + * job being processed). + * + * `X` points to the input vector @tensor{x} with length @math{N}. The address + * supplied for `X` should be the start address of the input vector (for any job + * being processed). + * + * `job` points to the (initialized) @oper{bsign_8} job to be performed with + * this call. + * + * @requires_word_alignment{Y,X} + * + * @param Y [out] The output vector @tensor{y} + * @param X [in] The input vector @tensor{x} + * @param plan [in] The @oper{bsign_8} plan to be processed + * @param job [in] The @oper{bsign_8} job to be processed + */ +void bsign_8(bnn_b32_t *Y, const int8_t *X, const int8_t *zero_point_vect, + const nn_bsign_8_job_t *job); + +/** + * Struct represents the parameters needed by each `pad_run()` job. + * + * Values are set by `pad_prepare()`. + * + * @note This struct is intended to be opaque. + */ +typedef struct nn_pad_plan_t { + unsigned top_pad_bytes; + unsigned mid_loop_count; + unsigned left_pad_bytes; + unsigned mid_copy_bytes; + unsigned right_pad_bytes; + unsigned bottom_pad_bytes; +} nn_pad_plan_t; + +typedef struct padding_sizes_t { + int32_t top; + int32_t bottom; + int32_t left; + int32_t right; +} padding_sizes_t; + +/** + * Func to calculate n_3 + */ +void pad_3_to_4_prepare(uint32_t *n_3, const unsigned height, + const unsigned width); + +/** Function that pads an image with 3-byte values with a 0. + * The output image must be word aligned. This function solves the general + * case and calls an optimised assembly version for the bulk copy. + * + * @param outputs output values, every word contains 3 bytes and a zero + * @param inputs input values, RGBRGBRGBRGB... + * @param N_3 number of blocks of 3 bytes to copy + * + * @returns The inner product + */ +extern void pad_3_to_4_run(int8_t outputs[], int8_t inputs[], uint32_t N_3, + uint32_t pad_val); +extern void pad_3_to_4_ref(int8_t outputs[], int8_t inputs[], uint32_t N_3, + uint32_t pad_val); + + +extern void pad_1_to_4_run(int8_t outputs[], int8_t inputs[], uint32_t N, + uint32_t pad_val); + +typedef struct nn_mul_params_t { + int8_t in1_zero_point; + int8_t in2_zero_point; + int16_t bias; + int16_t scalar; + int16_t vlashr_shr; +} nn_mul_params_t; + +void mul_boggle(nn_mul_params_t *params, double in1Scale, double in2Scale, + double outputScale, int8_t in1ZeroPoint, int8_t in2ZeroPoint, + int8_t outputZeroPoint); +void mul_elementwise(const int8_t *in1_data, const int8_t *in2_data, + int element_count, nn_mul_params_t *params, + int8_t *out_data); + +// /** +// * Describes the parameters needed for an @oper{add_elementwise} operator. +// @see add_elementwise(). +// */ +// typedef struct { +// /** +// * The parameters that are applied to each input element. +// */ +// +// /** +// * `m1` and `m2` are the multiplers for the inputs. +// */ +// int16_t m1[16]; +// int16_t m2[16]; + +// /** +// * `shift` is the number of bits the 32-bit accumulator is +// * right-shifted by to obtain a final result for each element. +// */ +// int16_t shift[16]; + +// /** +// * `bias_hi` and `bias_lo` are together, the 32-bit bias to +// * which the scaled inputs are added. +// */ +// int16_t bias_lo[16]; +// int16_t bias_hi[16]; + +// } nn_add_params_t; + +typedef struct { + int16_t m1[16]; + int16_t m2[16]; + int16_t shift[16]; + int16_t bias_hi[16]; + int16_t bias_lo[16]; +} nn_add_params_t; + +/** + * @brief Invoke an @oper{add_elementwise} job. + * + * The @oper{add_elementwise} operator adds together two quantized 8-bit input + * vectors, @tensor{x_0} and @tensor{x_1} element-by-element to produce the + * output vector @tensor{y}. This function assumes that the input vectors and + * the output vector each require different quantization parameters. + * + * In order to add together two quantized vectors, their quantization parameters + * must match. The contents of `params` indicate how to do this. + * + * @par Parameter Details + * + * `Y` points to the output vector @tensor{y} with shape @tensor_shape{N}. + * + * `X0` and `X1` respectively point to the first and second input vectors + * @tensor{x_0} and @tensor{x_1}, each with shape + * @tensor_shape{N}. + * + * `params` describes the parameters @math{s_i}, @math{m_i}, @math{b} and + * @math{s_{out}} which are applied for each output element. + * + * `elm_start` and `elm_count` together specify which output elements + * @math{y[k]} should be calculated by this invocation. Specifically, this + * invocation will calculate @math{y[k]} for which `elm_start` @math{\le k \lt} + * `(elm_start + elm_count)`. + * + * @param[out] Y The output vector @tensor{y} + * @param[in] X0 The first input vector @tensor{x_0} + * @param[in] X1 The second input vector @tensor{x_1} + * @param[in] params The scaling and bias parameters + * @param[in] elm_start Index of first output element to be computed + * @param[in] elm_count Number of output elements to be computed + */ +void add_elementwise(int8_t Y[], const int8_t X1[], const int8_t X2[], + nn_add_params_t *p, const int elm_start, + const int elm_count); + +/** + * @brief Execute @oper{lookup8} job. + * + * See @oper_ref{lookup8} for more details about the @oper{lookup8} operator. + * + * Unlike other operators, instances of @oper{lookup8} do not require plans or + * jobs and no initialization is necessary. + * + * `Y` points to the output vector @tensor{y} with length @math{N}. + * + * `X` points to the input vector @tensor{x} with length @math{N}. + * + * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and + * dtype `int8`. + * + * `N` is the length @math{N} of the input vector @tensor{x}. + * + * @requires_word_alignment{Y,X} + * + * @param Y [out] The output vector @tensor{y} + * @param X [in] The input vector @tensor{x} + * @param lut [in] Look-up table @tensor{T} + * @param N [in] Length @math{N} of input and output vectors + */ +void lookup8(uint8_t *Y, const uint8_t *X, const uint8_t *lut, + const unsigned elm_start, const unsigned elm_count); + +/** + * @brief Execute @oper{softmax_exp_sum} job. + * + * `Y` points to the output scalar. + * + * `X` points to the input vector @tensor{x} with length @math{N}. + * + * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and + * dtype `float32`. + * + * `N` is the length @math{N} of the input vector @tensor{x}. + * + * `elm_start` and `elm_count` together specify which output elements should be + * summed into the output scalar. + */ +void softmax_exp_sum(float *Y, const int8_t *X, const float *lut, + const unsigned elm_start, const unsigned elm_count); + +/** + * @brief Execute @oper{softmax_exp_div} job. + * + * `Y` points to the output vector @tensor{y} with length @math{N}. + * + * `X` points to the input vector @tensor{x} with length @math{N}. + * + * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and + * dtype `float32`. + * + * `inv_sum` is the reciprocal of the sum of the exponentials of the inputs. + * + * `elm_start` and `elm_count` together specify which output elements should be + * calculated by this invocation. + */ +void softmax_exp_div(int8_t *Y, const int8_t *X, const float *lut, + const float inv_sum, const unsigned elm_start, + const unsigned elm_count); + +void softmax_calculate_inv_sum(float *inv_sum, const float sums[]); + +void softmax_generate_exp_lut(int zero_point, float scale, float *lut); + +void softmax_ref(int8_t *Y, const int8_t *X, const float zero_point, + const float scale, const int length); + +void softmax_single(int8_t *Y, const int8_t *X, const float *lut, + const int offset); + +void mean_int8(const int8_t *input, int8_t *output, const int start_dim_size, + const int mean_dim_size, const int end_dim_size, + const float in_zero_point, const float out_zero_point, + const float scale_mul); + +void mean_int16(const int16_t *input, int16_t *output, const int start_dim_size, + const int mean_dim_size, const int end_dim_size, + const float scale_mul); + +#endif // LAYERS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_op_helper.h b/new_ai_tools/include/lib_nn/api/nn_op_helper.h new file mode 100644 index 00000000..86e2c811 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_op_helper.h @@ -0,0 +1,138 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef NN_OP_HELPER_H_ +#define NN_OP_HELPER_H_ +#include + +#include "xs3_vpu.h" + +/** Get address of array element. + * + * For compatibility with (non-xcore) builds with 64-bit addresses, when getting + * the address of an array element, the indexes should be signed, or else + * badness ensues. + * + * @note This macro gets the address of an array *element*. So if `V` is + * `int16_t`, the address is adjusted by `2*INDEX` *bytes*. + * + * @param V Array + * @param INDEX Element index within array. + */ +#define ADDR(V, INDEX) &(V)[((int)(INDEX))] + +static inline int8_t sat_s8(const int32_t acc32, const int8_t sat_lo, + const int8_t sat_hi) { + if (acc32 > VPU_INT8_MAX) return sat_hi; + if (acc32 < VPU_INT8_MIN) return sat_lo; + + return (int8_t)acc32; +} + +static inline int16_t sat_s16(const int32_t acc32) { + if (acc32 >= VPU_INT16_MAX) return VPU_INT16_MAX; + if (acc32 <= VPU_INT16_MIN) return VPU_INT16_MIN; + + return (int16_t)acc32; +} + +static inline int32_t sat_s32(const int64_t acc64) { + if (acc64 >= VPU_INT32_MAX) return VPU_INT32_MAX; + if (acc64 <= VPU_INT32_MIN) return VPU_INT32_MIN; + + return (int32_t)acc64; +} + +// static inline void mulsat_s32(int32_t* acc32, const int8_t a, const int8_t b) +// { +// int64_t acc64 = *acc32 + a*b; +// *acc32 = sat_s32(acc64); +// } + +static inline int8_t vlsat_single_s8(int32_t acc, uint16_t shr, + const int8_t sat_lo, const int8_t sat_hi) { + int64_t acc64 = acc; + if (shr > 0) acc64 += 1 << (shr - 1); + return sat_s8(acc64 >> shr, sat_lo, sat_hi); +} + +static inline int16_t vlsat_single_s16(int32_t acc, uint16_t shr) { + if (shr > 0) acc += 1 << (shr - 1); + return sat_s16(acc >> shr); +} + +static inline int16_t vlmul_single_s16(int16_t vR, int16_t mem) { + int32_t p = ((int32_t)vR) * mem; + p = vlsat_single_s16(p, 14); + return (int16_t)p; +} + +static inline int8_t vlmul_single_s8(int8_t vR, int8_t mem) { + int32_t p = ((int32_t)vR) * mem; + p = vlsat_single_s8(p, 6, VPU_INT8_MIN, VPU_INT8_MAX); + return (int8_t)p; +} + +static inline int8_t vdepth8_single_s16(int16_t vR) { + return vlsat_single_s8(vR, 8, VPU_INT8_MIN, VPU_INT8_MAX); +} + +static inline unsigned in_bounds( + const unsigned region_top, const unsigned region_left, + const unsigned region_bottom, const unsigned region_right, + const unsigned bounds_top, const unsigned bounds_left, + const unsigned bounds_bottom, const unsigned bounds_right) { + return region_top > bounds_bottom || region_bottom < bounds_top || + region_left > bounds_right || region_right < bounds_left; +} + +static inline unsigned clip(const unsigned low, const unsigned val, + const unsigned high) { + if (val <= low) + return low; + else if (val >= high) + return high; + else + return val; +} + +static inline unsigned smax(const unsigned a, const unsigned b) { + return (a >= b) ? a : b; +} + +static inline unsigned smin(const unsigned a, const unsigned b) { + return (a <= b) ? a : b; +} + +static inline int ceil_log2(uint32_t a) { + if (a == 0) return -1; +#ifdef __xcore__ + unsigned x; + #ifdef __XS3A__ + asm("clz %0, %1" : "=r"(x) : "r"(a)); + #endif + #ifdef __VX4A__ + asm("xm.clz %0, %1" : "=r"(x) : "r"(a)); + #endif + + unsigned y = 31 - x; + + // clz(1) = 31 -> 31-31 = 0 -> 2^0 = 1 + // clz(2) = 30 -> 31-30 = 1 -> 2^1 = 2 + // clz(3) = 30 -> 31-30 = 1 -> 2^1 = 2 + // 2^(y) <= a < 2^(y+1) + // check for the lower bound, which yields a different result + if (a == (1 << y)) return y; + return y + 1; + +#else + for (unsigned i = 0; i < 31; i++) { + if ((((unsigned)1) << i) >= a) { + return i; + } + } +#endif + return -1; +} + +#endif // NN_OP_HELPER_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_op_utils.h b/new_ai_tools/include/lib_nn/api/nn_op_utils.h new file mode 100644 index 00000000..f5c000a1 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_op_utils.h @@ -0,0 +1,153 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef NN_OP_UTILS_H_ +#define NN_OP_UTILS_H_ + +#include +#include + +#include "xs3_vpu.h" + +C_API int calculateAlignedThreadSplit(int tc, int split_size, int split_start[], int split_end[]); +C_API int calculateThreadSplit(int tc, int split_size, int split_start[], int split_end[], int alignment); + +#ifdef __XC__ +extern "C" { +#endif + +/** Helper for computing offsets between pixels in an 8-bit image. + * + * Gives the address delta associated with moving the specified number of + * rows, columns and channels through an image with the specified parameters. + * + * \param IMG (nn_image_params_t*) Pointer to image params. + * \param DELTA_ROWS (signed int) Number of rows + * \param DELTA_COLS (signed int) Number of columns + * \param DELTA_CHANS (signed int) Number of channels + */ +#define IMG_ADDRESS_VECT(IMG, DELTA_ROWS, DELTA_COLS, DELTA_CHANS) \ + (((DELTA_ROWS) * (IMG)->width * (IMG)->channels) + \ + ((DELTA_COLS) * (IMG)->channels) + (DELTA_CHANS)) + +/** Get the number of output channel groups given the number of output channels. + * + * This macro gives the minimum number of groups required to handle `CHANNELS` + * channels, which means it is effectively `(int) ceil(CHANNELS / 16.0f)`. + * + * \param CHANNELS Number of channels + */ +#define OUT_CHANNEL_GROUPS(CHANNELS) \ + (((CHANNELS) + (VPU_INT8_ACC_PERIOD - 1)) >> VPU_INT8_ACC_PERIOD_LOG2) + +#ifdef NN_USE_REF +#define USING_C_REFERENCE (1) +#else +#define USING_C_REFERENCE (0) +#endif // NN_USE_REF + +#define MEMCPY_VECT_EXT_BYTES (128) +#define MEMCPY_VECT_INT_BYTES (32) + +/** + * @brief Copy `size` bytes from `src` to `dst`. + * + * `dst` and `src` both must be word-aligned addresses. + * + * `size` need not be an integer number of words. + * + * @param dst [out] Destination address + * @param src [in] Source address + * @param byte_count [in] Number of bytes to be copied + */ +void vpu_memcpy(void *dst, const void *src, size_t byte_count); + +/** + * @brief Copy `size` bytes from `src` to `dst`. + * Faster for copies from internal SRAM. + * + * `dst` and `src` both must be word-aligned addresses. + * + * `size` need not be an integer number of words. + * + * @param dst [out] Destination address + * @param src [in] Source address + * @param byte_count [in] Number of bytes to be copied + */ +void vpu_memcpy_int(void *dst, const void *src, size_t byte_count); + +/** + * @brief Copy `size` bytes from `src` to `dst`. + * Faster for copies from external flash and DDR. + * + * `dst` and `src` both must be word-aligned addresses. + * + * `size` need not be an integer number of words. + * + * @param dst [out] Destination address + * @param src [in] Source address + * @param byte_count [in] Number of bytes to be copied + */ +void vpu_memcpy_ext(void *dst, const void *src, size_t byte_count); + +/** + * @brief Copy `vector_count` multiples of MEMCPY_VECT_EXT_BYTES bytes + * from `src` to `dst`. + * Faster for copies from external flash and DDR. + * + * `dst` and `src` both must be word-aligned addresses. + * + * `size` need not be an integer number of words. + * + * @param dst [out] Destination address + * @param src [in] Source address + * @param vector_count [in] Number of MEMCPY_VECT_EXT_BYTES bytes copies to + * be bytes to be performed + */ +void vpu_memcpy_vector_ext(void *dst, const void *src, int vector_count); + +/** + * @brief Copy `vector_count` multiples of MEMCPY_VECT_INT_BYTES bytes + * from `src` to `dst`. + * Faster for copies from internal SRAM. + * + * `dst` and `src` both must be word-aligned addresses. + * + * `size` need not be an integer number of words. + * + * @param dst [out] Destination address + * @param src [in] Source address + * @param vector_count [in] Number of MEMCPY_VECT_INT_BYTES bytes copies to + * be bytes to be performed + */ +void vpu_memcpy_vector_int(void *dst, const void *src, int vector_count); + +/** + * @brief set `word_count` words from `value` to `dst`. + * + * `dst` must be a word-aligned address. + * + * @param dst [out] Destination address, must be word aligned + * @param value [in] Source value. + * @param size [in] Number of 32 bit words to be copied + */ +void vpu_memset_32(void *dst, const int32_t value, const int word_count); + +#define VPU_MEMSET_VECTOR_WORDS XS3_VPU_VREG_WIDTH_WORDS + +/** + * @brief set `vector_count` vector words from `value` to `dst`. + * + * `dst` must be a word-aligned address. + * + * @param dst [out] Destination address, must be word aligned + * @param value [in] Source value. + * @param vector_count [in] Number of VPU_MEMSET_VECTOR_WORDS words vectors + * to be copied. + */ +void vpu_memset_vector(void *dst, const int32_t value, const int vector_count); + +#ifdef __XC__ +} // extern "C" +#endif + +#endif // NN_OP_UTILS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_operator.h b/new_ai_tools/include/lib_nn/api/nn_operator.h new file mode 100644 index 00000000..81741993 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_operator.h @@ -0,0 +1,18 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef NN_OPERATOR_H_ +#define NN_OPERATOR_H_ + +#ifdef __XC__ +extern "C" { +#endif + +#include "nn_layers.h" +#include "nn_op_utils.h" +#include "nn_pooling.h" + +#ifdef __XC__ +} // extern "C" +#endif + +#endif // NN_OPERATOR_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_pooling.h b/new_ai_tools/include/lib_nn/api/nn_pooling.h new file mode 100644 index 00000000..745fc276 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_pooling.h @@ -0,0 +1,551 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef POOLING_H_ +#define POOLING_H_ + +#include "nn_conv2d_structs.h" +#include "nn_image.h" +#include "nn_types.h" +#include "nn_window_params.h" + +typedef nn_window_op_job_params_t nn_conv2d_job_params_t; + +/** + * Flags used with maxpool2d_ext() for advanced scenarios. + */ +typedef enum { + /** + * Placeholder flag used to indicate no other flags are needed. + */ + MAXPOOL2D_FLAG_NONE = 0, +} nn_maxpool2d_flags_e; + +/** + * Flags used with avgpool2d_ext() for advanced scenarios. + */ +typedef enum { + /** + * Placeholder flag used to indicate no other flags are needed. + */ + AVGPOOL2D_FLAG_NONE = 0, +} nn_avgpool2d_flags_e; + +/** + * Flags used with avgpool2d_global_ext() for advanced scenarios. + */ +typedef enum { + /** + * Placeholder flag used to indicate no other flags are needed. + */ + AVGPOOL2D_GLOBAL_FLAG_NONE = 0, +} nn_avgpool2d_global_flags_e; + +// /** +// * @brief Initialize an instance of the @oper{avgpool2d_global} operator. +// * +// * See @oper_ref{avgpool2d_global} for more details about the +// @oper{avgpool2d_global} operator. To invoke a +// * @oper{avgpool2d_global} job, call avgpool2d_global(). +// * +// * When avgpool2d_global() is called, a plan (`nn_avgpool2d_global_plan_t`) +// and a job (`nn_avgpool2d_global_job_t`) must +// * be supplied to tell it how to do its work. This function initializes that +// plan and one or more jobs to be supplied in +// * subsequent calls to avgpool2d_global(). +// * +// * A plan contains information shared by all jobs of an instance of +// @oper{avgpool2d_global}. Each job computes a range +// * of channels in the output vector (possibly the entire vector). +// * +// * `plan` points to the plan to be initialized. It need only be initialized +// once for many calls to avgpool2d_global(). +// * +// * `jobs` points to an array of `nn_avgpool2d_global_job_t` to be +// initialized. Each element represents one job. There +// * should be `job_count` elements in the array. +// * +// * `x_params` points to the image parameters for the instance's input (and +// output) image @tensor{X}. +// * +// * `job_params` points to either an array of +// `nn_avgpool2d_global_job_params_t` structs or else is `NULL`. A +// * `job_params` value of `NULL` indicates that there will only be a single +// job which computes the entire output image. +// * If `job_params` is `NULL`, then `job_count` must be `1`. If `job_params` +// is not `NULL`, it must point to an array +// * of `job_count` `nn_avgpool2d_global_job_params_t` elements. +// * +// * In particular, job `k` will compute the output elements @math{y[p]} for +// which: +// * @inlinecode +// * job_params[k].start_channel <= p < job_params[k].start_channel + +// job_params[k].out.channels +// * @endinlinecode +// * +// * `job_count` indicates the number of jobs to be initialized (and thus the +// number of elements in the `jobs` array), as +// * well the number of elements in the `job_params` array if it is not `NULL`. +// * +// * @param plan [out] The plan to be initialized. +// * @param jobs [out] Array of jobs to be initialized. +// * @param x_params [in] Parameters describing the shape of each input +// (and output) image tensor @tensor{X}. +// * @param job_params [in] An array of +// `nn_avgpool2d_global_job_params_t` structs, or NULL +// * @param job_count [in] The number of jobs to be initialized. +// */ +// void avgpool2d_global_init( +// nn_avgpool2d_global_plan_t* plan, +// nn_avgpool2d_global_job_t* jobs, +// const nn_image_params_t* x_params, +// const nn_avgpool2d_global_job_params_t* job_params, +// const unsigned job_count); + +/** + * @brief Invoke @oper{maxpool2d} job. + * + * See @oper_ref{maxpool2d} for more details about the @oper{maxpool2d} + * operator. + * + * @par Parameter Details + * + * `Y` points to the output image @tensor{Y} with shape @tensor_shape{Y_h, Y_w, + * X_c}. + * + * `X` points to the input image @tensor{X} with shape @tensor_shape{X_h, X_w, + * X_c}. + * + * The memory layout of @tensor{Y} and @tensor{X} are the standard memory layout + * for image tensors (see @ref standard_layout). + * + * `x_params` points to the image parameters describing the shape of the input + * image @tensor{X}. The size of each of + * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to + * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. + * + * `y_params` points to the image parameters describing the shape of the output + * image @tensor{Y}. The size of each of + * @tensor{Y}'s dimensions, @math{Y_h}, @math{Y_w}, and @math{X_c} correspond to + * `y_params->height`, `y_params->width`, and `x_params->channels` respectively. + * + * `pooling_window` points to a `nn_window_params_t` struct containing the + * instance's @math{W_h}, @math{W_w}, + * @math{W_{vert}}, @math{W_{hori}}, @math{W_{r0}} and @math{W_{c0}} + * hyperparameters (see @ref maxpool2d_hyperparameters) which describe the + * spacial relationship between the input image, the pooling window window and + * the output image. `pooling_window->dilation` is ignored. + * + * `pooling_window->shape` specifies @math{W_h} and @math{W_w}, the height and + * width of the pooling window. + * + * `pooling_window->start` specifies @math{W_{r0}} and @math{W_{c0}}, the + * starting row and column of the pooling window in @tensor{X}'s coordinate + * space. For example, a `start` value of `(0,0)` indicates that the top-left + * pixel of the output image has the pooling window aligned with the top-left + * corner of the input image, with no implied padding at the top or left sides + * of the input image. A `start` value of `(1,1)`, on the other hand, indicates + * that the top-left pixel of the output image has the pooling window shifted + * one pixel right and one pixel down relative to the top-left corner of the + * input image. + * + * `pooling_window->stride.horizontal` specifies @math{W_{vert}} and + * @math{W_{hori}}, the vertical and horizontal strides of the pooling window. + * The strides describe the number of pixels the pooling window moves (across + * the input image) with each pixel in the output image. + * + * @par Parameter Constraints + * + * The arguments `Y` and `X` must each point to a word-aligned address. + * + * The input and output images must have the same number of channels. As such, + * it is required that `y_params->channels == x_params->channels`. + * + * Due to memory alignment requirements, @math{X_c} must be a multiple of + * @math{4}, which forces all pixels to begin at word-aligned addresses. + * + * Padding is not supported by this operator. + * + * @par Splitting the Workload + * + * See maxpool2d_ext() for more advanced scenarios which allow the the work to + * be split across multiple invocations (which can be parallelized across + * cores). + * + * @par Additional Remarks + * + * Internally, maxpool2d() calls maxpool2d_ext() with a `job_params` argument + * that computes the entire output image, and with no flags set. For more + * advanced scenarios, use maxpool2d_ext(). + * + * By default this operator uses the standard 8-bit limits @math([-128, 127]) + * when applying saturation logic. Instead, it can be configured to use + * symmetric saturation bounds @math([-127, 127]) by defining + * `CONFIG_SYMMETRIC_SATURATION_maxpool2d` appropriately. See @ref nn_config.h + * for more details. Note that this configures _all_ instances of the + * @oper{maxpool2d} operator. + * + * If @math{X_c} is not a multiple of @math{32}, this operator may read up to 28 + * bytes following the end of @tensor{X}. This is not ordinarily a problem. + * However, if the object to which `X` points is located very near the end of a + * valid memory address range, it is possible memory access exceptions may occur + * when this operator is invoked. + * + * If necessary, this can be avoided by manually forcing a buffer region (no + * more than @math{28} bytes are necessary) following @tensor{X}. There are + * various ways this can be accomplished, including embedding these objects in + * larger structures. + * + * @param[out] Y The output image @tensor{Y} + * @param[in] X The input image @tensor{X} + * @param[in] x_params Parameters describing the shape of input image + * tensor @tensor{X} + * @param[in] y_params Parameters describing the shape of output image + * tensor @tensor{Y} + * @param[in] pooling_window Parameters describing the relationship between + * the pooling window, the input image, and the output image + */ +void maxpool2d(nn_image_t* Y, const nn_image_t* X, + const nn_image_params_t* x_params, + const nn_image_params_t* y_params, + const nn_window_params_t* pooling_window); + +/** + * @brief Invoke @oper{maxpool2d} job. + * + * See @oper_ref{maxpool2d} for more details about the @oper{maxpool2d} + * operator. + * + * @par Parameter Details + * + * `Y` points to the output image @tensor{Y} with shape @tensor_shape{Y_h, Y_w, + * X_c}. + * + * `X` points to the input image @tensor{X} with shape @tensor_shape{X_h, X_w, + * X_c}. + * + * The memory layout of @tensor{Y} and @tensor{X} are the standard memory layout + * for image tensors (see @ref standard_layout). + * + * `x_params` points to the image parameters describing the shape of the input + * image @tensor{X}. The size of each of + * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to + * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. + * + * `y_params` points to the image parameters describing the shape of the output + * image @tensor{Y}. The size of each of + * @tensor{Y}'s dimensions, @math{Y_h}, @math{Y_w}, and @math{X_c} correspond to + * `y_params->height`, `y_params->width`, and `x_params->channels` respectively. + * + * `pooling_window` points to a `nn_window_params_t` struct containing the + * instance's @math{W_h}, @math{W_w}, + * @math{W_{vert}}, @math{W_{hori}}, @math{W_{r0}} and @math{W_{c0}} + * hyperparameters (see @ref maxpool2d_hyperparameters) which describe the + * spacial relationship between the input image, the pooling window window and + * the output image. `pooling_window->dilation` is ignored. + * + * `pooling_window->shape` specifies @math{W_h} and @math{W_w}, the height and + * width of the pooling window. + * + * `pooling_window->start` specifies @math{W_{r0}} and @math{W_{c0}}, the + * starting row and column of the pooling window in @tensor{X}'s coordinate + * space. For example, a `start` value of `(0,0)` indicates that the top-left + * pixel of the output image has the pooling window aligned with the top-left + * corner of the input image, with no implied padding at the top or left sides + * of the input image. A `start` value of `(1,1)`, on the other hand, indicates + * that the top-left pixel of the output image has the pooling window shifted + * one pixel right and one pixel down relative to the top-left corner of the + * input image. + * + * `pooling_window->stride.horizontal` specifies @math{W_{vert}} and + * @math{W_{hori}}, the vertical and horizontal strides of the pooling window. + * The strides describe the number of pixels the pooling window moves (across + * the input image) with each pixel in the output image. + * + * `job_params` describes which elements of the output image will be computed by + * this invocation. This invocation computes the output elements @math{Y[r,c,p]} + * for which: + * @inlinecode + * job_params->start.rows <= r < job_params->start.rows + + * job_params->size.rows job_params->start.cols <= c < job_params->start.cols + + * job_params->size.cols job_params->start.channels <= p < + * job_params->start.channels + job_params->size.channels + * @endinlinecode + * + * `flags` is a collection of flags which modify the behavior of + * @oper{maxpool2d}. See `nn_maxpool2d_flags_e` for a description of each flag. + * `MAXPOOL2D_FLAG_NONE`(0) can be used for default behavior. + * + * @par Parameter Constraints + * + * The arguments `Y` and `X` must each point to a word-aligned address. + * + * The input and output images must have the same number of channels. As such, + * it is required that `y_params->channels == x_params->channels`. + * + * Due to memory alignment requirements, @math{X_c} must be a multiple of + * @math{4}, which forces all pixels to begin at word-aligned addresses. + * + * Padding is not supported by this operator. + * + * @par Splitting the Workload + * + * @todo Include information about how to split the work into multiple + * invocations (e.g. for parallelization), particularly any counter-intuitive + * aspects. + * + * @par Additional Remarks + * + * Internally, maxpool2d() calls maxpool2d_ext() with a `job_params` argument + * that computes the entire output image, and with no flags set. For more + * advanced scenarios, use maxpool2d_ext(). + * + * By default this operator uses the standard 8-bit limits @math([-128, 127]) + * when applying saturation logic. Instead, it can be configured to use + * symmetric saturation bounds @math([-127, 127]) by defining + * `CONFIG_SYMMETRIC_SATURATION_maxpool2d` appropriately. See @ref nn_config.h + * for more details. Note that this configures _all_ instances of the + * @oper{maxpool2d} operator. + * + * If @math{X_c} is not a multiple of @math{32}, this operator may read up to 28 + * bytes following the end of @tensor{X}. This is not ordinarily a problem. + * However, if the object to which `X` points is located very near the end of a + * valid memory address range, it is possible memory access exceptions may occur + * when this operator is invoked. + * + * If necessary, this can be avoided by manually forcing a buffer region (no + * more than @math{28} bytes are necessary) following @tensor{X}. There are + * various ways this can be accomplished, including embedding these objects in + * larger structures. + * + * @param[out] Y The output image @tensor{Y} + * @param[in] X The input image @tensor{X} + * @param[in] x_params Parameters describing the shape of input image + * tensor @tensor{X} + * @param[in] y_params Parameters describing the shape of output image + * tensor @tensor{Y} + * @param[in] pooling_window Parameters describing the relationship between + * the pooling window, the input image, and the output image + * @param[in] job_params Indicates which output elements will be computed + * by this invocation + * @param[in] flags Flags which modify the behavior of + * maxpool2d_ext() + */ +void maxpool2d_ext(nn_image_t* Y, const nn_image_t* X, + const nn_image_params_t* x_params, + const nn_image_params_t* y_params, + const nn_window_params_t* window_config, + const nn_window_op_job_params_t* job_params, + const nn_maxpool2d_flags_e flags); + +/** + * @brief Invoke a @oper{avgpool2d_global} job. + * + * The @oper{avgpool2d_global} operator computes a scaled and biased sum of + * pixel values for each channel of an input image, producing an 8-bit vector of + * outputs. + * + * See @oper_ref{avgpool2d_global} for more details about the + * @oper{avgpool2d_global} operator, including the mathematical details of the + * operation performed. + * + * @par Parameter Details + * + * `Y` points to the 8-bit output vector @tensor{y} with length + * @tensor_shape{X_c}. + * + * `X` points to the 8-bit input image @tensor{X} with shape @tensor_shape{X_h, + * X_w, X_c}. The memory layout of + * @tensor{X} is the standard memory layout for image tensors (see @ref + * standard_layout). + * + * `bias` is the 32-bit bias @math{b} with which the accumulators are + * initialized for each output. Note that the right-shift by @math{r} bits is + * applied after all accumulation. To add an absolute offset of @math{b_0} to + * each result, then the value used for @math{b} should be @math{b_0 \cdot 2^r}. + * + * `scale` is the 8-bit coefficient @math{s}. All pixel values are multiplied by + * @math{s} and added to the 32-bit accumulator. + * + * `shift` is the (rounding) right-shift @math{r} applied to each 32-bit + * accumulator to yield an 8-bit result. Note that this is a saturating + * right-shift which will saturate to 8-bit bounds (see additional remarks + * below). + * + * `x_params` points to the image parameters describing the shape of the input + * image @tensor{X}. The size of each of + * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to + * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. + * + * @par Parameter Constraints + * + * The arguments `Y` and `X` must each point to a word-aligned address. + * + * The input and output images must have the same number of channels. As such, + * it is required that `y_params->channels == x_params->channels`. + * + * Due to memory alignment requirements, @math{X_c} must be a multiple of + * @math{4}, which forces all pixels to begin at a word-aligned address. + * + * Padding is not supported by this operator. + * + * @par Splitting the Workload + * + * See avgpool2d_ext() for more advanced scenarios which allow the the work to + * be split across multiple invocations (which can be parallelized across + * cores). + * + * @par Additional Remarks + * + * Internally, avgpool2d() calls avgpool2d_ext() with a `job_params` argument + * that computes the entire output image, and with no flags set. For more + * advanced scenarios, use avgpool2d_ext(). + * + * The arguments `Y` and `X` should both point at the beginning of their + * respective objects, even if the job being invoked does not start at the + * beginning of the output vector. + * + * By default this operator uses the standard 8-bit limits @math([-128, 127]) + * when applying saturation logic. Instead, it can be configured to use + * symmetric saturation bounds @math([-127, 127]) by defining + * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` appropriately. See @ref + * nn_config.h for more details. Note that this configures _all_ instances of + * the @oper{avgpool2d_global} operator. + * + * If @math{X_c} is not a multiple of @math{16}, this operator may read up to 12 + * bytes following the end of @tensor{X}. This is not ordinarily a problem. + * However, if the object to which `X` points is located very near the end of a + * valid memory address range, it is possible memory access exceptions may occur + * when this operator is invoked. + * + * If necessary, this can be avoided by manually forcing a buffer region (no + * more than @math{12} bytes are necessary) following @tensor{X}. There are + * various ways this can be accomplished, including embedding these objects in + * larger structures. + * + * @param[out] Y The output vector @tensor{y} + * @param[in] X The input image @tensor{X} + * @param[in] bias Initial 32-bit accumulator value @math{b}. Shared by + * all channels. + * @param[in] scale The factor @math{s} by which input pixel values are + * scaled. + * @param[in] shift The right-shift @math{r} applied to the 32-bit + * accumulators to yield an 8-bit result. + * @param[in] x_params Parameters describing the shape of input image + * tensor @tensor{X} + */ +void avgpool2d_global(nn_image_t* Y, const nn_image_t* X, const int32_t bias, + const int8_t scale, const uint16_t shift, + const nn_image_params_t* x_params); + +/** + * @brief Invoke a @oper{avgpool2d_global} job. + * + * The @oper{avgpool2d_global} operator computes a scaled and biased sum of + * pixel values for each channel of an input image, producing an 8-bit vector of + * outputs. + * + * See @oper_ref{avgpool2d_global} for more details about the + * @oper{avgpool2d_global} operator, including the mathematical details of the + * operation performed. + * + * @par Parameter Details + * + * `Y` points to the 8-bit output vector @tensor{y} with length + * @tensor_shape{X_c}. + * + * `X` points to the 8-bit input image @tensor{X} with shape @tensor_shape{X_h, + * X_w, X_c}. The memory layout of + * @tensor{X} is the standard memory layout for image tensors (see @ref + * standard_layout). + * + * `bias` is the 32-bit bias @math{b} with which the accumulators are + * initialized for each output. Note that the right-shift by @math{r} bits is + * applied after all accumulation. To add an absolute offset of @math{b_0} to + * each result, then the value used for @math{b} should be @math{b_0 \cdot 2^r}. + * + * `scale` is the 8-bit coefficient @math{s}. All pixel values are multiplied by + * @math{s} and added to the 32-bit accumulator. + * + * `shift` is the (rounding) right-shift @math{r} applied to each 32-bit + * accumulator to yield an 8-bit result. Note that this is a saturating + * right-shift which will saturate to 8-bit bounds (see additional remarks + * below). + * + * `x_params` points to the image parameters describing the shape of the input + * image @tensor{X}. The size of each of + * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to + * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. + * + * `chan_start` is the first output channel to be computed by this invocation. + * + * `chan_count` is the number of channels to be computed by this invocation. + * + * `flags` is a collection of flags which modify the behavior of + * @oper{avgpool2d_global}. See `nn_avgpool2d_flags_e` for a description of each + * flag. `AVGPOOL2D_GLOBAL_FLAG_NONE`(0) can be used for default behavior. + * + * @par Parameter Constraints + * + * The arguments `Y` and `X` must each point to a word-aligned address. + * + * The input and output images must have the same number of channels. As such, + * it is required that `y_params->channels == x_params->channels`. + * + * Due to memory alignment requirements, @math{X_c} must be a multiple of + * @math{4}, which forces all pixels to begin at a word-aligned address. + * + * Padding is not supported by this operator. + * + * @par Splitting the Workload + * + * @todo Include information about how to split the work into multiple + * invocations (e.g. for parallelization), particularly any counter-intuitive + * aspects. + * + * @par Additional Remarks + * + * Internally, avgpool2d() calls avgpool2d_ext() with a `job_params` argument + * that computes the entire output image, and with no flags set. For more + * advanced scenarios, use avgpool2d_ext(). + * + * The arguments `Y` and `X` should both point at the beginning of their + * respective objects, even if the job being invoked does not start at the + * beginning of the output vector. + * + * By default this operator uses the standard 8-bit limits @math([-128, 127]) + * when applying saturation logic. Instead, it can be configured to use + * symmetric saturation bounds @math([-127, 127]) by defining + * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` appropriately. See @ref + * nn_config.h for more details. Note that this configures _all_ instances of + * the @oper{avgpool2d_global} operator. + * + * If @math{X_c} is not a multiple of @math{16}, this operator may read up to 12 + * bytes following the end of @tensor{X}. This is not ordinarily a problem. + * However, if the object to which `X` points is located very near the end of a + * valid memory address range, it is possible memory access exceptions may occur + * when this operator is invoked. + * + * If necessary, this can be avoided by manually forcing a buffer region (no + * more than @math{12} bytes are necessary) following @tensor{X}. There are + * various ways this can be accomplished, including embedding these objects in + * larger structures. + * + * @param[out] Y The output vector @tensor{y} + * @param[in] X The input image @tensor{X} + * @param[in] bias Initial 32-bit accumulator value @math{b}. Shared by + * all channels. + * @param[in] scale The factor @math{s} by which input pixel values are + * scaled. + * @param[in] shift The right-shift @math{r} applied to the 32-bit + * accumulators to yield an 8-bit result. + * @param[in] x_params Parameters describing the shape of input image + * tensor @tensor{X} + */ +void avgpool2d_global_ext(nn_image_t* Y, const nn_image_t* X, + const int32_t bias, const int8_t scale, + const uint16_t shift, + const nn_image_params_t* x_params, + const unsigned chan_start, const unsigned chan_count, + const nn_avgpool2d_global_flags_e flags); + +#endif // POOLING_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_types.h b/new_ai_tools/include/lib_nn/api/nn_types.h new file mode 100644 index 00000000..c67a09ff --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_types.h @@ -0,0 +1,83 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef NN_TYPES_H_ +#define NN_TYPES_H_ + +#include + +/** + * Padding modes + */ +typedef enum { + PADDING_VALID = 0, + PADDING_SAME = 1, +} padding_mode_t; + +/** + * Where functions in the API take an array of values, where the array index + * corresponds to a padding direction, the indices shall be as indicated in + * `nn_pad_index_t`, with the order being top padding, left padding, bottom + * padding, right padding. Rather than hard-coding those values, this enum can + * be used instead. + */ +enum { + NN_PAD_TOP = 0, + NN_PAD_LEFT, + NN_PAD_BOTTOM, + NN_PAD_RIGHT, +}; + +/** + * Alias for `uint16_t`. + * + * `data16_t` is used to hint that a struct field or function parameter is + * opaque 16-bit data. + */ +typedef uint16_t data16_t; + +/** + * Alias for `int8_t`. + * + * `nn_tensor_t*` is used to hint that a struct field or function parameter is + * to be interpreted as a tensor. + */ +typedef int8_t nn_tensor_t; + +/** + * Alias for `int8_t`. + * + * `nn_image_t*` is used to hint that a struct field or function parameter will + * be interpreted as an image-like tensor. + */ +typedef nn_tensor_t nn_image_t; + +/** + * Alias for `int32_t`. + * + * `mem_stride_t` is used to hint that a struct field or function parameter + * indicates the signed offset between memory addresses, expressed in bytes. + */ +typedef int32_t mem_stride_t; + +/** + * Alias for `unsigned`. + * + * `channel_count_t` is used to hint that a struct field or function parameter + * indicates a number of channels. + */ +typedef uint32_t channel_count_t; + +/** + * + * Function pointer to unary op kernel functions for int16 + */ +typedef void (*UnaryI16FnType)(void *output, void *input, int tensor_length, void *blob); + + +/** + * + * Function pointer to binary op kernel functions for int16 + */ +typedef void (*BinaryI16FnType)(void *output, void *input1, void *input2, int tensor_length, void *blob); + +#endif // NN_TYPES_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_window_params.h b/new_ai_tools/include/lib_nn/api/nn_window_params.h new file mode 100644 index 00000000..b2ee7760 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/nn_window_params.h @@ -0,0 +1,55 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef STRUCTS_H_ +#define STRUCTS_H_ + +#include + +/** + * This struct represents an indexing vector for an image. + */ +typedef struct { + /** Number of image pixel rows */ + int32_t rows; + /** Number of image pixel columns */ + int32_t cols; + /** Number of image pixel channels */ + int32_t channels; +} nn_image_vect_t; + +/** + * Some of the functions in this API can have their work split into + * multiple parts, each called a "job". This is useful, for example, + * for parallelizing a computation across multiple cores, or to reduce + * the delay between which the calling function can service some other + * resource. + * + * This struct contains the parameters required to specify how work + * can be split according to the region of the output image in which + * the job operates. + * + * For an output image `Y` with shape `(Y_height, Y_width, Y_chans)`, + * the value of this struct indicates that a particular job should + * compute the output values in the rectangular region + * `Y[ start.rows : (start.rows + size.rows), + * start.cols : (start.cols + size.cols), + * start.channels : (start.channels + size.channels) ]` + */ +typedef struct { + /** + * Indices in an output image at which to begin producing output. + * + * Typically channels must be a multiple of 4. + */ + nn_image_vect_t start; + + /** + * The number of rows, columns and channels of output to produce. + * + * Typically channels must be a multiple of 4. + */ + nn_image_vect_t size; + +} nn_window_op_job_params_t; + +#endif // STRUCTS_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h new file mode 100644 index 00000000..298ffedc --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h @@ -0,0 +1,54 @@ +#ifndef _output_transform_fn_int16_h_ +#define _output_transform_fn_int16_h_ + +#include + +typedef struct { + int32_t output_slice_channel_count; +} otfn_int16_params_t; + + +/** Function that transform a ring buffer accumulator + * into a vector of 16 bit numbers after multiplying and scaling. + * In the name of efficiency the inputs shoudl be provided in the following + * order (K indicates the value that affects output channel K): + * + * vD/vR: 0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15. + * (each second and third element are swapped) + * If only few channels are being used, eg two, they + * must be: 0, X, 1, X, X, X, X, X X, X, X, X X, X, X, X. + * + * mul_add: m1, m3, m5, m7, m9, m11, m13, m15, + * a1, a3, a5, a7, a9, a11, a13, a15, + * m0, m2, m4, m6, m8, m10, m12, m14 + * a0, a2, a4, a6, a8, a10, a12, a14. + * If only few channels are being used, eg two, they + * must be: m1, X, X, X, X, X, X, X, a1, X, X, X, X, X, X, X, + * m0, X, X, X, X, X, X, X, a0, X, X, X, X, X, X, X. + * + * output: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 + * Only the channels used are written, eg, for two channels + * 0, 1. + * + * + * + * \param vDvR Pointer to 32 shorts storing 16 upper halfs + * of the ring buffer, and 16 lower halfs in that + * order + * + * \param mul_add Pointer to four vectors of length N/2 integers, the first + * and third vector are multipliers (Q2.30), the second + * and fourth vectors are adders (Q16.16). + * + * \param output Pointer to the desired place where N values will be + * outputted + * + * \param N Number of vector elements to process; N <= 16. + */ +extern int16_t *output_transform_fn_int16(otfn_int16_params_t *params, + int16_t *output, + int16_t *vDvR, + int32_t output_channel_group, + int32_t *mul_add); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h new file mode 100644 index 00000000..0fc13fb3 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h @@ -0,0 +1,37 @@ +#ifndef _output_transform_fn_int16_kernel_transform_h_ +#define _output_transform_fn_int16_kernel_transform_h_ + +#include + +/** + * Function that performs all the transformations needed for a 16-bit convolution + * 1. It transforms the weight ordering to reverse the weights in groups of 16 in preparation of VLMACCR shift-and-rotate + * 2. It convers the channel multpliers to integer multipliers + * 3. It interleaves the channel multpliers and channel biases into a single blob + * The number of kernels is assumed to be input_channels x output_channels elements + * The number of bias terms and multipliers is assumed to be output_channels + * The number of elements in the array mul_add_out should be 2xoutput_channels + * The number of elements in the kernel_weights_out array should be input_channels x output_channels + * + * @param kernel_weights_in kernel weights input + * + * @param channel_multpliers_in per-channel multipliers. + * + * @param channel_bias_terms_in per-channel bias terms. + * + * @param kernel_weights_out reordered kernel weights + * + * @param mul_add_out mixed per-channel multipliers and bias-terms + * + * @param input_channels number of input channels + * + * @param output_channels number of output_channels + */ + +extern void output_transform_fn_int16_kernel_transform( + const int8_t *kernel_weights_in, + const float *channel_multipliers_in, const int *channel_bias_terms_in, + int8_t *kernel_weights_out, int32_t *mul_add_out, + int input_channels, int output_channels); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h new file mode 100644 index 00000000..b7500f4b --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h @@ -0,0 +1,13 @@ +#ifndef _output_transform_fn_int16_mappings_h_ +#define _output_transform_fn_int16_mappings_h_ + +// This defines the mapping of the output transform multipliers from output channels +extern int ot_int16_mul_index_used_for_output[]; + +// This defines the mapping of the output transform biases from output channels +extern int ot_int16_add_index_used_for_output[]; + +// This defines the kernel mapping from output channels +extern int aggr_ot_int16_input_channel_used_for_output[]; + +#endif diff --git a/new_ai_tools/include/lib_nn/api/quadratic_approximation.h b/new_ai_tools/include/lib_nn/api/quadratic_approximation.h new file mode 100644 index 00000000..3d6bc92c --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/quadratic_approximation.h @@ -0,0 +1,83 @@ +#ifndef _quadratic_approximation_h_ +#define _quadratic_approximation_h_ + +#ifdef __xcore__ +#define ACTIVATION_FUNCTION __attribute__(( fptrgroup("activation_functions") )) +#else +#define ACTIVATION_FUNCTION /**/ +#endif + +#include "nn_api.h" +#include + +#define QUADRATIC_APPROXIMATION_MAX_CHUNKS 2048 + +/** Type that stores an approximation table. + * Must be stored 64-bit aligned when presented to assembly code. + * Use: + * * ``quadratic_function_table_number_bytes()`` to query the size + * * ``quadratic_function_table_bytes()`` to obtain a pointer to the table + */ +struct quadratic_function_table { + struct { // The order matters - this is how the assembly code expects them + int32_t c; + int8_t a; + int8_t padding; + int16_t b; + } coefficients[QUADRATIC_APPROXIMATION_MAX_CHUNKS]; + int data_bytes; +}; + +typedef struct quadratic_function_table quadratic_function_table_t; + +/* Function pointer type - any float -> float + */ +typedef float (*float_function_t)(float x); + +/** Function that builds a quadratic approximation table + * The function passed in must be monotinuous + * Any number of chunks will work but the assembly implementiaton assumes 128. + * The function returns a table pointer, and through two arguments the max error, + * and the sqrt of the sum of squared errors as a goodness metric. + * + * \param table interpolation table to be filled in + * \param av function to be interpolated + * \param input_scaler scale that is applied to the input, eg 8.0/32768.0 + * \param outptu_scaler scale that is applied to the output, eg 32768.0 + * \param chunks number of interpolations. Set to 128. + * \param max_error maximum error, ought to be 1 + * \param error sqrt of sum of squared errors. + */ +C_API void quadratic_approximation_generator( + quadratic_function_table_t *table, + ACTIVATION_FUNCTION float_function_t av, + double input_scaler, + double output_scaler, + int chunks, + int *max_error, + double *error); + +/** Function that returns the number of bytes in an approximation table + * + * \param x the table + * \returns The number of bytes in the table + */ +C_API uint32_t quadratic_function_table_number_bytes(quadratic_function_table_t *x); + + +/** Function that returns a pointer to the bytes in an approximation table + * + * \param x the table + * \returns Pointer to the bytes in the table + */ +C_API uint8_t *quadratic_function_table_bytes(quadratic_function_table_t *x); + +/** Example functions that can be passed in + */ +C_API float approximation_function_tanh(float x); +C_API float approximation_function_logistics(float x); +C_API float approximation_function_elu(float x); +C_API float approximation_function_relu(float x); +C_API float approximation_function_relu6(float x); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h b/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h new file mode 100644 index 00000000..836c83a7 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h @@ -0,0 +1,23 @@ +#ifndef _quadratic_interpolation_h_ +#define _quadratic_interpolation_h_ + +#include + +/** Function that performs a quadratic interpolation on a vector of inputs given a table + * of coefficients + * + * \param outputs Output vector, 16-bit signed integers + * + * \param inputs Output vector, 16-bit signed integers + * + * \param coefficients The bytes comprising the table of coefficients produced with + * ``quadratic_approximation_generator()``. + * Must be 64-bit aligned. + * + * \param N Number of 16-bit elements in the vector + */ +extern void quadratic_interpolation_128(int16_t *outputs, int16_t *inputs, + uint8_t *coeffs, + uint32_t N); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/quantize_int16.h b/new_ai_tools/include/lib_nn/api/quantize_int16.h new file mode 100644 index 00000000..3678ee52 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/quantize_int16.h @@ -0,0 +1,22 @@ +#ifndef _quantize_int16_h_ +#define _quantize_int16_h_ + +/** + * Function that implements quantization of a 16-bit tensor to a 32-bit tensor. + * The blob must have been created by a call to ``quantize_int16_blob()`` + * + * @param output Output tensor + * + * @param input Input tensor + * Must be word-aligned + * + * @param blob Transformed constant input tensor + * Must be word-aligned + * + * @param tensor_length Number of elements in the tensor (product of all dimensions) + * There are no constraints on this number. + */ +void quantize_int16_tensor(int16_t *output, float *input, + int tensor_length, void *blob); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h b/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h new file mode 100644 index 00000000..c1b3d89e --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h @@ -0,0 +1,33 @@ +#ifndef _quantize_int16_transform_h_ +#define _quantize_int16_transform_h_ + +#include "nn_api.h" +#include + +/** + * Function that performs the compile time transformation of an int16 addition + * between two tensors. + * + * this function should be + * called at compile-time, and at run-time the output of this function shall be passed + * as the second input tensor of ``quantize_int16_tensor`` + * + * @param output Output of the function; a blob of + * ``QUANTIZE_INT16_TENSOR_BYTES()`` bytes. + * Must be word-aligned. + * + * @param output_scaler Quantisation scaler for the output + * + * @returns 1 on success, 0 on fail (fallback required) + */ +C_API int quantize_int16_tensor_blob(void *output, + float output_scaler); + +/** + * Macro that calculates the number of int16_t that should be allocated to + * store the output of ``quantize_int16_tensor_blob()`` + */ +#define QUANTIZE_INT16_TENSOR_BYTES() (1 * sizeof(float)) + + +#endif diff --git a/new_ai_tools/include/lib_nn/api/version.h b/new_ai_tools/include/lib_nn/api/version.h new file mode 100644 index 00000000..d59e5842 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/version.h @@ -0,0 +1,13 @@ +// Copyright (c) 2024, XMOS Ltd, All rights reserved +#ifndef LIB_NN_VERSION_H_ +#define LIB_NN_VERSION_H_ + +namespace lib_nn { + +static const unsigned major_version = 0; +static const unsigned minor_version = 5; +static const unsigned patch_version = 0; + +} // namespace lib_nn + +#endif // LIB_NN_VERSION_H_ diff --git a/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h b/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h new file mode 100644 index 00000000..fa8ae950 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h @@ -0,0 +1,15 @@ +#ifndef _vpu_memmove_word_aligned_h_ +#define _vpu_memmove_word_aligned_h_ + +/** + * Function that copies a block of memory. Both source and destination + * address must be word aligned. Any number of bytes can be copied. There + * may be an overlap between the destination and source. + * + * @param dst Destination address, must be word aligned. + * @param src Source address, must be word aligned. + * @param byte_count Number of bytes to copy - may be zero + */ +void vpu_memmove_word_aligned(void * dst, const void * src, unsigned int byte_count); + +#endif diff --git a/new_ai_tools/include/lib_nn/api/vpu_memset_256.h b/new_ai_tools/include/lib_nn/api/vpu_memset_256.h new file mode 100644 index 00000000..187c4b7b --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/vpu_memset_256.h @@ -0,0 +1,55 @@ +#include + +#ifndef _vpu_memset_256_h_ +#define _vpu_memset_256_h_ + +/** + * Function that replicates a vector. The source address must be word + * aligned, the destination address is assumed to be aligned with the + * replication pattern in the source. Any number of bytes can be copied. + * There should not be an overlap between the destination and source. + * + * It is assumed that the source address contains 32 replicated bytes (if + * the destination address is byte aligned), or that it contains 16 + * replicated shorts (if the destination address is 16-bit aligned), or + * that it contains 8 replicated ints. + * + * broadcast_32_to_256() and BROADCAST_8_TO_32() cane be used to + * create the source vector + * + * @param dst Destination address + * @param src Source address, must be word aligned. + * @param byte_count Number of bytes to copy - may be zero + */ +void vpu_memset_256(void *dst, const void *src, unsigned int byte_count); + +/** + * Function that replicates an int over a vector. The vector must be + * aligned on an 8-byte boundary. In order to replicate a byte or short over + * a vector, combine this with a call to BROADCAST_8_TO_32() or + * BROADCAST_16_TO_32(). Declare the vector as a uint64_t x[] in order to + * guarantee 8-byte alignement. + * + * @param dst Destination address, must be 8-byte aligned + * @param from Value to be replicated + */ +void broadcast_32_to_256(void *dst, uint32_t from); + +/** + * Macro that replicates a byte over an int. + * Use with broadcast_32_to_256() in order to replicate a byte over a vector + */ +#define BROADCAST_8_TO_32(f) (((uint8_t)f) * 0x01010101) + +/** + * Macro that replicates a short over an int + * Use with broadcast_32_to_256() in order to replicate a short over a vector + */ +#define BROADCAST_16_TO_32(f) (((uint16_t)f) * 0x00010001) + +/** + * Macro that replicates a byte over a short + */ +#define BROADCAST_8_TO_16(f) (((uint8_t)f) * 0x00000101) + +#endif diff --git a/new_ai_tools/include/lib_nn/api/vpu_sim.h b/new_ai_tools/include/lib_nn/api/vpu_sim.h new file mode 100644 index 00000000..8a3e6fd5 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/vpu_sim.h @@ -0,0 +1,119 @@ +// Copyright 2020 XMOS LIMITED. This Software is subject to the terms of the +// XMOS Public License: Version 1 + +#ifndef LIB_NN_VPU_SIM_H_ +#define LIB_NN_VPU_SIM_H_ + +#include +#include +#include + +#include "nn_types.h" +#include "xs3_vpu.h" + +C_API +typedef union { + uint8_t u8[VPU_INT8_EPV]; + int8_t s8[VPU_INT8_EPV]; + + uint16_t u16[VPU_INT16_EPV]; + int16_t s16[VPU_INT16_EPV]; + + uint32_t u32[VPU_INT32_EPV]; + int32_t s32[VPU_INT32_EPV]; +} vpu_vector_t; + +C_API +typedef enum { + MODE_S32 = 0x00, + MODE_S16 = 0x100, + MODE_S8 = 0x200, + MODE_S16x8 = 0x400, +} vector_mode; + +C_API +typedef struct { + vector_mode mode; + vpu_vector_t vR; + vpu_vector_t vD; + vpu_vector_t vC; +} xs3_vpu; + +C_API void VSETC(xs3_vpu* vpu, const vector_mode mode); +C_API void VCLRDR(xs3_vpu* vpu); +C_API void VLDR(xs3_vpu* vpu, const void* addr); +C_API void VLDD(xs3_vpu* vpu, const void* addr); +C_API void VLDC(xs3_vpu* vpu, const void* addr); +C_API void VSTR(const xs3_vpu* vpu, void* addr); +C_API void VSTD(const xs3_vpu* vpu, void* addr); +C_API void VSTC(const xs3_vpu* vpu, void* addr); +C_API void VSTRPV(const xs3_vpu* vpu, void* addr, unsigned mask); +C_API void VLMACC(xs3_vpu* vpu, const void* addr); +C_API void VLMACCR(xs3_vpu* vpu, const void* addr); +C_API void VLMACCR1(xs3_vpu* vpu, const void* addr); +C_API void VPOS(xs3_vpu* vpu); +C_API void VLSAT(xs3_vpu* vpu, const void* addr); +C_API void VLSAT_FIXED(xs3_vpu* vpu, const void* addr); +C_API void VLASHR(xs3_vpu* vpu, const void* addr, const int32_t shr); +C_API void VLADD(xs3_vpu* vpu, const void* addr); +C_API void VLSUB(xs3_vpu* vpu, const void* addr); +C_API void VLMUL(xs3_vpu* vpu, const void* addr); +C_API void VDEPTH1(xs3_vpu* vpu); +C_API void VDEPTH8(xs3_vpu* vpu); +C_API void VDEPTH16(xs3_vpu* vpu); + +/** Print vector register contents based on current vector_mode **/ +C_API void vpu_accu_print(xs3_vpu* vpu); +C_API void vpu_sim_print(xs3_vpu* vpu); +C_API void vpu_sim_mem_print(void* address, vector_mode mode); + +// Function for implementing the saturation logic within the VPU. +C_API int64_t vpu_saturate(const int64_t input, const unsigned bits); + +// Assert if the memory access is non-word aligned +// void assert_word_aligned(const void* address); +#define assert_word_aligned(address) assert(((uintptr_t)address & 0x3) == 0); + +#ifdef __cplusplus + +namespace nn { + +class VPU { + private: + xs3_vpu vpu; + + public: + vpu_vector_t& vD; + vpu_vector_t& vR; + vpu_vector_t& vC; + vector_mode& mode; + + public: + VPU() : vD(vpu.vD), vR(vpu.vR), vC(vpu.vC), mode(vpu.mode) {} + + /** `mode` should be one of `MODE_S32`, `MODE_S16` or `MODE_S8` */ + void vsetc(const vector_mode mode) { VSETC(&this->vpu, mode); } + void vclrdr() { VCLRDR(&this->vpu); } + void vldr(void const* addr) { VLDR(&this->vpu, addr); } + void vldd(void const* addr) { VLDD(&this->vpu, addr); } + void vldc(void const* addr) { VLDC(&this->vpu, addr); } + void vstr(void* addr) { VSTR(&this->vpu, addr); } + void vstd(void* addr) { VSTD(&this->vpu, addr); } + void vstc(void* addr) { VSTC(&this->vpu, addr); } + void vstrpv(void* addr, uint32_t mask) { VSTRPV(&this->vpu, addr, mask); } + void vlmacc(void const* addr) { VLMACC(&this->vpu, addr); } + void vlmaccr(void const* addr) { VLMACCR(&this->vpu, addr); } + void vlmaccr1(void const* addr) { VLMACCR1(&this->vpu, addr); } + void vlsat(void const* addr) { VLSAT(&this->vpu, addr); } + void vlashr(void const* addr, int32_t shr) { VLASHR(&this->vpu, addr, shr); } + void vladd(void const* addr) { VLADD(&this->vpu, addr); } + void vlsub(void const* addr) { VLSUB(&this->vpu, addr); } + void vlmul(void const* addr) { VLMUL(&this->vpu, addr); } + void vdepth1() { VDEPTH1(&this->vpu); } + void vdepth8() { VDEPTH8(&this->vpu); } + void vdepth16() { VDEPTH16(&this->vpu); } +}; +} // namespace nn + +#endif +#endif // LIB_NN_VPU_SIM_H_ diff --git a/new_ai_tools/include/lib_nn/api/xs3_vpu.h b/new_ai_tools/include/lib_nn/api/xs3_vpu.h new file mode 100644 index 00000000..7107cb94 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/xs3_vpu.h @@ -0,0 +1,216 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef XS3_VPU_H_ +#define XS3_VPU_H_ + +#include + +#include "nn_api.h" + +#ifndef XS1_VSR_TYPE + +/* TODO use from xs3a_kernel.h in a future tools release */ +#define XS1_VSR_HEADROOM_SHIFT 0x0 +#define XS1_VSR_HEADROOM_SIZE 0x5 +#define XS1_VSR_HEADROOM_MASK \ + (((1 << XS1_VSR_HEADROOM_SIZE) - 1) << XS1_VSR_HEADROOM_SHIFT) +#define XS1_VSR_HEADROOM(x) \ + (((x)&XS1_VSR_HEADROOM_MASK) >> XS1_VSR_HEADROOM_SHIFT) +#define XS1_VSR_HEADROOM_SET(x, v) \ + (((x) & ~XS1_VSR_HEADROOM_MASK) | \ + (((v) << XS1_VSR_HEADROOM_SHIFT) & XS1_VSR_HEADROOM_MASK)) +#define XS1_VSR_SHIFT_SHIFT 0x6 +#define XS1_VSR_SHIFT_SIZE 0x2 +#define XS1_VSR_SHIFT_MASK \ + (((1 << XS1_VSR_SHIFT_SIZE) - 1) << XS1_VSR_SHIFT_SHIFT) +#define XS1_VSR_SHIFT(x) (((x)&XS1_VSR_SHIFT_MASK) >> XS1_VSR_SHIFT_SHIFT) +#define XS1_VSR_SHIFT_SET(x, v) \ + (((x) & ~XS1_VSR_SHIFT_MASK) | \ + (((v) << XS1_VSR_SHIFT_SHIFT) & XS1_VSR_SHIFT_MASK)) +#define XS1_VSR_TYPE_SHIFT 0x8 +#define XS1_VSR_TYPE_SIZE 0x4 +#define XS1_VSR_TYPE_MASK (((1 << XS1_VSR_TYPE_SIZE) - 1) << XS1_VSR_TYPE_SHIFT) +#define XS1_VSR_TYPE(x) (((x)&XS1_VSR_TYPE_MASK) >> XS1_VSR_TYPE_SHIFT) +#define XS1_VSR_TYPE_SET(x, v) \ + (((x) & ~XS1_VSR_TYPE_MASK) | \ + (((v) << XS1_VSR_TYPE_SHIFT) & XS1_VSR_TYPE_MASK)) +#define XS1_VSR_LENGTH_SHIFT 0xc +#define XS1_VSR_LENGTH_SIZE 0x4 +#define XS1_VSR_LENGTH_MASK \ + (((1 << XS1_VSR_LENGTH_SIZE) - 1) << XS1_VSR_LENGTH_SHIFT) +#define XS1_VSR_LENGTH(x) (((x)&XS1_VSR_LENGTH_MASK) >> XS1_VSR_LENGTH_SHIFT) +#define XS1_VSR_LENGTH_SET(x, v) \ + (((x) & ~XS1_VSR_LENGTH_MASK) | \ + (((v) << XS1_VSR_LENGTH_SHIFT) & XS1_VSR_LENGTH_MASK)) + +#endif + +#define XS1_VSETC_SHIFT_NOSHIFT 0x0 +#define XS1_VSETC_SHIFT_SHIFTLEFT 0x1 +#define XS1_VSETC_SHIFT_SHIFTRIGHT 0x2 +#define XS1_VSETC_TYPE_INT32 0x0 +#define XS1_VSETC_TYPE_INT16 0x1 +#define XS1_VSETC_TYPE_INT8 0x2 + +#define XS1_NUM_WORDS_PER_VECTOR 0x8 + +/* End of xs3a_kernel.h */ + +#define XS3_VPU_VREG_WIDTH_BITS (XS1_NUM_WORDS_PER_VECTOR * XS1_ALL_BITS_SIZE) +#define XS3_VPU_VREG_WIDTH_BYTES (XS3_VPU_VREG_WIDTH_BITS >> 3) +#define XS3_VPU_VREG_WIDTH_WORDS (XS3_VPU_VREG_WIDTH_BYTES >> 2) + +#ifndef __ASSEMBLER__ + +C_API enum { + VEC_INT_32 = 0, /**< 0 */ + VEC_INT_16 = 1, /**< 1 */ + VEC_INT_8 = 2, /**< 2 */ + VEC_FLT_32 = 4, /**< 4 */ + VEC_FLT_16 = 5, /**< 5 */ + VEC_FLT_8 = 6, /**< 6 */ +}; + +C_API enum { + VEC_SH0 = 0, /**< 0 */ + VEC_SHL = 1, /**< 1 */ + VEC_SHR = 2, /**< 2 */ +}; + +/** + * The saturation bounds for signed integers in each VPU operating mode. + */ +C_API enum { + VPU_INT8_MAX = 0x7F, /**< 0x7F */ + VPU_INT8_MIN = -0x7F, /**< -0x7F */ + + VPU_INT16_MAX = 0x7FFF, /**< 0x7FFF */ + VPU_INT16_MIN = -0x7FFF, /**< -0x7FFF */ + + VPU_INT32_MAX = 0x7FFFFFFF, /**< 0x7FFFFFFF */ + VPU_INT32_MIN = -0x7FFFFFFF, /**< -0x7FFFFFFF */ +}; + +/** + * Number of accumulator bits in each operating mode. + * + * In each operating mode, the VLMACC, VLMACCR and VLSAT instructions operate on + * an array of accumulators in the vector registers vR and vD. In each case, the + * most significant bits are stored in vD, and the least significant bits are + * stored in vR. + */ +C_API enum { + VPU_INT8_ACC_SIZE = 32, /**< 32 */ + VPU_INT16_ACC_SIZE = 32, /**< 32 */ + VPU_INT32_ACC_SIZE = 40, /**< 40 */ +}; + +/** + * When vD and vR contain accumulators, the values in this enum indicate how + * many least significant bits are stored in vR, with the remaining bits stored + * in vD. + */ +C_API enum { + VPU_INT8_ACC_VR_BITS = 16, /**< 16 */ + VPU_INT16_ACC_VR_BITS = 16, /**< 16 */ + VPU_INT32_ACC_VR_BITS = 32, /**< 32 */ +}; +/** + * When vD and vR contain accumulators, the values in this enum can be used to + * mask off the bits of the accumulator value which correspond to the portion in + * vR. + */ +C_API enum { + VPU_INT8_ACC_VR_MASK = 0xFFFF, /**< 0xFFFF */ + VPU_INT16_ACC_VR_MASK = 0xFFFF, /**< 0xFFFF */ + VPU_INT32_ACC_VR_MASK = 0xFFFFFFFF, /**< 0xFFFFFFFF */ +}; + +/** + * Integer type which fits a single accumulator (32-bits) corresponding to the + * 8-bit VPU mode. + */ +C_API typedef int32_t vpu_int8_acc_t; + +/** + * Integer type which fits a single accumulator (32-bits) corresponding to the + * 16-bit VPU mode. + */ +C_API typedef int32_t vpu_int16_acc_t; + +/** + * Integer type which fits a single accumulator (40-bits) corresponding to the + * 32-bit VPU mode. + */ +C_API typedef int64_t vpu_int32_acc_t; + +/** + * The number of elements which fit into a vector register for each operating + * mode. + * + * This is also the number of elements which are operated on in the following + * instructions: VDEPTH1, VDEPTH16, VDEPTH8, VLADD, VLADDD, VLASHR, VLMACCR, + * VLMUL, VLSUB, VPOS, VSIGN + * + */ +C_API enum { + VPU_INT8_EPV = 32, /**< 32 */ + VPU_INT16_EPV = 16, /**< 16 */ + VPU_INT32_EPV = 8, /**< 8 */ +}; + +/** + * log-base-2 of the corresponding VPU_INT*_EPV values. + */ +C_API enum { + VPU_INT8_EPV_LOG2 = 5, /**< 5 */ + VPU_INT16_EPV_LOG2 = 4, /**< 4 */ + VPU_INT32_EPV_LOG2 = 3, /**< 3 */ +}; + +/** + * The number of accumulators, spread across vR and vD, in each operating mode. + * + * This is also the number of elements consumed (number of multiplies) by the + * VLMACC instruction. + */ +C_API enum { + VPU_BIN_ACC_PERIOD = 16, /**< 16 */ + VPU_INT8_ACC_PERIOD = 16, /**< 16 */ + VPU_INT16_ACC_PERIOD = 16, /**< 16 */ + VPU_INT32_ACC_PERIOD = 8, /**< 8 */ +}; + +/** + * log-base-2 of the corresponding VPU_INT*_ACC_PERIOD values. + */ +C_API enum { + VPU_INT8_ACC_PERIOD_LOG2 = 4, /**< 4 */ + VPU_INT16_ACC_PERIOD_LOG2 = 4, /**< 4 */ + VPU_INT32_ACC_PERIOD_LOG2 = 3, /**< 3 */ +}; + +/** + * The number of elements consumed by a VLMACC instruction in each operating + * mode. In other words, the number of simultaneous multiply-accumulates + * performed by the VLMACC instruction. + */ +C_API enum { + VPU_INT8_VLMACC_ELMS = 16, /**< 16 */ + VPU_INT16_VLMACC_ELMS = 16, /**< 16 */ + VPU_INT32_VLMACC_ELMS = 8, /**< 8 */ +}; + +/** + * log-base-2 of the corresponding VPU_INT*_VLMACC_ELMS values. + */ +C_API enum { + VPU_INT8_VLMACC_ELMS_LOG2 = 4, /**< 4 */ + VPU_INT16_VLMACC_ELMS_LOG2 = 4, /**< 4 */ + VPU_INT32_VLMACC_ELMS_LOG2 = 3, /**< 3 */ +}; + +#endif //__ASM__ + +#endif // XS3_VPU_H_ diff --git a/new_ai_tools/include/lib_nn/api/xs3a_registers.h b/new_ai_tools/include/lib_nn/api/xs3a_registers.h new file mode 100644 index 00000000..10131171 --- /dev/null +++ b/new_ai_tools/include/lib_nn/api/xs3a_registers.h @@ -0,0 +1,2869 @@ +/* + * + * Copyright XMOS - (c) 2007 - 2018 + * + * AUTOGENERATED - DO NOT EDIT + * by lib_xmosutils/Scripts/CreateXS1.pl + * + */ +#ifndef _xs3a_registers_h_ +#define _xs3a_registers_h_ +#define XS1_PS_RAM_BASE 0xb +#define XS1_PS_VECTOR_BASE 0x10b +#define XS1_PS_XCORE_CTRL0 0x20b +#define XS1_PS_BOOT_CONFIG 0x30b +#define XS1_PS_BOOT_STATUS 0x40b +#define XS1_PS_SECURITY_CONFIG 0x50b +#define XS1_PS_RING_OSC_CTRL 0x60b +#define XS1_PS_RING_OSC_DATA0 0x70b +#define XS1_PS_RING_OSC_DATA1 0x80b +#define XS1_PS_RING_OSC_DATA2 0x90b +#define XS1_PS_RING_OSC_DATA3 0xa0b +#define XS1_PS_UNAVAILABLE_RESOURCE 0xb0b +#define XS1_PS_RAM_SIZE 0xc0b +#define XS1_PS_ROM_RMA 0xf0b +#define XS1_PS_DBG_SSR 0x100b +#define XS1_PS_DBG_SPC 0x110b +#define XS1_PS_DBG_SSP 0x120b +#define XS1_PS_DBG_T_NUM_NUM 0x13 +#define XS1_PS_DBG_T_NUM 0x130b +#define XS1_PS_DBG_T_REG 0x140b +#define XS1_PS_DBG_TYPE 0x150b +#define XS1_PS_DBG_DATA 0x160b +#define XS1_PS_DBG_RUN_CTRL 0x180b +#define XS1_PS_DBG_SCRATCH_0 0x200b +#define XS1_PS_DBG_SCRATCH_1 0x210b +#define XS1_PS_DBG_SCRATCH_2 0x220b +#define XS1_PS_DBG_SCRATCH_3 0x230b +#define XS1_PS_DBG_SCRATCH_4 0x240b +#define XS1_PS_DBG_SCRATCH_5 0x250b +#define XS1_PS_DBG_SCRATCH_6 0x260b +#define XS1_PS_DBG_SCRATCH_7 0x270b +#define XS1_PS_DBG_IBREAK_ADDR_0 0x300b +#define XS1_NUM_PS_DBG_IBREAK_ADDR 0x4 +#define XS1_PS_DBG_IBREAK_ADDR_1 0x310b +#define XS1_PS_DBG_IBREAK_ADDR_2 0x320b +#define XS1_PS_DBG_IBREAK_ADDR_3 0x330b +#define XS1_PS_DBG_IBREAK_CTRL_0 0x400b +#define XS1_NUM_PS_DBG_IBREAK_CTRL 0x4 +#define XS1_PS_DBG_IBREAK_CTRL_1 0x410b +#define XS1_PS_DBG_IBREAK_CTRL_2 0x420b +#define XS1_PS_DBG_IBREAK_CTRL_3 0x430b +#define XS1_PS_DBG_DWATCH_ADDR1_0 0x500b +#define XS1_NUM_PS_DBG_DWATCH_ADDR1 0x4 +#define XS1_PS_DBG_DWATCH_ADDR1_1 0x510b +#define XS1_PS_DBG_DWATCH_ADDR1_2 0x520b +#define XS1_PS_DBG_DWATCH_ADDR1_3 0x530b +#define XS1_PS_DBG_DWATCH_ADDR2_0 0x600b +#define XS1_NUM_PS_DBG_DWATCH_ADDR2 0x4 +#define XS1_PS_DBG_DWATCH_ADDR2_1 0x610b +#define XS1_PS_DBG_DWATCH_ADDR2_2 0x620b +#define XS1_PS_DBG_DWATCH_ADDR2_3 0x630b +#define XS1_PS_DBG_DWATCH_CTRL_0 0x700b +#define XS1_NUM_PS_DBG_DWATCH_CTRL 0x4 +#define XS1_PS_DBG_DWATCH_CTRL_1 0x710b +#define XS1_PS_DBG_DWATCH_CTRL_2 0x720b +#define XS1_PS_DBG_DWATCH_CTRL_3 0x730b +#define XS1_PS_DBG_RWATCH_ADDR1_0 0x800b +#define XS1_NUM_PS_DBG_RWATCH_ADDR1 0x4 +#define XS1_PS_DBG_RWATCH_ADDR1_1 0x810b +#define XS1_PS_DBG_RWATCH_ADDR1_2 0x820b +#define XS1_PS_DBG_RWATCH_ADDR1_3 0x830b +#define XS1_PS_DBG_RWATCH_ADDR2_0 0x900b +#define XS1_NUM_PS_DBG_RWATCH_ADDR2 0x4 +#define XS1_PS_DBG_RWATCH_ADDR2_1 0x910b +#define XS1_PS_DBG_RWATCH_ADDR2_2 0x920b +#define XS1_PS_DBG_RWATCH_ADDR2_3 0x930b +#define XS1_PS_DBG_RWATCH_CTRL_0 0x9c0b +#define XS1_NUM_PS_DBG_RWATCH_CTRL 0x4 +#define XS1_PS_DBG_RWATCH_CTRL_1 0x9d0b +#define XS1_PS_DBG_RWATCH_CTRL_2 0x9e0b +#define XS1_PS_DBG_RWATCH_CTRL_3 0x9f0b +#define XS1_PS_CACHE_MISS_CNT 0xa00b +#define XS1_PS_CACHE_ACCESS_CNT 0xa10b +#define XS1_SSWITCH_DEVICE_ID0_NUM 0x0 +#define XS1_SSWITCH_DEVICE_ID1_NUM 0x1 +#define XS1_SSWITCH_DEVICE_ID2_NUM 0x2 +#define XS1_SSWITCH_DEVICE_ID3_NUM 0x3 +#define XS1_SSWITCH_NODE_CONFIG_NUM 0x4 +#define XS1_SSWITCH_NODE_ID_NUM 0x5 +#define XS1_SSWITCH_PLL_CTL_NUM 0x6 +#define XS1_SSWITCH_CLK_DIVIDER_NUM 0x7 +#define XS1_SSWITCH_REF_CLK_DIVIDER_NUM 0x8 +#define XS1_SSWITCH_JTAG_DEVICE_ID_NUM 0x9 +#define XS1_SSWITCH_JTAG_USERCODE_NUM 0xa +#define XS1_SSWITCH_DDR_CLK_DIVIDER_NUM 0xb +#define XS1_SSWITCH_DIMENSION_DIRECTION0_NUM 0xc +#define XS1_SSWITCH_DIMENSION_DIRECTION1_NUM 0xd +#define XS1_SSWITCH_SS_APP_CLK_DIVIDER_NUM 0xe +#define XS1_SSWITCH_SS_APP_PLL_CTL_NUM 0xf +#define XS1_SSWITCH_XCORE0_GLOBAL_DEBUG_CONFIG_NUM 0x10 +#define XS1_SSWITCH_XCORE1_GLOBAL_DEBUG_CONFIG_NUM 0x11 +#define XS1_SSWITCH_SS_APP_PLL_FRAC_N_DIVIDER_NUM 0x12 +#define XS1_SSWITCH_MIPI_CLK_DIVIDER_NUM 0x14 +#define XS1_SSWITCH_MIPI_CFG_CLK_DIVIDER_NUM 0x15 +#define XS1_SSWITCH_GLOBAL_DEBUG_SOURCE_NUM 0x1f +#define XS1_SSWITCH_SLINK_0_NUM 0x20 +#define XS1_NUM_SSWITCH_SLINK 0x9 +#define XS1_SSWITCH_SLINK_1_NUM 0x21 +#define XS1_SSWITCH_SLINK_2_NUM 0x22 +#define XS1_SSWITCH_SLINK_3_NUM 0x23 +#define XS1_SSWITCH_SLINK_4_NUM 0x24 +#define XS1_SSWITCH_SLINK_5_NUM 0x25 +#define XS1_SSWITCH_SLINK_6_NUM 0x26 +#define XS1_SSWITCH_SLINK_7_NUM 0x27 +#define XS1_SSWITCH_SLINK_8_NUM 0x28 +#define XS1_SSWITCH_PLINK_0_NUM 0x40 +#define XS1_NUM_SSWITCH_PLINK 0x8 +#define XS1_SSWITCH_PLINK_1_NUM 0x41 +#define XS1_SSWITCH_PLINK_2_NUM 0x42 +#define XS1_SSWITCH_PLINK_3_NUM 0x43 +#define XS1_SSWITCH_PLINK_4_NUM 0x44 +#define XS1_SSWITCH_PLINK_5_NUM 0x45 +#define XS1_SSWITCH_PLINK_6_NUM 0x46 +#define XS1_SSWITCH_PLINK_7_NUM 0x47 +#define XS1_SSWITCH_XLINK_0_NUM 0x80 +#define XS1_NUM_SSWITCH_XLINK 0x9 +#define XS1_SSWITCH_XLINK_1_NUM 0x81 +#define XS1_SSWITCH_XLINK_2_NUM 0x82 +#define XS1_SSWITCH_XLINK_3_NUM 0x83 +#define XS1_SSWITCH_XLINK_4_NUM 0x84 +#define XS1_SSWITCH_XLINK_5_NUM 0x85 +#define XS1_SSWITCH_XLINK_6_NUM 0x86 +#define XS1_SSWITCH_XLINK_7_NUM 0x87 +#define XS1_SSWITCH_XLINK_8_NUM 0x88 +#define XS1_SSWITCH_XSTATIC_0_NUM 0xa0 +#define XS1_NUM_SSWITCH_XSTATIC 0x8 +#define XS1_SSWITCH_XSTATIC_1_NUM 0xa1 +#define XS1_SSWITCH_XSTATIC_2_NUM 0xa2 +#define XS1_SSWITCH_XSTATIC_3_NUM 0xa3 +#define XS1_SSWITCH_XSTATIC_4_NUM 0xa4 +#define XS1_SSWITCH_XSTATIC_5_NUM 0xa5 +#define XS1_SSWITCH_XSTATIC_6_NUM 0xa6 +#define XS1_SSWITCH_XSTATIC_7_NUM 0xa7 +#define XS1_SSWITCH_USB_XCFGI_REG0_NUM 0xf000 +#define XS1_SSWITCH_USB_XCFGI_REG1_NUM 0xf001 +#define XS1_SSWITCH_USB_XCFGI_REG2_NUM 0xf002 +#define XS1_SSWITCH_USB_XCFG_COARSE_TUNE_NUM 0xf003 +#define XS1_SSWITCH_USB_XCFG_FINE_TUNE_NUM 0xf004 +#define XS1_SSWITCH_USB_XCFG_LOCK_RANGE_MAX_NUM 0xf005 +#define XS1_SSWITCH_USB_XCFG_LOCK_RANGE_MIN_NUM 0xf006 +#define XS1_SSWITCH_USB_PHY_CFG0_NUM 0xf008 +#define XS1_SSWITCH_USB_PHY_CFG1_NUM 0xf009 +#define XS1_SSWITCH_USB_PHY_CFG2_NUM 0xf00a +#define XS1_SSWITCH_USB_PHY_CFG3_NUM 0xf00b +#define XS1_SSWITCH_USB_SHIM_CFG_NUM 0xf00c +#define XS1_SSWITCH_USB_PHY_XCFGO_REG0_NUM 0xf010 +#define XS1_SSWITCH_USB_PHY_STATUS_NUM 0xf011 +#define XS1_SSWITCH_WATCHDOG_CFG_NUM 0xf020 +#define XS1_SSWITCH_WATCHDOG_PRESCALER_NUM 0xf021 +#define XS1_SSWITCH_WATCHDOG_PRESCALER_WRAP_NUM 0xf022 +#define XS1_SSWITCH_WATCHDOG_COUNT_NUM 0xf023 +#define XS1_SSWITCH_WATCHDOG_STATUS_NUM 0xf024 +#define XS1_SSWITCH_MIPI_XCFGI_REG0_NUM 0xe000 +#define XS1_SSWITCH_MIPI_XCFGI_REG1_NUM 0xe001 +#define XS1_SSWITCH_MIPI_XCFGI_REG2_NUM 0xe002 +#define XS1_SSWITCH_MIPI_XCFGI_REG3_NUM 0xe003 +#define XS1_SSWITCH_MIPI_XCFGI_REG4_NUM 0xe004 +#define XS1_SSWITCH_MIPI_XCFGI_REG5_NUM 0xe005 +#define XS1_SSWITCH_MIPI_XCFGI_REG6_NUM 0xe006 +#define XS1_SSWITCH_MIPI_XCFGI_REG7_NUM 0xe007 +#define XS1_SSWITCH_MIPI_XCFGI_REG8_NUM 0xe008 +#define XS1_SSWITCH_MIPI_XCFGI_REG9_NUM 0xe009 +#define XS1_SSWITCH_MIPI_XCFGI_REG10_NUM 0xe00a +#define XS1_SSWITCH_MIPI_XCFGI_REG11_NUM 0xe00b +#define XS1_SSWITCH_MIPI_XCFGI_REG12_NUM 0xe00c +#define XS1_SSWITCH_MIPI_XCFGI_REG13_NUM 0xe00d +#define XS1_SSWITCH_MIPI_XCFGI_REG14_NUM 0xe00e +#define XS1_SSWITCH_MIPI_XCFGI_REG15_NUM 0xe00f +#define XS1_SSWITCH_MIPI_XCFGI_REG16_NUM 0xe010 +#define XS1_SSWITCH_MIPI_XCFGI_REG17_NUM 0xe011 +#define XS1_SSWITCH_MIPI_XCFGI_REG18_NUM 0xe012 +#define XS1_SSWITCH_MIPI_STATUS0_NUM 0xe013 +#define XS1_SSWITCH_MIPI_SHIM_STATUS_NUM 0xe014 +#define XS1_SSWITCH_MIPI_DPHY_CFG0_NUM 0xe018 +#define XS1_SSWITCH_MIPI_DPHY_CFG1_NUM 0xe019 +#define XS1_SSWITCH_MIPI_DPHY_CFG2_NUM 0xe01a +#define XS1_SSWITCH_MIPI_DPHY_CFG3_NUM 0xe01b +#define XS1_SSWITCH_MIPI_DPHY_CFG4_NUM 0xe01c +#define XS1_SSWITCH_MIPI_DPHY_CFG5_NUM 0xe01d +#define XS1_SSWITCH_MIPI_SHIM_CFG0_NUM 0xe01f +#define XS1_SSWITCH_MIPI_XCFGO_REG0_NUM 0xe020 +#define XS1_SSWITCH_MIPI_XCFGO_REG1_NUM 0xe021 +#define XS1_SSWITCH_MIPI_XCFGO_REG2_NUM 0xe022 +#define XS1_SSWITCH_LPDDR_IID_ENABLE_NUM 0xc000 +#define XS1_SSWITCH_LPDDR_IID_0_7_NUM 0xc001 +#define XS1_SSWITCH_LPDDR_IID_8_15_NUM 0xc002 +#define XS1_SSWITCH_LPDDR_QUEUE_CONT_NUM 0xc003 +#define XS1_SSWITCH_LPDDR_RO_COMMAND_QUEUE_PRIORITY_NUM 0xc008 +#define XS1_SSWITCH_LPDDR_RW_COMMAND_QUEUE_PRIORITY_NUM 0xc009 +#define XS1_SSWITCH_LPDDR_ARBITRATION_TIMEOUT_NUM 0xc00a +#define XS1_SSWITCH_LPDDR_ARBITRATION_MTG_COMMAND_NUM 0xc010 +#define XS1_SSWITCH_LPDDR_DLL_CONTROL_NUM 0xc014 +#define XS1_SSWITCH_LPDDR_DLL_MEASUREMENT_STATUS_NUM 0xc015 +#define XS1_SSWITCH_LPDDR_DLL_MANUAL_CONTROL_NUM 0xc016 +#define XS1_SSWITCH_LPDDR_DLL_PHY_CALIBRATION_DATA_NUM 0xc017 +#define XS1_SSWITCH_LPDDR_PHY_CONTROL_NUM 0xc01d +#define XS1_SSWITCH_LPDDR_LMR_OPCODE_NUM 0xc01e +#define XS1_SSWITCH_LPDDR_EMR_OPCODE_NUM 0xc01f +#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_CONF_0_NUM 0xc020 +#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_CONF_1_NUM 0xc021 +#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_STATUS_NUM 0xc022 +#define XS1_SSWITCH_PADCTRL_CLK_NUM 0xd000 +#define XS1_SSWITCH_PADCTRL_CKE_NUM 0xd001 +#define XS1_SSWITCH_PADCTRL_CS_N_NUM 0xd002 +#define XS1_SSWITCH_PADCTRL_WE_N_NUM 0xd003 +#define XS1_SSWITCH_PADCTRL_CAS_N_NUM 0xd004 +#define XS1_SSWITCH_PADCTRL_RAS_N_NUM 0xd005 +#define XS1_SSWITCH_PADCTRL_ADDR_NUM 0xd006 +#define XS1_SSWITCH_PADCTRL_BA_NUM 0xd007 +#define XS1_SSWITCH_PADCTRL_DQ_NUM 0xd008 +#define XS1_SSWITCH_PADCTRL_DQS_NUM 0xd009 +#define XS1_SSWITCH_PADCTRL_DM_NUM 0xd00a +#define XS1_PSWITCH_DEVICE_ID0_NUM 0x0 +#define XS1_PSWITCH_DEVICE_ID1_NUM 0x1 +#define XS1_PSWITCH_DEVICE_ID2_NUM 0x2 +#define XS1_PSWITCH_DEVICE_ID3_NUM 0x3 +#define XS1_PSWITCH_DBG_CTRL_NUM 0x4 +#define XS1_PSWITCH_DBG_INT_NUM 0x5 +#define XS1_PSWITCH_PLL_CLK_DIVIDER_NUM 0x6 +#define XS1_PSWITCH_SECU_CONFIG_NUM 0x7 +#define XS1_PSWITCH_DBG_SCRATCH_0_NUM 0x20 +#define XS1_NUM_PSWITCH_DBG_SCRATCH 0x8 +#define XS1_PSWITCH_DBG_SCRATCH_1_NUM 0x21 +#define XS1_PSWITCH_DBG_SCRATCH_2_NUM 0x22 +#define XS1_PSWITCH_DBG_SCRATCH_3_NUM 0x23 +#define XS1_PSWITCH_DBG_SCRATCH_4_NUM 0x24 +#define XS1_PSWITCH_DBG_SCRATCH_5_NUM 0x25 +#define XS1_PSWITCH_DBG_SCRATCH_6_NUM 0x26 +#define XS1_PSWITCH_DBG_SCRATCH_7_NUM 0x27 +#define XS1_PSWITCH_T0_PC_NUM 0x40 +#define XS1_PSWITCH_T1_PC_NUM 0x41 +#define XS1_PSWITCH_T2_PC_NUM 0x42 +#define XS1_PSWITCH_T3_PC_NUM 0x43 +#define XS1_PSWITCH_T4_PC_NUM 0x44 +#define XS1_PSWITCH_T5_PC_NUM 0x45 +#define XS1_PSWITCH_T6_PC_NUM 0x46 +#define XS1_PSWITCH_T7_PC_NUM 0x47 +#define XS1_PSWITCH_T0_SR_NUM 0x60 +#define XS1_PSWITCH_T1_SR_NUM 0x61 +#define XS1_PSWITCH_T2_SR_NUM 0x62 +#define XS1_PSWITCH_T3_SR_NUM 0x63 +#define XS1_PSWITCH_T4_SR_NUM 0x64 +#define XS1_PSWITCH_T5_SR_NUM 0x65 +#define XS1_PSWITCH_T6_SR_NUM 0x66 +#define XS1_PSWITCH_T7_SR_NUM 0x67 +#define XS1_ID_ID_SHIFT 0x0 +#define XS1_ID_ID_SIZE 0x6 +#define XS1_ID_ID_MASK (((1 << XS1_ID_ID_SIZE) - 1) << XS1_ID_ID_SHIFT) +#define XS1_ID_ID(x) (((x)&XS1_ID_ID_MASK) >> XS1_ID_ID_SHIFT) +#define XS1_ID_ID_SET(x, v) \ + (((x) & ~XS1_ID_ID_MASK) | (((v) << XS1_ID_ID_SHIFT) & XS1_ID_ID_MASK)) +#define XS1_EXCEPTION_TYPE_SHIFT 0x0 +#define XS1_EXCEPTION_TYPE_SIZE 0x5 +#define XS1_EXCEPTION_TYPE_MASK \ + (((1 << XS1_EXCEPTION_TYPE_SIZE) - 1) << XS1_EXCEPTION_TYPE_SHIFT) +#define XS1_EXCEPTION_TYPE(x) \ + (((x)&XS1_EXCEPTION_TYPE_MASK) >> XS1_EXCEPTION_TYPE_SHIFT) +#define XS1_EXCEPTION_TYPE_SET(x, v) \ + (((x) & ~XS1_EXCEPTION_TYPE_MASK) | \ + (((v) << XS1_EXCEPTION_TYPE_SHIFT) & XS1_EXCEPTION_TYPE_MASK)) +#define XS1_DBG_T_NUM_NUM_SHIFT 0x0 +#define XS1_DBG_T_NUM_NUM_SIZE 0x8 +#define XS1_DBG_T_NUM_NUM_MASK \ + (((1 << XS1_DBG_T_NUM_NUM_SIZE) - 1) << XS1_DBG_T_NUM_NUM_SHIFT) +#define XS1_DBG_T_NUM_NUM(x) \ + (((x)&XS1_DBG_T_NUM_NUM_MASK) >> XS1_DBG_T_NUM_NUM_SHIFT) +#define XS1_DBG_T_NUM_NUM_SET(x, v) \ + (((x) & ~XS1_DBG_T_NUM_NUM_MASK) | \ + (((v) << XS1_DBG_T_NUM_NUM_SHIFT) & XS1_DBG_T_NUM_NUM_MASK)) +#define XS1_DBG_T_REG_REG_SHIFT 0x0 +#define XS1_DBG_T_REG_REG_SIZE 0x5 +#define XS1_DBG_T_REG_REG_MASK \ + (((1 << XS1_DBG_T_REG_REG_SIZE) - 1) << XS1_DBG_T_REG_REG_SHIFT) +#define XS1_DBG_T_REG_REG(x) \ + (((x)&XS1_DBG_T_REG_REG_MASK) >> XS1_DBG_T_REG_REG_SHIFT) +#define XS1_DBG_T_REG_REG_SET(x, v) \ + (((x) & ~XS1_DBG_T_REG_REG_MASK) | \ + (((v) << XS1_DBG_T_REG_REG_SHIFT) & XS1_DBG_T_REG_REG_MASK)) +#define XS1_BRK_ENABLE_SHIFT 0x0 +#define XS1_BRK_ENABLE_SIZE 0x1 +#define XS1_BRK_ENABLE_MASK \ + (((1 << XS1_BRK_ENABLE_SIZE) - 1) << XS1_BRK_ENABLE_SHIFT) +#define XS1_BRK_ENABLE(x) (((x)&XS1_BRK_ENABLE_MASK) >> XS1_BRK_ENABLE_SHIFT) +#define XS1_BRK_ENABLE_SET(x, v) \ + (((x) & ~XS1_BRK_ENABLE_MASK) | \ + (((v) << XS1_BRK_ENABLE_SHIFT) & XS1_BRK_ENABLE_MASK)) +#define XS1_ALL_BITS_SHIFT 0x0 +#define XS1_ALL_BITS_SIZE 0x20 +#define XS1_ALL_BITS_MASK (((1 << XS1_ALL_BITS_SIZE) - 1) << XS1_ALL_BITS_SHIFT) +#define XS1_ALL_BITS(x) (((x)&XS1_ALL_BITS_MASK) >> XS1_ALL_BITS_SHIFT) +#define XS1_ALL_BITS_SET(x, v) \ + (((x) & ~XS1_ALL_BITS_MASK) | \ + (((v) << XS1_ALL_BITS_SHIFT) & XS1_ALL_BITS_MASK)) +#define XS1_KEP_ADDRESS_BITS_SHIFT 0x7 +#define XS1_KEP_ADDRESS_BITS_SIZE 0x19 +#define XS1_KEP_ADDRESS_BITS_MASK \ + (((1 << XS1_KEP_ADDRESS_BITS_SIZE) - 1) << XS1_KEP_ADDRESS_BITS_SHIFT) +#define XS1_KEP_ADDRESS_BITS(x) \ + (((x)&XS1_KEP_ADDRESS_BITS_MASK) >> XS1_KEP_ADDRESS_BITS_SHIFT) +#define XS1_KEP_ADDRESS_BITS_SET(x, v) \ + (((x) & ~XS1_KEP_ADDRESS_BITS_MASK) | \ + (((v) << XS1_KEP_ADDRESS_BITS_SHIFT) & XS1_KEP_ADDRESS_BITS_MASK)) +#define XS1_WORD_ADDRESS_BITS_SHIFT 0x2 +#define XS1_WORD_ADDRESS_BITS_SIZE 0x1e +#define XS1_WORD_ADDRESS_BITS_MASK \ + (((1 << XS1_WORD_ADDRESS_BITS_SIZE) - 1) << XS1_WORD_ADDRESS_BITS_SHIFT) +#define XS1_WORD_ADDRESS_BITS(x) \ + (((x)&XS1_WORD_ADDRESS_BITS_MASK) >> XS1_WORD_ADDRESS_BITS_SHIFT) +#define XS1_WORD_ADDRESS_BITS_SET(x, v) \ + (((x) & ~XS1_WORD_ADDRESS_BITS_MASK) | \ + (((v) << XS1_WORD_ADDRESS_BITS_SHIFT) & XS1_WORD_ADDRESS_BITS_MASK)) +#define XS1_VECTOR_BASE_SHIFT 0x13 +#define XS1_VECTOR_BASE_SIZE 0xd +#define XS1_VECTOR_BASE_MASK \ + (((1 << XS1_VECTOR_BASE_SIZE) - 1) << XS1_VECTOR_BASE_SHIFT) +#define XS1_VECTOR_BASE(x) (((x)&XS1_VECTOR_BASE_MASK) >> XS1_VECTOR_BASE_SHIFT) +#define XS1_VECTOR_BASE_SET(x, v) \ + (((x) & ~XS1_VECTOR_BASE_MASK) | \ + (((v) << XS1_VECTOR_BASE_SHIFT) & XS1_VECTOR_BASE_MASK)) +#define XS1_IBRK_CONDITION_SHIFT 0x1 +#define XS1_IBRK_CONDITION_SIZE 0x1 +#define XS1_IBRK_CONDITION_MASK \ + (((1 << XS1_IBRK_CONDITION_SIZE) - 1) << XS1_IBRK_CONDITION_SHIFT) +#define XS1_IBRK_CONDITION(x) \ + (((x)&XS1_IBRK_CONDITION_MASK) >> XS1_IBRK_CONDITION_SHIFT) +#define XS1_IBRK_CONDITION_SET(x, v) \ + (((x) & ~XS1_IBRK_CONDITION_MASK) | \ + (((v) << XS1_IBRK_CONDITION_SHIFT) & XS1_IBRK_CONDITION_MASK)) +#define XS1_DBRK_CONDITION_SHIFT 0x1 +#define XS1_DBRK_CONDITION_SIZE 0x1 +#define XS1_DBRK_CONDITION_MASK \ + (((1 << XS1_DBRK_CONDITION_SIZE) - 1) << XS1_DBRK_CONDITION_SHIFT) +#define XS1_DBRK_CONDITION(x) \ + (((x)&XS1_DBRK_CONDITION_MASK) >> XS1_DBRK_CONDITION_SHIFT) +#define XS1_DBRK_CONDITION_SET(x, v) \ + (((x) & ~XS1_DBRK_CONDITION_MASK) | \ + (((v) << XS1_DBRK_CONDITION_SHIFT) & XS1_DBRK_CONDITION_MASK)) +#define XS1_RBRK_CONDITION_SHIFT 0x1 +#define XS1_RBRK_CONDITION_SIZE 0x1 +#define XS1_RBRK_CONDITION_MASK \ + (((1 << XS1_RBRK_CONDITION_SIZE) - 1) << XS1_RBRK_CONDITION_SHIFT) +#define XS1_RBRK_CONDITION(x) \ + (((x)&XS1_RBRK_CONDITION_MASK) >> XS1_RBRK_CONDITION_SHIFT) +#define XS1_RBRK_CONDITION_SET(x, v) \ + (((x) & ~XS1_RBRK_CONDITION_MASK) | \ + (((v) << XS1_RBRK_CONDITION_SHIFT) & XS1_RBRK_CONDITION_MASK)) +#define XS1_BRK_LOAD_SHIFT 0x2 +#define XS1_BRK_LOAD_SIZE 0x1 +#define XS1_BRK_LOAD_MASK (((1 << XS1_BRK_LOAD_SIZE) - 1) << XS1_BRK_LOAD_SHIFT) +#define XS1_BRK_LOAD(x) (((x)&XS1_BRK_LOAD_MASK) >> XS1_BRK_LOAD_SHIFT) +#define XS1_BRK_LOAD_SET(x, v) \ + (((x) & ~XS1_BRK_LOAD_MASK) | \ + (((v) << XS1_BRK_LOAD_SHIFT) & XS1_BRK_LOAD_MASK)) +#define XS1_BRK_THREADS_SHIFT 0x10 +#define XS1_BRK_THREADS_SIZE 0x8 +#define XS1_BRK_THREADS_MASK \ + (((1 << XS1_BRK_THREADS_SIZE) - 1) << XS1_BRK_THREADS_SHIFT) +#define XS1_BRK_THREADS(x) (((x)&XS1_BRK_THREADS_MASK) >> XS1_BRK_THREADS_SHIFT) +#define XS1_BRK_THREADS_SET(x, v) \ + (((x) & ~XS1_BRK_THREADS_MASK) | \ + (((v) << XS1_BRK_THREADS_SHIFT) & XS1_BRK_THREADS_MASK)) +#define XS1_DBG_TYPE_CAUSE_SHIFT 0x0 +#define XS1_DBG_TYPE_CAUSE_SIZE 0x3 +#define XS1_DBG_TYPE_CAUSE_MASK \ + (((1 << XS1_DBG_TYPE_CAUSE_SIZE) - 1) << XS1_DBG_TYPE_CAUSE_SHIFT) +#define XS1_DBG_TYPE_CAUSE(x) \ + (((x)&XS1_DBG_TYPE_CAUSE_MASK) >> XS1_DBG_TYPE_CAUSE_SHIFT) +#define XS1_DBG_TYPE_CAUSE_SET(x, v) \ + (((x) & ~XS1_DBG_TYPE_CAUSE_MASK) | \ + (((v) << XS1_DBG_TYPE_CAUSE_SHIFT) & XS1_DBG_TYPE_CAUSE_MASK)) +#define XS1_DBG_TYPE_T_NUM_SHIFT 0x8 +#define XS1_DBG_TYPE_T_NUM_SIZE 0x8 +#define XS1_DBG_TYPE_T_NUM_MASK \ + (((1 << XS1_DBG_TYPE_T_NUM_SIZE) - 1) << XS1_DBG_TYPE_T_NUM_SHIFT) +#define XS1_DBG_TYPE_T_NUM(x) \ + (((x)&XS1_DBG_TYPE_T_NUM_MASK) >> XS1_DBG_TYPE_T_NUM_SHIFT) +#define XS1_DBG_TYPE_T_NUM_SET(x, v) \ + (((x) & ~XS1_DBG_TYPE_T_NUM_MASK) | \ + (((v) << XS1_DBG_TYPE_T_NUM_SHIFT) & XS1_DBG_TYPE_T_NUM_MASK)) +#define XS1_DBG_TYPE_HW_NUM_SHIFT 0x10 +#define XS1_DBG_TYPE_HW_NUM_SIZE 0x2 +#define XS1_DBG_TYPE_HW_NUM_MASK \ + (((1 << XS1_DBG_TYPE_HW_NUM_SIZE) - 1) << XS1_DBG_TYPE_HW_NUM_SHIFT) +#define XS1_DBG_TYPE_HW_NUM(x) \ + (((x)&XS1_DBG_TYPE_HW_NUM_MASK) >> XS1_DBG_TYPE_HW_NUM_SHIFT) +#define XS1_DBG_TYPE_HW_NUM_SET(x, v) \ + (((x) & ~XS1_DBG_TYPE_HW_NUM_MASK) | \ + (((v) << XS1_DBG_TYPE_HW_NUM_SHIFT) & XS1_DBG_TYPE_HW_NUM_MASK)) +#define XS1_DBG_RUN_CTRL_STOP_SHIFT 0x0 +#define XS1_DBG_RUN_CTRL_STOP_SIZE 0x8 +#define XS1_DBG_RUN_CTRL_STOP_MASK \ + (((1 << XS1_DBG_RUN_CTRL_STOP_SIZE) - 1) << XS1_DBG_RUN_CTRL_STOP_SHIFT) +#define XS1_DBG_RUN_CTRL_STOP(x) \ + (((x)&XS1_DBG_RUN_CTRL_STOP_MASK) >> XS1_DBG_RUN_CTRL_STOP_SHIFT) +#define XS1_DBG_RUN_CTRL_STOP_SET(x, v) \ + (((x) & ~XS1_DBG_RUN_CTRL_STOP_MASK) | \ + (((v) << XS1_DBG_RUN_CTRL_STOP_SHIFT) & XS1_DBG_RUN_CTRL_STOP_MASK)) +#define XS1_XCORE_CTRL0_USB_ENABLE_SHIFT 0x1 +#define XS1_XCORE_CTRL0_USB_ENABLE_SIZE 0x1 +#define XS1_XCORE_CTRL0_USB_ENABLE_MASK \ + (((1 << XS1_XCORE_CTRL0_USB_ENABLE_SIZE) - 1) \ + << XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) +#define XS1_XCORE_CTRL0_USB_ENABLE(x) \ + (((x)&XS1_XCORE_CTRL0_USB_ENABLE_MASK) >> XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) +#define XS1_XCORE_CTRL0_USB_ENABLE_SET(x, v) \ + (((x) & ~XS1_XCORE_CTRL0_USB_ENABLE_MASK) | \ + (((v) << XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) & \ + XS1_XCORE_CTRL0_USB_ENABLE_MASK)) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT 0x4 +#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SIZE 0x1 +#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK \ + (((1 << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SIZE) - 1) \ + << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN(x) \ + (((x)&XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK) >> \ + XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SET(x, v) \ + (((x) & ~XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK) | \ + (((v) << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) & \ + XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK)) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT 0x5 +#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SIZE 0x1 +#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK \ + (((1 << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SIZE) - 1) \ + << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN(x) \ + (((x)&XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK) >> \ + XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) +#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SET(x, v) \ + (((x) & ~XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK) | \ + (((v) << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) & \ + XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK)) +#define XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT 0x8 +#define XS1_BOOT_CONFIG_SECURE_BOOT_SIZE 0x1 +#define XS1_BOOT_CONFIG_SECURE_BOOT_MASK \ + (((1 << XS1_BOOT_CONFIG_SECURE_BOOT_SIZE) - 1) \ + << XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) +#define XS1_BOOT_CONFIG_SECURE_BOOT(x) \ + (((x)&XS1_BOOT_CONFIG_SECURE_BOOT_MASK) >> XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) +#define XS1_BOOT_CONFIG_SECURE_BOOT_SET(x, v) \ + (((x) & ~XS1_BOOT_CONFIG_SECURE_BOOT_MASK) | \ + (((v) << XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) & \ + XS1_BOOT_CONFIG_SECURE_BOOT_MASK)) +#define XS1_BOOT_CONFIG_PROCESSOR_SHIFT 0x10 +#define XS1_BOOT_CONFIG_PROCESSOR_SIZE 0x8 +#define XS1_BOOT_CONFIG_PROCESSOR_MASK \ + (((1 << XS1_BOOT_CONFIG_PROCESSOR_SIZE) - 1) \ + << XS1_BOOT_CONFIG_PROCESSOR_SHIFT) +#define XS1_BOOT_CONFIG_PROCESSOR(x) \ + (((x)&XS1_BOOT_CONFIG_PROCESSOR_MASK) >> XS1_BOOT_CONFIG_PROCESSOR_SHIFT) +#define XS1_BOOT_CONFIG_PROCESSOR_SET(x, v) \ + (((x) & ~XS1_BOOT_CONFIG_PROCESSOR_MASK) | \ + (((v) << XS1_BOOT_CONFIG_PROCESSOR_SHIFT) & \ + XS1_BOOT_CONFIG_PROCESSOR_MASK)) +#define XS1_BOOT_STATUS_LEDS_SHIFT 0x0 +#define XS1_BOOT_STATUS_LEDS_SIZE 0x4 +#define XS1_BOOT_STATUS_LEDS_MASK \ + (((1 << XS1_BOOT_STATUS_LEDS_SIZE) - 1) << XS1_BOOT_STATUS_LEDS_SHIFT) +#define XS1_BOOT_STATUS_LEDS(x) \ + (((x)&XS1_BOOT_STATUS_LEDS_MASK) >> XS1_BOOT_STATUS_LEDS_SHIFT) +#define XS1_BOOT_STATUS_LEDS_SET(x, v) \ + (((x) & ~XS1_BOOT_STATUS_LEDS_MASK) | \ + (((v) << XS1_BOOT_STATUS_LEDS_SHIFT) & XS1_BOOT_STATUS_LEDS_MASK)) +#define XS1_BOOT_STATUS_BITS_SHIFT 0x4 +#define XS1_BOOT_STATUS_BITS_SIZE 0x1c +#define XS1_BOOT_STATUS_BITS_MASK \ + (((1 << XS1_BOOT_STATUS_BITS_SIZE) - 1) << XS1_BOOT_STATUS_BITS_SHIFT) +#define XS1_BOOT_STATUS_BITS(x) \ + (((x)&XS1_BOOT_STATUS_BITS_MASK) >> XS1_BOOT_STATUS_BITS_SHIFT) +#define XS1_BOOT_STATUS_BITS_SET(x, v) \ + (((x) & ~XS1_BOOT_STATUS_BITS_MASK) | \ + (((v) << XS1_BOOT_STATUS_BITS_SHIFT) & XS1_BOOT_STATUS_BITS_MASK)) +#define XS1_RING_OSC_PERPH_ENABLE_SHIFT 0x0 +#define XS1_RING_OSC_PERPH_ENABLE_SIZE 0x1 +#define XS1_RING_OSC_PERPH_ENABLE_MASK \ + (((1 << XS1_RING_OSC_PERPH_ENABLE_SIZE) - 1) \ + << XS1_RING_OSC_PERPH_ENABLE_SHIFT) +#define XS1_RING_OSC_PERPH_ENABLE(x) \ + (((x)&XS1_RING_OSC_PERPH_ENABLE_MASK) >> XS1_RING_OSC_PERPH_ENABLE_SHIFT) +#define XS1_RING_OSC_PERPH_ENABLE_SET(x, v) \ + (((x) & ~XS1_RING_OSC_PERPH_ENABLE_MASK) | \ + (((v) << XS1_RING_OSC_PERPH_ENABLE_SHIFT) & \ + XS1_RING_OSC_PERPH_ENABLE_MASK)) +#define XS1_RING_OSC_CORE_ENABLE_SHIFT 0x1 +#define XS1_RING_OSC_CORE_ENABLE_SIZE 0x1 +#define XS1_RING_OSC_CORE_ENABLE_MASK \ + (((1 << XS1_RING_OSC_CORE_ENABLE_SIZE) - 1) << XS1_RING_OSC_CORE_ENABLE_SHIFT) +#define XS1_RING_OSC_CORE_ENABLE(x) \ + (((x)&XS1_RING_OSC_CORE_ENABLE_MASK) >> XS1_RING_OSC_CORE_ENABLE_SHIFT) +#define XS1_RING_OSC_CORE_ENABLE_SET(x, v) \ + (((x) & ~XS1_RING_OSC_CORE_ENABLE_MASK) | \ + (((v) << XS1_RING_OSC_CORE_ENABLE_SHIFT) & XS1_RING_OSC_CORE_ENABLE_MASK)) +#define XS1_RING_OSC_DATA_SHIFT 0x0 +#define XS1_RING_OSC_DATA_SIZE 0x10 +#define XS1_RING_OSC_DATA_MASK \ + (((1 << XS1_RING_OSC_DATA_SIZE) - 1) << XS1_RING_OSC_DATA_SHIFT) +#define XS1_RING_OSC_DATA(x) \ + (((x)&XS1_RING_OSC_DATA_MASK) >> XS1_RING_OSC_DATA_SHIFT) +#define XS1_RING_OSC_DATA_SET(x, v) \ + (((x) & ~XS1_RING_OSC_DATA_MASK) | \ + (((v) << XS1_RING_OSC_DATA_SHIFT) & XS1_RING_OSC_DATA_MASK)) +#define XS1_PLL_CLK_DIVIDER_SHIFT 0x0 +#define XS1_PLL_CLK_DIVIDER_SIZE 0x10 +#define XS1_PLL_CLK_DIVIDER_MASK \ + (((1 << XS1_PLL_CLK_DIVIDER_SIZE) - 1) << XS1_PLL_CLK_DIVIDER_SHIFT) +#define XS1_PLL_CLK_DIVIDER(x) \ + (((x)&XS1_PLL_CLK_DIVIDER_MASK) >> XS1_PLL_CLK_DIVIDER_SHIFT) +#define XS1_PLL_CLK_DIVIDER_SET(x, v) \ + (((x) & ~XS1_PLL_CLK_DIVIDER_MASK) | \ + (((v) << XS1_PLL_CLK_DIVIDER_SHIFT) & XS1_PLL_CLK_DIVIDER_MASK)) +#define XS1_PLL_CLK_DISABLE_SHIFT 0x1f +#define XS1_PLL_CLK_DISABLE_SIZE 0x1 +#define XS1_PLL_CLK_DISABLE_MASK \ + (((1 << XS1_PLL_CLK_DISABLE_SIZE) - 1) << XS1_PLL_CLK_DISABLE_SHIFT) +#define XS1_PLL_CLK_DISABLE(x) \ + (((x)&XS1_PLL_CLK_DISABLE_MASK) >> XS1_PLL_CLK_DISABLE_SHIFT) +#define XS1_PLL_CLK_DISABLE_SET(x, v) \ + (((x) & ~XS1_PLL_CLK_DISABLE_MASK) | \ + (((v) << XS1_PLL_CLK_DISABLE_SHIFT) & XS1_PLL_CLK_DISABLE_MASK)) +#define XS1_THREAD_CTRL0_INUSE_SHIFT 0x0 +#define XS1_THREAD_CTRL0_INUSE_SIZE 0x1 +#define XS1_THREAD_CTRL0_INUSE_MASK \ + (((1 << XS1_THREAD_CTRL0_INUSE_SIZE) - 1) << XS1_THREAD_CTRL0_INUSE_SHIFT) +#define XS1_THREAD_CTRL0_INUSE(x) \ + (((x)&XS1_THREAD_CTRL0_INUSE_MASK) >> XS1_THREAD_CTRL0_INUSE_SHIFT) +#define XS1_THREAD_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_THREAD_CTRL0_INUSE_MASK) | \ + (((v) << XS1_THREAD_CTRL0_INUSE_SHIFT) & XS1_THREAD_CTRL0_INUSE_MASK)) +#define XS1_THREAD_CTRL0_MSYNC_SHIFT 0x1 +#define XS1_THREAD_CTRL0_MSYNC_SIZE 0x1 +#define XS1_THREAD_CTRL0_MSYNC_MASK \ + (((1 << XS1_THREAD_CTRL0_MSYNC_SIZE) - 1) << XS1_THREAD_CTRL0_MSYNC_SHIFT) +#define XS1_THREAD_CTRL0_MSYNC(x) \ + (((x)&XS1_THREAD_CTRL0_MSYNC_MASK) >> XS1_THREAD_CTRL0_MSYNC_SHIFT) +#define XS1_THREAD_CTRL0_MSYNC_SET(x, v) \ + (((x) & ~XS1_THREAD_CTRL0_MSYNC_MASK) | \ + (((v) << XS1_THREAD_CTRL0_MSYNC_SHIFT) & XS1_THREAD_CTRL0_MSYNC_MASK)) +#define XS1_THREAD_CTRL0_SSYNC_SHIFT 0x2 +#define XS1_THREAD_CTRL0_SSYNC_SIZE 0x1 +#define XS1_THREAD_CTRL0_SSYNC_MASK \ + (((1 << XS1_THREAD_CTRL0_SSYNC_SIZE) - 1) << XS1_THREAD_CTRL0_SSYNC_SHIFT) +#define XS1_THREAD_CTRL0_SSYNC(x) \ + (((x)&XS1_THREAD_CTRL0_SSYNC_MASK) >> XS1_THREAD_CTRL0_SSYNC_SHIFT) +#define XS1_THREAD_CTRL0_SSYNC_SET(x, v) \ + (((x) & ~XS1_THREAD_CTRL0_SSYNC_MASK) | \ + (((v) << XS1_THREAD_CTRL0_SSYNC_SHIFT) & XS1_THREAD_CTRL0_SSYNC_MASK)) +#define XS1_THREAD_CTRL0_MASTER_SHIFT 0x8 +#define XS1_THREAD_CTRL0_MASTER_SIZE 0x8 +#define XS1_THREAD_CTRL0_MASTER_MASK \ + (((1 << XS1_THREAD_CTRL0_MASTER_SIZE) - 1) << XS1_THREAD_CTRL0_MASTER_SHIFT) +#define XS1_THREAD_CTRL0_MASTER(x) \ + (((x)&XS1_THREAD_CTRL0_MASTER_MASK) >> XS1_THREAD_CTRL0_MASTER_SHIFT) +#define XS1_THREAD_CTRL0_MASTER_SET(x, v) \ + (((x) & ~XS1_THREAD_CTRL0_MASTER_MASK) | \ + (((v) << XS1_THREAD_CTRL0_MASTER_SHIFT) & XS1_THREAD_CTRL0_MASTER_MASK)) +#define XS1_PORT_CTRL0_INUSE_SHIFT 0x0 +#define XS1_PORT_CTRL0_INUSE_SIZE 0x1 +#define XS1_PORT_CTRL0_INUSE_MASK \ + (((1 << XS1_PORT_CTRL0_INUSE_SIZE) - 1) << XS1_PORT_CTRL0_INUSE_SHIFT) +#define XS1_PORT_CTRL0_INUSE(x) \ + (((x)&XS1_PORT_CTRL0_INUSE_MASK) >> XS1_PORT_CTRL0_INUSE_SHIFT) +#define XS1_PORT_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_INUSE_MASK) | \ + (((v) << XS1_PORT_CTRL0_INUSE_SHIFT) & XS1_PORT_CTRL0_INUSE_MASK)) +#define XS1_PORT_CTRL0_IE_MODE_SHIFT 0x1 +#define XS1_PORT_CTRL0_IE_MODE_SIZE 0x1 +#define XS1_PORT_CTRL0_IE_MODE_MASK \ + (((1 << XS1_PORT_CTRL0_IE_MODE_SIZE) - 1) << XS1_PORT_CTRL0_IE_MODE_SHIFT) +#define XS1_PORT_CTRL0_IE_MODE(x) \ + (((x)&XS1_PORT_CTRL0_IE_MODE_MASK) >> XS1_PORT_CTRL0_IE_MODE_SHIFT) +#define XS1_PORT_CTRL0_IE_MODE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_IE_MODE_MASK) | \ + (((v) << XS1_PORT_CTRL0_IE_MODE_SHIFT) & XS1_PORT_CTRL0_IE_MODE_MASK)) +#define XS1_PORT_CTRL0_IE_ENABLED_SHIFT 0x2 +#define XS1_PORT_CTRL0_IE_ENABLED_SIZE 0x1 +#define XS1_PORT_CTRL0_IE_ENABLED_MASK \ + (((1 << XS1_PORT_CTRL0_IE_ENABLED_SIZE) - 1) \ + << XS1_PORT_CTRL0_IE_ENABLED_SHIFT) +#define XS1_PORT_CTRL0_IE_ENABLED(x) \ + (((x)&XS1_PORT_CTRL0_IE_ENABLED_MASK) >> XS1_PORT_CTRL0_IE_ENABLED_SHIFT) +#define XS1_PORT_CTRL0_IE_ENABLED_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_IE_ENABLED_MASK) | \ + (((v) << XS1_PORT_CTRL0_IE_ENABLED_SHIFT) & \ + XS1_PORT_CTRL0_IE_ENABLED_MASK)) +#define XS1_PORT_CTRL0_DIRECTION_SHIFT 0x3 +#define XS1_PORT_CTRL0_DIRECTION_SIZE 0x1 +#define XS1_PORT_CTRL0_DIRECTION_MASK \ + (((1 << XS1_PORT_CTRL0_DIRECTION_SIZE) - 1) << XS1_PORT_CTRL0_DIRECTION_SHIFT) +#define XS1_PORT_CTRL0_DIRECTION(x) \ + (((x)&XS1_PORT_CTRL0_DIRECTION_MASK) >> XS1_PORT_CTRL0_DIRECTION_SHIFT) +#define XS1_PORT_CTRL0_DIRECTION_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_DIRECTION_MASK) | \ + (((v) << XS1_PORT_CTRL0_DIRECTION_SHIFT) & XS1_PORT_CTRL0_DIRECTION_MASK)) +#define XS1_PORT_CTRL0_COND_SHIFT 0x4 +#define XS1_PORT_CTRL0_COND_SIZE 0x4 +#define XS1_PORT_CTRL0_COND_MASK \ + (((1 << XS1_PORT_CTRL0_COND_SIZE) - 1) << XS1_PORT_CTRL0_COND_SHIFT) +#define XS1_PORT_CTRL0_COND(x) \ + (((x)&XS1_PORT_CTRL0_COND_MASK) >> XS1_PORT_CTRL0_COND_SHIFT) +#define XS1_PORT_CTRL0_COND_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_COND_MASK) | \ + (((v) << XS1_PORT_CTRL0_COND_SHIFT) & XS1_PORT_CTRL0_COND_MASK)) +#define XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT 0x8 +#define XS1_PORT_CTRL0_MASTER_SLAVE_SIZE 0x1 +#define XS1_PORT_CTRL0_MASTER_SLAVE_MASK \ + (((1 << XS1_PORT_CTRL0_MASTER_SLAVE_SIZE) - 1) \ + << XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) +#define XS1_PORT_CTRL0_MASTER_SLAVE(x) \ + (((x)&XS1_PORT_CTRL0_MASTER_SLAVE_MASK) >> XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) +#define XS1_PORT_CTRL0_MASTER_SLAVE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_MASTER_SLAVE_MASK) | \ + (((v) << XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) & \ + XS1_PORT_CTRL0_MASTER_SLAVE_MASK)) +#define XS1_PORT_CTRL0_BUFFERS_SHIFT 0x9 +#define XS1_PORT_CTRL0_BUFFERS_SIZE 0x1 +#define XS1_PORT_CTRL0_BUFFERS_MASK \ + (((1 << XS1_PORT_CTRL0_BUFFERS_SIZE) - 1) << XS1_PORT_CTRL0_BUFFERS_SHIFT) +#define XS1_PORT_CTRL0_BUFFERS(x) \ + (((x)&XS1_PORT_CTRL0_BUFFERS_MASK) >> XS1_PORT_CTRL0_BUFFERS_SHIFT) +#define XS1_PORT_CTRL0_BUFFERS_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_BUFFERS_MASK) | \ + (((v) << XS1_PORT_CTRL0_BUFFERS_SHIFT) & XS1_PORT_CTRL0_BUFFERS_MASK)) +#define XS1_PORT_CTRL0_READY_MODE_SHIFT 0xa +#define XS1_PORT_CTRL0_READY_MODE_SIZE 0x2 +#define XS1_PORT_CTRL0_READY_MODE_MASK \ + (((1 << XS1_PORT_CTRL0_READY_MODE_SIZE) - 1) \ + << XS1_PORT_CTRL0_READY_MODE_SHIFT) +#define XS1_PORT_CTRL0_READY_MODE(x) \ + (((x)&XS1_PORT_CTRL0_READY_MODE_MASK) >> XS1_PORT_CTRL0_READY_MODE_SHIFT) +#define XS1_PORT_CTRL0_READY_MODE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_READY_MODE_MASK) | \ + (((v) << XS1_PORT_CTRL0_READY_MODE_SHIFT) & \ + XS1_PORT_CTRL0_READY_MODE_MASK)) +#define XS1_PORT_CTRL0_PORT_TYPE_SHIFT 0xc +#define XS1_PORT_CTRL0_PORT_TYPE_SIZE 0x2 +#define XS1_PORT_CTRL0_PORT_TYPE_MASK \ + (((1 << XS1_PORT_CTRL0_PORT_TYPE_SIZE) - 1) << XS1_PORT_CTRL0_PORT_TYPE_SHIFT) +#define XS1_PORT_CTRL0_PORT_TYPE(x) \ + (((x)&XS1_PORT_CTRL0_PORT_TYPE_MASK) >> XS1_PORT_CTRL0_PORT_TYPE_SHIFT) +#define XS1_PORT_CTRL0_PORT_TYPE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_PORT_TYPE_MASK) | \ + (((v) << XS1_PORT_CTRL0_PORT_TYPE_SHIFT) & XS1_PORT_CTRL0_PORT_TYPE_MASK)) +#define XS1_PORT_CTRL0_INVERT_SHIFT 0xe +#define XS1_PORT_CTRL0_INVERT_SIZE 0x1 +#define XS1_PORT_CTRL0_INVERT_MASK \ + (((1 << XS1_PORT_CTRL0_INVERT_SIZE) - 1) << XS1_PORT_CTRL0_INVERT_SHIFT) +#define XS1_PORT_CTRL0_INVERT(x) \ + (((x)&XS1_PORT_CTRL0_INVERT_MASK) >> XS1_PORT_CTRL0_INVERT_SHIFT) +#define XS1_PORT_CTRL0_INVERT_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_INVERT_MASK) | \ + (((v) << XS1_PORT_CTRL0_INVERT_SHIFT) & XS1_PORT_CTRL0_INVERT_MASK)) +#define XS1_PORT_CTRL0_SDELAY_SHIFT 0xf +#define XS1_PORT_CTRL0_SDELAY_SIZE 0x1 +#define XS1_PORT_CTRL0_SDELAY_MASK \ + (((1 << XS1_PORT_CTRL0_SDELAY_SIZE) - 1) << XS1_PORT_CTRL0_SDELAY_SHIFT) +#define XS1_PORT_CTRL0_SDELAY(x) \ + (((x)&XS1_PORT_CTRL0_SDELAY_MASK) >> XS1_PORT_CTRL0_SDELAY_SHIFT) +#define XS1_PORT_CTRL0_SDELAY_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_SDELAY_MASK) | \ + (((v) << XS1_PORT_CTRL0_SDELAY_SHIFT) & XS1_PORT_CTRL0_SDELAY_MASK)) +#define XS1_PORT_CTRL0_EV_VALID_SHIFT 0x16 +#define XS1_PORT_CTRL0_EV_VALID_SIZE 0x1 +#define XS1_PORT_CTRL0_EV_VALID_MASK \ + (((1 << XS1_PORT_CTRL0_EV_VALID_SIZE) - 1) << XS1_PORT_CTRL0_EV_VALID_SHIFT) +#define XS1_PORT_CTRL0_EV_VALID(x) \ + (((x)&XS1_PORT_CTRL0_EV_VALID_MASK) >> XS1_PORT_CTRL0_EV_VALID_SHIFT) +#define XS1_PORT_CTRL0_EV_VALID_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_EV_VALID_MASK) | \ + (((v) << XS1_PORT_CTRL0_EV_VALID_SHIFT) & XS1_PORT_CTRL0_EV_VALID_MASK)) +#define XS1_PORT_CTRL0_T_WAITING_SHIFT 0x17 +#define XS1_PORT_CTRL0_T_WAITING_SIZE 0x1 +#define XS1_PORT_CTRL0_T_WAITING_MASK \ + (((1 << XS1_PORT_CTRL0_T_WAITING_SIZE) - 1) << XS1_PORT_CTRL0_T_WAITING_SHIFT) +#define XS1_PORT_CTRL0_T_WAITING(x) \ + (((x)&XS1_PORT_CTRL0_T_WAITING_MASK) >> XS1_PORT_CTRL0_T_WAITING_SHIFT) +#define XS1_PORT_CTRL0_T_WAITING_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_T_WAITING_MASK) | \ + (((v) << XS1_PORT_CTRL0_T_WAITING_SHIFT) & XS1_PORT_CTRL0_T_WAITING_MASK)) +#define XS1_PORT_CTRL0_T_NUM_SHIFT 0x18 +#define XS1_PORT_CTRL0_T_NUM_SIZE 0x8 +#define XS1_PORT_CTRL0_T_NUM_MASK \ + (((1 << XS1_PORT_CTRL0_T_NUM_SIZE) - 1) << XS1_PORT_CTRL0_T_NUM_SHIFT) +#define XS1_PORT_CTRL0_T_NUM(x) \ + (((x)&XS1_PORT_CTRL0_T_NUM_MASK) >> XS1_PORT_CTRL0_T_NUM_SHIFT) +#define XS1_PORT_CTRL0_T_NUM_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL0_T_NUM_MASK) | \ + (((v) << XS1_PORT_CTRL0_T_NUM_SHIFT) & XS1_PORT_CTRL0_T_NUM_MASK)) +#define XS1_PORT_CTRL1_DRIVE_SHIFT 0x0 +#define XS1_PORT_CTRL1_DRIVE_SIZE 0x8 +#define XS1_PORT_CTRL1_DRIVE_MASK \ + (((1 << XS1_PORT_CTRL1_DRIVE_SIZE) - 1) << XS1_PORT_CTRL1_DRIVE_SHIFT) +#define XS1_PORT_CTRL1_DRIVE(x) \ + (((x)&XS1_PORT_CTRL1_DRIVE_MASK) >> XS1_PORT_CTRL1_DRIVE_SHIFT) +#define XS1_PORT_CTRL1_DRIVE_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_DRIVE_MASK) | \ + (((v) << XS1_PORT_CTRL1_DRIVE_SHIFT) & XS1_PORT_CTRL1_DRIVE_MASK)) +#define XS1_PORT_CTRL1_TWIDTH_SHIFT 0x8 +#define XS1_PORT_CTRL1_TWIDTH_SIZE 0x8 +#define XS1_PORT_CTRL1_TWIDTH_MASK \ + (((1 << XS1_PORT_CTRL1_TWIDTH_SIZE) - 1) << XS1_PORT_CTRL1_TWIDTH_SHIFT) +#define XS1_PORT_CTRL1_TWIDTH(x) \ + (((x)&XS1_PORT_CTRL1_TWIDTH_MASK) >> XS1_PORT_CTRL1_TWIDTH_SHIFT) +#define XS1_PORT_CTRL1_TWIDTH_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_TWIDTH_MASK) | \ + (((v) << XS1_PORT_CTRL1_TWIDTH_SHIFT) & XS1_PORT_CTRL1_TWIDTH_MASK)) +#define XS1_PORT_CTRL1_SREG_COUNT_SHIFT 0x10 +#define XS1_PORT_CTRL1_SREG_COUNT_SIZE 0x8 +#define XS1_PORT_CTRL1_SREG_COUNT_MASK \ + (((1 << XS1_PORT_CTRL1_SREG_COUNT_SIZE) - 1) \ + << XS1_PORT_CTRL1_SREG_COUNT_SHIFT) +#define XS1_PORT_CTRL1_SREG_COUNT(x) \ + (((x)&XS1_PORT_CTRL1_SREG_COUNT_MASK) >> XS1_PORT_CTRL1_SREG_COUNT_SHIFT) +#define XS1_PORT_CTRL1_SREG_COUNT_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_SREG_COUNT_MASK) | \ + (((v) << XS1_PORT_CTRL1_SREG_COUNT_SHIFT) & \ + XS1_PORT_CTRL1_SREG_COUNT_MASK)) +#define XS1_PORT_CTRL1_TREG_FULL_SHIFT 0x18 +#define XS1_PORT_CTRL1_TREG_FULL_SIZE 0x1 +#define XS1_PORT_CTRL1_TREG_FULL_MASK \ + (((1 << XS1_PORT_CTRL1_TREG_FULL_SIZE) - 1) << XS1_PORT_CTRL1_TREG_FULL_SHIFT) +#define XS1_PORT_CTRL1_TREG_FULL(x) \ + (((x)&XS1_PORT_CTRL1_TREG_FULL_MASK) >> XS1_PORT_CTRL1_TREG_FULL_SHIFT) +#define XS1_PORT_CTRL1_TREG_FULL_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_TREG_FULL_MASK) | \ + (((v) << XS1_PORT_CTRL1_TREG_FULL_SHIFT) & XS1_PORT_CTRL1_TREG_FULL_MASK)) +#define XS1_PORT_CTRL1_CHANGE_DIR_SHIFT 0x19 +#define XS1_PORT_CTRL1_CHANGE_DIR_SIZE 0x1 +#define XS1_PORT_CTRL1_CHANGE_DIR_MASK \ + (((1 << XS1_PORT_CTRL1_CHANGE_DIR_SIZE) - 1) \ + << XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) +#define XS1_PORT_CTRL1_CHANGE_DIR(x) \ + (((x)&XS1_PORT_CTRL1_CHANGE_DIR_MASK) >> XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) +#define XS1_PORT_CTRL1_CHANGE_DIR_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_CHANGE_DIR_MASK) | \ + (((v) << XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) & \ + XS1_PORT_CTRL1_CHANGE_DIR_MASK)) +#define XS1_PORT_CTRL1_SYNCR_SHIFT 0x1a +#define XS1_PORT_CTRL1_SYNCR_SIZE 0x1 +#define XS1_PORT_CTRL1_SYNCR_MASK \ + (((1 << XS1_PORT_CTRL1_SYNCR_SIZE) - 1) << XS1_PORT_CTRL1_SYNCR_SHIFT) +#define XS1_PORT_CTRL1_SYNCR(x) \ + (((x)&XS1_PORT_CTRL1_SYNCR_MASK) >> XS1_PORT_CTRL1_SYNCR_SHIFT) +#define XS1_PORT_CTRL1_SYNCR_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_SYNCR_MASK) | \ + (((v) << XS1_PORT_CTRL1_SYNCR_SHIFT) & XS1_PORT_CTRL1_SYNCR_MASK)) +#define XS1_PORT_CTRL1_INST_COMMITTED_SHIFT 0x1b +#define XS1_PORT_CTRL1_INST_COMMITTED_SIZE 0x1 +#define XS1_PORT_CTRL1_INST_COMMITTED_MASK \ + (((1 << XS1_PORT_CTRL1_INST_COMMITTED_SIZE) - 1) \ + << XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) +#define XS1_PORT_CTRL1_INST_COMMITTED(x) \ + (((x)&XS1_PORT_CTRL1_INST_COMMITTED_MASK) >> \ + XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) +#define XS1_PORT_CTRL1_INST_COMMITTED_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_INST_COMMITTED_MASK) | \ + (((v) << XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) & \ + XS1_PORT_CTRL1_INST_COMMITTED_MASK)) +#define XS1_PORT_CTRL1_HOLD_DATA_SHIFT 0x1c +#define XS1_PORT_CTRL1_HOLD_DATA_SIZE 0x1 +#define XS1_PORT_CTRL1_HOLD_DATA_MASK \ + (((1 << XS1_PORT_CTRL1_HOLD_DATA_SIZE) - 1) << XS1_PORT_CTRL1_HOLD_DATA_SHIFT) +#define XS1_PORT_CTRL1_HOLD_DATA(x) \ + (((x)&XS1_PORT_CTRL1_HOLD_DATA_MASK) >> XS1_PORT_CTRL1_HOLD_DATA_SHIFT) +#define XS1_PORT_CTRL1_HOLD_DATA_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_HOLD_DATA_MASK) | \ + (((v) << XS1_PORT_CTRL1_HOLD_DATA_SHIFT) & XS1_PORT_CTRL1_HOLD_DATA_MASK)) +#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT 0x1d +#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SIZE 0x1 +#define XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK \ + (((1 << XS1_PORT_CTRL1_WAIT_FOR_TIME_SIZE) - 1) \ + << XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) +#define XS1_PORT_CTRL1_WAIT_FOR_TIME(x) \ + (((x)&XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK) >> \ + XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) +#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK) | \ + (((v) << XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) & \ + XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK)) +#define XS1_PORT_CTRL1_TIMEMET_SHIFT 0x1e +#define XS1_PORT_CTRL1_TIMEMET_SIZE 0x1 +#define XS1_PORT_CTRL1_TIMEMET_MASK \ + (((1 << XS1_PORT_CTRL1_TIMEMET_SIZE) - 1) << XS1_PORT_CTRL1_TIMEMET_SHIFT) +#define XS1_PORT_CTRL1_TIMEMET(x) \ + (((x)&XS1_PORT_CTRL1_TIMEMET_MASK) >> XS1_PORT_CTRL1_TIMEMET_SHIFT) +#define XS1_PORT_CTRL1_TIMEMET_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_TIMEMET_MASK) | \ + (((v) << XS1_PORT_CTRL1_TIMEMET_SHIFT) & XS1_PORT_CTRL1_TIMEMET_MASK)) +#define XS1_PORT_CTRL1_ENDIN_SHIFT 0x1f +#define XS1_PORT_CTRL1_ENDIN_SIZE 0x1 +#define XS1_PORT_CTRL1_ENDIN_MASK \ + (((1 << XS1_PORT_CTRL1_ENDIN_SIZE) - 1) << XS1_PORT_CTRL1_ENDIN_SHIFT) +#define XS1_PORT_CTRL1_ENDIN(x) \ + (((x)&XS1_PORT_CTRL1_ENDIN_MASK) >> XS1_PORT_CTRL1_ENDIN_SHIFT) +#define XS1_PORT_CTRL1_ENDIN_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL1_ENDIN_MASK) | \ + (((v) << XS1_PORT_CTRL1_ENDIN_SHIFT) & XS1_PORT_CTRL1_ENDIN_MASK)) +#define XS1_PORT_CTRL2_TIME_SHIFT 0x0 +#define XS1_PORT_CTRL2_TIME_SIZE 0x10 +#define XS1_PORT_CTRL2_TIME_MASK \ + (((1 << XS1_PORT_CTRL2_TIME_SIZE) - 1) << XS1_PORT_CTRL2_TIME_SHIFT) +#define XS1_PORT_CTRL2_TIME(x) \ + (((x)&XS1_PORT_CTRL2_TIME_MASK) >> XS1_PORT_CTRL2_TIME_SHIFT) +#define XS1_PORT_CTRL2_TIME_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL2_TIME_MASK) | \ + (((v) << XS1_PORT_CTRL2_TIME_SHIFT) & XS1_PORT_CTRL2_TIME_MASK)) +#define XS1_PORT_CTRL2_PIN_DELAY_SHIFT 0x10 +#define XS1_PORT_CTRL2_PIN_DELAY_SIZE 0x3 +#define XS1_PORT_CTRL2_PIN_DELAY_MASK \ + (((1 << XS1_PORT_CTRL2_PIN_DELAY_SIZE) - 1) << XS1_PORT_CTRL2_PIN_DELAY_SHIFT) +#define XS1_PORT_CTRL2_PIN_DELAY(x) \ + (((x)&XS1_PORT_CTRL2_PIN_DELAY_MASK) >> XS1_PORT_CTRL2_PIN_DELAY_SHIFT) +#define XS1_PORT_CTRL2_PIN_DELAY_SET(x, v) \ + (((x) & ~XS1_PORT_CTRL2_PIN_DELAY_MASK) | \ + (((v) << XS1_PORT_CTRL2_PIN_DELAY_SHIFT) & XS1_PORT_CTRL2_PIN_DELAY_MASK)) +#define XS1_PORT_PAD_CTRL_OEN_SHIFT 0x0 +#define XS1_PORT_PAD_CTRL_OEN_SIZE 0x1 +#define XS1_PORT_PAD_CTRL_OEN_MASK \ + (((1 << XS1_PORT_PAD_CTRL_OEN_SIZE) - 1) << XS1_PORT_PAD_CTRL_OEN_SHIFT) +#define XS1_PORT_PAD_CTRL_OEN(x) \ + (((x)&XS1_PORT_PAD_CTRL_OEN_MASK) >> XS1_PORT_PAD_CTRL_OEN_SHIFT) +#define XS1_PORT_PAD_CTRL_OEN_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_OEN_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_OEN_SHIFT) & XS1_PORT_PAD_CTRL_OEN_MASK)) +#define XS1_PORT_PAD_CTRL_REN_SHIFT 0x1 +#define XS1_PORT_PAD_CTRL_REN_SIZE 0x1 +#define XS1_PORT_PAD_CTRL_REN_MASK \ + (((1 << XS1_PORT_PAD_CTRL_REN_SIZE) - 1) << XS1_PORT_PAD_CTRL_REN_SHIFT) +#define XS1_PORT_PAD_CTRL_REN(x) \ + (((x)&XS1_PORT_PAD_CTRL_REN_MASK) >> XS1_PORT_PAD_CTRL_REN_SHIFT) +#define XS1_PORT_PAD_CTRL_REN_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_REN_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_REN_SHIFT) & XS1_PORT_PAD_CTRL_REN_MASK)) +#define XS1_PORT_PAD_CTRL_P_SHIFT 0x2 +#define XS1_PORT_PAD_CTRL_P_SIZE 0x2 +#define XS1_PORT_PAD_CTRL_P_MASK \ + (((1 << XS1_PORT_PAD_CTRL_P_SIZE) - 1) << XS1_PORT_PAD_CTRL_P_SHIFT) +#define XS1_PORT_PAD_CTRL_P(x) \ + (((x)&XS1_PORT_PAD_CTRL_P_MASK) >> XS1_PORT_PAD_CTRL_P_SHIFT) +#define XS1_PORT_PAD_CTRL_P_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_P_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_P_SHIFT) & XS1_PORT_PAD_CTRL_P_MASK)) +#define XS1_PORT_PAD_CTRL_E_SHIFT 0x4 +#define XS1_PORT_PAD_CTRL_E_SIZE 0x2 +#define XS1_PORT_PAD_CTRL_E_MASK \ + (((1 << XS1_PORT_PAD_CTRL_E_SIZE) - 1) << XS1_PORT_PAD_CTRL_E_SHIFT) +#define XS1_PORT_PAD_CTRL_E(x) \ + (((x)&XS1_PORT_PAD_CTRL_E_MASK) >> XS1_PORT_PAD_CTRL_E_SHIFT) +#define XS1_PORT_PAD_CTRL_E_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_E_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_E_SHIFT) & XS1_PORT_PAD_CTRL_E_MASK)) +#define XS1_PORT_PAD_CTRL_SR_SHIFT 0x6 +#define XS1_PORT_PAD_CTRL_SR_SIZE 0x1 +#define XS1_PORT_PAD_CTRL_SR_MASK \ + (((1 << XS1_PORT_PAD_CTRL_SR_SIZE) - 1) << XS1_PORT_PAD_CTRL_SR_SHIFT) +#define XS1_PORT_PAD_CTRL_SR(x) \ + (((x)&XS1_PORT_PAD_CTRL_SR_MASK) >> XS1_PORT_PAD_CTRL_SR_SHIFT) +#define XS1_PORT_PAD_CTRL_SR_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_SR_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_SR_SHIFT) & XS1_PORT_PAD_CTRL_SR_MASK)) +#define XS1_PORT_PAD_CTRL_SMT_SHIFT 0x7 +#define XS1_PORT_PAD_CTRL_SMT_SIZE 0x1 +#define XS1_PORT_PAD_CTRL_SMT_MASK \ + (((1 << XS1_PORT_PAD_CTRL_SMT_SIZE) - 1) << XS1_PORT_PAD_CTRL_SMT_SHIFT) +#define XS1_PORT_PAD_CTRL_SMT(x) \ + (((x)&XS1_PORT_PAD_CTRL_SMT_MASK) >> XS1_PORT_PAD_CTRL_SMT_SHIFT) +#define XS1_PORT_PAD_CTRL_SMT_SET(x, v) \ + (((x) & ~XS1_PORT_PAD_CTRL_SMT_MASK) | \ + (((v) << XS1_PORT_PAD_CTRL_SMT_SHIFT) & XS1_PORT_PAD_CTRL_SMT_MASK)) +#define XS1_TIMER_CTRL0_INUSE_SHIFT 0x0 +#define XS1_TIMER_CTRL0_INUSE_SIZE 0x1 +#define XS1_TIMER_CTRL0_INUSE_MASK \ + (((1 << XS1_TIMER_CTRL0_INUSE_SIZE) - 1) << XS1_TIMER_CTRL0_INUSE_SHIFT) +#define XS1_TIMER_CTRL0_INUSE(x) \ + (((x)&XS1_TIMER_CTRL0_INUSE_MASK) >> XS1_TIMER_CTRL0_INUSE_SHIFT) +#define XS1_TIMER_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_INUSE_MASK) | \ + (((v) << XS1_TIMER_CTRL0_INUSE_SHIFT) & XS1_TIMER_CTRL0_INUSE_MASK)) +#define XS1_TIMER_CTRL0_IE_MODE_SHIFT 0x1 +#define XS1_TIMER_CTRL0_IE_MODE_SIZE 0x1 +#define XS1_TIMER_CTRL0_IE_MODE_MASK \ + (((1 << XS1_TIMER_CTRL0_IE_MODE_SIZE) - 1) << XS1_TIMER_CTRL0_IE_MODE_SHIFT) +#define XS1_TIMER_CTRL0_IE_MODE(x) \ + (((x)&XS1_TIMER_CTRL0_IE_MODE_MASK) >> XS1_TIMER_CTRL0_IE_MODE_SHIFT) +#define XS1_TIMER_CTRL0_IE_MODE_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_IE_MODE_MASK) | \ + (((v) << XS1_TIMER_CTRL0_IE_MODE_SHIFT) & XS1_TIMER_CTRL0_IE_MODE_MASK)) +#define XS1_TIMER_CTRL0_IE_ENABLED_SHIFT 0x2 +#define XS1_TIMER_CTRL0_IE_ENABLED_SIZE 0x1 +#define XS1_TIMER_CTRL0_IE_ENABLED_MASK \ + (((1 << XS1_TIMER_CTRL0_IE_ENABLED_SIZE) - 1) \ + << XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) +#define XS1_TIMER_CTRL0_IE_ENABLED(x) \ + (((x)&XS1_TIMER_CTRL0_IE_ENABLED_MASK) >> XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) +#define XS1_TIMER_CTRL0_IE_ENABLED_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_IE_ENABLED_MASK) | \ + (((v) << XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) & \ + XS1_TIMER_CTRL0_IE_ENABLED_MASK)) +#define XS1_TIMER_CTRL0_READY_SHIFT 0x3 +#define XS1_TIMER_CTRL0_READY_SIZE 0x1 +#define XS1_TIMER_CTRL0_READY_MASK \ + (((1 << XS1_TIMER_CTRL0_READY_SIZE) - 1) << XS1_TIMER_CTRL0_READY_SHIFT) +#define XS1_TIMER_CTRL0_READY(x) \ + (((x)&XS1_TIMER_CTRL0_READY_MASK) >> XS1_TIMER_CTRL0_READY_SHIFT) +#define XS1_TIMER_CTRL0_READY_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_READY_MASK) | \ + (((v) << XS1_TIMER_CTRL0_READY_SHIFT) & XS1_TIMER_CTRL0_READY_MASK)) +#define XS1_TIMER_CTRL0_COND_SHIFT 0x4 +#define XS1_TIMER_CTRL0_COND_SIZE 0x1 +#define XS1_TIMER_CTRL0_COND_MASK \ + (((1 << XS1_TIMER_CTRL0_COND_SIZE) - 1) << XS1_TIMER_CTRL0_COND_SHIFT) +#define XS1_TIMER_CTRL0_COND(x) \ + (((x)&XS1_TIMER_CTRL0_COND_MASK) >> XS1_TIMER_CTRL0_COND_SHIFT) +#define XS1_TIMER_CTRL0_COND_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_COND_MASK) | \ + (((v) << XS1_TIMER_CTRL0_COND_SHIFT) & XS1_TIMER_CTRL0_COND_MASK)) +#define XS1_TIMER_CTRL0_EV_VALID_SHIFT 0x8 +#define XS1_TIMER_CTRL0_EV_VALID_SIZE 0x1 +#define XS1_TIMER_CTRL0_EV_VALID_MASK \ + (((1 << XS1_TIMER_CTRL0_EV_VALID_SIZE) - 1) << XS1_TIMER_CTRL0_EV_VALID_SHIFT) +#define XS1_TIMER_CTRL0_EV_VALID(x) \ + (((x)&XS1_TIMER_CTRL0_EV_VALID_MASK) >> XS1_TIMER_CTRL0_EV_VALID_SHIFT) +#define XS1_TIMER_CTRL0_EV_VALID_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_EV_VALID_MASK) | \ + (((v) << XS1_TIMER_CTRL0_EV_VALID_SHIFT) & XS1_TIMER_CTRL0_EV_VALID_MASK)) +#define XS1_TIMER_CTRL0_T_WAITING_SHIFT 0x17 +#define XS1_TIMER_CTRL0_T_WAITING_SIZE 0x1 +#define XS1_TIMER_CTRL0_T_WAITING_MASK \ + (((1 << XS1_TIMER_CTRL0_T_WAITING_SIZE) - 1) \ + << XS1_TIMER_CTRL0_T_WAITING_SHIFT) +#define XS1_TIMER_CTRL0_T_WAITING(x) \ + (((x)&XS1_TIMER_CTRL0_T_WAITING_MASK) >> XS1_TIMER_CTRL0_T_WAITING_SHIFT) +#define XS1_TIMER_CTRL0_T_WAITING_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_T_WAITING_MASK) | \ + (((v) << XS1_TIMER_CTRL0_T_WAITING_SHIFT) & \ + XS1_TIMER_CTRL0_T_WAITING_MASK)) +#define XS1_TIMER_CTRL0_T_NUM_SHIFT 0x18 +#define XS1_TIMER_CTRL0_T_NUM_SIZE 0x8 +#define XS1_TIMER_CTRL0_T_NUM_MASK \ + (((1 << XS1_TIMER_CTRL0_T_NUM_SIZE) - 1) << XS1_TIMER_CTRL0_T_NUM_SHIFT) +#define XS1_TIMER_CTRL0_T_NUM(x) \ + (((x)&XS1_TIMER_CTRL0_T_NUM_MASK) >> XS1_TIMER_CTRL0_T_NUM_SHIFT) +#define XS1_TIMER_CTRL0_T_NUM_SET(x, v) \ + (((x) & ~XS1_TIMER_CTRL0_T_NUM_MASK) | \ + (((v) << XS1_TIMER_CTRL0_T_NUM_SHIFT) & XS1_TIMER_CTRL0_T_NUM_MASK)) +#define XS1_SWMEM_CTRL0_INUSE_SHIFT 0x0 +#define XS1_SWMEM_CTRL0_INUSE_SIZE 0x1 +#define XS1_SWMEM_CTRL0_INUSE_MASK \ + (((1 << XS1_SWMEM_CTRL0_INUSE_SIZE) - 1) << XS1_SWMEM_CTRL0_INUSE_SHIFT) +#define XS1_SWMEM_CTRL0_INUSE(x) \ + (((x)&XS1_SWMEM_CTRL0_INUSE_MASK) >> XS1_SWMEM_CTRL0_INUSE_SHIFT) +#define XS1_SWMEM_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_INUSE_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_INUSE_SHIFT) & XS1_SWMEM_CTRL0_INUSE_MASK)) +#define XS1_SWMEM_CTRL0_IE_MODE_SHIFT 0x1 +#define XS1_SWMEM_CTRL0_IE_MODE_SIZE 0x1 +#define XS1_SWMEM_CTRL0_IE_MODE_MASK \ + (((1 << XS1_SWMEM_CTRL0_IE_MODE_SIZE) - 1) << XS1_SWMEM_CTRL0_IE_MODE_SHIFT) +#define XS1_SWMEM_CTRL0_IE_MODE(x) \ + (((x)&XS1_SWMEM_CTRL0_IE_MODE_MASK) >> XS1_SWMEM_CTRL0_IE_MODE_SHIFT) +#define XS1_SWMEM_CTRL0_IE_MODE_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_IE_MODE_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_IE_MODE_SHIFT) & XS1_SWMEM_CTRL0_IE_MODE_MASK)) +#define XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT 0x2 +#define XS1_SWMEM_CTRL0_IE_ENABLED_SIZE 0x1 +#define XS1_SWMEM_CTRL0_IE_ENABLED_MASK \ + (((1 << XS1_SWMEM_CTRL0_IE_ENABLED_SIZE) - 1) \ + << XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) +#define XS1_SWMEM_CTRL0_IE_ENABLED(x) \ + (((x)&XS1_SWMEM_CTRL0_IE_ENABLED_MASK) >> XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) +#define XS1_SWMEM_CTRL0_IE_ENABLED_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_IE_ENABLED_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) & \ + XS1_SWMEM_CTRL0_IE_ENABLED_MASK)) +#define XS1_SWMEM_CTRL0_READY_SHIFT 0x3 +#define XS1_SWMEM_CTRL0_READY_SIZE 0x1 +#define XS1_SWMEM_CTRL0_READY_MASK \ + (((1 << XS1_SWMEM_CTRL0_READY_SIZE) - 1) << XS1_SWMEM_CTRL0_READY_SHIFT) +#define XS1_SWMEM_CTRL0_READY(x) \ + (((x)&XS1_SWMEM_CTRL0_READY_MASK) >> XS1_SWMEM_CTRL0_READY_SHIFT) +#define XS1_SWMEM_CTRL0_READY_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_READY_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_READY_SHIFT) & XS1_SWMEM_CTRL0_READY_MASK)) +#define XS1_SWMEM_CTRL0_COND_SHIFT 0x4 +#define XS1_SWMEM_CTRL0_COND_SIZE 0x1 +#define XS1_SWMEM_CTRL0_COND_MASK \ + (((1 << XS1_SWMEM_CTRL0_COND_SIZE) - 1) << XS1_SWMEM_CTRL0_COND_SHIFT) +#define XS1_SWMEM_CTRL0_COND(x) \ + (((x)&XS1_SWMEM_CTRL0_COND_MASK) >> XS1_SWMEM_CTRL0_COND_SHIFT) +#define XS1_SWMEM_CTRL0_COND_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_COND_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_COND_SHIFT) & XS1_SWMEM_CTRL0_COND_MASK)) +#define XS1_SWMEM_CTRL0_EV_VALID_SHIFT 0x8 +#define XS1_SWMEM_CTRL0_EV_VALID_SIZE 0x1 +#define XS1_SWMEM_CTRL0_EV_VALID_MASK \ + (((1 << XS1_SWMEM_CTRL0_EV_VALID_SIZE) - 1) << XS1_SWMEM_CTRL0_EV_VALID_SHIFT) +#define XS1_SWMEM_CTRL0_EV_VALID(x) \ + (((x)&XS1_SWMEM_CTRL0_EV_VALID_MASK) >> XS1_SWMEM_CTRL0_EV_VALID_SHIFT) +#define XS1_SWMEM_CTRL0_EV_VALID_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_EV_VALID_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_EV_VALID_SHIFT) & XS1_SWMEM_CTRL0_EV_VALID_MASK)) +#define XS1_SWMEM_CTRL0_T_WAITING_SHIFT 0x17 +#define XS1_SWMEM_CTRL0_T_WAITING_SIZE 0x1 +#define XS1_SWMEM_CTRL0_T_WAITING_MASK \ + (((1 << XS1_SWMEM_CTRL0_T_WAITING_SIZE) - 1) \ + << XS1_SWMEM_CTRL0_T_WAITING_SHIFT) +#define XS1_SWMEM_CTRL0_T_WAITING(x) \ + (((x)&XS1_SWMEM_CTRL0_T_WAITING_MASK) >> XS1_SWMEM_CTRL0_T_WAITING_SHIFT) +#define XS1_SWMEM_CTRL0_T_WAITING_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_T_WAITING_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_T_WAITING_SHIFT) & \ + XS1_SWMEM_CTRL0_T_WAITING_MASK)) +#define XS1_SWMEM_CTRL0_T_NUM_SHIFT 0x18 +#define XS1_SWMEM_CTRL0_T_NUM_SIZE 0x8 +#define XS1_SWMEM_CTRL0_T_NUM_MASK \ + (((1 << XS1_SWMEM_CTRL0_T_NUM_SIZE) - 1) << XS1_SWMEM_CTRL0_T_NUM_SHIFT) +#define XS1_SWMEM_CTRL0_T_NUM(x) \ + (((x)&XS1_SWMEM_CTRL0_T_NUM_MASK) >> XS1_SWMEM_CTRL0_T_NUM_SHIFT) +#define XS1_SWMEM_CTRL0_T_NUM_SET(x, v) \ + (((x) & ~XS1_SWMEM_CTRL0_T_NUM_MASK) | \ + (((v) << XS1_SWMEM_CTRL0_T_NUM_SHIFT) & XS1_SWMEM_CTRL0_T_NUM_MASK)) +#define XS1_OTPA_MOSI_LSB_SHIFT 0x0 +#define XS1_OTPA_MOSI_LSB_SIZE 0x1 +#define XS1_OTPA_MOSI_LSB_MASK \ + (((1 << XS1_OTPA_MOSI_LSB_SIZE) - 1) << XS1_OTPA_MOSI_LSB_SHIFT) +#define XS1_OTPA_MOSI_LSB(x) \ + (((x)&XS1_OTPA_MOSI_LSB_MASK) >> XS1_OTPA_MOSI_LSB_SHIFT) +#define XS1_OTPA_MOSI_LSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MOSI_LSB_MASK) | \ + (((v) << XS1_OTPA_MOSI_LSB_SHIFT) & XS1_OTPA_MOSI_LSB_MASK)) +#define XS1_OTPA_MOSI_MSB_SHIFT 0x7 +#define XS1_OTPA_MOSI_MSB_SIZE 0x1 +#define XS1_OTPA_MOSI_MSB_MASK \ + (((1 << XS1_OTPA_MOSI_MSB_SIZE) - 1) << XS1_OTPA_MOSI_MSB_SHIFT) +#define XS1_OTPA_MOSI_MSB(x) \ + (((x)&XS1_OTPA_MOSI_MSB_MASK) >> XS1_OTPA_MOSI_MSB_SHIFT) +#define XS1_OTPA_MOSI_MSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MOSI_MSB_MASK) | \ + (((v) << XS1_OTPA_MOSI_MSB_SHIFT) & XS1_OTPA_MOSI_MSB_MASK)) +#define XS1_OTPA_CLK_IDX_SHIFT 0x8 +#define XS1_OTPA_CLK_IDX_SIZE 0x1 +#define XS1_OTPA_CLK_IDX_MASK \ + (((1 << XS1_OTPA_CLK_IDX_SIZE) - 1) << XS1_OTPA_CLK_IDX_SHIFT) +#define XS1_OTPA_CLK_IDX(x) \ + (((x)&XS1_OTPA_CLK_IDX_MASK) >> XS1_OTPA_CLK_IDX_SHIFT) +#define XS1_OTPA_CLK_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_CLK_IDX_MASK) | \ + (((v) << XS1_OTPA_CLK_IDX_SHIFT) & XS1_OTPA_CLK_IDX_MASK)) +#define XS1_OTPA_SP_IDX_SHIFT 0x9 +#define XS1_OTPA_SP_IDX_SIZE 0x1 +#define XS1_OTPA_SP_IDX_MASK \ + (((1 << XS1_OTPA_SP_IDX_SIZE) - 1) << XS1_OTPA_SP_IDX_SHIFT) +#define XS1_OTPA_SP_IDX(x) (((x)&XS1_OTPA_SP_IDX_MASK) >> XS1_OTPA_SP_IDX_SHIFT) +#define XS1_OTPA_SP_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_SP_IDX_MASK) | \ + (((v) << XS1_OTPA_SP_IDX_SHIFT) & XS1_OTPA_SP_IDX_MASK)) +#define XS1_OTPA_CS_IDX_SHIFT 0xa +#define XS1_OTPA_CS_IDX_SIZE 0x1 +#define XS1_OTPA_CS_IDX_MASK \ + (((1 << XS1_OTPA_CS_IDX_SIZE) - 1) << XS1_OTPA_CS_IDX_SHIFT) +#define XS1_OTPA_CS_IDX(x) (((x)&XS1_OTPA_CS_IDX_MASK) >> XS1_OTPA_CS_IDX_SHIFT) +#define XS1_OTPA_CS_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_CS_IDX_MASK) | \ + (((v) << XS1_OTPA_CS_IDX_SHIFT) & XS1_OTPA_CS_IDX_MASK)) +#define XS1_OTPA_CKE_IDX_SHIFT 0xb +#define XS1_OTPA_CKE_IDX_SIZE 0x1 +#define XS1_OTPA_CKE_IDX_MASK \ + (((1 << XS1_OTPA_CKE_IDX_SIZE) - 1) << XS1_OTPA_CKE_IDX_SHIFT) +#define XS1_OTPA_CKE_IDX(x) \ + (((x)&XS1_OTPA_CKE_IDX_MASK) >> XS1_OTPA_CKE_IDX_SHIFT) +#define XS1_OTPA_CKE_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_CKE_IDX_MASK) | \ + (((v) << XS1_OTPA_CKE_IDX_SHIFT) & XS1_OTPA_CKE_IDX_MASK)) +#define XS1_OTPA_DCTRL_IDX_SHIFT 0xc +#define XS1_OTPA_DCTRL_IDX_SIZE 0x1 +#define XS1_OTPA_DCTRL_IDX_MASK \ + (((1 << XS1_OTPA_DCTRL_IDX_SIZE) - 1) << XS1_OTPA_DCTRL_IDX_SHIFT) +#define XS1_OTPA_DCTRL_IDX(x) \ + (((x)&XS1_OTPA_DCTRL_IDX_MASK) >> XS1_OTPA_DCTRL_IDX_SHIFT) +#define XS1_OTPA_DCTRL_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_DCTRL_IDX_MASK) | \ + (((v) << XS1_OTPA_DCTRL_IDX_SHIFT) & XS1_OTPA_DCTRL_IDX_MASK)) +#define XS1_OTPA_PD_IDX_SHIFT 0xd +#define XS1_OTPA_PD_IDX_SIZE 0x1 +#define XS1_OTPA_PD_IDX_MASK \ + (((1 << XS1_OTPA_PD_IDX_SIZE) - 1) << XS1_OTPA_PD_IDX_SHIFT) +#define XS1_OTPA_PD_IDX(x) (((x)&XS1_OTPA_PD_IDX_MASK) >> XS1_OTPA_PD_IDX_SHIFT) +#define XS1_OTPA_PD_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_PD_IDX_MASK) | \ + (((v) << XS1_OTPA_PD_IDX_SHIFT) & XS1_OTPA_PD_IDX_MASK)) +#define XS1_OTPA_SEL_IDX_SHIFT 0xe +#define XS1_OTPA_SEL_IDX_SIZE 0x1 +#define XS1_OTPA_SEL_IDX_MASK \ + (((1 << XS1_OTPA_SEL_IDX_SIZE) - 1) << XS1_OTPA_SEL_IDX_SHIFT) +#define XS1_OTPA_SEL_IDX(x) \ + (((x)&XS1_OTPA_SEL_IDX_MASK) >> XS1_OTPA_SEL_IDX_SHIFT) +#define XS1_OTPA_SEL_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_SEL_IDX_MASK) | \ + (((v) << XS1_OTPA_SEL_IDX_SHIFT) & XS1_OTPA_SEL_IDX_MASK)) +#define XS1_OTPA_CK_IDX_SHIFT 0xf +#define XS1_OTPA_CK_IDX_SIZE 0x1 +#define XS1_OTPA_CK_IDX_MASK \ + (((1 << XS1_OTPA_CK_IDX_SIZE) - 1) << XS1_OTPA_CK_IDX_SHIFT) +#define XS1_OTPA_CK_IDX(x) (((x)&XS1_OTPA_CK_IDX_MASK) >> XS1_OTPA_CK_IDX_SHIFT) +#define XS1_OTPA_CK_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_CK_IDX_MASK) | \ + (((v) << XS1_OTPA_CK_IDX_SHIFT) & XS1_OTPA_CK_IDX_MASK)) +#define XS1_OTPA_A_LSB_SHIFT 0x10 +#define XS1_OTPA_A_LSB_SIZE 0x1 +#define XS1_OTPA_A_LSB_MASK \ + (((1 << XS1_OTPA_A_LSB_SIZE) - 1) << XS1_OTPA_A_LSB_SHIFT) +#define XS1_OTPA_A_LSB(x) (((x)&XS1_OTPA_A_LSB_MASK) >> XS1_OTPA_A_LSB_SHIFT) +#define XS1_OTPA_A_LSB_SET(x, v) \ + (((x) & ~XS1_OTPA_A_LSB_MASK) | \ + (((v) << XS1_OTPA_A_LSB_SHIFT) & XS1_OTPA_A_LSB_MASK)) +#define XS1_OTPA_A_MSB_SHIFT 0x1a +#define XS1_OTPA_A_MSB_SIZE 0x1 +#define XS1_OTPA_A_MSB_MASK \ + (((1 << XS1_OTPA_A_MSB_SIZE) - 1) << XS1_OTPA_A_MSB_SHIFT) +#define XS1_OTPA_A_MSB(x) (((x)&XS1_OTPA_A_MSB_MASK) >> XS1_OTPA_A_MSB_SHIFT) +#define XS1_OTPA_A_MSB_SET(x, v) \ + (((x) & ~XS1_OTPA_A_MSB_MASK) | \ + (((v) << XS1_OTPA_A_MSB_SHIFT) & XS1_OTPA_A_MSB_MASK)) +#define XS1_OTPA_RST_IDX_SHIFT 0x1b +#define XS1_OTPA_RST_IDX_SIZE 0x1 +#define XS1_OTPA_RST_IDX_MASK \ + (((1 << XS1_OTPA_RST_IDX_SIZE) - 1) << XS1_OTPA_RST_IDX_SHIFT) +#define XS1_OTPA_RST_IDX(x) \ + (((x)&XS1_OTPA_RST_IDX_MASK) >> XS1_OTPA_RST_IDX_SHIFT) +#define XS1_OTPA_RST_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_RST_IDX_MASK) | \ + (((v) << XS1_OTPA_RST_IDX_SHIFT) & XS1_OTPA_RST_IDX_MASK)) +#define XS1_OTPA_ARB_REQ_IDX_SHIFT 0x1c +#define XS1_OTPA_ARB_REQ_IDX_SIZE 0x1 +#define XS1_OTPA_ARB_REQ_IDX_MASK \ + (((1 << XS1_OTPA_ARB_REQ_IDX_SIZE) - 1) << XS1_OTPA_ARB_REQ_IDX_SHIFT) +#define XS1_OTPA_ARB_REQ_IDX(x) \ + (((x)&XS1_OTPA_ARB_REQ_IDX_MASK) >> XS1_OTPA_ARB_REQ_IDX_SHIFT) +#define XS1_OTPA_ARB_REQ_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_ARB_REQ_IDX_MASK) | \ + (((v) << XS1_OTPA_ARB_REQ_IDX_SHIFT) & XS1_OTPA_ARB_REQ_IDX_MASK)) +#define XS1_OTPA_MUXSEL_LSB_SHIFT 0x1d +#define XS1_OTPA_MUXSEL_LSB_SIZE 0x1 +#define XS1_OTPA_MUXSEL_LSB_MASK \ + (((1 << XS1_OTPA_MUXSEL_LSB_SIZE) - 1) << XS1_OTPA_MUXSEL_LSB_SHIFT) +#define XS1_OTPA_MUXSEL_LSB(x) \ + (((x)&XS1_OTPA_MUXSEL_LSB_MASK) >> XS1_OTPA_MUXSEL_LSB_SHIFT) +#define XS1_OTPA_MUXSEL_LSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MUXSEL_LSB_MASK) | \ + (((v) << XS1_OTPA_MUXSEL_LSB_SHIFT) & XS1_OTPA_MUXSEL_LSB_MASK)) +#define XS1_OTPA_MUXSEL_MSB_SHIFT 0x1f +#define XS1_OTPA_MUXSEL_MSB_SIZE 0x1 +#define XS1_OTPA_MUXSEL_MSB_MASK \ + (((1 << XS1_OTPA_MUXSEL_MSB_SIZE) - 1) << XS1_OTPA_MUXSEL_MSB_SHIFT) +#define XS1_OTPA_MUXSEL_MSB(x) \ + (((x)&XS1_OTPA_MUXSEL_MSB_MASK) >> XS1_OTPA_MUXSEL_MSB_SHIFT) +#define XS1_OTPA_MUXSEL_MSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MUXSEL_MSB_MASK) | \ + (((v) << XS1_OTPA_MUXSEL_MSB_SHIFT) & XS1_OTPA_MUXSEL_MSB_MASK)) +#define XS1_OTPA_MISO_LSB_SHIFT 0x0 +#define XS1_OTPA_MISO_LSB_SIZE 0x1 +#define XS1_OTPA_MISO_LSB_MASK \ + (((1 << XS1_OTPA_MISO_LSB_SIZE) - 1) << XS1_OTPA_MISO_LSB_SHIFT) +#define XS1_OTPA_MISO_LSB(x) \ + (((x)&XS1_OTPA_MISO_LSB_MASK) >> XS1_OTPA_MISO_LSB_SHIFT) +#define XS1_OTPA_MISO_LSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MISO_LSB_MASK) | \ + (((v) << XS1_OTPA_MISO_LSB_SHIFT) & XS1_OTPA_MISO_LSB_MASK)) +#define XS1_OTPA_MISO_MSB_SHIFT 0x7 +#define XS1_OTPA_MISO_MSB_SIZE 0x1 +#define XS1_OTPA_MISO_MSB_MASK \ + (((1 << XS1_OTPA_MISO_MSB_SIZE) - 1) << XS1_OTPA_MISO_MSB_SHIFT) +#define XS1_OTPA_MISO_MSB(x) \ + (((x)&XS1_OTPA_MISO_MSB_MASK) >> XS1_OTPA_MISO_MSB_SHIFT) +#define XS1_OTPA_MISO_MSB_SET(x, v) \ + (((x) & ~XS1_OTPA_MISO_MSB_MASK) | \ + (((v) << XS1_OTPA_MISO_MSB_SHIFT) & XS1_OTPA_MISO_MSB_MASK)) +#define XS1_OTPA_FLAG_IDX_SHIFT 0x8 +#define XS1_OTPA_FLAG_IDX_SIZE 0x1 +#define XS1_OTPA_FLAG_IDX_MASK \ + (((1 << XS1_OTPA_FLAG_IDX_SIZE) - 1) << XS1_OTPA_FLAG_IDX_SHIFT) +#define XS1_OTPA_FLAG_IDX(x) \ + (((x)&XS1_OTPA_FLAG_IDX_MASK) >> XS1_OTPA_FLAG_IDX_SHIFT) +#define XS1_OTPA_FLAG_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_FLAG_IDX_MASK) | \ + (((v) << XS1_OTPA_FLAG_IDX_SHIFT) & XS1_OTPA_FLAG_IDX_MASK)) +#define XS1_OTPA_ARB_GNT_IDX_SHIFT 0x9 +#define XS1_OTPA_ARB_GNT_IDX_SIZE 0x1 +#define XS1_OTPA_ARB_GNT_IDX_MASK \ + (((1 << XS1_OTPA_ARB_GNT_IDX_SIZE) - 1) << XS1_OTPA_ARB_GNT_IDX_SHIFT) +#define XS1_OTPA_ARB_GNT_IDX(x) \ + (((x)&XS1_OTPA_ARB_GNT_IDX_MASK) >> XS1_OTPA_ARB_GNT_IDX_SHIFT) +#define XS1_OTPA_ARB_GNT_IDX_SET(x, v) \ + (((x) & ~XS1_OTPA_ARB_GNT_IDX_MASK) | \ + (((v) << XS1_OTPA_ARB_GNT_IDX_SHIFT) & XS1_OTPA_ARB_GNT_IDX_MASK)) +#define XS1_SYNC_CTRL0_INUSE_SHIFT 0x0 +#define XS1_SYNC_CTRL0_INUSE_SIZE 0x1 +#define XS1_SYNC_CTRL0_INUSE_MASK \ + (((1 << XS1_SYNC_CTRL0_INUSE_SIZE) - 1) << XS1_SYNC_CTRL0_INUSE_SHIFT) +#define XS1_SYNC_CTRL0_INUSE(x) \ + (((x)&XS1_SYNC_CTRL0_INUSE_MASK) >> XS1_SYNC_CTRL0_INUSE_SHIFT) +#define XS1_SYNC_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_SYNC_CTRL0_INUSE_MASK) | \ + (((v) << XS1_SYNC_CTRL0_INUSE_SHIFT) & XS1_SYNC_CTRL0_INUSE_MASK)) +#define XS1_SYNC_CTRL0_MSYNCED_SHIFT 0x1 +#define XS1_SYNC_CTRL0_MSYNCED_SIZE 0x1 +#define XS1_SYNC_CTRL0_MSYNCED_MASK \ + (((1 << XS1_SYNC_CTRL0_MSYNCED_SIZE) - 1) << XS1_SYNC_CTRL0_MSYNCED_SHIFT) +#define XS1_SYNC_CTRL0_MSYNCED(x) \ + (((x)&XS1_SYNC_CTRL0_MSYNCED_MASK) >> XS1_SYNC_CTRL0_MSYNCED_SHIFT) +#define XS1_SYNC_CTRL0_MSYNCED_SET(x, v) \ + (((x) & ~XS1_SYNC_CTRL0_MSYNCED_MASK) | \ + (((v) << XS1_SYNC_CTRL0_MSYNCED_SHIFT) & XS1_SYNC_CTRL0_MSYNCED_MASK)) +#define XS1_SYNC_CTRL0_JOIN_SHIFT 0x2 +#define XS1_SYNC_CTRL0_JOIN_SIZE 0x1 +#define XS1_SYNC_CTRL0_JOIN_MASK \ + (((1 << XS1_SYNC_CTRL0_JOIN_SIZE) - 1) << XS1_SYNC_CTRL0_JOIN_SHIFT) +#define XS1_SYNC_CTRL0_JOIN(x) \ + (((x)&XS1_SYNC_CTRL0_JOIN_MASK) >> XS1_SYNC_CTRL0_JOIN_SHIFT) +#define XS1_SYNC_CTRL0_JOIN_SET(x, v) \ + (((x) & ~XS1_SYNC_CTRL0_JOIN_MASK) | \ + (((v) << XS1_SYNC_CTRL0_JOIN_SHIFT) & XS1_SYNC_CTRL0_JOIN_MASK)) +#define XS1_SYNC_CTRL0_MASTER_SHIFT 0x8 +#define XS1_SYNC_CTRL0_MASTER_SIZE 0x8 +#define XS1_SYNC_CTRL0_MASTER_MASK \ + (((1 << XS1_SYNC_CTRL0_MASTER_SIZE) - 1) << XS1_SYNC_CTRL0_MASTER_SHIFT) +#define XS1_SYNC_CTRL0_MASTER(x) \ + (((x)&XS1_SYNC_CTRL0_MASTER_MASK) >> XS1_SYNC_CTRL0_MASTER_SHIFT) +#define XS1_SYNC_CTRL0_MASTER_SET(x, v) \ + (((x) & ~XS1_SYNC_CTRL0_MASTER_MASK) | \ + (((v) << XS1_SYNC_CTRL0_MASTER_SHIFT) & XS1_SYNC_CTRL0_MASTER_MASK)) +#define XS1_SYNC_TBV0_SLAVES_SHIFT 0x0 +#define XS1_SYNC_TBV0_SLAVES_SIZE 0x8 +#define XS1_SYNC_TBV0_SLAVES_MASK \ + (((1 << XS1_SYNC_TBV0_SLAVES_SIZE) - 1) << XS1_SYNC_TBV0_SLAVES_SHIFT) +#define XS1_SYNC_TBV0_SLAVES(x) \ + (((x)&XS1_SYNC_TBV0_SLAVES_MASK) >> XS1_SYNC_TBV0_SLAVES_SHIFT) +#define XS1_SYNC_TBV0_SLAVES_SET(x, v) \ + (((x) & ~XS1_SYNC_TBV0_SLAVES_MASK) | \ + (((v) << XS1_SYNC_TBV0_SLAVES_SHIFT) & XS1_SYNC_TBV0_SLAVES_MASK)) +#define XS1_LOCK_CTRL0_INUSE_SHIFT 0x0 +#define XS1_LOCK_CTRL0_INUSE_SIZE 0x1 +#define XS1_LOCK_CTRL0_INUSE_MASK \ + (((1 << XS1_LOCK_CTRL0_INUSE_SIZE) - 1) << XS1_LOCK_CTRL0_INUSE_SHIFT) +#define XS1_LOCK_CTRL0_INUSE(x) \ + (((x)&XS1_LOCK_CTRL0_INUSE_MASK) >> XS1_LOCK_CTRL0_INUSE_SHIFT) +#define XS1_LOCK_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_LOCK_CTRL0_INUSE_MASK) | \ + (((v) << XS1_LOCK_CTRL0_INUSE_SHIFT) & XS1_LOCK_CTRL0_INUSE_MASK)) +#define XS1_LOCK_CTRL0_OWNT_V_SHIFT 0x1 +#define XS1_LOCK_CTRL0_OWNT_V_SIZE 0x1 +#define XS1_LOCK_CTRL0_OWNT_V_MASK \ + (((1 << XS1_LOCK_CTRL0_OWNT_V_SIZE) - 1) << XS1_LOCK_CTRL0_OWNT_V_SHIFT) +#define XS1_LOCK_CTRL0_OWNT_V(x) \ + (((x)&XS1_LOCK_CTRL0_OWNT_V_MASK) >> XS1_LOCK_CTRL0_OWNT_V_SHIFT) +#define XS1_LOCK_CTRL0_OWNT_V_SET(x, v) \ + (((x) & ~XS1_LOCK_CTRL0_OWNT_V_MASK) | \ + (((v) << XS1_LOCK_CTRL0_OWNT_V_SHIFT) & XS1_LOCK_CTRL0_OWNT_V_MASK)) +#define XS1_LOCK_CTRL0_OWNT_SHIFT 0x8 +#define XS1_LOCK_CTRL0_OWNT_SIZE 0x8 +#define XS1_LOCK_CTRL0_OWNT_MASK \ + (((1 << XS1_LOCK_CTRL0_OWNT_SIZE) - 1) << XS1_LOCK_CTRL0_OWNT_SHIFT) +#define XS1_LOCK_CTRL0_OWNT(x) \ + (((x)&XS1_LOCK_CTRL0_OWNT_MASK) >> XS1_LOCK_CTRL0_OWNT_SHIFT) +#define XS1_LOCK_CTRL0_OWNT_SET(x, v) \ + (((x) & ~XS1_LOCK_CTRL0_OWNT_MASK) | \ + (((v) << XS1_LOCK_CTRL0_OWNT_SHIFT) & XS1_LOCK_CTRL0_OWNT_MASK)) +#define XS1_LOCK_TBV0_WAITING_SHIFT 0x0 +#define XS1_LOCK_TBV0_WAITING_SIZE 0x8 +#define XS1_LOCK_TBV0_WAITING_MASK \ + (((1 << XS1_LOCK_TBV0_WAITING_SIZE) - 1) << XS1_LOCK_TBV0_WAITING_SHIFT) +#define XS1_LOCK_TBV0_WAITING(x) \ + (((x)&XS1_LOCK_TBV0_WAITING_MASK) >> XS1_LOCK_TBV0_WAITING_SHIFT) +#define XS1_LOCK_TBV0_WAITING_SET(x, v) \ + (((x) & ~XS1_LOCK_TBV0_WAITING_MASK) | \ + (((v) << XS1_LOCK_TBV0_WAITING_SHIFT) & XS1_LOCK_TBV0_WAITING_MASK)) +#define XS1_MMAP_CTRL0_INUSE_SHIFT 0x0 +#define XS1_MMAP_CTRL0_INUSE_SIZE 0x1 +#define XS1_MMAP_CTRL0_INUSE_MASK \ + (((1 << XS1_MMAP_CTRL0_INUSE_SIZE) - 1) << XS1_MMAP_CTRL0_INUSE_SHIFT) +#define XS1_MMAP_CTRL0_INUSE(x) \ + (((x)&XS1_MMAP_CTRL0_INUSE_MASK) >> XS1_MMAP_CTRL0_INUSE_SHIFT) +#define XS1_MMAP_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_INUSE_MASK) | \ + (((v) << XS1_MMAP_CTRL0_INUSE_SHIFT) & XS1_MMAP_CTRL0_INUSE_MASK)) +#define XS1_MMAP_CTRL0_RO_SHIFT 0x1 +#define XS1_MMAP_CTRL0_RO_SIZE 0x1 +#define XS1_MMAP_CTRL0_RO_MASK \ + (((1 << XS1_MMAP_CTRL0_RO_SIZE) - 1) << XS1_MMAP_CTRL0_RO_SHIFT) +#define XS1_MMAP_CTRL0_RO(x) \ + (((x)&XS1_MMAP_CTRL0_RO_MASK) >> XS1_MMAP_CTRL0_RO_SHIFT) +#define XS1_MMAP_CTRL0_RO_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_RO_MASK) | \ + (((v) << XS1_MMAP_CTRL0_RO_SHIFT) & XS1_MMAP_CTRL0_RO_MASK)) +#define XS1_MMAP_CTRL0_LOCK_SHIFT 0x2 +#define XS1_MMAP_CTRL0_LOCK_SIZE 0x1 +#define XS1_MMAP_CTRL0_LOCK_MASK \ + (((1 << XS1_MMAP_CTRL0_LOCK_SIZE) - 1) << XS1_MMAP_CTRL0_LOCK_SHIFT) +#define XS1_MMAP_CTRL0_LOCK(x) \ + (((x)&XS1_MMAP_CTRL0_LOCK_MASK) >> XS1_MMAP_CTRL0_LOCK_SHIFT) +#define XS1_MMAP_CTRL0_LOCK_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_LOCK_MASK) | \ + (((v) << XS1_MMAP_CTRL0_LOCK_SHIFT) & XS1_MMAP_CTRL0_LOCK_MASK)) +#define XS1_MMAP_CTRL0_GLOBAL_SHIFT 0x3 +#define XS1_MMAP_CTRL0_GLOBAL_SIZE 0x1 +#define XS1_MMAP_CTRL0_GLOBAL_MASK \ + (((1 << XS1_MMAP_CTRL0_GLOBAL_SIZE) - 1) << XS1_MMAP_CTRL0_GLOBAL_SHIFT) +#define XS1_MMAP_CTRL0_GLOBAL(x) \ + (((x)&XS1_MMAP_CTRL0_GLOBAL_MASK) >> XS1_MMAP_CTRL0_GLOBAL_SHIFT) +#define XS1_MMAP_CTRL0_GLOBAL_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_GLOBAL_MASK) | \ + (((v) << XS1_MMAP_CTRL0_GLOBAL_SHIFT) & XS1_MMAP_CTRL0_GLOBAL_MASK)) +#define XS1_MMAP_CTRL0_ASID_SHIFT 0x4 +#define XS1_MMAP_CTRL0_ASID_SIZE 0x2 +#define XS1_MMAP_CTRL0_ASID_MASK \ + (((1 << XS1_MMAP_CTRL0_ASID_SIZE) - 1) << XS1_MMAP_CTRL0_ASID_SHIFT) +#define XS1_MMAP_CTRL0_ASID(x) \ + (((x)&XS1_MMAP_CTRL0_ASID_MASK) >> XS1_MMAP_CTRL0_ASID_SHIFT) +#define XS1_MMAP_CTRL0_ASID_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_ASID_MASK) | \ + (((v) << XS1_MMAP_CTRL0_ASID_SHIFT) & XS1_MMAP_CTRL0_ASID_MASK)) +#define XS1_MMAP_CTRL0_LENGTH_SHIFT 0x6 +#define XS1_MMAP_CTRL0_LENGTH_SIZE 0x2 +#define XS1_MMAP_CTRL0_LENGTH_MASK \ + (((1 << XS1_MMAP_CTRL0_LENGTH_SIZE) - 1) << XS1_MMAP_CTRL0_LENGTH_SHIFT) +#define XS1_MMAP_CTRL0_LENGTH(x) \ + (((x)&XS1_MMAP_CTRL0_LENGTH_MASK) >> XS1_MMAP_CTRL0_LENGTH_SHIFT) +#define XS1_MMAP_CTRL0_LENGTH_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_LENGTH_MASK) | \ + (((v) << XS1_MMAP_CTRL0_LENGTH_SHIFT) & XS1_MMAP_CTRL0_LENGTH_MASK)) +#define XS1_MMAP_CTRL0_PHY_ADDR_SHIFT 0x8 +#define XS1_MMAP_CTRL0_PHY_ADDR_SIZE 0x18 +#define XS1_MMAP_CTRL0_PHY_ADDR_MASK \ + (((1 << XS1_MMAP_CTRL0_PHY_ADDR_SIZE) - 1) << XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) +#define XS1_MMAP_CTRL0_PHY_ADDR(x) \ + (((x)&XS1_MMAP_CTRL0_PHY_ADDR_MASK) >> XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) +#define XS1_MMAP_CTRL0_PHY_ADDR_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL0_PHY_ADDR_MASK) | \ + (((v) << XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) & XS1_MMAP_CTRL0_PHY_ADDR_MASK)) +#define XS1_MMAP_CTRL1_THREADS_EN_SHIFT 0x0 +#define XS1_MMAP_CTRL1_THREADS_EN_SIZE 0x8 +#define XS1_MMAP_CTRL1_THREADS_EN_MASK \ + (((1 << XS1_MMAP_CTRL1_THREADS_EN_SIZE) - 1) \ + << XS1_MMAP_CTRL1_THREADS_EN_SHIFT) +#define XS1_MMAP_CTRL1_THREADS_EN(x) \ + (((x)&XS1_MMAP_CTRL1_THREADS_EN_MASK) >> XS1_MMAP_CTRL1_THREADS_EN_SHIFT) +#define XS1_MMAP_CTRL1_THREADS_EN_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL1_THREADS_EN_MASK) | \ + (((v) << XS1_MMAP_CTRL1_THREADS_EN_SHIFT) & \ + XS1_MMAP_CTRL1_THREADS_EN_MASK)) +#define XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT 0x8 +#define XS1_MMAP_CTRL1_VIRT_ADDR_SIZE 0x18 +#define XS1_MMAP_CTRL1_VIRT_ADDR_MASK \ + (((1 << XS1_MMAP_CTRL1_VIRT_ADDR_SIZE) - 1) << XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) +#define XS1_MMAP_CTRL1_VIRT_ADDR(x) \ + (((x)&XS1_MMAP_CTRL1_VIRT_ADDR_MASK) >> XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) +#define XS1_MMAP_CTRL1_VIRT_ADDR_SET(x, v) \ + (((x) & ~XS1_MMAP_CTRL1_VIRT_ADDR_MASK) | \ + (((v) << XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) & XS1_MMAP_CTRL1_VIRT_ADDR_MASK)) +#define XS1_CHANEND_CTRL0_INUSE_SHIFT 0x0 +#define XS1_CHANEND_CTRL0_INUSE_SIZE 0x1 +#define XS1_CHANEND_CTRL0_INUSE_MASK \ + (((1 << XS1_CHANEND_CTRL0_INUSE_SIZE) - 1) << XS1_CHANEND_CTRL0_INUSE_SHIFT) +#define XS1_CHANEND_CTRL0_INUSE(x) \ + (((x)&XS1_CHANEND_CTRL0_INUSE_MASK) >> XS1_CHANEND_CTRL0_INUSE_SHIFT) +#define XS1_CHANEND_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_INUSE_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_INUSE_SHIFT) & XS1_CHANEND_CTRL0_INUSE_MASK)) +#define XS1_CHANEND_CTRL0_IE_MODE_SHIFT 0x1 +#define XS1_CHANEND_CTRL0_IE_MODE_SIZE 0x1 +#define XS1_CHANEND_CTRL0_IE_MODE_MASK \ + (((1 << XS1_CHANEND_CTRL0_IE_MODE_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_IE_MODE_SHIFT) +#define XS1_CHANEND_CTRL0_IE_MODE(x) \ + (((x)&XS1_CHANEND_CTRL0_IE_MODE_MASK) >> XS1_CHANEND_CTRL0_IE_MODE_SHIFT) +#define XS1_CHANEND_CTRL0_IE_MODE_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_IE_MODE_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_IE_MODE_SHIFT) & \ + XS1_CHANEND_CTRL0_IE_MODE_MASK)) +#define XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT 0x2 +#define XS1_CHANEND_CTRL0_IE_ENABLED_SIZE 0x1 +#define XS1_CHANEND_CTRL0_IE_ENABLED_MASK \ + (((1 << XS1_CHANEND_CTRL0_IE_ENABLED_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) +#define XS1_CHANEND_CTRL0_IE_ENABLED(x) \ + (((x)&XS1_CHANEND_CTRL0_IE_ENABLED_MASK) >> \ + XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) +#define XS1_CHANEND_CTRL0_IE_ENABLED_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_IE_ENABLED_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) & \ + XS1_CHANEND_CTRL0_IE_ENABLED_MASK)) +#define XS1_CHANEND_CTRL0_IN_READY_SHIFT 0x4 +#define XS1_CHANEND_CTRL0_IN_READY_SIZE 0x1 +#define XS1_CHANEND_CTRL0_IN_READY_MASK \ + (((1 << XS1_CHANEND_CTRL0_IN_READY_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_IN_READY_SHIFT) +#define XS1_CHANEND_CTRL0_IN_READY(x) \ + (((x)&XS1_CHANEND_CTRL0_IN_READY_MASK) >> XS1_CHANEND_CTRL0_IN_READY_SHIFT) +#define XS1_CHANEND_CTRL0_IN_READY_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_IN_READY_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_IN_READY_SHIFT) & \ + XS1_CHANEND_CTRL0_IN_READY_MASK)) +#define XS1_CHANEND_CTRL0_IN_WAITING_SHIFT 0x5 +#define XS1_CHANEND_CTRL0_IN_WAITING_SIZE 0x1 +#define XS1_CHANEND_CTRL0_IN_WAITING_MASK \ + (((1 << XS1_CHANEND_CTRL0_IN_WAITING_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) +#define XS1_CHANEND_CTRL0_IN_WAITING(x) \ + (((x)&XS1_CHANEND_CTRL0_IN_WAITING_MASK) >> \ + XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) +#define XS1_CHANEND_CTRL0_IN_WAITING_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_IN_WAITING_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) & \ + XS1_CHANEND_CTRL0_IN_WAITING_MASK)) +#define XS1_CHANEND_CTRL0_OUT_READY_SHIFT 0x6 +#define XS1_CHANEND_CTRL0_OUT_READY_SIZE 0x1 +#define XS1_CHANEND_CTRL0_OUT_READY_MASK \ + (((1 << XS1_CHANEND_CTRL0_OUT_READY_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_OUT_READY_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_READY(x) \ + (((x)&XS1_CHANEND_CTRL0_OUT_READY_MASK) >> XS1_CHANEND_CTRL0_OUT_READY_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_READY_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_OUT_READY_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_OUT_READY_SHIFT) & \ + XS1_CHANEND_CTRL0_OUT_READY_MASK)) +#define XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT 0x7 +#define XS1_CHANEND_CTRL0_OUT_WAITING_SIZE 0x1 +#define XS1_CHANEND_CTRL0_OUT_WAITING_MASK \ + (((1 << XS1_CHANEND_CTRL0_OUT_WAITING_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_WAITING(x) \ + (((x)&XS1_CHANEND_CTRL0_OUT_WAITING_MASK) >> \ + XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_WAITING_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_OUT_WAITING_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) & \ + XS1_CHANEND_CTRL0_OUT_WAITING_MASK)) +#define XS1_CHANEND_CTRL0_EV_VALID_SHIFT 0x8 +#define XS1_CHANEND_CTRL0_EV_VALID_SIZE 0x1 +#define XS1_CHANEND_CTRL0_EV_VALID_MASK \ + (((1 << XS1_CHANEND_CTRL0_EV_VALID_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_EV_VALID_SHIFT) +#define XS1_CHANEND_CTRL0_EV_VALID(x) \ + (((x)&XS1_CHANEND_CTRL0_EV_VALID_MASK) >> XS1_CHANEND_CTRL0_EV_VALID_SHIFT) +#define XS1_CHANEND_CTRL0_EV_VALID_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_EV_VALID_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_EV_VALID_SHIFT) & \ + XS1_CHANEND_CTRL0_EV_VALID_MASK)) +#define XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT 0x10 +#define XS1_CHANEND_CTRL0_IN_T_NUM_SIZE 0x8 +#define XS1_CHANEND_CTRL0_IN_T_NUM_MASK \ + (((1 << XS1_CHANEND_CTRL0_IN_T_NUM_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) +#define XS1_CHANEND_CTRL0_IN_T_NUM(x) \ + (((x)&XS1_CHANEND_CTRL0_IN_T_NUM_MASK) >> XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) +#define XS1_CHANEND_CTRL0_IN_T_NUM_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_IN_T_NUM_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) & \ + XS1_CHANEND_CTRL0_IN_T_NUM_MASK)) +#define XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT 0x18 +#define XS1_CHANEND_CTRL0_OUT_T_NUM_SIZE 0x8 +#define XS1_CHANEND_CTRL0_OUT_T_NUM_MASK \ + (((1 << XS1_CHANEND_CTRL0_OUT_T_NUM_SIZE) - 1) \ + << XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_T_NUM(x) \ + (((x)&XS1_CHANEND_CTRL0_OUT_T_NUM_MASK) >> XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) +#define XS1_CHANEND_CTRL0_OUT_T_NUM_SET(x, v) \ + (((x) & ~XS1_CHANEND_CTRL0_OUT_T_NUM_MASK) | \ + (((v) << XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) & \ + XS1_CHANEND_CTRL0_OUT_T_NUM_MASK)) +#define XS1_CLKBLK_CTRL0_INUSE_SHIFT 0x0 +#define XS1_CLKBLK_CTRL0_INUSE_SIZE 0x1 +#define XS1_CLKBLK_CTRL0_INUSE_MASK \ + (((1 << XS1_CLKBLK_CTRL0_INUSE_SIZE) - 1) << XS1_CLKBLK_CTRL0_INUSE_SHIFT) +#define XS1_CLKBLK_CTRL0_INUSE(x) \ + (((x)&XS1_CLKBLK_CTRL0_INUSE_MASK) >> XS1_CLKBLK_CTRL0_INUSE_SHIFT) +#define XS1_CLKBLK_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL0_INUSE_MASK) | \ + (((v) << XS1_CLKBLK_CTRL0_INUSE_SHIFT) & XS1_CLKBLK_CTRL0_INUSE_MASK)) +#define XS1_CLKBLK_CTRL0_STARTED_SHIFT 0x1 +#define XS1_CLKBLK_CTRL0_STARTED_SIZE 0x1 +#define XS1_CLKBLK_CTRL0_STARTED_MASK \ + (((1 << XS1_CLKBLK_CTRL0_STARTED_SIZE) - 1) << XS1_CLKBLK_CTRL0_STARTED_SHIFT) +#define XS1_CLKBLK_CTRL0_STARTED(x) \ + (((x)&XS1_CLKBLK_CTRL0_STARTED_MASK) >> XS1_CLKBLK_CTRL0_STARTED_SHIFT) +#define XS1_CLKBLK_CTRL0_STARTED_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL0_STARTED_MASK) | \ + (((v) << XS1_CLKBLK_CTRL0_STARTED_SHIFT) & XS1_CLKBLK_CTRL0_STARTED_MASK)) +#define XS1_CLKBLK_CTRL0_STOPPING_SHIFT 0x2 +#define XS1_CLKBLK_CTRL0_STOPPING_SIZE 0x1 +#define XS1_CLKBLK_CTRL0_STOPPING_MASK \ + (((1 << XS1_CLKBLK_CTRL0_STOPPING_SIZE) - 1) \ + << XS1_CLKBLK_CTRL0_STOPPING_SHIFT) +#define XS1_CLKBLK_CTRL0_STOPPING(x) \ + (((x)&XS1_CLKBLK_CTRL0_STOPPING_MASK) >> XS1_CLKBLK_CTRL0_STOPPING_SHIFT) +#define XS1_CLKBLK_CTRL0_STOPPING_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL0_STOPPING_MASK) | \ + (((v) << XS1_CLKBLK_CTRL0_STOPPING_SHIFT) & \ + XS1_CLKBLK_CTRL0_STOPPING_MASK)) +#define XS1_CLKBLK_CTRL0_T_WAITING_SHIFT 0x17 +#define XS1_CLKBLK_CTRL0_T_WAITING_SIZE 0x1 +#define XS1_CLKBLK_CTRL0_T_WAITING_MASK \ + (((1 << XS1_CLKBLK_CTRL0_T_WAITING_SIZE) - 1) \ + << XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) +#define XS1_CLKBLK_CTRL0_T_WAITING(x) \ + (((x)&XS1_CLKBLK_CTRL0_T_WAITING_MASK) >> XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) +#define XS1_CLKBLK_CTRL0_T_WAITING_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL0_T_WAITING_MASK) | \ + (((v) << XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) & \ + XS1_CLKBLK_CTRL0_T_WAITING_MASK)) +#define XS1_CLKBLK_CTRL0_T_NUM_SHIFT 0x18 +#define XS1_CLKBLK_CTRL0_T_NUM_SIZE 0x8 +#define XS1_CLKBLK_CTRL0_T_NUM_MASK \ + (((1 << XS1_CLKBLK_CTRL0_T_NUM_SIZE) - 1) << XS1_CLKBLK_CTRL0_T_NUM_SHIFT) +#define XS1_CLKBLK_CTRL0_T_NUM(x) \ + (((x)&XS1_CLKBLK_CTRL0_T_NUM_MASK) >> XS1_CLKBLK_CTRL0_T_NUM_SHIFT) +#define XS1_CLKBLK_CTRL0_T_NUM_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL0_T_NUM_MASK) | \ + (((v) << XS1_CLKBLK_CTRL0_T_NUM_SHIFT) & XS1_CLKBLK_CTRL0_T_NUM_MASK)) +#define XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT 0x0 +#define XS1_CLKBLK_CTRL1_FALL_DELAY_SIZE 0x9 +#define XS1_CLKBLK_CTRL1_FALL_DELAY_MASK \ + (((1 << XS1_CLKBLK_CTRL1_FALL_DELAY_SIZE) - 1) \ + << XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) +#define XS1_CLKBLK_CTRL1_FALL_DELAY(x) \ + (((x)&XS1_CLKBLK_CTRL1_FALL_DELAY_MASK) >> XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) +#define XS1_CLKBLK_CTRL1_FALL_DELAY_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL1_FALL_DELAY_MASK) | \ + (((v) << XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) & \ + XS1_CLKBLK_CTRL1_FALL_DELAY_MASK)) +#define XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT 0x10 +#define XS1_CLKBLK_CTRL1_RISE_DELAY_SIZE 0x9 +#define XS1_CLKBLK_CTRL1_RISE_DELAY_MASK \ + (((1 << XS1_CLKBLK_CTRL1_RISE_DELAY_SIZE) - 1) \ + << XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) +#define XS1_CLKBLK_CTRL1_RISE_DELAY(x) \ + (((x)&XS1_CLKBLK_CTRL1_RISE_DELAY_MASK) >> XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) +#define XS1_CLKBLK_CTRL1_RISE_DELAY_SET(x, v) \ + (((x) & ~XS1_CLKBLK_CTRL1_RISE_DELAY_MASK) | \ + (((v) << XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) & \ + XS1_CLKBLK_CTRL1_RISE_DELAY_MASK)) +#define XS1_COPROC_CTRL0_INUSE_SHIFT 0x0 +#define XS1_COPROC_CTRL0_INUSE_SIZE 0x1 +#define XS1_COPROC_CTRL0_INUSE_MASK \ + (((1 << XS1_COPROC_CTRL0_INUSE_SIZE) - 1) << XS1_COPROC_CTRL0_INUSE_SHIFT) +#define XS1_COPROC_CTRL0_INUSE(x) \ + (((x)&XS1_COPROC_CTRL0_INUSE_MASK) >> XS1_COPROC_CTRL0_INUSE_SHIFT) +#define XS1_COPROC_CTRL0_INUSE_SET(x, v) \ + (((x) & ~XS1_COPROC_CTRL0_INUSE_MASK) | \ + (((v) << XS1_COPROC_CTRL0_INUSE_SHIFT) & XS1_COPROC_CTRL0_INUSE_MASK)) +#define XS1_COPROC_CTRL0_OWNT_V_SHIFT 0x1 +#define XS1_COPROC_CTRL0_OWNT_V_SIZE 0x1 +#define XS1_COPROC_CTRL0_OWNT_V_MASK \ + (((1 << XS1_COPROC_CTRL0_OWNT_V_SIZE) - 1) << XS1_COPROC_CTRL0_OWNT_V_SHIFT) +#define XS1_COPROC_CTRL0_OWNT_V(x) \ + (((x)&XS1_COPROC_CTRL0_OWNT_V_MASK) >> XS1_COPROC_CTRL0_OWNT_V_SHIFT) +#define XS1_COPROC_CTRL0_OWNT_V_SET(x, v) \ + (((x) & ~XS1_COPROC_CTRL0_OWNT_V_MASK) | \ + (((v) << XS1_COPROC_CTRL0_OWNT_V_SHIFT) & XS1_COPROC_CTRL0_OWNT_V_MASK)) +#define XS1_COPROC_CTRL0_OWNT_SHIFT 0x8 +#define XS1_COPROC_CTRL0_OWNT_SIZE 0x8 +#define XS1_COPROC_CTRL0_OWNT_MASK \ + (((1 << XS1_COPROC_CTRL0_OWNT_SIZE) - 1) << XS1_COPROC_CTRL0_OWNT_SHIFT) +#define XS1_COPROC_CTRL0_OWNT(x) \ + (((x)&XS1_COPROC_CTRL0_OWNT_MASK) >> XS1_COPROC_CTRL0_OWNT_SHIFT) +#define XS1_COPROC_CTRL0_OWNT_SET(x, v) \ + (((x) & ~XS1_COPROC_CTRL0_OWNT_MASK) | \ + (((v) << XS1_COPROC_CTRL0_OWNT_SHIFT) & XS1_COPROC_CTRL0_OWNT_MASK)) +#define XS1_COPROC_TBV0_WAITING_SHIFT 0x0 +#define XS1_COPROC_TBV0_WAITING_SIZE 0x8 +#define XS1_COPROC_TBV0_WAITING_MASK \ + (((1 << XS1_COPROC_TBV0_WAITING_SIZE) - 1) << XS1_COPROC_TBV0_WAITING_SHIFT) +#define XS1_COPROC_TBV0_WAITING(x) \ + (((x)&XS1_COPROC_TBV0_WAITING_MASK) >> XS1_COPROC_TBV0_WAITING_SHIFT) +#define XS1_COPROC_TBV0_WAITING_SET(x, v) \ + (((x) & ~XS1_COPROC_TBV0_WAITING_MASK) | \ + (((v) << XS1_COPROC_TBV0_WAITING_SHIFT) & XS1_COPROC_TBV0_WAITING_MASK)) +#define XS1_DBG_INT_REQ_DBG_SHIFT 0x0 +#define XS1_DBG_INT_REQ_DBG_SIZE 0x1 +#define XS1_DBG_INT_REQ_DBG_MASK \ + (((1 << XS1_DBG_INT_REQ_DBG_SIZE) - 1) << XS1_DBG_INT_REQ_DBG_SHIFT) +#define XS1_DBG_INT_REQ_DBG(x) \ + (((x)&XS1_DBG_INT_REQ_DBG_MASK) >> XS1_DBG_INT_REQ_DBG_SHIFT) +#define XS1_DBG_INT_REQ_DBG_SET(x, v) \ + (((x) & ~XS1_DBG_INT_REQ_DBG_MASK) | \ + (((v) << XS1_DBG_INT_REQ_DBG_SHIFT) & XS1_DBG_INT_REQ_DBG_MASK)) +#define XS1_DBG_INT_IN_DBG_SHIFT 0x1 +#define XS1_DBG_INT_IN_DBG_SIZE 0x1 +#define XS1_DBG_INT_IN_DBG_MASK \ + (((1 << XS1_DBG_INT_IN_DBG_SIZE) - 1) << XS1_DBG_INT_IN_DBG_SHIFT) +#define XS1_DBG_INT_IN_DBG(x) \ + (((x)&XS1_DBG_INT_IN_DBG_MASK) >> XS1_DBG_INT_IN_DBG_SHIFT) +#define XS1_DBG_INT_IN_DBG_SET(x, v) \ + (((x) & ~XS1_DBG_INT_IN_DBG_MASK) | \ + (((v) << XS1_DBG_INT_IN_DBG_SHIFT) & XS1_DBG_INT_IN_DBG_MASK)) +#define XS1_DBG_CTRL_PSWITCH_RO_SHIFT 0x1f +#define XS1_DBG_CTRL_PSWITCH_RO_SIZE 0x1 +#define XS1_DBG_CTRL_PSWITCH_RO_MASK \ + (((1 << XS1_DBG_CTRL_PSWITCH_RO_SIZE) - 1) << XS1_DBG_CTRL_PSWITCH_RO_SHIFT) +#define XS1_DBG_CTRL_PSWITCH_RO(x) \ + (((x)&XS1_DBG_CTRL_PSWITCH_RO_MASK) >> XS1_DBG_CTRL_PSWITCH_RO_SHIFT) +#define XS1_DBG_CTRL_PSWITCH_RO_SET(x, v) \ + (((x) & ~XS1_DBG_CTRL_PSWITCH_RO_MASK) | \ + (((v) << XS1_DBG_CTRL_PSWITCH_RO_SHIFT) & XS1_DBG_CTRL_PSWITCH_RO_MASK)) +#define XS1_DEVICE_ID0_VERSION_SHIFT 0x0 +#define XS1_DEVICE_ID0_VERSION_SIZE 0x8 +#define XS1_DEVICE_ID0_VERSION_MASK \ + (((1 << XS1_DEVICE_ID0_VERSION_SIZE) - 1) << XS1_DEVICE_ID0_VERSION_SHIFT) +#define XS1_DEVICE_ID0_VERSION(x) \ + (((x)&XS1_DEVICE_ID0_VERSION_MASK) >> XS1_DEVICE_ID0_VERSION_SHIFT) +#define XS1_DEVICE_ID0_VERSION_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID0_VERSION_MASK) | \ + (((v) << XS1_DEVICE_ID0_VERSION_SHIFT) & XS1_DEVICE_ID0_VERSION_MASK)) +#define XS1_DEVICE_ID0_REVISION_SHIFT 0x8 +#define XS1_DEVICE_ID0_REVISION_SIZE 0x8 +#define XS1_DEVICE_ID0_REVISION_MASK \ + (((1 << XS1_DEVICE_ID0_REVISION_SIZE) - 1) << XS1_DEVICE_ID0_REVISION_SHIFT) +#define XS1_DEVICE_ID0_REVISION(x) \ + (((x)&XS1_DEVICE_ID0_REVISION_MASK) >> XS1_DEVICE_ID0_REVISION_SHIFT) +#define XS1_DEVICE_ID0_REVISION_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID0_REVISION_MASK) | \ + (((v) << XS1_DEVICE_ID0_REVISION_SHIFT) & XS1_DEVICE_ID0_REVISION_MASK)) +#define XS1_DEVICE_ID0_NODE_SHIFT 0x10 +#define XS1_DEVICE_ID0_NODE_SIZE 0x8 +#define XS1_DEVICE_ID0_NODE_MASK \ + (((1 << XS1_DEVICE_ID0_NODE_SIZE) - 1) << XS1_DEVICE_ID0_NODE_SHIFT) +#define XS1_DEVICE_ID0_NODE(x) \ + (((x)&XS1_DEVICE_ID0_NODE_MASK) >> XS1_DEVICE_ID0_NODE_SHIFT) +#define XS1_DEVICE_ID0_NODE_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID0_NODE_MASK) | \ + (((v) << XS1_DEVICE_ID0_NODE_SHIFT) & XS1_DEVICE_ID0_NODE_MASK)) +#define XS1_DEVICE_ID0_PID_SHIFT 0x18 +#define XS1_DEVICE_ID0_PID_SIZE 0x8 +#define XS1_DEVICE_ID0_PID_MASK \ + (((1 << XS1_DEVICE_ID0_PID_SIZE) - 1) << XS1_DEVICE_ID0_PID_SHIFT) +#define XS1_DEVICE_ID0_PID(x) \ + (((x)&XS1_DEVICE_ID0_PID_MASK) >> XS1_DEVICE_ID0_PID_SHIFT) +#define XS1_DEVICE_ID0_PID_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID0_PID_MASK) | \ + (((v) << XS1_DEVICE_ID0_PID_SHIFT) & XS1_DEVICE_ID0_PID_MASK)) +#define XS1_DEVICE_ID1_NUM_THREADS_SHIFT 0x0 +#define XS1_DEVICE_ID1_NUM_THREADS_SIZE 0x8 +#define XS1_DEVICE_ID1_NUM_THREADS_MASK \ + (((1 << XS1_DEVICE_ID1_NUM_THREADS_SIZE) - 1) \ + << XS1_DEVICE_ID1_NUM_THREADS_SHIFT) +#define XS1_DEVICE_ID1_NUM_THREADS(x) \ + (((x)&XS1_DEVICE_ID1_NUM_THREADS_MASK) >> XS1_DEVICE_ID1_NUM_THREADS_SHIFT) +#define XS1_DEVICE_ID1_NUM_THREADS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID1_NUM_THREADS_MASK) | \ + (((v) << XS1_DEVICE_ID1_NUM_THREADS_SHIFT) & \ + XS1_DEVICE_ID1_NUM_THREADS_MASK)) +#define XS1_DEVICE_ID1_NUM_SYNCS_SHIFT 0x8 +#define XS1_DEVICE_ID1_NUM_SYNCS_SIZE 0x8 +#define XS1_DEVICE_ID1_NUM_SYNCS_MASK \ + (((1 << XS1_DEVICE_ID1_NUM_SYNCS_SIZE) - 1) << XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) +#define XS1_DEVICE_ID1_NUM_SYNCS(x) \ + (((x)&XS1_DEVICE_ID1_NUM_SYNCS_MASK) >> XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) +#define XS1_DEVICE_ID1_NUM_SYNCS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID1_NUM_SYNCS_MASK) | \ + (((v) << XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) & XS1_DEVICE_ID1_NUM_SYNCS_MASK)) +#define XS1_DEVICE_ID1_NUM_LOCKS_SHIFT 0x10 +#define XS1_DEVICE_ID1_NUM_LOCKS_SIZE 0x8 +#define XS1_DEVICE_ID1_NUM_LOCKS_MASK \ + (((1 << XS1_DEVICE_ID1_NUM_LOCKS_SIZE) - 1) << XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) +#define XS1_DEVICE_ID1_NUM_LOCKS(x) \ + (((x)&XS1_DEVICE_ID1_NUM_LOCKS_MASK) >> XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) +#define XS1_DEVICE_ID1_NUM_LOCKS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID1_NUM_LOCKS_MASK) | \ + (((v) << XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) & XS1_DEVICE_ID1_NUM_LOCKS_MASK)) +#define XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT 0x18 +#define XS1_DEVICE_ID1_NUM_CHANENDS_SIZE 0x8 +#define XS1_DEVICE_ID1_NUM_CHANENDS_MASK \ + (((1 << XS1_DEVICE_ID1_NUM_CHANENDS_SIZE) - 1) \ + << XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) +#define XS1_DEVICE_ID1_NUM_CHANENDS(x) \ + (((x)&XS1_DEVICE_ID1_NUM_CHANENDS_MASK) >> XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) +#define XS1_DEVICE_ID1_NUM_CHANENDS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID1_NUM_CHANENDS_MASK) | \ + (((v) << XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) & \ + XS1_DEVICE_ID1_NUM_CHANENDS_MASK)) +#define XS1_DEVICE_ID2_NUM_TIMERS_SHIFT 0x0 +#define XS1_DEVICE_ID2_NUM_TIMERS_SIZE 0x8 +#define XS1_DEVICE_ID2_NUM_TIMERS_MASK \ + (((1 << XS1_DEVICE_ID2_NUM_TIMERS_SIZE) - 1) \ + << XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) +#define XS1_DEVICE_ID2_NUM_TIMERS(x) \ + (((x)&XS1_DEVICE_ID2_NUM_TIMERS_MASK) >> XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) +#define XS1_DEVICE_ID2_NUM_TIMERS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID2_NUM_TIMERS_MASK) | \ + (((v) << XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) & \ + XS1_DEVICE_ID2_NUM_TIMERS_MASK)) +#define XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT 0x8 +#define XS1_DEVICE_ID2_NUM_CLKBLKS_SIZE 0x8 +#define XS1_DEVICE_ID2_NUM_CLKBLKS_MASK \ + (((1 << XS1_DEVICE_ID2_NUM_CLKBLKS_SIZE) - 1) \ + << XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) +#define XS1_DEVICE_ID2_NUM_CLKBLKS(x) \ + (((x)&XS1_DEVICE_ID2_NUM_CLKBLKS_MASK) >> XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) +#define XS1_DEVICE_ID2_NUM_CLKBLKS_SET(x, v) \ + (((x) & ~XS1_DEVICE_ID2_NUM_CLKBLKS_MASK) | \ + (((v) << XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) & \ + XS1_DEVICE_ID2_NUM_CLKBLKS_MASK)) +#define XS1_JUNK_SHIFT 0x2 +#define XS1_JUNK_SIZE 0x1 +#define XS1_JUNK_MASK (((1 << XS1_JUNK_SIZE) - 1) << XS1_JUNK_SHIFT) +#define XS1_JUNK(x) (((x)&XS1_JUNK_MASK) >> XS1_JUNK_SHIFT) +#define XS1_JUNK_SET(x, v) \ + (((x) & ~XS1_JUNK_MASK) | (((v) << XS1_JUNK_SHIFT) & XS1_JUNK_MASK)) +#define XS1_NETWORK_SHIFT 0x4 +#define XS1_NETWORK_SIZE 0x2 +#define XS1_NETWORK_MASK (((1 << XS1_NETWORK_SIZE) - 1) << XS1_NETWORK_SHIFT) +#define XS1_NETWORK(x) (((x)&XS1_NETWORK_MASK) >> XS1_NETWORK_SHIFT) +#define XS1_NETWORK_SET(x, v) \ + (((x) & ~XS1_NETWORK_MASK) | (((v) << XS1_NETWORK_SHIFT) & XS1_NETWORK_MASK)) +#define XS1_SRC_TARGET_ID_SHIFT 0x10 +#define XS1_SRC_TARGET_ID_SIZE 0x8 +#define XS1_SRC_TARGET_ID_MASK \ + (((1 << XS1_SRC_TARGET_ID_SIZE) - 1) << XS1_SRC_TARGET_ID_SHIFT) +#define XS1_SRC_TARGET_ID(x) \ + (((x)&XS1_SRC_TARGET_ID_MASK) >> XS1_SRC_TARGET_ID_SHIFT) +#define XS1_SRC_TARGET_ID_SET(x, v) \ + (((x) & ~XS1_SRC_TARGET_ID_MASK) | \ + (((v) << XS1_SRC_TARGET_ID_SHIFT) & XS1_SRC_TARGET_ID_MASK)) +#define XS1_SRC_TARGET_TYPE_SHIFT 0x18 +#define XS1_SRC_TARGET_TYPE_SIZE 0x2 +#define XS1_SRC_TARGET_TYPE_MASK \ + (((1 << XS1_SRC_TARGET_TYPE_SIZE) - 1) << XS1_SRC_TARGET_TYPE_SHIFT) +#define XS1_SRC_TARGET_TYPE(x) \ + (((x)&XS1_SRC_TARGET_TYPE_MASK) >> XS1_SRC_TARGET_TYPE_SHIFT) +#define XS1_SRC_TARGET_TYPE_SET(x, v) \ + (((x) & ~XS1_SRC_TARGET_TYPE_MASK) | \ + (((v) << XS1_SRC_TARGET_TYPE_SHIFT) & XS1_SRC_TARGET_TYPE_MASK)) +#define XS1_SS_DEVICE_ID0_VERSION_SHIFT 0x0 +#define XS1_SS_DEVICE_ID0_VERSION_SIZE 0x8 +#define XS1_SS_DEVICE_ID0_VERSION_MASK \ + (((1 << XS1_SS_DEVICE_ID0_VERSION_SIZE) - 1) \ + << XS1_SS_DEVICE_ID0_VERSION_SHIFT) +#define XS1_SS_DEVICE_ID0_VERSION(x) \ + (((x)&XS1_SS_DEVICE_ID0_VERSION_MASK) >> XS1_SS_DEVICE_ID0_VERSION_SHIFT) +#define XS1_SS_DEVICE_ID0_VERSION_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID0_VERSION_MASK) | \ + (((v) << XS1_SS_DEVICE_ID0_VERSION_SHIFT) & \ + XS1_SS_DEVICE_ID0_VERSION_MASK)) +#define XS1_SS_DEVICE_ID0_REVISION_SHIFT 0x8 +#define XS1_SS_DEVICE_ID0_REVISION_SIZE 0x8 +#define XS1_SS_DEVICE_ID0_REVISION_MASK \ + (((1 << XS1_SS_DEVICE_ID0_REVISION_SIZE) - 1) \ + << XS1_SS_DEVICE_ID0_REVISION_SHIFT) +#define XS1_SS_DEVICE_ID0_REVISION(x) \ + (((x)&XS1_SS_DEVICE_ID0_REVISION_MASK) >> XS1_SS_DEVICE_ID0_REVISION_SHIFT) +#define XS1_SS_DEVICE_ID0_REVISION_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID0_REVISION_MASK) | \ + (((v) << XS1_SS_DEVICE_ID0_REVISION_SHIFT) & \ + XS1_SS_DEVICE_ID0_REVISION_MASK)) +#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT 0x10 +#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SIZE 0x8 +#define XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK \ + (((1 << XS1_SS_DEVICE_ID0_BOOT_CTRL_SIZE) - 1) \ + << XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) +#define XS1_SS_DEVICE_ID0_BOOT_CTRL(x) \ + (((x)&XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK) >> XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) +#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK) | \ + (((v) << XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) & \ + XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK)) +#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT 0x0 +#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SIZE 0x8 +#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK \ + (((1 << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SIZE) - 1) \ + << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC(x) \ + (((x)&XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK) >> \ + XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK) | \ + (((v) << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) & \ + XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK)) +#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT 0x8 +#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SIZE 0x8 +#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK \ + (((1 << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SIZE) - 1) \ + << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS(x) \ + (((x)&XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK) >> \ + XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK) | \ + (((v) << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) & \ + XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK)) +#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT 0x10 +#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SIZE 0x8 +#define XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK \ + (((1 << XS1_SS_DEVICE_ID1_NUM_SLINKS_SIZE) - 1) \ + << XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_SLINKS(x) \ + (((x)&XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK) >> \ + XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) +#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SET(x, v) \ + (((x) & ~XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK) | \ + (((v) << XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) & \ + XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK)) +#define XS1_SS_NODE_CONFIG_HEADERS_SHIFT 0x0 +#define XS1_SS_NODE_CONFIG_HEADERS_SIZE 0x1 +#define XS1_SS_NODE_CONFIG_HEADERS_MASK \ + (((1 << XS1_SS_NODE_CONFIG_HEADERS_SIZE) - 1) \ + << XS1_SS_NODE_CONFIG_HEADERS_SHIFT) +#define XS1_SS_NODE_CONFIG_HEADERS(x) \ + (((x)&XS1_SS_NODE_CONFIG_HEADERS_MASK) >> XS1_SS_NODE_CONFIG_HEADERS_SHIFT) +#define XS1_SS_NODE_CONFIG_HEADERS_SET(x, v) \ + (((x) & ~XS1_SS_NODE_CONFIG_HEADERS_MASK) | \ + (((v) << XS1_SS_NODE_CONFIG_HEADERS_SHIFT) & \ + XS1_SS_NODE_CONFIG_HEADERS_MASK)) +#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT 0x8 +#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SIZE 0x1 +#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK \ + (((1 << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SIZE) - 1) \ + << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) +#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG(x) \ + (((x)&XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK) >> \ + XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) +#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SET(x, v) \ + (((x) & ~XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK) | \ + (((v) << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) & \ + XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK)) +#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT 0x1f +#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SIZE 0x1 +#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK \ + (((1 << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SIZE) - 1) \ + << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) +#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE(x) \ + (((x)&XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK) >> \ + XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) +#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SET(x, v) \ + (((x) & ~XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK) | \ + (((v) << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) & \ + XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK)) +#define XS1_SS_NODE_ID_ID_SHIFT 0x0 +#define XS1_SS_NODE_ID_ID_SIZE 0x10 +#define XS1_SS_NODE_ID_ID_MASK \ + (((1 << XS1_SS_NODE_ID_ID_SIZE) - 1) << XS1_SS_NODE_ID_ID_SHIFT) +#define XS1_SS_NODE_ID_ID(x) \ + (((x)&XS1_SS_NODE_ID_ID_MASK) >> XS1_SS_NODE_ID_ID_SHIFT) +#define XS1_SS_NODE_ID_ID_SET(x, v) \ + (((x) & ~XS1_SS_NODE_ID_ID_MASK) | \ + (((v) << XS1_SS_NODE_ID_ID_SHIFT) & XS1_SS_NODE_ID_ID_MASK)) +#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT 0x0 +#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SIZE 0x7 +#define XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK \ + (((1 << XS1_SS_PLL_CTL_INPUT_DIVISOR_SIZE) - 1) \ + << XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) +#define XS1_SS_PLL_CTL_INPUT_DIVISOR(x) \ + (((x)&XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK) >> \ + XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) +#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SET(x, v) \ + (((x) & ~XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK) | \ + (((v) << XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) & \ + XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK)) +#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT 0x8 +#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SIZE 0xd +#define XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK \ + (((1 << XS1_SS_PLL_CTL_FEEDBACK_MUL_SIZE) - 1) \ + << XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) +#define XS1_SS_PLL_CTL_FEEDBACK_MUL(x) \ + (((x)&XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK) >> XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) +#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SET(x, v) \ + (((x) & ~XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK) | \ + (((v) << XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) & \ + XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK)) +#define XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT 0x17 +#define XS1_SS_PLL_CTL_POST_DIVISOR_SIZE 0x3 +#define XS1_SS_PLL_CTL_POST_DIVISOR_MASK \ + (((1 << XS1_SS_PLL_CTL_POST_DIVISOR_SIZE) - 1) \ + << XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) +#define XS1_SS_PLL_CTL_POST_DIVISOR(x) \ + (((x)&XS1_SS_PLL_CTL_POST_DIVISOR_MASK) >> XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) +#define XS1_SS_PLL_CTL_POST_DIVISOR_SET(x, v) \ + (((x) & ~XS1_SS_PLL_CTL_POST_DIVISOR_MASK) | \ + (((v) << XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) & \ + XS1_SS_PLL_CTL_POST_DIVISOR_MASK)) +#define XS1_SS_APP_PLL_ENABLE_SHIFT 0x1b +#define XS1_SS_APP_PLL_ENABLE_SIZE 0x1 +#define XS1_SS_APP_PLL_ENABLE_MASK \ + (((1 << XS1_SS_APP_PLL_ENABLE_SIZE) - 1) << XS1_SS_APP_PLL_ENABLE_SHIFT) +#define XS1_SS_APP_PLL_ENABLE(x) \ + (((x)&XS1_SS_APP_PLL_ENABLE_MASK) >> XS1_SS_APP_PLL_ENABLE_SHIFT) +#define XS1_SS_APP_PLL_ENABLE_SET(x, v) \ + (((x) & ~XS1_SS_APP_PLL_ENABLE_MASK) | \ + (((v) << XS1_SS_APP_PLL_ENABLE_SHIFT) & XS1_SS_APP_PLL_ENABLE_MASK)) +#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT 0x0 +#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SIZE 0x8 +#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK \ + (((1 << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SIZE) - 1) \ + << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) +#define XS1_SS_FRAC_N_PERIOD_CYC_CNT(x) \ + (((x)&XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK) >> \ + XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) +#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SET(x, v) \ + (((x) & ~XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK) | \ + (((v) << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) & \ + XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK)) +#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT 0x8 +#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SIZE 0x8 +#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK \ + (((1 << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SIZE) - 1) \ + << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) +#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT(x) \ + (((x)&XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK) >> \ + XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) +#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SET(x, v) \ + (((x) & ~XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK) | \ + (((v) << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) & \ + XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK)) +#define XS1_SS_FRAC_N_ENABLE_SHIFT 0x1f +#define XS1_SS_FRAC_N_ENABLE_SIZE 0x1 +#define XS1_SS_FRAC_N_ENABLE_MASK \ + (((1 << XS1_SS_FRAC_N_ENABLE_SIZE) - 1) << XS1_SS_FRAC_N_ENABLE_SHIFT) +#define XS1_SS_FRAC_N_ENABLE(x) \ + (((x)&XS1_SS_FRAC_N_ENABLE_MASK) >> XS1_SS_FRAC_N_ENABLE_SHIFT) +#define XS1_SS_FRAC_N_ENABLE_SET(x, v) \ + (((x) & ~XS1_SS_FRAC_N_ENABLE_MASK) | \ + (((v) << XS1_SS_FRAC_N_ENABLE_SHIFT) & XS1_SS_FRAC_N_ENABLE_MASK)) +#define XS1_SS_PLL_CTL_NLOCK_SHIFT 0x1e +#define XS1_SS_PLL_CTL_NRESET_SHIFT 0x1f +#define XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT 0x0 +#define XS1_SS_CLK_DIVIDER_CLK_DIV_SIZE 0x10 +#define XS1_SS_CLK_DIVIDER_CLK_DIV_MASK \ + (((1 << XS1_SS_CLK_DIVIDER_CLK_DIV_SIZE) - 1) \ + << XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) +#define XS1_SS_CLK_DIVIDER_CLK_DIV(x) \ + (((x)&XS1_SS_CLK_DIVIDER_CLK_DIV_MASK) >> XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) +#define XS1_SS_CLK_DIVIDER_CLK_DIV_SET(x, v) \ + (((x) & ~XS1_SS_CLK_DIVIDER_CLK_DIV_MASK) | \ + (((v) << XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) & \ + XS1_SS_CLK_DIVIDER_CLK_DIV_MASK)) +#define XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT 0x0 +#define XS1_SS_SSWITCH_REF_CLK_DIV_SIZE 0x10 +#define XS1_SS_SSWITCH_REF_CLK_DIV_MASK \ + (((1 << XS1_SS_SSWITCH_REF_CLK_DIV_SIZE) - 1) \ + << XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) +#define XS1_SS_SSWITCH_REF_CLK_DIV(x) \ + (((x)&XS1_SS_SSWITCH_REF_CLK_DIV_MASK) >> XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) +#define XS1_SS_SSWITCH_REF_CLK_DIV_SET(x, v) \ + (((x) & ~XS1_SS_SSWITCH_REF_CLK_DIV_MASK) | \ + (((v) << XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) & \ + XS1_SS_SSWITCH_REF_CLK_DIV_MASK)) +#define XS1_DIM0_DIR_SHIFT 0x0 +#define XS1_DIM0_DIR_SIZE 0x4 +#define XS1_DIM0_DIR_MASK (((1 << XS1_DIM0_DIR_SIZE) - 1) << XS1_DIM0_DIR_SHIFT) +#define XS1_DIM0_DIR(x) (((x)&XS1_DIM0_DIR_MASK) >> XS1_DIM0_DIR_SHIFT) +#define XS1_DIM0_DIR_SET(x, v) \ + (((x) & ~XS1_DIM0_DIR_MASK) | \ + (((v) << XS1_DIM0_DIR_SHIFT) & XS1_DIM0_DIR_MASK)) +#define XS1_DIM1_DIR_SHIFT 0x4 +#define XS1_DIM1_DIR_SIZE 0x4 +#define XS1_DIM1_DIR_MASK (((1 << XS1_DIM1_DIR_SIZE) - 1) << XS1_DIM1_DIR_SHIFT) +#define XS1_DIM1_DIR(x) (((x)&XS1_DIM1_DIR_MASK) >> XS1_DIM1_DIR_SHIFT) +#define XS1_DIM1_DIR_SET(x, v) \ + (((x) & ~XS1_DIM1_DIR_MASK) | \ + (((v) << XS1_DIM1_DIR_SHIFT) & XS1_DIM1_DIR_MASK)) +#define XS1_DIM2_DIR_SHIFT 0x8 +#define XS1_DIM2_DIR_SIZE 0x4 +#define XS1_DIM2_DIR_MASK (((1 << XS1_DIM2_DIR_SIZE) - 1) << XS1_DIM2_DIR_SHIFT) +#define XS1_DIM2_DIR(x) (((x)&XS1_DIM2_DIR_MASK) >> XS1_DIM2_DIR_SHIFT) +#define XS1_DIM2_DIR_SET(x, v) \ + (((x) & ~XS1_DIM2_DIR_MASK) | \ + (((v) << XS1_DIM2_DIR_SHIFT) & XS1_DIM2_DIR_MASK)) +#define XS1_DIM3_DIR_SHIFT 0xc +#define XS1_DIM3_DIR_SIZE 0x4 +#define XS1_DIM3_DIR_MASK (((1 << XS1_DIM3_DIR_SIZE) - 1) << XS1_DIM3_DIR_SHIFT) +#define XS1_DIM3_DIR(x) (((x)&XS1_DIM3_DIR_MASK) >> XS1_DIM3_DIR_SHIFT) +#define XS1_DIM3_DIR_SET(x, v) \ + (((x) & ~XS1_DIM3_DIR_MASK) | \ + (((v) << XS1_DIM3_DIR_SHIFT) & XS1_DIM3_DIR_MASK)) +#define XS1_DIM4_DIR_SHIFT 0x10 +#define XS1_DIM4_DIR_SIZE 0x4 +#define XS1_DIM4_DIR_MASK (((1 << XS1_DIM4_DIR_SIZE) - 1) << XS1_DIM4_DIR_SHIFT) +#define XS1_DIM4_DIR(x) (((x)&XS1_DIM4_DIR_MASK) >> XS1_DIM4_DIR_SHIFT) +#define XS1_DIM4_DIR_SET(x, v) \ + (((x) & ~XS1_DIM4_DIR_MASK) | \ + (((v) << XS1_DIM4_DIR_SHIFT) & XS1_DIM4_DIR_MASK)) +#define XS1_DIM5_DIR_SHIFT 0x14 +#define XS1_DIM5_DIR_SIZE 0x4 +#define XS1_DIM5_DIR_MASK (((1 << XS1_DIM5_DIR_SIZE) - 1) << XS1_DIM5_DIR_SHIFT) +#define XS1_DIM5_DIR(x) (((x)&XS1_DIM5_DIR_MASK) >> XS1_DIM5_DIR_SHIFT) +#define XS1_DIM5_DIR_SET(x, v) \ + (((x) & ~XS1_DIM5_DIR_MASK) | \ + (((v) << XS1_DIM5_DIR_SHIFT) & XS1_DIM5_DIR_MASK)) +#define XS1_DIM6_DIR_SHIFT 0x18 +#define XS1_DIM6_DIR_SIZE 0x4 +#define XS1_DIM6_DIR_MASK (((1 << XS1_DIM6_DIR_SIZE) - 1) << XS1_DIM6_DIR_SHIFT) +#define XS1_DIM6_DIR(x) (((x)&XS1_DIM6_DIR_MASK) >> XS1_DIM6_DIR_SHIFT) +#define XS1_DIM6_DIR_SET(x, v) \ + (((x) & ~XS1_DIM6_DIR_MASK) | \ + (((v) << XS1_DIM6_DIR_SHIFT) & XS1_DIM6_DIR_MASK)) +#define XS1_DIM7_DIR_SHIFT 0x1c +#define XS1_DIM7_DIR_SIZE 0x4 +#define XS1_DIM7_DIR_MASK (((1 << XS1_DIM7_DIR_SIZE) - 1) << XS1_DIM7_DIR_SHIFT) +#define XS1_DIM7_DIR(x) (((x)&XS1_DIM7_DIR_MASK) >> XS1_DIM7_DIR_SHIFT) +#define XS1_DIM7_DIR_SET(x, v) \ + (((x) & ~XS1_DIM7_DIR_MASK) | \ + (((v) << XS1_DIM7_DIR_SHIFT) & XS1_DIM7_DIR_MASK)) +#define XS1_DIM8_DIR_SHIFT 0x0 +#define XS1_DIM8_DIR_SIZE 0x4 +#define XS1_DIM8_DIR_MASK (((1 << XS1_DIM8_DIR_SIZE) - 1) << XS1_DIM8_DIR_SHIFT) +#define XS1_DIM8_DIR(x) (((x)&XS1_DIM8_DIR_MASK) >> XS1_DIM8_DIR_SHIFT) +#define XS1_DIM8_DIR_SET(x, v) \ + (((x) & ~XS1_DIM8_DIR_MASK) | \ + (((v) << XS1_DIM8_DIR_SHIFT) & XS1_DIM8_DIR_MASK)) +#define XS1_DIM9_DIR_SHIFT 0x4 +#define XS1_DIM9_DIR_SIZE 0x4 +#define XS1_DIM9_DIR_MASK (((1 << XS1_DIM9_DIR_SIZE) - 1) << XS1_DIM9_DIR_SHIFT) +#define XS1_DIM9_DIR(x) (((x)&XS1_DIM9_DIR_MASK) >> XS1_DIM9_DIR_SHIFT) +#define XS1_DIM9_DIR_SET(x, v) \ + (((x) & ~XS1_DIM9_DIR_MASK) | \ + (((v) << XS1_DIM9_DIR_SHIFT) & XS1_DIM9_DIR_MASK)) +#define XS1_DIMA_DIR_SHIFT 0x8 +#define XS1_DIMA_DIR_SIZE 0x4 +#define XS1_DIMA_DIR_MASK (((1 << XS1_DIMA_DIR_SIZE) - 1) << XS1_DIMA_DIR_SHIFT) +#define XS1_DIMA_DIR(x) (((x)&XS1_DIMA_DIR_MASK) >> XS1_DIMA_DIR_SHIFT) +#define XS1_DIMA_DIR_SET(x, v) \ + (((x) & ~XS1_DIMA_DIR_MASK) | \ + (((v) << XS1_DIMA_DIR_SHIFT) & XS1_DIMA_DIR_MASK)) +#define XS1_DIMB_DIR_SHIFT 0xc +#define XS1_DIMB_DIR_SIZE 0x4 +#define XS1_DIMB_DIR_MASK (((1 << XS1_DIMB_DIR_SIZE) - 1) << XS1_DIMB_DIR_SHIFT) +#define XS1_DIMB_DIR(x) (((x)&XS1_DIMB_DIR_MASK) >> XS1_DIMB_DIR_SHIFT) +#define XS1_DIMB_DIR_SET(x, v) \ + (((x) & ~XS1_DIMB_DIR_MASK) | \ + (((v) << XS1_DIMB_DIR_SHIFT) & XS1_DIMB_DIR_MASK)) +#define XS1_DIMC_DIR_SHIFT 0x10 +#define XS1_DIMC_DIR_SIZE 0x4 +#define XS1_DIMC_DIR_MASK (((1 << XS1_DIMC_DIR_SIZE) - 1) << XS1_DIMC_DIR_SHIFT) +#define XS1_DIMC_DIR(x) (((x)&XS1_DIMC_DIR_MASK) >> XS1_DIMC_DIR_SHIFT) +#define XS1_DIMC_DIR_SET(x, v) \ + (((x) & ~XS1_DIMC_DIR_MASK) | \ + (((v) << XS1_DIMC_DIR_SHIFT) & XS1_DIMC_DIR_MASK)) +#define XS1_DIMD_DIR_SHIFT 0x14 +#define XS1_DIMD_DIR_SIZE 0x4 +#define XS1_DIMD_DIR_MASK (((1 << XS1_DIMD_DIR_SIZE) - 1) << XS1_DIMD_DIR_SHIFT) +#define XS1_DIMD_DIR(x) (((x)&XS1_DIMD_DIR_MASK) >> XS1_DIMD_DIR_SHIFT) +#define XS1_DIMD_DIR_SET(x, v) \ + (((x) & ~XS1_DIMD_DIR_MASK) | \ + (((v) << XS1_DIMD_DIR_SHIFT) & XS1_DIMD_DIR_MASK)) +#define XS1_DIME_DIR_SHIFT 0x18 +#define XS1_DIME_DIR_SIZE 0x4 +#define XS1_DIME_DIR_MASK (((1 << XS1_DIME_DIR_SIZE) - 1) << XS1_DIME_DIR_SHIFT) +#define XS1_DIME_DIR(x) (((x)&XS1_DIME_DIR_MASK) >> XS1_DIME_DIR_SHIFT) +#define XS1_DIME_DIR_SET(x, v) \ + (((x) & ~XS1_DIME_DIR_MASK) | \ + (((v) << XS1_DIME_DIR_SHIFT) & XS1_DIME_DIR_MASK)) +#define XS1_DIMF_DIR_SHIFT 0x1c +#define XS1_DIMF_DIR_SIZE 0x4 +#define XS1_DIMF_DIR_MASK (((1 << XS1_DIMF_DIR_SIZE) - 1) << XS1_DIMF_DIR_SHIFT) +#define XS1_DIMF_DIR(x) (((x)&XS1_DIMF_DIR_MASK) >> XS1_DIMF_DIR_SHIFT) +#define XS1_DIMF_DIR_SET(x, v) \ + (((x) & ~XS1_DIMF_DIR_MASK) | \ + (((v) << XS1_DIMF_DIR_SHIFT) & XS1_DIMF_DIR_MASK)) +#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT 0x0 +#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SIZE 0x1 +#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK \ + (((1 << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SIZE) - 1) \ + << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG(x) \ + (((x)&XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK) >> \ + XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SET(x, v) \ + (((x) & ~XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK) | \ + (((v) << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) & \ + XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK)) +#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT 0x1 +#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SIZE 0x1 +#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK \ + (((1 << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SIZE) - 1) \ + << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) +#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ(x) \ + (((x)&XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK) >> \ + XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) +#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SET(x, v) \ + (((x) & ~XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK) | \ + (((v) << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) & \ + XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK)) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT 0x0 +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SIZE 0x1 +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK \ + (((1 << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SIZE) - 1) \ + << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG(x) \ + (((x)&XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK) >> \ + XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SET(x, v) \ + (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK) | \ + (((v) << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) & \ + XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK)) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT 0x1 +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SIZE 0x1 +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK \ + (((1 << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SIZE) - 1) \ + << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG(x) \ + (((x)&XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK) >> \ + XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SET(x, v) \ + (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK) | \ + (((v) << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) & \ + XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK)) +#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT 0x4 +#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SIZE 0x1 +#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK \ + (((1 << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SIZE) - 1) \ + << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG(x) \ + (((x)&XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK) >> \ + XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) +#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SET(x, v) \ + (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK) | \ + (((v) << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) & \ + XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK)) +#define XS1_LINK_SRC_INUSE_SHIFT 0x0 +#define XS1_LINK_SRC_INUSE_SIZE 0x1 +#define XS1_LINK_SRC_INUSE_MASK \ + (((1 << XS1_LINK_SRC_INUSE_SIZE) - 1) << XS1_LINK_SRC_INUSE_SHIFT) +#define XS1_LINK_SRC_INUSE(x) \ + (((x)&XS1_LINK_SRC_INUSE_MASK) >> XS1_LINK_SRC_INUSE_SHIFT) +#define XS1_LINK_SRC_INUSE_SET(x, v) \ + (((x) & ~XS1_LINK_SRC_INUSE_MASK) | \ + (((v) << XS1_LINK_SRC_INUSE_SHIFT) & XS1_LINK_SRC_INUSE_MASK)) +#define XS1_LINK_DST_INUSE_SHIFT 0x1 +#define XS1_LINK_DST_INUSE_SIZE 0x1 +#define XS1_LINK_DST_INUSE_MASK \ + (((1 << XS1_LINK_DST_INUSE_SIZE) - 1) << XS1_LINK_DST_INUSE_SHIFT) +#define XS1_LINK_DST_INUSE(x) \ + (((x)&XS1_LINK_DST_INUSE_MASK) >> XS1_LINK_DST_INUSE_SHIFT) +#define XS1_LINK_DST_INUSE_SET(x, v) \ + (((x) & ~XS1_LINK_DST_INUSE_MASK) | \ + (((v) << XS1_LINK_DST_INUSE_SHIFT) & XS1_LINK_DST_INUSE_MASK)) +#define XS1_LINK_JUNK_SHIFT 0x2 +#define XS1_LINK_JUNK_SIZE 0x1 +#define XS1_LINK_JUNK_MASK \ + (((1 << XS1_LINK_JUNK_SIZE) - 1) << XS1_LINK_JUNK_SHIFT) +#define XS1_LINK_JUNK(x) (((x)&XS1_LINK_JUNK_MASK) >> XS1_LINK_JUNK_SHIFT) +#define XS1_LINK_JUNK_SET(x, v) \ + (((x) & ~XS1_LINK_JUNK_MASK) | \ + (((v) << XS1_LINK_JUNK_SHIFT) & XS1_LINK_JUNK_MASK)) +#define XS1_LINK_NETWORK_SHIFT 0x4 +#define XS1_LINK_NETWORK_SIZE 0x2 +#define XS1_LINK_NETWORK_MASK \ + (((1 << XS1_LINK_NETWORK_SIZE) - 1) << XS1_LINK_NETWORK_SHIFT) +#define XS1_LINK_NETWORK(x) \ + (((x)&XS1_LINK_NETWORK_MASK) >> XS1_LINK_NETWORK_SHIFT) +#define XS1_LINK_NETWORK_SET(x, v) \ + (((x) & ~XS1_LINK_NETWORK_MASK) | \ + (((v) << XS1_LINK_NETWORK_SHIFT) & XS1_LINK_NETWORK_MASK)) +#define XS1_LINK_DIRECTION_SHIFT 0x8 +#define XS1_LINK_DIRECTION_SIZE 0x4 +#define XS1_LINK_DIRECTION_MASK \ + (((1 << XS1_LINK_DIRECTION_SIZE) - 1) << XS1_LINK_DIRECTION_SHIFT) +#define XS1_LINK_DIRECTION(x) \ + (((x)&XS1_LINK_DIRECTION_MASK) >> XS1_LINK_DIRECTION_SHIFT) +#define XS1_LINK_DIRECTION_SET(x, v) \ + (((x) & ~XS1_LINK_DIRECTION_MASK) | \ + (((v) << XS1_LINK_DIRECTION_SHIFT) & XS1_LINK_DIRECTION_MASK)) +#define XS1_XLINK_INTER_TOKEN_DELAY_SHIFT 0x0 +#define XS1_XLINK_INTER_TOKEN_DELAY_SIZE 0xb +#define XS1_XLINK_INTER_TOKEN_DELAY_MASK \ + (((1 << XS1_XLINK_INTER_TOKEN_DELAY_SIZE) - 1) \ + << XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) +#define XS1_XLINK_INTER_TOKEN_DELAY(x) \ + (((x)&XS1_XLINK_INTER_TOKEN_DELAY_MASK) >> XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) +#define XS1_XLINK_INTER_TOKEN_DELAY_SET(x, v) \ + (((x) & ~XS1_XLINK_INTER_TOKEN_DELAY_MASK) | \ + (((v) << XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) & \ + XS1_XLINK_INTER_TOKEN_DELAY_MASK)) +#define XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT 0xb +#define XS1_XLINK_INTRA_TOKEN_DELAY_SIZE 0xb +#define XS1_XLINK_INTRA_TOKEN_DELAY_MASK \ + (((1 << XS1_XLINK_INTRA_TOKEN_DELAY_SIZE) - 1) \ + << XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) +#define XS1_XLINK_INTRA_TOKEN_DELAY(x) \ + (((x)&XS1_XLINK_INTRA_TOKEN_DELAY_MASK) >> XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) +#define XS1_XLINK_INTRA_TOKEN_DELAY_SET(x, v) \ + (((x) & ~XS1_XLINK_INTRA_TOKEN_DELAY_MASK) | \ + (((v) << XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) & \ + XS1_XLINK_INTRA_TOKEN_DELAY_MASK)) +#define XS1_XLINK_RX_RESET_SHIFT 0x17 +#define XS1_XLINK_RX_RESET_SIZE 0x1 +#define XS1_XLINK_RX_RESET_MASK \ + (((1 << XS1_XLINK_RX_RESET_SIZE) - 1) << XS1_XLINK_RX_RESET_SHIFT) +#define XS1_XLINK_RX_RESET(x) \ + (((x)&XS1_XLINK_RX_RESET_MASK) >> XS1_XLINK_RX_RESET_SHIFT) +#define XS1_XLINK_RX_RESET_SET(x, v) \ + (((x) & ~XS1_XLINK_RX_RESET_MASK) | \ + (((v) << XS1_XLINK_RX_RESET_SHIFT) & XS1_XLINK_RX_RESET_MASK)) +#define XS1_XLINK_HELLO_SHIFT 0x18 +#define XS1_XLINK_HELLO_SIZE 0x1 +#define XS1_XLINK_HELLO_MASK \ + (((1 << XS1_XLINK_HELLO_SIZE) - 1) << XS1_XLINK_HELLO_SHIFT) +#define XS1_XLINK_HELLO(x) (((x)&XS1_XLINK_HELLO_MASK) >> XS1_XLINK_HELLO_SHIFT) +#define XS1_XLINK_HELLO_SET(x, v) \ + (((x) & ~XS1_XLINK_HELLO_MASK) | \ + (((v) << XS1_XLINK_HELLO_SHIFT) & XS1_XLINK_HELLO_MASK)) +#define XS1_TX_CREDIT_SHIFT 0x19 +#define XS1_TX_CREDIT_SIZE 0x1 +#define XS1_TX_CREDIT_MASK \ + (((1 << XS1_TX_CREDIT_SIZE) - 1) << XS1_TX_CREDIT_SHIFT) +#define XS1_TX_CREDIT(x) (((x)&XS1_TX_CREDIT_MASK) >> XS1_TX_CREDIT_SHIFT) +#define XS1_TX_CREDIT_SET(x, v) \ + (((x) & ~XS1_TX_CREDIT_MASK) | \ + (((v) << XS1_TX_CREDIT_SHIFT) & XS1_TX_CREDIT_MASK)) +#define XS1_RX_CREDIT_SHIFT 0x1a +#define XS1_RX_CREDIT_SIZE 0x1 +#define XS1_RX_CREDIT_MASK \ + (((1 << XS1_RX_CREDIT_SIZE) - 1) << XS1_RX_CREDIT_SHIFT) +#define XS1_RX_CREDIT(x) (((x)&XS1_RX_CREDIT_MASK) >> XS1_RX_CREDIT_SHIFT) +#define XS1_RX_CREDIT_SET(x, v) \ + (((x) & ~XS1_RX_CREDIT_MASK) | \ + (((v) << XS1_RX_CREDIT_SHIFT) & XS1_RX_CREDIT_MASK)) +#define XS1_XLINK_RX_ERROR_SHIFT 0x1b +#define XS1_XLINK_RX_ERROR_SIZE 0x1 +#define XS1_XLINK_RX_ERROR_MASK \ + (((1 << XS1_XLINK_RX_ERROR_SIZE) - 1) << XS1_XLINK_RX_ERROR_SHIFT) +#define XS1_XLINK_RX_ERROR(x) \ + (((x)&XS1_XLINK_RX_ERROR_MASK) >> XS1_XLINK_RX_ERROR_SHIFT) +#define XS1_XLINK_RX_ERROR_SET(x, v) \ + (((x) & ~XS1_XLINK_RX_ERROR_MASK) | \ + (((v) << XS1_XLINK_RX_ERROR_SHIFT) & XS1_XLINK_RX_ERROR_MASK)) +#define XS1_XLINK_WIDE_SHIFT 0x1e +#define XS1_XLINK_WIDE_SIZE 0x1 +#define XS1_XLINK_WIDE_MASK \ + (((1 << XS1_XLINK_WIDE_SIZE) - 1) << XS1_XLINK_WIDE_SHIFT) +#define XS1_XLINK_WIDE(x) (((x)&XS1_XLINK_WIDE_MASK) >> XS1_XLINK_WIDE_SHIFT) +#define XS1_XLINK_WIDE_SET(x, v) \ + (((x) & ~XS1_XLINK_WIDE_MASK) | \ + (((v) << XS1_XLINK_WIDE_SHIFT) & XS1_XLINK_WIDE_MASK)) +#define XS1_XLINK_ENABLE_SHIFT 0x1f +#define XS1_XLINK_ENABLE_SIZE 0x1 +#define XS1_XLINK_ENABLE_MASK \ + (((1 << XS1_XLINK_ENABLE_SIZE) - 1) << XS1_XLINK_ENABLE_SHIFT) +#define XS1_XLINK_ENABLE(x) \ + (((x)&XS1_XLINK_ENABLE_MASK) >> XS1_XLINK_ENABLE_SHIFT) +#define XS1_XLINK_ENABLE_SET(x, v) \ + (((x) & ~XS1_XLINK_ENABLE_MASK) | \ + (((v) << XS1_XLINK_ENABLE_SHIFT) & XS1_XLINK_ENABLE_MASK)) +#define XS1_XSTATIC_DEST_CHAN_END_SHIFT 0x0 +#define XS1_XSTATIC_DEST_CHAN_END_SIZE 0x5 +#define XS1_XSTATIC_DEST_CHAN_END_MASK \ + (((1 << XS1_XSTATIC_DEST_CHAN_END_SIZE) - 1) \ + << XS1_XSTATIC_DEST_CHAN_END_SHIFT) +#define XS1_XSTATIC_DEST_CHAN_END(x) \ + (((x)&XS1_XSTATIC_DEST_CHAN_END_MASK) >> XS1_XSTATIC_DEST_CHAN_END_SHIFT) +#define XS1_XSTATIC_DEST_CHAN_END_SET(x, v) \ + (((x) & ~XS1_XSTATIC_DEST_CHAN_END_MASK) | \ + (((v) << XS1_XSTATIC_DEST_CHAN_END_SHIFT) & \ + XS1_XSTATIC_DEST_CHAN_END_MASK)) +#define XS1_XSTATIC_ENABLE_SHIFT 0x1f +#define XS1_XSTATIC_ENABLE_SIZE 0x1 +#define XS1_XSTATIC_ENABLE_MASK \ + (((1 << XS1_XSTATIC_ENABLE_SIZE) - 1) << XS1_XSTATIC_ENABLE_SHIFT) +#define XS1_XSTATIC_ENABLE(x) \ + (((x)&XS1_XSTATIC_ENABLE_MASK) >> XS1_XSTATIC_ENABLE_SHIFT) +#define XS1_XSTATIC_ENABLE_SET(x, v) \ + (((x) & ~XS1_XSTATIC_ENABLE_MASK) | \ + (((v) << XS1_XSTATIC_ENABLE_SHIFT) & XS1_XSTATIC_ENABLE_MASK)) +#define XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT 0x8 +#define XS1_SSCTRL_PSCTRL_CORE_NUM_SIZE 0x8 +#define XS1_SSCTRL_PSCTRL_CORE_NUM_MASK \ + (((1 << XS1_SSCTRL_PSCTRL_CORE_NUM_SIZE) - 1) \ + << XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) +#define XS1_SSCTRL_PSCTRL_CORE_NUM(x) \ + (((x)&XS1_SSCTRL_PSCTRL_CORE_NUM_MASK) >> XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) +#define XS1_SSCTRL_PSCTRL_CORE_NUM_SET(x, v) \ + (((x) & ~XS1_SSCTRL_PSCTRL_CORE_NUM_MASK) | \ + (((v) << XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) & \ + XS1_SSCTRL_PSCTRL_CORE_NUM_MASK)) +#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT 0x0 +#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SIZE 0x2 +#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK \ + (((1 << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT(x) \ + (((x)&XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK) >> \ + XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) & \ + XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK)) +#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT 0x2 +#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SIZE 0x1 +#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK \ + (((1 << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT(x) \ + (((x)&XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK) >> \ + XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) & \ + XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK)) +#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT 0x3 +#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SIZE 0x2 +#define XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK \ + (((1 << XS1_USB_PHY_CFG0_UTMI_OPMODE_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_OPMODE(x) \ + (((x)&XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK) >> \ + XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) & \ + XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK)) +#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT 0x5 +#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SIZE 0x1 +#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK \ + (((1 << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM(x) \ + (((x)&XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK) >> \ + XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) +#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) & \ + XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK)) +#define XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT 0x6 +#define XS1_USB_PHY_CFG0_DPPULLDOWN_SIZE 0x1 +#define XS1_USB_PHY_CFG0_DPPULLDOWN_MASK \ + (((1 << XS1_USB_PHY_CFG0_DPPULLDOWN_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) +#define XS1_USB_PHY_CFG0_DPPULLDOWN(x) \ + (((x)&XS1_USB_PHY_CFG0_DPPULLDOWN_MASK) >> XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) +#define XS1_USB_PHY_CFG0_DPPULLDOWN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_DPPULLDOWN_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) & \ + XS1_USB_PHY_CFG0_DPPULLDOWN_MASK)) +#define XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT 0x7 +#define XS1_USB_PHY_CFG0_DMPULLDOWN_SIZE 0x1 +#define XS1_USB_PHY_CFG0_DMPULLDOWN_MASK \ + (((1 << XS1_USB_PHY_CFG0_DMPULLDOWN_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) +#define XS1_USB_PHY_CFG0_DMPULLDOWN(x) \ + (((x)&XS1_USB_PHY_CFG0_DMPULLDOWN_MASK) >> XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) +#define XS1_USB_PHY_CFG0_DMPULLDOWN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_DMPULLDOWN_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) & \ + XS1_USB_PHY_CFG0_DMPULLDOWN_MASK)) +#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT 0x8 +#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SIZE 0x1 +#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK \ + (((1 << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) +#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN(x) \ + (((x)&XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK) >> \ + XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) +#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) & \ + XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK)) +#define XS1_USB_PHY_CFG0_PLL_EN_SHIFT 0x9 +#define XS1_USB_PHY_CFG0_PLL_EN_SIZE 0x1 +#define XS1_USB_PHY_CFG0_PLL_EN_MASK \ + (((1 << XS1_USB_PHY_CFG0_PLL_EN_SIZE) - 1) << XS1_USB_PHY_CFG0_PLL_EN_SHIFT) +#define XS1_USB_PHY_CFG0_PLL_EN(x) \ + (((x)&XS1_USB_PHY_CFG0_PLL_EN_MASK) >> XS1_USB_PHY_CFG0_PLL_EN_SHIFT) +#define XS1_USB_PHY_CFG0_PLL_EN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_PLL_EN_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_PLL_EN_SHIFT) & XS1_USB_PHY_CFG0_PLL_EN_MASK)) +#define XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT 0xa +#define XS1_USB_PHY_CFG0_LPM_ALIVE_SIZE 0x1 +#define XS1_USB_PHY_CFG0_LPM_ALIVE_MASK \ + (((1 << XS1_USB_PHY_CFG0_LPM_ALIVE_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) +#define XS1_USB_PHY_CFG0_LPM_ALIVE(x) \ + (((x)&XS1_USB_PHY_CFG0_LPM_ALIVE_MASK) >> XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) +#define XS1_USB_PHY_CFG0_LPM_ALIVE_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_LPM_ALIVE_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) & \ + XS1_USB_PHY_CFG0_LPM_ALIVE_MASK)) +#define XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT 0xb +#define XS1_USB_PHY_CFG0_IDPAD_EN_SIZE 0x1 +#define XS1_USB_PHY_CFG0_IDPAD_EN_MASK \ + (((1 << XS1_USB_PHY_CFG0_IDPAD_EN_SIZE) - 1) \ + << XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) +#define XS1_USB_PHY_CFG0_IDPAD_EN(x) \ + (((x)&XS1_USB_PHY_CFG0_IDPAD_EN_MASK) >> XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) +#define XS1_USB_PHY_CFG0_IDPAD_EN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_IDPAD_EN_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) & \ + XS1_USB_PHY_CFG0_IDPAD_EN_MASK)) +#define XS1_USB_PHY_CFG0_XTLSEL_SHIFT 0xc +#define XS1_USB_PHY_CFG0_XTLSEL_SIZE 0x3 +#define XS1_USB_PHY_CFG0_XTLSEL_MASK \ + (((1 << XS1_USB_PHY_CFG0_XTLSEL_SIZE) - 1) << XS1_USB_PHY_CFG0_XTLSEL_SHIFT) +#define XS1_USB_PHY_CFG0_XTLSEL(x) \ + (((x)&XS1_USB_PHY_CFG0_XTLSEL_MASK) >> XS1_USB_PHY_CFG0_XTLSEL_SHIFT) +#define XS1_USB_PHY_CFG0_XTLSEL_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG0_XTLSEL_MASK) | \ + (((v) << XS1_USB_PHY_CFG0_XTLSEL_SHIFT) & XS1_USB_PHY_CFG0_XTLSEL_MASK)) +#define XS1_USB_PHY_CFG2_PONRST_SHIFT 0x0 +#define XS1_USB_PHY_CFG2_PONRST_SIZE 0x1 +#define XS1_USB_PHY_CFG2_PONRST_MASK \ + (((1 << XS1_USB_PHY_CFG2_PONRST_SIZE) - 1) << XS1_USB_PHY_CFG2_PONRST_SHIFT) +#define XS1_USB_PHY_CFG2_PONRST(x) \ + (((x)&XS1_USB_PHY_CFG2_PONRST_MASK) >> XS1_USB_PHY_CFG2_PONRST_SHIFT) +#define XS1_USB_PHY_CFG2_PONRST_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG2_PONRST_MASK) | \ + (((v) << XS1_USB_PHY_CFG2_PONRST_SHIFT) & XS1_USB_PHY_CFG2_PONRST_MASK)) +#define XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT 0x1 +#define XS1_USB_PHY_CFG2_UTMI_RESET_SIZE 0x1 +#define XS1_USB_PHY_CFG2_UTMI_RESET_MASK \ + (((1 << XS1_USB_PHY_CFG2_UTMI_RESET_SIZE) - 1) \ + << XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) +#define XS1_USB_PHY_CFG2_UTMI_RESET(x) \ + (((x)&XS1_USB_PHY_CFG2_UTMI_RESET_MASK) >> XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) +#define XS1_USB_PHY_CFG2_UTMI_RESET_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG2_UTMI_RESET_MASK) | \ + (((v) << XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) & \ + XS1_USB_PHY_CFG2_UTMI_RESET_MASK)) +#define XS1_USB_PHY_CFG3_VCONTROL_SHIFT 0x0 +#define XS1_USB_PHY_CFG3_VCONTROL_SIZE 0x4 +#define XS1_USB_PHY_CFG3_VCONTROL_MASK \ + (((1 << XS1_USB_PHY_CFG3_VCONTROL_SIZE) - 1) \ + << XS1_USB_PHY_CFG3_VCONTROL_SHIFT) +#define XS1_USB_PHY_CFG3_VCONTROL(x) \ + (((x)&XS1_USB_PHY_CFG3_VCONTROL_MASK) >> XS1_USB_PHY_CFG3_VCONTROL_SHIFT) +#define XS1_USB_PHY_CFG3_VCONTROL_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG3_VCONTROL_MASK) | \ + (((v) << XS1_USB_PHY_CFG3_VCONTROL_SHIFT) & \ + XS1_USB_PHY_CFG3_VCONTROL_MASK)) +#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT 0x4 +#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SIZE 0x1 +#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK \ + (((1 << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SIZE) - 1) \ + << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) +#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE(x) \ + (((x)&XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK) >> \ + XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) +#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK) | \ + (((v) << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) & \ + XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK)) +#define XS1_USB_PHY_CFG3_LS_EN_SHIFT 0x5 +#define XS1_USB_PHY_CFG3_LS_EN_SIZE 0x1 +#define XS1_USB_PHY_CFG3_LS_EN_MASK \ + (((1 << XS1_USB_PHY_CFG3_LS_EN_SIZE) - 1) << XS1_USB_PHY_CFG3_LS_EN_SHIFT) +#define XS1_USB_PHY_CFG3_LS_EN(x) \ + (((x)&XS1_USB_PHY_CFG3_LS_EN_MASK) >> XS1_USB_PHY_CFG3_LS_EN_SHIFT) +#define XS1_USB_PHY_CFG3_LS_EN_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG3_LS_EN_MASK) | \ + (((v) << XS1_USB_PHY_CFG3_LS_EN_SHIFT) & XS1_USB_PHY_CFG3_LS_EN_MASK)) +#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT 0x6 +#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SIZE 0x1 +#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK \ + (((1 << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SIZE) - 1) \ + << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) +#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM(x) \ + (((x)&XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK) >> \ + XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) +#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK) | \ + (((v) << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) & \ + XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK)) +#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT 0x7 +#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SIZE 0x1 +#define XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK \ + (((1 << XS1_USB_PHY_CFG3_HS_BIST_MODE_SIZE) - 1) \ + << XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) +#define XS1_USB_PHY_CFG3_HS_BIST_MODE(x) \ + (((x)&XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK) >> \ + XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) +#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SET(x, v) \ + (((x) & ~XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK) | \ + (((v) << XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) & \ + XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK)) +#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT 0x0 +#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SIZE 0x1 +#define XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK \ + (((1 << XS1_USB_SHIM_CFG_AND_RXV_RXA_SIZE) - 1) \ + << XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) +#define XS1_USB_SHIM_CFG_AND_RXV_RXA(x) \ + (((x)&XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK) >> \ + XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) +#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SET(x, v) \ + (((x) & ~XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK) | \ + (((v) << XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) & \ + XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK)) +#define XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT 0x1 +#define XS1_USB_SHIM_CFG_FLAG_MODE_SIZE 0x1 +#define XS1_USB_SHIM_CFG_FLAG_MODE_MASK \ + (((1 << XS1_USB_SHIM_CFG_FLAG_MODE_SIZE) - 1) \ + << XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) +#define XS1_USB_SHIM_CFG_FLAG_MODE(x) \ + (((x)&XS1_USB_SHIM_CFG_FLAG_MODE_MASK) >> XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) +#define XS1_USB_SHIM_CFG_FLAG_MODE_SET(x, v) \ + (((x) & ~XS1_USB_SHIM_CFG_FLAG_MODE_MASK) | \ + (((v) << XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) & \ + XS1_USB_SHIM_CFG_FLAG_MODE_MASK)) +#define XS1_WATCHDOG_COUNT_ENABLE_SHIFT 0x0 +#define XS1_WATCHDOG_COUNT_ENABLE_SIZE 0x1 +#define XS1_WATCHDOG_COUNT_ENABLE_MASK \ + (((1 << XS1_WATCHDOG_COUNT_ENABLE_SIZE) - 1) \ + << XS1_WATCHDOG_COUNT_ENABLE_SHIFT) +#define XS1_WATCHDOG_COUNT_ENABLE(x) \ + (((x)&XS1_WATCHDOG_COUNT_ENABLE_MASK) >> XS1_WATCHDOG_COUNT_ENABLE_SHIFT) +#define XS1_WATCHDOG_COUNT_ENABLE_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_COUNT_ENABLE_MASK) | \ + (((v) << XS1_WATCHDOG_COUNT_ENABLE_SHIFT) & \ + XS1_WATCHDOG_COUNT_ENABLE_MASK)) +#define XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT 0x1 +#define XS1_WATCHDOG_TRIGGER_ENABLE_SIZE 0x1 +#define XS1_WATCHDOG_TRIGGER_ENABLE_MASK \ + (((1 << XS1_WATCHDOG_TRIGGER_ENABLE_SIZE) - 1) \ + << XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) +#define XS1_WATCHDOG_TRIGGER_ENABLE(x) \ + (((x)&XS1_WATCHDOG_TRIGGER_ENABLE_MASK) >> XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) +#define XS1_WATCHDOG_TRIGGER_ENABLE_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_TRIGGER_ENABLE_MASK) | \ + (((v) << XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) & \ + XS1_WATCHDOG_TRIGGER_ENABLE_MASK)) +#define XS1_WATCHDOG_PRESCALER_VALUE_SHIFT 0x0 +#define XS1_WATCHDOG_PRESCALER_VALUE_SIZE 0x10 +#define XS1_WATCHDOG_PRESCALER_VALUE_MASK \ + (((1 << XS1_WATCHDOG_PRESCALER_VALUE_SIZE) - 1) \ + << XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) +#define XS1_WATCHDOG_PRESCALER_VALUE(x) \ + (((x)&XS1_WATCHDOG_PRESCALER_VALUE_MASK) >> \ + XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) +#define XS1_WATCHDOG_PRESCALER_VALUE_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_PRESCALER_VALUE_MASK) | \ + (((v) << XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) & \ + XS1_WATCHDOG_PRESCALER_VALUE_MASK)) +#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT 0x0 +#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SIZE 0x10 +#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK \ + (((1 << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SIZE) - 1) \ + << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) +#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE(x) \ + (((x)&XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK) >> \ + XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) +#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK) | \ + (((v) << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) & \ + XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK)) +#define XS1_WATCHDOG_COUNT_VALUE_SHIFT 0x0 +#define XS1_WATCHDOG_COUNT_VALUE_SIZE 0xc +#define XS1_WATCHDOG_COUNT_VALUE_MASK \ + (((1 << XS1_WATCHDOG_COUNT_VALUE_SIZE) - 1) << XS1_WATCHDOG_COUNT_VALUE_SHIFT) +#define XS1_WATCHDOG_COUNT_VALUE(x) \ + (((x)&XS1_WATCHDOG_COUNT_VALUE_MASK) >> XS1_WATCHDOG_COUNT_VALUE_SHIFT) +#define XS1_WATCHDOG_COUNT_VALUE_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_COUNT_VALUE_MASK) | \ + (((v) << XS1_WATCHDOG_COUNT_VALUE_SHIFT) & XS1_WATCHDOG_COUNT_VALUE_MASK)) +#define XS1_WATCHDOG_HAS_TRIGGERED_SHIFT 0x0 +#define XS1_WATCHDOG_HAS_TRIGGERED_SIZE 0x1 +#define XS1_WATCHDOG_HAS_TRIGGERED_MASK \ + (((1 << XS1_WATCHDOG_HAS_TRIGGERED_SIZE) - 1) \ + << XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) +#define XS1_WATCHDOG_HAS_TRIGGERED(x) \ + (((x)&XS1_WATCHDOG_HAS_TRIGGERED_MASK) >> XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) +#define XS1_WATCHDOG_HAS_TRIGGERED_SET(x, v) \ + (((x) & ~XS1_WATCHDOG_HAS_TRIGGERED_MASK) | \ + (((v) << XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) & \ + XS1_WATCHDOG_HAS_TRIGGERED_MASK)) +#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT 0x0 +#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SIZE 0x1 +#define XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK \ + (((1 << XS1_MIPI_STATUS0_OSC_CLK_ACT_SIZE) - 1) \ + << XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) +#define XS1_MIPI_STATUS0_OSC_CLK_ACT(x) \ + (((x)&XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK) >> \ + XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) +#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK) | \ + (((v) << XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) & \ + XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK)) +#define XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT 0x1 +#define XS1_MIPI_STATUS0_OSC_CLK_READY_SIZE 0x1 +#define XS1_MIPI_STATUS0_OSC_CLK_READY_MASK \ + (((1 << XS1_MIPI_STATUS0_OSC_CLK_READY_SIZE) - 1) \ + << XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) +#define XS1_MIPI_STATUS0_OSC_CLK_READY(x) \ + (((x)&XS1_MIPI_STATUS0_OSC_CLK_READY_MASK) >> \ + XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) +#define XS1_MIPI_STATUS0_OSC_CLK_READY_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_OSC_CLK_READY_MASK) | \ + (((v) << XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) & \ + XS1_MIPI_STATUS0_OSC_CLK_READY_MASK)) +#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT 0x2 +#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SIZE 0x1 +#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK \ + (((1 << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SIZE) - 1) \ + << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) +#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G(x) \ + (((x)&XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK) >> \ + XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) +#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK) | \ + (((v) << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) & \ + XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK)) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT 0x3 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SIZE 0x1 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK \ + (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SIZE) - 1) \ + << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0(x) \ + (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK) >> \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK) | \ + (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) & \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK)) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT 0x4 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SIZE 0x1 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK \ + (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SIZE) - 1) \ + << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1(x) \ + (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK) >> \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK) | \ + (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) & \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK)) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT 0x5 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SIZE 0x1 +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK \ + (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SIZE) - 1) \ + << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2(x) \ + (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK) >> \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) +#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK) | \ + (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) & \ + XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK)) +#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT 0xc +#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SIZE 0x1 +#define XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK \ + (((1 << XS1_MIPI_STATUS0_STOPSTATE_CLK_SIZE) - 1) \ + << XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_CLK(x) \ + (((x)&XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK) >> \ + XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK) | \ + (((v) << XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) & \ + XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK)) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT 0xd +#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SIZE 0x1 +#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK \ + (((1 << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SIZE) - 1) \ + << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN0(x) \ + (((x)&XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK) >> \ + XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK) | \ + (((v) << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) & \ + XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK)) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT 0xe +#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SIZE 0x1 +#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK \ + (((1 << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SIZE) - 1) \ + << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN1(x) \ + (((x)&XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK) >> \ + XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) +#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SET(x, v) \ + (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK) | \ + (((v) << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) & \ + XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK)) +#define XS1_MIPI_SHIM_STATUS_REG_SHIFT 0x0 +#define XS1_MIPI_SHIM_STATUS_REG_SIZE 0x1 +#define XS1_MIPI_SHIM_STATUS_REG_MASK \ + (((1 << XS1_MIPI_SHIM_STATUS_REG_SIZE) - 1) << XS1_MIPI_SHIM_STATUS_REG_SHIFT) +#define XS1_MIPI_SHIM_STATUS_REG(x) \ + (((x)&XS1_MIPI_SHIM_STATUS_REG_MASK) >> XS1_MIPI_SHIM_STATUS_REG_SHIFT) +#define XS1_MIPI_SHIM_STATUS_REG_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_STATUS_REG_MASK) | \ + (((v) << XS1_MIPI_SHIM_STATUS_REG_SHIFT) & XS1_MIPI_SHIM_STATUS_REG_MASK)) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT 0x0 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SIZE 0x1 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK \ + (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SIZE) - 1) \ + << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN(x) \ + (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK) >> \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK) | \ + (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) & \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK)) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT 0x8 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SIZE 0x8 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK \ + (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SIZE) - 1) \ + << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE(x) \ + (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK) >> \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK) | \ + (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) & \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK)) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT 0x10 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SIZE 0x6 +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK \ + (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SIZE) - 1) \ + << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE(x) \ + (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK) >> \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) +#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK) | \ + (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) & \ + XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK)) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT 0x19 +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SIZE 0x1 +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK \ + (((1 << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SIZE) - 1) \ + << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT(x) \ + (((x)&XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK) >> \ + XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK) | \ + (((v) << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) & \ + XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK)) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT 0x1a +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SIZE 0x1 +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK \ + (((1 << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SIZE) - 1) \ + << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG(x) \ + (((x)&XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK) >> \ + XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) +#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SET(x, v) \ + (((x) & ~XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK) | \ + (((v) << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) & \ + XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK)) +#define XS1_LPDDR_PE_TREFI_CNT_SHIFT 0x0 +#define XS1_LPDDR_PE_TREFI_CNT_SIZE 0xb +#define XS1_LPDDR_PE_TREFI_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TREFI_CNT_SIZE) - 1) << XS1_LPDDR_PE_TREFI_CNT_SHIFT) +#define XS1_LPDDR_PE_TREFI_CNT(x) \ + (((x)&XS1_LPDDR_PE_TREFI_CNT_MASK) >> XS1_LPDDR_PE_TREFI_CNT_SHIFT) +#define XS1_LPDDR_PE_TREFI_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TREFI_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TREFI_CNT_SHIFT) & XS1_LPDDR_PE_TREFI_CNT_MASK)) +#define XS1_LPDDR_PE_TRAS_CNT_SHIFT 0xb +#define XS1_LPDDR_PE_TRAS_CNT_SIZE 0x4 +#define XS1_LPDDR_PE_TRAS_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRAS_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRAS_CNT_SHIFT) +#define XS1_LPDDR_PE_TRAS_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRAS_CNT_MASK) >> XS1_LPDDR_PE_TRAS_CNT_SHIFT) +#define XS1_LPDDR_PE_TRAS_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRAS_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRAS_CNT_SHIFT) & XS1_LPDDR_PE_TRAS_CNT_MASK)) +#define XS1_LPDDR_PE_TXSR_CNT_SHIFT 0xf +#define XS1_LPDDR_PE_TXSR_CNT_SIZE 0x6 +#define XS1_LPDDR_PE_TXSR_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TXSR_CNT_SIZE) - 1) << XS1_LPDDR_PE_TXSR_CNT_SHIFT) +#define XS1_LPDDR_PE_TXSR_CNT(x) \ + (((x)&XS1_LPDDR_PE_TXSR_CNT_MASK) >> XS1_LPDDR_PE_TXSR_CNT_SHIFT) +#define XS1_LPDDR_PE_TXSR_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TXSR_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TXSR_CNT_SHIFT) & XS1_LPDDR_PE_TXSR_CNT_MASK)) +#define XS1_LPDDR_PE_TWR_CNT_SHIFT 0x15 +#define XS1_LPDDR_PE_TWR_CNT_SIZE 0x3 +#define XS1_LPDDR_PE_TWR_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TWR_CNT_SIZE) - 1) << XS1_LPDDR_PE_TWR_CNT_SHIFT) +#define XS1_LPDDR_PE_TWR_CNT(x) \ + (((x)&XS1_LPDDR_PE_TWR_CNT_MASK) >> XS1_LPDDR_PE_TWR_CNT_SHIFT) +#define XS1_LPDDR_PE_TWR_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TWR_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TWR_CNT_SHIFT) & XS1_LPDDR_PE_TWR_CNT_MASK)) +#define XS1_LPDDR_PE_TRC_CNT_SHIFT 0x0 +#define XS1_LPDDR_PE_TRC_CNT_SIZE 0x4 +#define XS1_LPDDR_PE_TRC_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRC_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRC_CNT_SHIFT) +#define XS1_LPDDR_PE_TRC_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRC_CNT_MASK) >> XS1_LPDDR_PE_TRC_CNT_SHIFT) +#define XS1_LPDDR_PE_TRC_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRC_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRC_CNT_SHIFT) & XS1_LPDDR_PE_TRC_CNT_MASK)) +#define XS1_LPDDR_PE_TRCD_CNT_SHIFT 0x4 +#define XS1_LPDDR_PE_TRCD_CNT_SIZE 0x3 +#define XS1_LPDDR_PE_TRCD_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRCD_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRCD_CNT_SHIFT) +#define XS1_LPDDR_PE_TRCD_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRCD_CNT_MASK) >> XS1_LPDDR_PE_TRCD_CNT_SHIFT) +#define XS1_LPDDR_PE_TRCD_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRCD_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRCD_CNT_SHIFT) & XS1_LPDDR_PE_TRCD_CNT_MASK)) +#define XS1_LPDDR_PE_TRP_CNT_SHIFT 0x7 +#define XS1_LPDDR_PE_TRP_CNT_SIZE 0x3 +#define XS1_LPDDR_PE_TRP_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRP_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRP_CNT_SHIFT) +#define XS1_LPDDR_PE_TRP_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRP_CNT_MASK) >> XS1_LPDDR_PE_TRP_CNT_SHIFT) +#define XS1_LPDDR_PE_TRP_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRP_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRP_CNT_SHIFT) & XS1_LPDDR_PE_TRP_CNT_MASK)) +#define XS1_LPDDR_PE_TRFC_CNT_SHIFT 0xa +#define XS1_LPDDR_PE_TRFC_CNT_SIZE 0x5 +#define XS1_LPDDR_PE_TRFC_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRFC_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRFC_CNT_SHIFT) +#define XS1_LPDDR_PE_TRFC_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRFC_CNT_MASK) >> XS1_LPDDR_PE_TRFC_CNT_SHIFT) +#define XS1_LPDDR_PE_TRFC_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRFC_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRFC_CNT_SHIFT) & XS1_LPDDR_PE_TRFC_CNT_MASK)) +#define XS1_LPDDR_PE_TRRD_CNT_SHIFT 0xf +#define XS1_LPDDR_PE_TRRD_CNT_SIZE 0x2 +#define XS1_LPDDR_PE_TRRD_CNT_MASK \ + (((1 << XS1_LPDDR_PE_TRRD_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRRD_CNT_SHIFT) +#define XS1_LPDDR_PE_TRRD_CNT(x) \ + (((x)&XS1_LPDDR_PE_TRRD_CNT_MASK) >> XS1_LPDDR_PE_TRRD_CNT_SHIFT) +#define XS1_LPDDR_PE_TRRD_CNT_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_TRRD_CNT_MASK) | \ + (((v) << XS1_LPDDR_PE_TRRD_CNT_SHIFT) & XS1_LPDDR_PE_TRRD_CNT_MASK)) +#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT 0x11 +#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SIZE 0x1 +#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK \ + (((1 << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SIZE) - 1) \ + << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) +#define XS1_LPDDR_PE_EN_256M_DEV_SIZE(x) \ + (((x)&XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK) >> \ + XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) +#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SET(x, v) \ + (((x) & ~XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK) | \ + (((v) << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) & \ + XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK)) +#define XS1_DEVICE_ID0_VERSION_VALUE 0x0 +#define XS1_DEVICE_ID0_REVISION_VALUE 0x4 +#define XS1_PS_DBG_HANDLER XS1_PS_DBG_SCRATCH_0 +#define XS1_PS_DBG_COMMAND XS1_PS_DBG_SCRATCH_1 +#define XS1_PS_DBG_ARG0_REG XS1_PS_DBG_SCRATCH_2 +#define XS1_PS_DBG_ARG1_REG XS1_PS_DBG_SCRATCH_3 +#define XS1_PS_DBG_ARG2_REG XS1_PS_DBG_SCRATCH_4 +#define XS1_PS_DBG_ARG3_REG XS1_PS_DBG_SCRATCH_5 +#define XS1_PS_DBG_ARG4_REG XS1_PS_DBG_SCRATCH_6 +#define XS1_PS_DBG_ARG5_REG XS1_PS_DBG_SCRATCH_7 +#define XS1_PSWITCH_DBG_HANDLER_NUM XS1_PSWITCH_DBG_SCRATCH_0_NUM +#define XS1_PSWITCH_DBG_COMMAND_NUM XS1_PSWITCH_DBG_SCRATCH_1_NUM +#define XS1_PSWITCH_DBG_ARG0_NUM XS1_PSWITCH_DBG_SCRATCH_2_NUM +#define XS1_PSWITCH_DBG_ARG1_NUM XS1_PSWITCH_DBG_SCRATCH_3_NUM +#define XS1_PSWITCH_DBG_ARG2_NUM XS1_PSWITCH_DBG_SCRATCH_4_NUM +#define XS1_PSWITCH_DBG_ARG3_NUM XS1_PSWITCH_DBG_SCRATCH_5_NUM +#define XS1_PSWITCH_DBG_ARG4_NUM XS1_PSWITCH_DBG_SCRATCH_6_NUM +#define XS1_PSWITCH_DBG_ARG5_NUM XS1_PSWITCH_DBG_SCRATCH_7_NUM +#define XS1_XMOS_PHY_CONF_WIDTH 0x3 +#define XS1_USB_PHY_VCONTROL_SETUP_LENGTH 0x6 +#define XS1_USB_PHY_ENTER_BIST_LENGTH 0x40 +#define XS1_USB_PHY_CLKCNT_WIDTH 0x4 +#define XS1_USB_BISTGO_CTR_WIDTH 0x4 +#define XS1_USB_TESTGO_CTR_WIDTH 0x4 +#define XS1_USB_TESTGO_PULSE_LENGTH 0x8 +#define XS1_USB_BISTGO_PULSE_LENGTH 0x8 +#define XS1_CRC5_RESULT_WIDTH 0x5 +#define XS1_MS_NIBBLE "7:4" +#define XS1_LS_NIBBLE "3:0" +#endif /* _xs3a_registers_h_ */ diff --git a/new_ai_tools/include/lib_nn/src/asm/asm_constants.h b/new_ai_tools/include/lib_nn/src/asm/asm_constants.h new file mode 100644 index 00000000..288e85b2 --- /dev/null +++ b/new_ai_tools/include/lib_nn/src/asm/asm_constants.h @@ -0,0 +1,41 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#pragma once + +#ifndef __ASSEMBLER__ + +#include + +#include "xs3_vpu.h" + +typedef struct { + // Word offset = 0 + int16_t vec_0x007F[VPU_INT8_ACC_PERIOD]; + // Word offset = 8 + int8_t vec_0x01[VPU_INT8_ACC_PERIOD]; + // Word offset = 12 + int16_t vec_0x0002[VPU_INT8_ACC_PERIOD]; + // Word offset = 20 + int8_t vec_0x80[VPU_INT8_EPV]; + // Word offset = 28 + +} vpu_constants_t; + +extern const vpu_constants_t vpu_vects; + +extern const uint32_t vpu_vect_zero[VPU_INT32_EPV]; +extern const int16_t vpu_vect_0x007F[VPU_INT16_EPV]; +extern const int8_t vpu_vect_0x01[VPU_INT8_EPV]; +extern const int8_t vpu_vect_0x02[VPU_INT8_EPV]; +extern const int8_t vpu_vect_0x80[VPU_INT8_EPV]; + +#endif // __ASSEMBLER__ + +#define VPU_MODE_32BIT 0x0000 +#define VPU_MODE_16BIT 0x0100 +#define VPU_MODE_8BIT 0x0200 + +#define VPU_VEC_0x007F (0) +#define VPU_VEC_0x01 (8) +#define VPU_VEC_0x0002 (12) +#define VPU_VEC_0x80 (20) diff --git a/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h b/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h new file mode 100644 index 00000000..57c28fd8 --- /dev/null +++ b/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h @@ -0,0 +1,25 @@ +// Copyright 2020-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef WINDOW_OP_PLAN_H_ +#define WINDOW_OP_PLAN_H_ + +#define WOP_OUTPUT_ROWS (0) +#define WOP_OUTPUT_COLS (1) +#define WOP_OUTPUT_CHANS (2) +#define WOP_WINDOW_ROWS (3) +#define WOP_WINDOW_COLS (4) +#define WOP_START_STRIDE_X (5) +#define WOP_START_STRIDE_Y (6) +#define WOP_INNER_STRIDE_VERT (7) +#define WOP_INNER_STRIDE_HORI (8) +#define WOP_OUTER_STRIDE_VERT_X (9) +#define WOP_OUTER_STRIDE_VERT_Y (10) +#define WOP_OUTER_STRIDE_HORI_X (11) +#define WOP_OUTER_STRIDE_HORI_Y (12) +#define WOP_CHAN_STRIDE_X (13) +#define WOP_CHAN_STRIDE_Y (14) + +#define WOP_WORDS (15) + +#endif // WINDOW_OP_PLAN_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h b/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h new file mode 100644 index 00000000..4556363b --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h @@ -0,0 +1,217 @@ +// Copyright (c) 2020, XMOS Ltd, All rights reserved +#ifndef INFERENCE_ENGINE_H_ +#define INFERENCE_ENGINE_H_ + +#ifndef __XC__ +#define UNSAFE /**/ +#else +#define UNSAFE unsafe +#endif + +#if !defined(XTFLM_DISABLED) + +#if defined(__xtflm_conf_h_exists__) +#include "xtflm_conf.h" +#else + +#ifndef XTFLM_OPERATORS +#define XTFLM_OPERATORS 10 +#endif + +#endif + +#include "tensorflow/lite/c/c_api_types.h" +#include "xcore_config.h" + +#ifdef __cplusplus + +#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" +#include "xcore_device_memory.h" +#include "xcore_error_reporter.h" +#include "xcore_interpreter.h" +#include "xcore_ops.h" +#include "xcore_profiler.h" + +/** Structure that contains all the TensorFlowLite for Micro objects that must + * be allocated to create an interpreter. One of these structures has to be + * allocated for each inference engine. This structure contains C++ objects, and + * must therefore be allocated inside a C++ source file. + */ +struct tflite_micro_objects { + tflite_micro::micro::xcore::XCoreErrorReporter error_reporter; + tflite_micro::micro::xcore::XCoreProfiler xcore_profiler; + uint64_t interpreter_buffer + [(sizeof(tflite_micro::micro::xcore::XCoreInterpreter) + sizeof(uint64_t) - 1) / + sizeof(uint64_t)]; // This needs to be aligned on a double word boundary + tflite_micro::MicroMutableOpResolver resolver; + + tflite_micro::micro::xcore::XCoreInterpreter *interpreter; + const tflite_micro::Model *model; +}; +#endif + +#endif + +// Opaque definition for the C++ struct above, used in C. +struct tflite_micro_objects; + +/** Structure that contains all the data needed to describe an inference engine + * This structure contains no C++, just standard C pointers and arrays. + */ +typedef struct inference_engine { + uint32_t *UNSAFE + tensor_arena; ///< Pointer to space for tensor arena + uint32_t *UNSAFE + external_memory; ///< Pointer to external memory + uint32_t + outputs; ///< Number of output tensors, initialised on loading a model. + uint32_t inputs; ///< Number of input tensors, initialised on loading a model. + uint32_t *UNSAFE + output_buffers[NUM_OUTPUT_TENSORS]; ///< Pointers to output tensors. + uint32_t *UNSAFE + input_buffers[NUM_INPUT_TENSORS]; ///< Pointers to input tensors. + uint32_t output_sizes[NUM_OUTPUT_TENSORS]; ///< Size of each output tensor in + ///< bytes. + uint32_t + input_sizes[NUM_INPUT_TENSORS]; ///< Size of each input tensor in bytes. + uint32_t output_size; ///< Total size of all outputs - TODO: obsolete? + uint32_t input_size; ///< Total size of all inputs - TODO: obsolete? + uint32_t + tensor_arena_size; ///< Number of bytes availabe to be used for tensor_arena + uint32_t + external_memory_size; ///< Number of bytes available in external memory + uint32_t + output_times_size; ///< Number of bytes available to store profiling data + uint32_t operators_size; ///< ??? + uint32_t *UNSAFE output_times; ///< pointer to profiling data, one per layer + struct tflite_micro_objects *UNSAFE + xtflm; ///< Pointer to C++ XTFLM object - opaque to C + // status for the engine to maintain + uint32_t haveModel; ///< if 1: we have a model + uint32_t chainToNext; ///< if 1: we are chained (could be implicit in c_push + ///< being non-null?) + uint32_t acquireMode; ///< if non zero we're acquiring data + uint32_t outputGpioEn; // TODO: should this be here? Possibly not + int8_t outputGpioThresh[AISRV_GPIO_LENGTH]; + uint8_t outputGpioMode; + uint32_t debug_log_buffer[MAX_DEBUG_LOG_LENGTH / + sizeof(uint32_t)]; ///< buffer for error messages + uint32_t arena_needed_bytes; ///< Total arena needed in bytes. + uint32_t num_threads; + struct xc_context_config_t xc_config; +} inference_engine_t; + +#ifdef __cplusplus +#ifndef XTFLM_DISABLED +/** Function that initializes the inference_engine object, given a + * tflite_micro_objects object. This function has to be called from a C++ source + * files, and it performs the initialisation of the inference engine. This + * involves setting up basic pointers inside the IE object, nothing else. + * + * The function returns the operator-resolver, which must be be used to add all + * necessary operators to the inference engine. A typical calling sequence is as + * follows:: + * + * uint32_t data_ext[100000/sizeof(int)]; + * [...] + * static struct tflite_micro_objects s0; + * auto *resolver = inference_engine_initialize(ie, + * data_ext, 100000, + * nullptr, 0, + * &s0); + * resolver->AddAdd(); + * resolver->AddConv2D(); + * resolver->AddCustom(tflite_micro::ops::micro::xcore::Conv2D_V2_OpCode, + * tflite_micro::ops::micro::xcore::Register_Conv2D_V2()); + * [...] + * + * Note that when tensorflow lite for micro is disabled this function will not + * exist as it depends on all and sundry in XTFLM. + * + * Note that the lifetime of all spaces passed to this function should be longer + * than the lifetime of the inference engine. Typically all spaces are declared + * globally. + * + * \param ie Pointer to an uninitialized inference engine + * object, allocated by the caller. \param memory_primary Pointer to the + * space to be used for the tensor arena, allocated by the caller. If the fourth + * parameter is null, then this space will be used for both model and tensor + * arena. \param n_primary Number of bytes available in primary memory + * \param memory_secondary Pointer to the space to be used for the model + * allocated by the caller. If this parameter is + * null, then the primary memory will be used for both model and tensor arena. + * \param n_secondary Number of bytes available in secondary memory + * \param xtflmo C++ structure for storing the XTFLM data + * structures. Must be allocated by the caller. + * + */ +tflite_micro::MicroMutableOpResolver *inference_engine_initialize( + inference_engine_t *UNSAFE ie, uint32_t memory_primary[], + uint32_t n_memory_primary, uint32_t memory_secondary[], + uint32_t n_secondary, struct tflite_micro_objects *UNSAFE xtflmo); +#endif +extern "C" { +#endif +/** Function that unloads a model frmo the inference engine. This function + * must be called before before overwriting the model. For example, you + * have ran a model successfully, before you store a new model over the top + * of the model (from flash or anywhere else), you must first unload the + * model, then you can overwrite the model, and finally you can call the + * inference_engine_load_model to set up the new model. + * + * It is safe to call unload model if there is no model loaded. + * + * \param ie pointer to inference engine. + */ +void inference_engine_unload_model(inference_engine_t *UNSAFE ie); + +/** Function that loads a model into the inference engine. The model must be + * stored in either of the two spaces passed to the inference_engine_initialize + * function above: either the dedicated data_model space or the space shared + * with the tensor_arena. This funciton assumes the model is in place already + * and will simply parse it, not copy it. + * + * \param ie pointer to inference engine. + * \param model_bytes Number of bytes in the model + * \param model_data Pointer to the model (one of data_model or + * data_tensor_arena passed above) \param c_flash_or_tile Optional channel to flash + * or tile server + * + * \returns non zero indicates an error + */ +#ifdef __XC__ + int inference_engine_load_model(inference_engine_t * UNSAFE ie, uint32_t model_bytes, uint32_t * UNSAFE model_data, chanend ?c_flash_or_tile); +#else +int inference_engine_load_model(inference_engine_t *UNSAFE ie, + uint32_t model_bytes, + uint32_t *UNSAFE model_data, void *weights_data_ptr); +#endif + + /** Function that invokes the inference engine. This function will create an + * extra four threads enabling a model to run in 5 threads. + * + * \param ie pointer to inference engine. + */ + int interp_invoke_par_5(inference_engine_t *ie); + int interp_invoke_par_4(inference_engine_t *ie); + int interp_invoke_par_3(inference_engine_t *ie); + int interp_invoke_par_2(inference_engine_t *ie); + int interp_invoke(inference_engine_t *ie); + TfLiteStatus interp_invoke_internal(inference_engine_t *ie); + + /** Function that resets variable tensors. + * This should be called after invoking a model with stateful ops such as LSTM. + */ + int interp_reset(inference_engine_t *ie); + + /** Function that prints a summary of the time each operator took. This + * function uses printf - you may want to avoid calling it. + * + * \param ie pointer to inference engine. + */ + void print_profiler_summary(inference_engine_t *UNSAFE ie); +#ifdef __cplusplus + }; +#endif + +#endif // INFERENCE_ENGINE_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/load_weights.h b/new_ai_tools/include/lib_tflite_micro/api/load_weights.h new file mode 100644 index 00000000..8ff347f4 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/load_weights.h @@ -0,0 +1,64 @@ +#ifndef _load_weights_h_ +#define _load_weights_h_ + +#include +#include "thread_call.h" + +#define LOAD_WEIGHTS_MAX_BLOCKS 2 + +/** + * Function that connects to a flash or tile-ram server and loads a series of weights. + * This function completes when the data is loaded. + * + * @param c_flash_or_tile channel-end connecting to the flash server + * + * @param data_ptr array of pointers where the loaded data should be scattered + * + * @param data_sizes_in_words number of words where for each block + * + * @param N number of blocks in data_ptr and data_sizes_in_words + * + * @param external_addr address in flash or tile ram + * + * @param model_thread_count number of threads available + * + * @param tif thread_info structure for multithreading + */ +void load_weights_synchronous(chanend_t c_flash_or_tile, int *data_ptr[], int data_sizes_in_words[], + int N, int external_addr, int model_thread_count, thread_info_t *tif); + +/** + * Function that connects to a flash server and loads a series of weights. + * This function continues loading after the call completes + * + * @param c_flash_or_tile channel-end connecting to the flash server + * + * @param data_ptr array of pointers where the loaded data should be scattered + * + * @param data_sizes_in_words number of words where for each block + * + * @param N number of blocks in data_ptr and data_sizes_in_words + * + * @param external_addr address in flash or tile ram + * + * @param model_thread_count number of threads available + */ +void load_weights_asynchronous(chanend_t c_flash_or_tile, int *data_ptr[], int data_sizes_in_words[], + int N, int external_addr); + +/** + * Function that connects to a flash server and waits for the last outstanding load to complete + * Only one asynchronous load should be outstanding at any one time. + * + * @param c_flash_or_tile channel-end connecting to the flash server + */ +void load_weights_asynchronous_wait(chanend_t c_flash_or_tile); + +/** + * Function that connects to a flash or tile ram server and kills it. + * + * @param c_flash_or_tile channel-end connecting to the flash server + */ +void load_weights_quit(chanend_t c_flash_or_tile); + +#endif diff --git a/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h b/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h new file mode 100644 index 00000000..853fc23d --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h @@ -0,0 +1,52 @@ +#include +#include "thread_call.h" + +#ifdef __XC__ +#include +#else +#include +#endif + +#ifdef __XC__ + +extern void memory_parallel_receive(chanend c, uint32_t data[], uint32_t bytes); +extern void memory_parallel_receive_thread_call(chanend c, uint32_t data[], uint32_t bytes, thread_info_t &ptr); +extern void memory_parallel_send(chanend c, uint32_t data[], uint32_t bytes); + +#else + +/** Function that receives a block of data. + * The number of bytes must be a multiple of 4. + * This function creates three threads and three channel ends in order to + * make full use of the bandwidth of the switch. + * + * \param c channel end to the sender + * \param data pointer where data must be stored + * \param bytes number of bytes that will be received. + */ +extern void memory_parallel_receive(chanend_t c, uint32_t *data, uint32_t bytes); + +/** Function that receives a block of data. + * The number of bytes must be a multiple of 4. + * This function assumes that at least three threads have been created by the + * thread_call library and will use those together with three fresh channel + * ends in order to make full use of the bandwidth of the switch. + * + * \param c channel end to the sender + * \param data pointer where data must be stored + * \param bytes number of bytes that will be received. + */ +extern void memory_parallel_receive_thread_call(chanend_t c, uint32_t *data, uint32_t bytes, thread_info_t *ptr); + +/** Function that sends a block of data. + * The number of bytes must be a multiple of 4. + * This function creates three threads and three channel ends in order to + * make full use of the bandwidth of the switch. + * + * \param c channel end to the receiver + * \param data pointer where data must be loaded frmo + * \param bytes number of bytes that will be sent. + */ +extern void memory_parallel_send(chanend_t c, uint32_t *data, uint32_t bytes); + +#endif diff --git a/new_ai_tools/include/lib_tflite_micro/api/version.h b/new_ai_tools/include/lib_tflite_micro/api/version.h new file mode 100644 index 00000000..b7a7fd98 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/version.h @@ -0,0 +1,13 @@ +// Copyright (c) 2020, XMOS Ltd, All rights reserved +#ifndef XCORE_VERSION_H_ +#define XCORE_VERSION_H_ + +namespace lib_tflite_micro { + +static const unsigned major_version = 0; +static const unsigned minor_version = 8; +static const unsigned patch_version = 0; + +} // namespace lib_tflite_micro + +#endif // XCORE_VERSION_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h new file mode 100644 index 00000000..1662ce77 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h @@ -0,0 +1,18 @@ +#ifndef XCORE_CONFIG_H_ +#define XCORE_CONFIG_H_ + +#include "../src/thread_call.h" + +struct xc_context_config_t { + // This is the thread count specified in the compiler. + // It's used by lookup op, beta float ops etc to split up work + // in the Prepare phase. + // Conv ops have their own thread count as the thread work is + // calculated in the compiler. + int model_thread_count; + thread_info_t thread_info; + void *UNSAFE weights_data_ptr; // DDR ptr or channel to flash/tile server. + void *UNSAFE paging_ptr; // DDR ptr for paging in/out tensor arena. +}; + +#endif // XCORE_CONFIG_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h new file mode 100644 index 00000000..3e47731e --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h @@ -0,0 +1,62 @@ +// Copyright (c) 2020, XMOS Ltd, All rights reserved +#ifndef XCORE_DEVICE_MEMORY_H_ +#define XCORE_DEVICE_MEMORY_H_ + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef XCORE +#ifdef _TIME_H_ +#define _clock_defined +#endif +#include + +#define STRINGIFY(NAME) #NAME +#define GET_STACKWORDS(DEST, NAME) \ + asm("ldc %[__dest], " STRINGIFY(NAME) ".nstackwords" : [__dest] "=r"(DEST)) +#define GET_STACKSIZE(DEST, NAME) \ + { \ + size_t _stack_words; \ + asm("ldc %[__dest], " STRINGIFY(NAME) ".nstackwords" \ + : [__dest] "=r"(_stack_words)); \ + DEST = (_stack_words + 2) * 4; \ + } +#define IS_RAM(a) (((uintptr_t)a >= 0x80000) && ((uintptr_t)a <= 0x100000)) +#define IS_NOT_RAM(a) ((uintptr_t)a > 0x100000) +#define IS_EXTMEM(a) \ + (((uintptr_t)a >= 0x10000000) && (((uintptr_t)a <= 0x20000000))) +#define IS_SWMEM(a) \ + (((uintptr_t)a >= 0x40000000) && (((uintptr_t)a <= 0x80000000))) + +#ifdef USE_SWMEM +#ifndef USE_QSPI_SWMEM_DEV +void swmem_setup(); +#else +#include +void swmem_setup(chanend_t ctrl_swmem_c); +#endif // USE_QSPI_SWMEM_DEV +#endif // USE_SWMEM + +void swmem_handler(void *ignored); +void swmem_teardown(); + +#else // not XCORE + +#define GET_STACKSIZE(DEST, NAME) DEST = 0 +#define GET_STACKWORDS(DEST, NAME) DEST = 0 +#define IS_RAM(a) (1) +#define IS_NOT_RAM(a) (0) + +#endif // XCORE + +void memload(void *dest, void *src, size_t size); + +#ifdef __cplusplus +} +#endif + +#endif // XCORE_DEVICE_MEMORY_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h new file mode 100644 index 00000000..8663c1ed --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h @@ -0,0 +1,48 @@ +// Copyright (c) 2020, XMOS Ltd, All rights reserved +#ifndef XCORE_SHARED_CONFIG_H_ +#define XCORE_SHARED_CONFIG_H_ + +#include "lib_nn/api/nn_arch.h" + +namespace shared_config { + +// This string is used as a key to store the shared config +// between xformer and lib_tflite_micro in the flatbuffer +constexpr char xcoreMetadataName[] = "xcoreSharedConfig"; + +constexpr int xcoreMaxNumOfTensors = 25; + +struct tensor_info_t { + uint32_t index; + uint32_t external_address; + uint32_t size; +}; + +// The metadata struct must be aligned to 16 bytes +// We cannot use alignas(16) yet in xcore +struct xcore_metadata_t { + // Target arch can be XS3A = 0, or VX4A = 1 + nn_target_arch_t target_arch; + // Versions of libraries used to build the model + uint32_t lib_nn_major_version; + uint32_t lib_nn_minor_version; + uint32_t lib_nn_patch_version; + uint32_t lib_tflite_micro_major_version; + uint32_t lib_tflite_micro_minor_version; + uint32_t lib_tflite_micro_patch_version; + uint32_t xformer_major_version; + uint32_t xformer_minor_version; + uint32_t xformer_patch_version; + // Number of threads required from the runtime to execute the model + uint32_t required_thread_count; + // Number of input tensors loaded from external memory + uint32_t num_external_input_tensors; + // Number of output tensors loaded from external memory + uint32_t num_external_output_tensors; + tensor_info_t external_input_tensors_data[xcoreMaxNumOfTensors]; + tensor_info_t external_output_tensors_data[xcoreMaxNumOfTensors]; +}; + +} // namespace shared_config + +#endif // XCORE_SHARED_CONFIG_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h new file mode 100644 index 00000000..09b9a64e --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h @@ -0,0 +1,155 @@ +#ifndef _conv2d_float_h_ +#define _conv2d_float_h_ + +#ifdef __cplusplus +extern "C" { +#endif + +/** Function that calculates a fully connected. + * + * @param outputs pointer to the output data, the output data will + * be stored as an array [out_features] + * @param inputs pointer to the input data, the input data must + * be stored as an array [input_features] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_features][input_features] + * @param out_features dimension 1 of the output array + * @param input_features dimension 1 of the input array + * @returns number of MACCs + */ +extern int xc_fc_float_ref(float *outputs, float *inputs, float *kernels, + int out_features, int input_features); + +/** Optimized function that calculates a fully connected. + * + * @param outputs pointer to the output data, the output data will + * be stored as an array [out_features] + * @param inputs pointer to the input data, the input data must + * be stored as an array [input_features] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_features][input_features] + * @param out_features dimension 1 of the output array + * @param input_features dimension 1 of the input array + * @param out_f_start output features to start at + * @param out_f_end output features to end at plus one + * @returns number of MACCs + */ +extern int xc_fc_float_opt(float *outputs, float *inputs, float *kernels, + int out_features, int input_features, int out_f_start, + int out_f_end); + +/** Function that calculates a convolution with a 5x2 filter with stride 2 + * over dimension 2 of a tensor over a tensor. + * + * @param outputs pointer to the output data, the output data will be + * stored as an array [out_w][out_h][out_depth] + * @param inputs pointer to the input data, the input data must be + * stored as an array [input_w][input_h][input_depth] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_depth][5][2][depth] + * @param bias pointer to the biases, the bias must be stored as an + * array [out_depth] + * @param out_w dimension 2 of the output array + * @param out_h dimension 1 of the output array + * @param out_depth dimension 3 of the output array + * @param input_w dimension 2 of the input array + * @param input_h dimension 1 of the input array + * @param input_depth dimension 3 of the input array + * @returns number of MACCs + */ +extern int xc_conv2d_float_kw5xh2_stride_w3_ref(float *outputs, float *inputs, + float *kernels, float *biases, + int out_w, int out_h, + int out_depth, int input_w, + int input_h, int input_depth); + +/** Optimised function that calculates a convolution with a 5x2 filter with + * stride 2 over dimension 2 of a tensor. For parallel usage, supply + * multiple invocations with different values of out_depth_start and + * out_depth_end so that the whole output depth is covered between all of + * them. + * + * @param outputs pointer to the output data, the output data will be + * stored as an array [out_w][out_h][out_depth] + * @param inputs pointer to the input data, the input data must be + * stored as an array [input_w][input_h][input_depth] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_depth][5][2][depth] + * @param bias pointer to the biases, the bias must be stored as an + * array [out_depth] + * @param out_w dimension 2 of the output array + * @param out_h dimension 1 of the output array + * @param out_depth dimension 3 of the output array + * @param input_w dimension 2 of the input array + * @param input_h dimension 1 of the input array + * @param input_depth dimension 3 of the input array + * @param out_depth_start output depth to start at + * @param out_depth_end output depth to end at plus one + */ +extern void xc_conv2d_float_kw5xh2_stride_w3_opt( + float *outputs, float *inputs, float *kernels, float *biases, int out_w, + int out_h, int out_depth, int input_w, int input_h, int input_depth, + int out_depth_start, int out_depth_end); + +/** Function that calculates a transposed convolution with a 5x2 filter with + * stride 3 in dimension 2 over a tensor. + * + * @param outputs pointer to the output data, the output data will be + * stored as an array [out_w][out_h][out_depth] + * @param inputs pointer to the input data, the input data must be + * stored as an array [input_w][input_h][input_depth] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_depth][5][2][depth] + * @param bias pointer to the biases, the bias must be stored as an + * array [out_depth] + * @param out_w dimension 2 of the output array + * @param out_h dimension 1 of the output array + * @param out_depth dimension 3 of the output array + * @param input_w dimension 2 of the input array + * @param input_h dimension 1 of the input array + * @param input_depth dimension 3 of the input array + * @returns number of MACCs + */ +extern int xc_transpose_conv2d_float_kw5xh2_stride_h3_ref( + float *outputs, float *inputs, float *kernels, float *biases, int out_w, + int out_h, int out_depth, int input_w, int input_h, int input_depth); + +/** Optimised function that calculates a transposed convolution with a 5x2 + * filter with stride 3 over dimension 2 of a tensor. For parallel usage, + * supply multiple invocations with different values of out_depth_start and + * out_depth_end so that the whole output depth is covered between all of + * them. + * + * @param outputs pointer to the output data, the output data will be + * stored as an array [out_w][out_h][out_depth] + * @param inputs pointer to the input data, the input data must be + * stored as an array [input_w][input_h][input_depth] + * @param kernels pointer to the kernels, the kernels + * must be stored as an array + * [out_depth][5][2][depth] + * @param bias pointer to the biases, the bias must be stored as an + * array [out_depth] + * @param out_w dimension 2 of the output array + * @param out_h dimension 1 of the output array + * @param out_depth dimension 3 of the output array + * @param input_w dimension 2 of the input array + * @param input_h dimension 1 of the input array + * @param input_depth dimension 3 of the input array + * @param out_depth_start output depth to start at + * @param out_depth_end output depth to end at plus one + */ +extern void xc_transpose_conv2d_float_kw5xh2_stride_h3_opt( + float *outputs, float *inputs, float *kernels, float *biases, int out_w, + int out_h, int out_depth, int input_w, int input_h, int input_depth, + int out_depth_start, int out_depth_end); + +#ifdef __cplusplus +}; +#endif + +#endif diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h new file mode 100644 index 00000000..27212df4 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h @@ -0,0 +1,28 @@ +#ifndef XCORE_CUSTOM_OPTIONS_H_ +#define XCORE_CUSTOM_OPTIONS_H_ + +#include "flatbuffers/flexbuffers.h" +#include "xcore_ops.h" + +namespace tflite_micro { +namespace ops { +namespace micro { +namespace xcore { + +class CustomOptionParser { +private: + flexbuffers::TypedVector keys_; + flexbuffers::Vector values_; + +public: + CustomOptionParser(const flexbuffers::Map &map); + CustomOptionParser(const char *buffer, size_t buffer_length); + flexbuffers::Reference parseNamedCustomOption(const char *name) const; +}; + +} // namespace xcore +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // XCORE_CUSTOM_OPTIONS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h new file mode 100644 index 00000000..7804f301 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h @@ -0,0 +1,32 @@ +// Copyright (c) 2019, XMOS Ltd, All rights reserved + +#ifndef XCORE_ERROR_REPORTER_H_ +#define XCORE_ERROR_REPORTER_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h" + +namespace tflite_micro { +namespace micro { +namespace xcore { + +class XCoreErrorReporter : public tflite_micro::MicroErrorReporter { +public: + explicit XCoreErrorReporter(){}; + ~XCoreErrorReporter() override = default; + void Init(char *debugBuffer, int debugBufferLength); + void Log(const char *format, va_list args); + int Report(const char *format, va_list args) override; + +private: + char *buffer; + int max_len; + int len = 0; + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace xcore +} // namespace micro +} // namespace tflite_micro + +#endif // XCORE_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h new file mode 100644 index 00000000..c07fd9a0 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h @@ -0,0 +1,49 @@ +// Copyright (c) 2020, XMOS Ltd, All rights reserved +#ifndef XCORE_INTERPRETER_H_ +#define XCORE_INTERPRETER_H_ + +#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" +#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_interpreter.h" +#include "xcore_profiler.h" + +namespace tflite_micro { +namespace micro { +namespace xcore { + +class XCoreInterpreter : public tflite_micro::MicroInterpreter { +public: + XCoreInterpreter(const tflite_micro::Model *model, + const tflite_micro::MicroOpResolver &resolver, + tflite_micro::MicroAllocator *allocator, + bool use_curent_thread = true, + XCoreProfiler *profiler = nullptr); + + static XCoreInterpreter * + Create(uint8_t interpreter_buffer[], const tflite_micro::Model *model, + const tflite_micro::MicroOpResolver &resolver, uint8_t *arena, + size_t arena_size, bool use_current_thread, XCoreProfiler *profiler); + + void PrintMemoryPlan(); + TfLiteTensor *tensor(size_t tensor_index); + const char *node_name(int sub_idx, int i); + + TfLiteStatus GetTensorDetails(size_t tensor_index, char *name, int name_len, + int *shape, int *type, float *scale, + int32_t *zero_point); + + TfLiteStatus GetTensorDetailsBufferSizes(size_t tensor_index, size_t *dims, + size_t *scales, size_t *zero_points); + + size_t input_tensor_index(size_t input_index); + size_t output_tensor_index(size_t output_index); + const Model *model__; + MicroAllocator *allocator_; +}; + +} // namespace xcore +} // namespace micro +} // namespace tflite_micro + +#endif // XCORE_INTERPRETER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h new file mode 100644 index 00000000..3e45f53b --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h @@ -0,0 +1,89 @@ +#ifndef XCORE_OPS_H_ +#define XCORE_OPS_H_ + +#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" + +namespace tflite_micro { +namespace ops { +namespace micro { +namespace xcore { + +constexpr const char *XC_beta_activationf32_OpCode = "XC_beta_activationf32"; +constexpr const char *XC_beta_concatf32_OpCode = "XC_beta_concatf32"; +constexpr const char *XC_beta_convf32_OpCode = "XC_beta_convf32"; +constexpr const char *XC_beta_transposeconvf32_OpCode = + "XC_beta_transposeconvf32"; +constexpr const char *XC_beta_fcf32_OpCode = "XC_beta_fcf32"; + +constexpr const char *XC_binaryi16_OpCode = "XC_binaryi16"; +constexpr const char *XC_unaryi16_OpCode = "XC_unaryi16"; + +constexpr const char *XC_conv2d_v2_OpCode = "XC_conv2d_v2"; +constexpr const char *XC_maxpool2d_OpCode = "XC_maxpool2d"; +constexpr const char *XC_softmax_OpCode = "XC_softmax"; +constexpr const char *XC_batched_softmax_OpCode = "XC_batched_softmax"; +constexpr const char *XC_ld_weights_OpCode = "XC_ld_weights"; +constexpr const char *XC_ld_weights_wait_OpCode = "XC_ld_weights_wait"; +constexpr const char *XC_add_OpCode = "XC_add"; +constexpr const char *XC_slice_OpCode = "XC_slice"; +constexpr const char *XC_broadcast_OpCode = "XC_broadcast"; +constexpr const char *XC_lookup_OpCode = "XC_lookup"; +constexpr const char *XC_pad_OpCode = "XC_pad"; +constexpr const char *XC_concat_OpCode = "XC_concat"; +constexpr const char *XC_transpose_OpCode = "XC_transpose"; +constexpr const char *XC_pad_3_to_4_OpCode = "XC_pad_3_to_4"; +constexpr const char *XC_pad_1_to_4_OpCode = "XC_pad_1_to_4"; +constexpr const char *XC_mul_OpCode = "XC_mul"; +constexpr const char *XC_mean_OpCode = "XC_mean"; +constexpr const char *XC_meani16_OpCode = "XC_meani16"; +constexpr const char *XC_expand_8_to_16_OpCode = "XC_expand_8_to_16"; +constexpr const char *XC_no_op_OpCode = "XC_no_op"; +constexpr const char *XC_store_tensor_OpCode = "XC_store_tensor"; +constexpr const char *XC_load_tensor_OpCode = "XC_load_tensor"; + +// Binarized ops +constexpr const char *XC_bsign_8_OpCode = "XC_bsign_8"; + +TFLMRegistration *Register_XC_beta_activationf32(); +TFLMRegistration *Register_XC_beta_concatf32(); +TFLMRegistration *Register_XC_beta_convf32(); +TFLMRegistration *Register_XC_beta_transposeconvf32(); +TFLMRegistration *Register_XC_beta_fcf32(); + +TFLMRegistration *Register_XC_binaryi16(); +TFLMRegistration *Register_XC_unaryi16(); + +TFLMRegistration *Register_XC_conv2d_v2(); +TFLMRegistration *Register_XC_maxpool2d(); +TFLMRegistration *Register_XC_softmax(); +TFLMRegistration *Register_XC_batched_softmax(); +TFLMRegistration *Register_XC_ld_weights(); +TFLMRegistration *Register_XC_ld_weights_wait(); +TFLMRegistration *Register_XC_add(); +TFLMRegistration *Register_XC_slice(); +TFLMRegistration *Register_XC_broadcast(); +TFLMRegistration *Register_XC_lookup(); +TFLMRegistration *Register_XC_pad(); +TFLMRegistration *Register_XC_concat(); +TFLMRegistration *Register_XC_transpose(); +TFLMRegistration *Register_XC_pad_3_to_4(); +TFLMRegistration *Register_XC_pad_1_to_4(); +TFLMRegistration *Register_XC_mul(); +TFLMRegistration *Register_XC_mean(); +TFLMRegistration *Register_XC_meani16(); +TFLMRegistration *Register_XC_expand_8_to_16(); +TFLMRegistration *Register_XC_no_op(); +TFLMRegistration *Register_XC_store_tensor(); +TFLMRegistration *Register_XC_load_tensor(); + +// Binarized ops +TFLMRegistration *Register_XC_bsign_8(); + +void RegisterXCOps(tflite_micro::MicroOpResolver *res); + +} // namespace xcore +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // XCORE_OPS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h new file mode 100644 index 00000000..79532c52 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h @@ -0,0 +1,49 @@ +// Copyright (c) 2019, XMOS Ltd, All rights reserved + +#ifndef XCORE_PROFILER_H_ +#define XCORE_PROFILER_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_profiler.h" + +#if !defined(XCORE_PROFILER_DEFAULT_MAX_LEVELS) +#define XCORE_PROFILER_DEFAULT_MAX_LEVELS (64) +#endif + +namespace tflite_micro { +namespace micro { +namespace xcore { + +class XCoreProfiler : public tflite_micro::MicroProfiler { +public: + explicit XCoreProfiler(){}; + ~XCoreProfiler() override = default; + + void Init(tflite_micro::MicroAllocator *allocator, + size_t max_event_count = XCORE_PROFILER_DEFAULT_MAX_LEVELS); + + void ClearEvents(); + + uint32_t BeginEvent(const char *tag) override; + + // Event_handle is ignored since TFLu does not support concurrent events. + void EndEvent(uint32_t event_handle) override; + + uint32_t const *GetEventDurations(); + size_t GetNumEvents(); + +private: + const char *event_tag_; + uint32_t event_start_time_; + size_t event_count_ = 0; + size_t max_event_count_ = 0; + uint32_t *event_durations_; + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace xcore +} // namespace micro +} // namespace tflite_micro + +#endif // XCORE_PROFILER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h new file mode 100644 index 00000000..c3316001 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h @@ -0,0 +1,156 @@ +#ifndef XCORE_UTILS_H_ +#define XCORE_UTILS_H_ + +#include +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/kernel_util.h" +#include "tensorflow/lite/micro/memory_helpers.h" +#include "tensorflow/lite/micro/micro_utils.h" + +namespace tflite_micro { +namespace ops { +namespace micro { +namespace xcore { +/* Unpack an integer data type from a byte array + * T data type to unpack + * + * Example usage: + * int32_t t0 = unpack(&my_buffer[23]); + * int32_t t1 = unpack(&my_buffer[27]); + */ +template T unpack(const uint8_t *buffer) { + T retval = 0; + for (int i = 0; i < sizeof(T); ++i) + retval |= buffer[i] << (8 * i); + return retval; +} + +template +static inline T *construct_persistent_object(TfLiteContext *context) { + return new (context->AllocatePersistentBuffer(context, sizeof(T))) T; +} + +static inline bool is_ram_address(uintptr_t a) { +#ifdef XCORE + return ((a >= 0x80000) && (a <= 0x100000)); +#else + return true; +#endif +} + +static inline TfLiteStatus request_scratch_if_needed(TfLiteContext *context, + const void *source_address, + const size_t size, + int &scratch_idx) { + if (source_address && !is_ram_address((uintptr_t)source_address)) { + return context->RequestScratchBufferInArena(context, size, &scratch_idx); + } + return kTfLiteOk; +} + +static inline TfLiteStatus request_scratch_if_needed(TfLiteContext *context, + const TfLiteTensor *tensor, + int &scratch_idx) { + return request_scratch_if_needed(context, tensor->data.data, tflite_micro::EvalTensorBytes((const TfLiteEvalTensor*)tensor), + scratch_idx); +} + +extern "C" { +static inline void memload(void *dest, void *src, size_t size) { + // printf("memload dest=%d src=%d size=%d\n", (long)dest, (long)src, + // size); + memcpy(dest, src, size); +} +} + +size_t FetchBuffer(int8_t **dest, int8_t const *src, size_t size); + +template +static inline TfLiteStatus +fetch_scratch_if_needed(TfLiteContext *context, T *&array, + const TfLiteEvalTensor *tensor, int scratch_idx) { + if (scratch_idx >= 0) { + array = + static_cast(context->GetScratchBuffer(context, scratch_idx)); + const RuntimeShape shape = tflite_micro::micro::GetTensorShape(tensor); + + size_t sizeof_tensor_type; + TfLiteTypeSizeOf(tensor->type, &sizeof_tensor_type); + FetchBuffer((int8_t **)&array, tflite_micro::micro::GetTensorData(tensor), + shape.FlatSize() * sizeof_tensor_type); + } else { + array = tflite_micro::micro::GetTensorData(tensor); + } + TF_LITE_ENSURE(context, array); + return kTfLiteOk; +} + +template class PersistentArray { +private: + size_t max_size_ = 0; + size_t size_ = 0; + T *data_ = nullptr; + +public: + // call this only in the Init phase of operators + PersistentArray &allocate(TfLiteContext *context, + size_t max_size) noexcept { + assert(data_ == nullptr); + assert(max_size > 0); + + max_size_ = max_size; + data_ = reinterpret_cast( + context->AllocatePersistentBuffer(context, sizeof(T) * max_size)); + + return *this; + }; + PersistentArray &initialize() noexcept { + assert(size_ == 0); + while (size_ < max_size_) { + this->append(T()); + } + + return *this; + }; + // TODO: begin and end would be better if returned an iterator object + inline T *begin() noexcept { + assert(size_ > 0); + return &data_[0]; + } + inline T *end() noexcept { + assert(size_ > 0); + return &data_[size_]; + } + inline T &operator[](int i) noexcept { + assert(i < size_); + return data_[i]; + } + inline void append(const T &element) noexcept { + assert(size_ < max_size_); + data_[size_++] = element; + } + inline void append(T &&element) noexcept { + assert(size_ < max_size_); + data_[size_++] = std::move(element); + } + inline size_t size() noexcept { return size_; } + inline size_t max_size() noexcept { return max_size_; } +}; + +#ifndef UNSUPPORTED_KERNEL_TYPE +#define UNSUPPORTED_KERNEL_TYPE(T) \ + { \ + DebugLog("Unsupported " #T " value"); \ + TFLITE_ABORT; \ + } +#endif /*UNSUPPORTED_KERNEL_TYPE*/ + +} // namespace xcore +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // XCORE_UTILS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/thread_call.h b/new_ai_tools/include/lib_tflite_micro/src/thread_call.h new file mode 100644 index 00000000..51d4bc51 --- /dev/null +++ b/new_ai_tools/include/lib_tflite_micro/src/thread_call.h @@ -0,0 +1,95 @@ +#if !defined(__micro_thread_library_h__) +#define __micro_thread_library_h__ + +#include +#ifdef __xcore__ +#include +#else +typedef unsigned synchronizer_t; +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +#define XCORE_MAX_NUM_THREADS 5 + +#ifdef __XC__ + #define UNSAFE unsafe +#else + #define UNSAFE /**/ +#endif + +typedef struct { // THIS STRUCT MUST BE IN SYNC WITH ASSEMBLY CODE. + union { + uint64_t id_aligned[2]; // Guarantee 64-bit alignment. + uint32_t id[4]; // Actual IDs + } thread_ids; // ids of at most 4 threads - live during invoke + uint32_t synchroniser; // synchroniser for threads - live during invoke +} thread_info_t; + + +#ifndef __XC__ + +typedef void (*thread_function_pointer_t)(void * arg0, void * arg1, void * arg2); +struct inference_engine; + +/** Function that runs the client task + */ +void thread_client(thread_info_t *ptr, int n); + +/** Function that runs the client task + */ +static inline void thread_store_sync(thread_info_t *ptr, uint32_t s) { + ptr->synchroniser = s; +} + +/** Function that sets up parameters for one of the client threads + * This particular one passes the second and third arguments to the thread. + * When the thread function is actually called (through thread_call) + * the thread function will be called with those two arguments, + * and the first shared argument provided by thread_call. + * Note - we can make versions with more or fewer parameters. + * Note - we could pass this function the thread-function itself + * + * \param arg1 Second argument for the thread function + * \param arg2 Third argument for the thread function + * \param thread_id The thread_id to initialise; one of ptr[0]..ptr[3] above + */ +#ifdef __xcore__ +static inline void thread_variable_setup(void * arg1, void * arg2, uint32_t thread_id) { +#ifdef __VX4A__ + asm volatile("xm.tsetr %0, 11, %1" :: "r" (thread_id), "r" (arg1)); + asm volatile("xm.tsetr %0, 12, %1" :: "r" (thread_id), "r" (arg2)); + asm volatile("xm.tsetr %0, 24, %1" :: "r" (thread_id), "r" (1)); +#else + asm volatile("set t[%0]:r1, %1" :: "r" (thread_id), "r" (arg1)); + asm volatile("set t[%0]:r2, %1" :: "r" (thread_id), "r" (arg2)); + asm volatile("set t[%0]:r10, %1" :: "r" (thread_id), "r" (1)); +#endif +} +#else +extern void thread_variable_setup(void * arg1, void * arg2, uint32_t thread_id); +#endif + +/** Function that starts all thread functions and runs them until completion. + * It is assumed that the variable parts have been set up per thread. + * by thread_variable_setup. + * This thread will also invoke the function with the given variable arguments. + * + * \param arg0 First argument shared among all threads (usually the output pointer) + * \param arg1 Second argument for the master thread function + * \param arg2 Third argument for the master thread function + * \param fp thread function to call on all threads. + * \param ptr Pointer to the thread info block held in the xcore + * interpreter. + */ +void thread_call(void * arg0, void * arg1, void * arg2, + thread_function_pointer_t fp, thread_info_t *ptr); +#ifdef __cplusplus +}; +#endif + +#endif // __XC__ + +#endif // __micro_thread_library_h__ diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h new file mode 100644 index 00000000..af9f8565 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h @@ -0,0 +1,4 @@ +// Copyright 2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#include "XUD_USB_Defines.h" + diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h new file mode 100644 index 00000000..b3aafdb0 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h @@ -0,0 +1,4 @@ +// Copyright 2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#include "xud_device.h" + diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h new file mode 100644 index 00000000..a1ae7002 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h @@ -0,0 +1,4 @@ +// Copyright 2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#include "xud_std_descriptors.h" + diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h new file mode 100644 index 00000000..54dbae9e --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h @@ -0,0 +1,4 @@ +// Copyright 2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#include "xud_std_requests.h" + diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud.h new file mode 100644 index 00000000..3f399301 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/xud.h @@ -0,0 +1,518 @@ +// Copyright 2011-2023 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +/* + * \brief User defines and functions for XMOS USB Device library + */ + +#ifndef _XUD_H_ +#define _XUD_H_ + +#include + +#if !defined(__XS2A__) +#define XUD_OPT_SOFTCRC5 (1) +#else +#define XUD_OPT_SOFTCRC5 (0) +#endif + +#ifdef __xud_conf_h_exists__ +#include "xud_conf.h" +#endif + +#ifndef XUD_STARTUP_ADDRESS +#define XUD_STARTUP_ADDRESS (0) +#endif + +#ifndef __ASSEMBLER__ + +#include +#include +#include +#include + +#ifndef XUD_WEAK_API +#define XUD_WEAK_API (0) +#endif + +#if defined(__STDC__) && XUD_WEAK_API +#define ATTRIB_WEAK __attribute__((weak)); +#else +#define ATTRIB_WEAK +#endif + +#if !defined(USB_TILE) + #define USB_TILE tile[0] +#endif + +// TODO use PLATFORM_REFERENCE_MHZ from platform.h +#ifndef REF_CLK_FREQ +#define REF_CLK_FREQ (100) +#endif + +#ifndef XUD_CORE_CLOCK + #ifdef __XS2A__ + //#warning XUD_CORE_CLOCK not defined, using default (500MHz) + #define XUD_CORE_CLOCK (500) + #else + //#warning XUD_CORE_CLOCK not defined, using default (600MHz) + #define XUD_CORE_CLOCK (600) + #endif +#endif + +#if !defined(PORT_USB_CLK) + /* Ports have not been defined in the .xn file */ + #define PORT_USB_CLK on USB_TILE: XS1_PORT_1J + #define PORT_USB_TXD on USB_TILE: XS1_PORT_8A + #define PORT_USB_RXD on USB_TILE: XS1_PORT_8B + #define PORT_USB_TX_READYOUT on USB_TILE: XS1_PORT_1K + #define PORT_USB_TX_READYIN on USB_TILE: XS1_PORT_1H + #define PORT_USB_RX_READY on USB_TILE: XS1_PORT_1I + #define PORT_USB_FLAG0 on USB_TILE: XS1_PORT_1E + #define PORT_USB_FLAG1 on USB_TILE: XS1_PORT_1F + #ifdef __XS2A__ + /* XS2A has an additional flag port */ + #define PORT_USB_FLAG2 on USB_TILE: XS1_PORT_1G + #endif +#endif // PORT_USB_CLK + +/** + * \var typedef XUD_EpTransferType + * \brief Typedef for endpoint data transfer types. Note: it is important that ISO is 0 + */ +typedef enum XUD_EpTransferType +{ + XUD_EPTYPE_ISO = 0, /**< Isoc */ + XUD_EPTYPE_INT, /**< Interrupt */ + XUD_EPTYPE_BUL, /**< Bulk */ + XUD_EPTYPE_CTL, /**< Control */ + XUD_EPTYPE_DIS, /**< Disabled */ +} XUD_EpTransferType; + +/** + * \var typedef XUD_EpType + * \brief Typedef for endpoint type + */ +typedef unsigned int XUD_EpType; + +/** + * \var typedef XUD_ep + * \brief Typedef for endpoint identifiers + */ +typedef unsigned int XUD_ep; + +/* Value to be or'ed in with EpTransferType to enable bus state notifications */ +#define XUD_STATUS_ENABLE 0x80000000 + +typedef enum XUD_BusSpeed +{ + XUD_SPEED_FS = 1, + XUD_SPEED_HS = 2, + XUD_SPEED_KILL = 3 +} XUD_BusSpeed_t; + +typedef enum XUD_PwrConfig +{ + XUD_PWR_BUS, + XUD_PWR_SELF +} XUD_PwrConfig; + +typedef enum XUD_Result +{ + XUD_RES_RST = -1, + XUD_RES_OKAY = 0, + //XUD_RES_CTL = 1, /* Received a control trans */ + XUD_RES_ERR = 2, +} XUD_Result_t; + +/** This performs the low-level USB I/O operations. Note that this + * needs to run in a thread with at least 80 MIPS worst case execution + * speed. + * + * \param c_epOut An array of channel ends, one channel end per + * output endpoint (USB OUT transaction); this includes + * a channel to obtain requests on Endpoint 0. + * \param noEpOut The number of output endpoints, should be at least 1 (for Endpoint 0). + * \param c_epIn An array of channel ends, one channel end per input endpoint (USB IN transaction); + * this includes a channel to respond to requests on Endpoint 0. + * \param noEpIn The number of input endpoints, should be at least 1 (for Endpoint 0). + * \param c_sof A channel to receive SOF tokens on. This channel must be connected to a process that + * can receive a token once every 125 ms. If tokens are not read, the USB layer will lock up. + * If no SOF tokens are required ``null`` should be used for this parameter. + * + * \param epTypeTableOut See ``epTypeTableIn``. + * \param epTypeTableIn This and ``epTypeTableOut`` are two arrays + * indicating the type of the endpoint. + * Legal types include: + * ``XUD_EPTYPE_CTL`` (Endpoint 0), + * ``XUD_EPTYPE_BUL`` (Bulk endpoint), + * ``XUD_EPTYPE_ISO`` (Isochronous endpoint), + * ``XUD_EPTYPE_INT`` (Interrupt endpoint), + * ``XUD_EPTYPE_DIS`` (Endpoint not used). + * The first array contains the + * endpoint types for each of the OUT + * endpoints, the second array contains the + * endpoint types for each of the IN + * endpoints. + * \param desiredSpeed This parameter specifies what speed the device will attempt to run at + * i.e. full-speed (ie 12Mbps) or high-speed (480Mbps) if supported + * by the host. Pass ``XUD_SPEED_HS`` if high-speed is desired or ``XUD_SPEED_FS`` + * if not. Low speed USB is not supported by XUD. + * \param pwrConfig Specifies whether the device is bus or self-powered. When self-powered the XUD + * will monitor the VBUS line for host disconnections. This is required for compliance reasons. + * Valid values are XUD_PWR_SELF and XUD_PWR_BUS. + * + */ +int XUD_Main(/*tileref * unsafe usbtileXUD_res_t &xudres, */ + chanend c_epOut[], int noEpOut, + chanend c_epIn[], int noEpIn, + NULLABLE_RESOURCE(chanend, c_sof), + XUD_EpType epTypeTableOut[], XUD_EpType epTypeTableIn[], + XUD_BusSpeed_t desiredSpeed, + XUD_PwrConfig pwrConfig); + +/* Legacy API support */ +int XUD_Manager(chanend c_epOut[], int noEpOut, + chanend c_epIn[], int noEpIn, + NULLABLE_RESOURCE(chanend, c_sof), + XUD_EpType epTypeTableOut[], XUD_EpType epTypeTableIn[], + NULLABLE_RESOURCE(port, p_usb_rst), + NULLABLE_RESOURCE(xcore_clock_t, clk), + unsigned rstMask, + XUD_BusSpeed_t desiredSpeed, + XUD_PwrConfig pwrConfig); + +/** + * \brief This function must be called by a thread that deals with an OUT endpoint. + * When the host sends data, the low-level driver will fill the buffer. It + * pauses until data is available. + * \param ep_out The OUT endpoint identifier (created by ``XUD_InitEP``). + * \param buffer The buffer in which to store data received from the host. + * The buffer is assumed to be word aligned. + * \param length The number of bytes written to the buffer + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. + **/ +XUD_Result_t XUD_GetBuffer(XUD_ep ep_out, unsigned char buffer[], REFERENCE_PARAM(unsigned, length)) ATTRIB_WEAK; + +/** + * \brief Request setup data from usb buffer for a specific endpoint, pauses until data is available. + * \param ep_out The OUT endpoint identifier (created by ``XUD_InitEP``). + * \param buffer A char buffer passed by ref into which data is returned. + * \param length Length of the buffer received (expect 8 bytes) + * \return XUD_RES_OKAY on success, for errors see ``Status Reporting``_. + **/ +XUD_Result_t XUD_GetSetupBuffer(XUD_ep ep_out, unsigned char buffer[], REFERENCE_PARAM(unsigned, length)) ATTRIB_WEAK; + +/** + * \brief This function must be called by a thread that deals with an IN endpoint. + * When the host asks for data, the low-level driver will transmit the buffer + * to the host. + * \param ep_in The endpoint identifier (created by ``XUD_InitEp``). + * \param buffer The buffer of data to transmit to the host. + * \param datalength The number of bytes in the buffer. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. + */ +XUD_Result_t XUD_SetBuffer(XUD_ep ep_in, unsigned char buffer[], unsigned datalength) ATTRIB_WEAK; + +/** + * \brief Similar to XUD_SetBuffer but breaks up data transfers into smaller packets. + * This function must be called by a thread that deals with an IN endpoint. + * When the host asks for data, the low-level driver will transmit the buffer + * to the host. + * \param ep_in The IN endpoint identifier (created by ``XUD_InitEp``). + * \param buffer The buffer of data to transmit to the host. + * \param datalength The number of bytes in the buffer. + * \param epMax The maximum packet size in bytes. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. + */ +XUD_Result_t XUD_SetBuffer_EpMax(XUD_ep ep_in, unsigned char buffer[], unsigned datalength, unsigned epMax) ATTRIB_WEAK; + +/** + * \brief Performs a combined ``XUD_SetBuffer`` and ``XUD_GetBuffer``. + * It transmits the buffer of the given length over the ``ep_in`` endpoint to + * answer an IN request, and then waits for a 0 length Status OUT transaction on ``ep_out``. + * This function is normally called to handle Get control requests to Endpoint 0. + * + * \param ep_out The endpoint identifier that handles Endpoint 0 OUT data in the XUD manager. + * \param ep_in The endpoint identifier that handles Endpoint 0 IN data in the XUD manager. + * \param buffer The data to send in response to the IN transaction. Note that this data + * is chopped up in fragments of at most 64 bytes. + * \param length Length of data to be sent. + * \param requested The length that the host requested, (Typically pass the value ``wLength``). + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_ + **/ +XUD_Result_t XUD_DoGetRequest(XUD_ep ep_out, XUD_ep ep_in, unsigned char buffer[], unsigned length, unsigned requested) ATTRIB_WEAK; + +/** + * \brief This function sends an empty packet back on the next IN request with + * PID1. It is normally used by Endpoint 0 to acknowledge success of a control transfer. + * \param ep_in The Endpoint 0 IN identifier to the XUD manager. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. + **/ +XUD_Result_t XUD_DoSetRequestStatus(XUD_ep ep_in) ATTRIB_WEAK; + +/** + * \brief This function will complete a reset on an endpoint. Can take + * one or two ``XUD_ep`` as parameters (the second parameter can be set to ``null``). + * The return value should be inspected to find the new bus-speed. + * In Endpoint 0 typically two endpoints are reset (IN and OUT). + * In other endpoints ``null`` can be passed as the second parameter. + * \param one IN or OUT endpoint identifier to perform the reset on. + * \param two Optional second IN or OUT endpoint structure to perform a reset on. + * \return Either ``XUD_SPEED_HS`` - the host has accepted that this device can execute + * at high speed, ``XUD_SPEED_FS`` - the device is running at full speed, + * or ``XUD_SPEED_KILL`` to indicate that the USB stack has been shut down + * by another part of the user code (using XUD_Kill). If the last value is + * returned, the endpoint code should call XUD_CloseEndpoint and then + * terminate. + */ +XUD_BusSpeed_t XUD_ResetEndpoint(XUD_ep one, NULLABLE_REFERENCE_PARAM(XUD_ep, two)); + +/** + * \brief This function closes an endpoint. It should be called when the USB stack + * is shutting down. It should be called on all endpoints, either in parallel + * or in numerical order, first all OUT and then all IN endpoints + * \param one endpoint to close. + */ +void XUD_CloseEndpoint(XUD_ep one); + +/** + * \brief Initialises an XUD_ep + * \param c_ep Endpoint channel to be connected to the XUD library. + * \return Endpoint identifier + */ +XUD_ep XUD_InitEp(chanend c_ep); + +/** + * \brief Mark an endpoint as STALL based on its EP address. Cleared automatically if a SETUP received on the endpoint. + * Note: the IN bit of the endpoint address is used. + * \param epNum Endpoint number. + * \warning Must be run on same tile as XUD core + */ +void XUD_SetStallByAddr(int epNum); + +/** + * \brief Mark an endpoint as NOT STALLed based on its EP address. + * Note: the IN bit of the endpoint address is used. + * \param epNum Endpoint number. + * \warning Must be run on same tile as XUD core + */ +void XUD_ClearStallByAddr(int epNum); + +/** + * \brief Mark an endpoint as STALLed. It is cleared automatically if a SETUP received on the endpoint. + * \param ep XUD_ep type. + * \warning Must be run on same tile as XUD core + */ +void XUD_SetStall(XUD_ep ep); + +/** + * \brief Mark an endpoint as NOT STALLed + * \param ep XUD_ep type. + * \warning Must be run on same tile as XUD core + */ +void XUD_ClearStall(XUD_ep ep); + +/* USB 2.0 Spec 9.1.1.5 states that configuring a device should cause all + * the status and configuration values associated with the endpoints in the + * affected interfaces to be set to their default values. This includes setting + * the data toggle of any endpoint using data toggles to the value DATA0 */ +/** + * \brief Reset an Endpoints state including data PID toggle + * Note: the IN bit of the endpoint address is used. + * \param epNum Endpoint number (including IN bit) + * \warning Must be run on same tile as XUD core + */ +void XUD_ResetEpStateByAddr(unsigned epNum); + +/** + * \brief Enable a specific USB test mode in XUD + * \param ep XUD_ep type (must be endpoint 0 in or out) + * \param testMode The desired test-mode + * \warning Must be run on same tile as XUD core + */ +void XUD_SetTestMode(XUD_ep ep, unsigned testMode); + +/** + * \brief Terminate XUD core + * \param ep XUD_ep type (must be endpoint 0 in or out) + * \warning Must be run on same tile as XUD core + */ +void XUD_Kill(XUD_ep ep); + +/***********************************************************************************************/ + +/* + * Advanced functions for supporting multple Endpoints in a single core + */ + + +/** + * \brief Marks an OUT endpoint as ready to receive data + * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). + * \param addr The address of the buffer in which to store data received from the host. + * The buffer is assumed to be word aligned. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +static inline int XUD_SetReady_OutPtr(XUD_ep ep, unsigned addr) +{ + int chan_array_ptr; + int reset; + + /* Firstly check if we have missed a USB reset - endpoint may would not want receive after a reset */ + asm volatile("ldw %0, %1[9]":"=r"(reset):"r"(ep)); + if(reset) + { + return XUD_RES_RST; + } + asm volatile("ldw %0, %1[0]":"=r"(chan_array_ptr):"r"(ep)); + asm volatile("stw %0, %1[3]"::"r"(addr),"r"(ep)); // Store buffer + asm volatile("stw %0, %1[0]"::"r"(ep),"r"(chan_array_ptr)); + + return XUD_RES_OKAY; +} + +/** + * \brief Marks an OUT endpoint as ready to receive data + * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). + * \param buffer The buffer in which to store data received from the host. + * The buffer is assumed to be word aligned. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +int XUD_SetReady_Out(XUD_ep ep, unsigned char buffer[]); + + +/** + * \brief Marks an IN endpoint as ready to transmit data + * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). + * \param addr The address of the buffer to transmit to the host. + * The buffer is assumed be word aligned. + * \param len The length of the data to transmit. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +static inline XUD_Result_t XUD_SetReady_InPtr(XUD_ep ep, unsigned addr, int len) +{ + int chan_array_ptr; + int tmp, tmp2; + int wordLength; + int tailLength; + + int reset; + + /* Firstly check if we have missed a USB reset - endpoint may not want to send out old data after a reset */ + asm volatile("ldw %0, %1[9]":"=r"(reset):"r"(ep)); + if(reset) + { + return XUD_RES_RST; + } + + /* Tail length bytes to bits */ +#ifdef __XC__ + tailLength = zext((len << 3),5); +#else + tailLength = (len << 3) & 0x1F; +#endif + + /* Datalength (bytes) --> datalength (words) */ + wordLength = len >> 2; + + /* If tail-length is 0 and word-length not 0. Make tail-length 32 and word-length-- */ + if ((tailLength == 0) && (wordLength != 0)) + { + wordLength = wordLength - 1; + tailLength = 32; + } + + /* Get end off buffer address */ + asm volatile("add %0, %1, %2":"=r"(tmp):"r"(addr),"r"(wordLength << 2)); + + /* Produce negative offset from end of buffer */ + asm volatile("neg %0, %1":"=r"(tmp2):"r"(wordLength)); + + /* Store neg index */ + asm volatile("stw %0, %1[6]"::"r"(tmp2),"r"(ep)); + + /* Store buffer pointer */ + asm volatile("stw %0, %1[3]"::"r"(tmp),"r"(ep)); + + /* Store tail len */ + asm volatile("stw %0, %1[7]"::"r"(tailLength),"r"(ep)); + + /* Finally, mark ready */ + asm volatile("ldw %0, %1[0]":"=r"(chan_array_ptr):"r"(ep)); + asm volatile("stw %0, %1[0]"::"r"(ep),"r"(chan_array_ptr)); + + return XUD_RES_OKAY; +} + +/** + * \brief Marks an IN endpoint as ready to transmit data + * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). + * \param buffer The buffer to transmit to the host. + * The buffer is assumed be word aligned. + * \param len The length of the data to transmit. + * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +static inline XUD_Result_t XUD_SetReady_In(XUD_ep ep, unsigned char buffer[], int len) +{ + unsigned addr; + + asm volatile("mov %0, %1":"=r"(addr):"r"(buffer)); + + return XUD_SetReady_InPtr(ep, addr, len); +} + +/** + * \brief Select handler function for receiving OUT endpoint data in a select. + * \param c The chanend related to the endpoint + * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). + * \param length Passed by reference. The number of bytes written to the buffer (that was passed into + * XUD_SetReady_Out()) + * \param result XUD_Result_t passed by reference. XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +#ifdef __XC__ +#pragma select handler +#endif +void XUD_GetData_Select(chanend c, XUD_ep ep, REFERENCE_PARAM(unsigned, length), REFERENCE_PARAM(XUD_Result_t, result)); + + +/** + * \brief Select handler function for transmitting IN endpoint data in a select. + * \param c The chanend related to the endpoint + * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). + * \param result Passed by reference. XUD_RES_OKAY on success, for errors see `Status Reporting`. + */ +#ifdef __XC__ +#pragma select handler +#endif +void XUD_SetData_Select(chanend c, XUD_ep ep, REFERENCE_PARAM(XUD_Result_t, result)); + +/* Control token defines - used to inform EPs of bus-state types */ +#define USB_RESET_TOKEN 8 /* Control token value that signals RESET */ + +#ifndef XUD_OSC_MHZ +#define XUD_OSC_MHZ (24) +#endif + +/* TODO pack this to save mem + * TODO size of this hardcoded in ResetEpStateByAddr_ + */ +typedef struct XUD_ep_info +{ + unsigned int array_ptr; // 0 + unsigned int xud_chanend; // 1 + unsigned int client_chanend; // 2 + unsigned int buffer; // 3 Pointer to buffer + unsigned int pid; // 4 Expected out PID + unsigned int epType; // 5 Data + unsigned int actualPid; // 6 Actual OUT PID received for OUT, Length (words) for IN. + unsigned int tailLength; // 7 "tail" length for IN (bytes) + unsigned int epAddress; // 8 EP address assigned by XUD (Used for marking stall etc) + unsigned int resetting; // 9 Flag to indicate to EP a bus-reset occured. + unsigned int halted; // 10 NAK or STALL + unsigned int saved_array_ptr; // 11 + unsigned int array_ptr_setup; // 12 +} XUD_ep_info; + +#endif +#endif // _XUD_H_ diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h new file mode 100644 index 00000000..76617f33 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h @@ -0,0 +1,11 @@ +// Copyright 2017-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef __XUD_CONF_DEFAULT_H__ +#define __XUD_CONF_DEFAULT_H__ + +#ifdef __xud_conf_h_exists__ + #include "xud_conf.h" +#endif + +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h new file mode 100644 index 00000000..d68b8dca --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h @@ -0,0 +1,87 @@ +// Copyright 2015-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +/* + * @brief USB Device helper functions + */ + +#ifndef _USB_DEVICE_H_ +#define _USB_DEVICE_H_ + +#include + +/* Low level XUD API for USB Device */ +#include "xud.h" + +/* Generic USB defines */ +#include "XUD_USB_Defines.h" + +/* Generic USB descriptor defines */ +#include "xud_std_descriptors.h" + +/* Generic USB descriptor defines */ +#include "xud_std_requests.h" + +/** + * \brief This function deals with common requests This includes Standard Device Requests listed + * in table 9-3 of the USB 2.0 Spec all devices must respond to these requests, in some + * cases a bare minimum implementation is provided and should be extended in the devices EP0 code + * It handles the following standard requests appropriately using values passed to it: + * + * Get Device Descriptor (using devDesc_hs/devDesc_fs arguments) + * + * Get Configuration Descriptor (using cfgDesc_hs/cfgDesc_fs arguments) + * + * String requests (using strDesc argument) + * + * Get Device_Qualifier Descriptor + * + * Get Other-Speed Configuration Descriptor + * + * Set/Clear Feature (Endpoint Halt) + * + * Get/Set Interface + * + * Set Configuration + * + * If the request is not recognised the endpoint is marked STALLED + * + * + * \param ep_out Endpoint from XUD (ep 0) + * \param ep_in Endpoint from XUD (ep 0) + * \param devDesc_hs The Device descriptor to use, encoded according to the USB standard + * \param devDescLength_hs Length of device descriptor in bytes + * \param cfgDesc_hs Configuration descriptor + * \param cfgDescLength_hs Length of config descriptor in bytes + * \param devDesc_fs The Device descriptor to use, encoded according to the USB standard + * \param devDescLength_fs Length of device descriptor in bytes. If 0 the HS device descriptor is used. + * \param cfgDesc_fs Configuration descriptor + * \param cfgDescLength_fs Length of config descriptor in bytes. If 0 the HS config descriptor is used. + * \param strDescs + * \param strDescsLength + * \param sp ``USB_SetupPacket_t`` (passed by ref) in which the setup data is returned + * \param usbBusSpeed The current bus speed (XUD_SPEED_HS or XUD_SPEED_FS) + * + * \return Returns XUD_RES_OKAY on success. + */ + +XUD_Result_t USB_StandardRequests(XUD_ep ep_out, XUD_ep ep_in, + NULLABLE_ARRAY_OF(unsigned char, devDesc_hs), int devDescLength_hs, + NULLABLE_ARRAY_OF(unsigned char, cfgDesc_hs), int cfgDescLength_hs, + NULLABLE_ARRAY_OF(unsigned char, devDesc_fs), int devDescLength_fs, + NULLABLE_ARRAY_OF(unsigned char, cfgDesc_fs), int cfgDescLength_fs, +#ifdef __XC__ + char * unsafe strDescs[], +#else + char * strDescs[], +#endif + int strDescsLength, REFERENCE_PARAM(USB_SetupPacket_t, sp), XUD_BusSpeed_t usbBusSpeed); +/** + * \brief Receives a Setup data packet and parses it into the passed USB_SetupPacket_t structure. + * \param ep_out OUT endpint from XUD + * \param ep_in IN endpoint to XUD + * \param sp SetupPacket structure to be filled in (passed by ref) + * \return Returns XUD_RES_OKAY on success, XUD_RES_RST on bus reset + */ +XUD_Result_t USB_GetSetupPacket(XUD_ep ep_out, XUD_ep ep_in, REFERENCE_PARAM(USB_SetupPacket_t, sp)); + +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h new file mode 100644 index 00000000..40e937ce --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h @@ -0,0 +1,191 @@ +// Copyright 2015-2022 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef _USB_DESCRIPTORS_H_ +#define _USB_DESCRIPTORS_H_ + +#if !defined (__ASSEMBLER__) +/* USB Class Codes (from usb.org) */ +typedef enum +{ + USB_CLASS_USE_CLASS = 0x00, /* Use class information in the interface descriptors */ + USB_CLASS_AUDIO = 0x01, + USB_CLASS_COMMUNICATIONS = 0x02, + USB_CLASS_HID = 0x03, + USB_CLASS_PHYSICAL = 0x05, + USB_CLASS_IMAGE = 0x06, + USB_CLASS_PRINTER = 0x07, + USB_CLASS_MASS_STORAGE = 0x08, + USB_CLASS_HUB = 0x09, + USB_CLASS_CDC_DATA = 0x0A, + USB_CLASS_SMART_CARD = 0x0B, + USB_CLASS_RESERVED = 0x0C, + USB_CLASS_CONTENT_SECURITY = 0x0D, + USB_CLASS_VIDEO = 0x0E, + USB_CLASS_PERSONAL_HEALTHCARE = 0x0F, + USB_CLASS_AUDIO_VIDEO = 0x10, +//... + USB_CLASS_MAPPED_INDEX_END = 17, + USB_CLASS_VENDOR_SPECIFIC = 0xFF +} USB_ClassCode_t; + +#endif + +/* USB Standard Descriptor types (Section 9.4, table 9-5) */ +enum USB_DescriptorTypes_t +{ + USB_DESCTYPE_DEVICE = 0x01, /* Device descriptor */ + USB_DESCTYPE_CONFIGURATION = 0x02, /* Configuration descriptor */ + USB_DESCTYPE_STRING = 0x03, /* String descriptor */ + USB_DESCTYPE_INTERFACE = 0x04, /* Interface descriptor */ + USB_DESCTYPE_ENDPOINT = 0x05, /* Endpoint descriptor */ + USB_DESCTYPE_DEVICE_QUALIFIER = 0x06, /* Device qualifier descriptor */ + USB_DESCTYPE_OTHER_SPEED = 0x07, + USB_DESCTYPE_INTERFACE_POWER = 0x08, /* Interface power descriptor */ + USB_DESCTYPE_OTG = 0x09, + USB_DESCTYPE_DEBUG = 0x0A, + USB_DESCTYPE_INTERFACE_ASSOCIATION = 0x0B, /* Interface association descriptor */ +}; + +#ifdef __STDC__ + +/* No current support for __attribute((packed)) in XC */ + +/* Generic USB Descriptor Header */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Descriptor type, either a value. See \ref USB_DescriptorTypes_t or + * a value given by the specific class */ +} __attribute__((packed)) USB_Descriptor_Header_t; + +/* USB Standard Device Descriptor (section 9.6.1, table 9-8) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t + * or a value given by the specific class */ + unsigned short bcdUSB; /* Supported USB version */ + unsigned char bDeviceClass; /* USB device class code */ + unsigned char bDeviceSubClass; /* USB device subclass code */ + unsigned char bDeviceProtocol; /* USB device protocol code */ + unsigned char bMaxPacketSize0; /* Maximum packet size for endpoint 0 (bytes) */ + unsigned short idVendor; /* Vendor ID */ + unsigned short idProduct; /* Product ID */ + unsigned short bcdDevice; /* Device release number in binary-coded decimal */ + unsigned char iManufacturer; /* Index of string descriptor describing manufacturer */ + unsigned char iProduct; /* Index of string descriptor describing product */ + unsigned char iSerialNumber; /* Index of String descriptor describing the devices serial number */ + unsigned char bNumConfigurations; /* Total number of configurations supported by the device */ +} __attribute__((packed)) USB_Descriptor_Device_t; + +/* USB Interface Association Descriptor (See IAD Engineering Change Notice) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t + or a value given by the specific class */ + unsigned char bFirstInterface; /* Index of the first associated interface */ + unsigned char bInterfaceCount; /* Total number of associated interfaces */ + unsigned char bFunctionClass; /* Interface class ID */ + unsigned char bFunctionSubClass; /* Interface subclass ID */ + unsigned char bFunctionProtocol; /* Interface protocol ID */ + unsigned char iFunction; /* Index of the string descriptor describing the + * interface association */ +} __attribute__((packed)) USB_Descriptor_Interface_Association_t; + +/* USB Standard Interface Descriptor (section 9.6.1 table 9-12) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Type of the descriptor, either a value in \ref USB_DescriptorTypes_t + * or a value given by the specific class */ + unsigned char bInterfaceNumber; /* Index of the interface in the current config */ + unsigned char bAlternateSetting; /* Alternate setting for this interface number. Multiple alternatives + * are supported per interface (with different EP configs) */ + unsigned char bNumEndpoints; /* Total endpoint count in this interface */ + unsigned char bInterfaceClass; /* Interface class code */ + + unsigned char bInterfaceSubClass; /* Interface subclass code */ + unsigned char bInterfaceProtocol; /* Interface protocol code */ + unsigned char iInterface; /* Index of the string descriptor in the string table */ +} __attribute__((packed)) USB_Descriptor_Interface_t; + +/* USB Standard Configuration Descriptor (section 9.6.1 table 9-10) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Type of the descriptor, either a value in \ref USB_DescriptorTypes_t or a value + * given by the specific class */ + unsigned short wTotalLength; /* Size of the configuration descriptor header and all sub descriptors inside + * the configuration */ + unsigned char bNumInterfaces; /* Total interface count in the configuration */ + unsigned char bConfigurationValue; /* Value to use as an argument to the SetConfiguration() request to select this + * configuration */ + unsigned char iConfiguration; /* Index of string descriptor describing this configuration */ + unsigned char bmAttributes; /* Configuration characteristics + * D7: Reserved (set to one) + * D6: Self-powered + * D5: Remote Wakeup + * D4...0: Reserved (reset to zero) + */ + unsigned char bMaxPower; /* Maximum power consumption of the USB device from the bus in this specific + * configuration when the device is fully operational. Expressed in 2 mA units + * (i.e., 50 = 100 mA) */ +} __attribute__((packed)) USB_Descriptor_Configuration_Header_t; + +/* USB Standard Endpoint Descriptor (section 9.6.1 table 9-13) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Descriptor type, either a value. See \ref USB_DescriptorTypes_t or + * a value given by the specific class */ + unsigned char bEndpointAddress; /* Address of the endpoint, includes a direction mask */ + unsigned char bmAttributes; /* Endpoint attributes, comprised of a mask of the endpoint type + * See EP_TYPE_ ad EP_ADDR) */ + unsigned short wMaxPacketSize; /* Maximum packet size (bytes) that the endpoint can receive */ + unsigned char bInterval; /* Polling interval in milliseconds for the endpoint. + * Relevant to Isochronous and Interrupt endpoints only */ +} __attribute__((packed)) USB_Descriptor_Endpoint_t; + +/* USB Standard Endpoint Descriptor (Section 9.6.6, table 9-13) */ +enum USB_Endpoint_TransType_t +{ + USB_ENDPOINT_TRANSTYPE_CTRL = 0x0, + USB_ENDPOINT_TRANSTYPE_ISO = 0x1, + USB_ENDPOINT_TRANSTYPE_BULK = 0x2, + USB_ENDPOINT_TRANSTYPE_INT = 0x3 +}; +#define USB_ENDPOINT_TRANSTYPE_SHIFT (0) + +enum USB_Endpoint_SyncType_t +{ + USB_ENDPOINT_SYNCTYPE_NONE = 0x0, + USB_ENDPOINT_SYNCTYPE_ASYNC = 0x1, + USB_ENDPOINT_SYNCTYPE_ADAPT = 0x2, + USB_ENDPOINT_SYNCTYPE_SYNC = 0x3 +}; +#define USB_ENDPOINT_SYNCTYPE_SHIFT (2) + +enum USB_Endpoint_UsageType_t +{ + USB_ENDPOINT_USAGETYPE_DATA = 0x0, + USB_ENDPOINT_USAGETYPE_FEEDBACK = 0x1, + USB_ENDPOINT_USAGETYPE_IMPLICIT = 0x2, + USB_ENDPOINT_USAGETYPE_RESERVED = 0x3 +}; +#define USB_ENDPOINT_USAGETYPE_SHIFT (4) + + + +/* USB String Descriptor (Section 9.6.7 table 9-15) */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t + * or a value given by the specific class */ + unsigned short bString[]; /* String data, (as unicode characters) - use array of chars instead of string. + * In GCC prefix string with "L" */ +} __attribute__((packed)) USB_Descriptor_String_t; +#endif +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h new file mode 100644 index 00000000..5fafbb21 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h @@ -0,0 +1,120 @@ +// Copyright 2015-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +#ifndef _USB_STD_REQUESTS_H_ +#define _USB_STD_REQUESTS_H_ + +#include +#include "XUD_USB_Defines.h" + +/* 9.3 USB Device Requests: Table 9-2 Format of Setup Data */ +/* bmRequestType: */ +#define USB_BM_REQTYPE_DIRECTION_H2D 0 /* Host to device */ +#define USB_BM_REQTYPE_DIRECTION_D2H 1 /* Device to host */ + +#define USB_BM_REQTYPE_TYPE_STANDARD 0x00 +#define USB_BM_REQTYPE_TYPE_CLASS 0x01 +#define USB_BM_REQTYPE_TYPE_VENDOR 0x02 + +#define USB_BM_REQTYPE_RECIP_DEV 0x00 +#define USB_BM_REQTYPE_RECIP_INTER 0x01 +#define USB_BM_REQTYPE_RECIP_EP 0x02 +#define USB_BM_REQTYPE_RECIP_OTHER 0x03 + +#define USB_BMREQ_H2D_STANDARD_DEV ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_DEV)) +#define USB_BMREQ_D2H_STANDARD_DEV ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_DEV)) +#define USB_BMREQ_H2D_STANDARD_INT ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_INTER)) +#define USB_BMREQ_D2H_STANDARD_INT ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_INTER)) +#define USB_BMREQ_H2D_STANDARD_EP ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_EP)) +#define USB_BMREQ_D2H_STANDARD_EP ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ + (USB_BM_REQTYPE_RECIP_EP)) + + +#define USB_BMREQ_H2D_CLASS_INT ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ + (USB_BM_REQTYPE_RECIP_INTER)) +#define USB_BMREQ_D2H_CLASS_INT ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ + (USB_BM_REQTYPE_RECIP_INTER)) +#define USB_BMREQ_H2D_CLASS_EP ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ + (USB_BM_REQTYPE_RECIP_EP)) +#define USB_BMREQ_D2H_CLASS_EP ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ + (USB_BM_REQTYPE_RECIP_EP)) + +#define USB_BMREQ_H2D_VENDOR_DEV ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ + (USB_BM_REQTYPE_TYPE_VENDOR << 5) | \ + (USB_BM_REQTYPE_RECIP_DEV)) +#define USB_BMREQ_D2H_VENDOR_DEV ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ + (USB_BM_REQTYPE_TYPE_VENDOR << 5) | \ + (USB_BM_REQTYPE_RECIP_DEV)) + +/* Table 9-4. Standard Request Codes */ +/* bRequest */ +#define USB_GET_STATUS 0x00 +#define USB_CLEAR_FEATURE 0x01 +#define USB_SET_FEATURE 0x03 +#define USB_SET_ADDRESS 0x05 +#define USB_GET_DESCRIPTOR 0x06 +#define USB_SET_DESCRIPTOR 0x07 +#define USB_GET_CONFIGURATION 0x08 +#define USB_SET_CONFIGURATION 0x09 +#define USB_GET_INTERFACE 0x0A +#define USB_SET_INTERFACE 0x0B +#define USB_SYNCH_FRAME 0x0C + +/** + * \var typedef USB_BmRequestType_t + * \brief Defines the Recepient, Type and Direction of a USB request. + */ +typedef struct USB_BmRequestType +{ + unsigned char Recipient; // [4..0] Request directed to: + // 0b00000: Device + // 0b00001: Specific interface + // 0b00010: Specific endpoint + // 0b00011: Other element in device + unsigned char Type; // [6..5] 0b00: Standard request + // 0b01: Class specific request + // 0b10: Request by vendor specific driver + unsigned char Direction; // [7] 0 (Host->Dev) + // 1 (Dev->Host) +} USB_BmRequestType_t; + +/** + * \var typedef USB_SetupPacket_t + * \brief Typedef for setup packet structure */ +typedef struct USB_SetupPacket +{ + USB_BmRequestType_t bmRequestType; /* (1 byte) Specifies direction of dataflow, + type of rquest and recipient */ + unsigned char bRequest; /* Specifies the request */ + unsigned short wValue; /* Host can use this to pass info to the + device in its own way */ + unsigned short wIndex; /* Typically used to pass index/offset such + as interface or EP no */ + unsigned short wLength; /* Number of data bytes in the data stage + (for Host -> Device this this is exact + count, for Dev->Host is a max. */ +} USB_SetupPacket_t; + +/** + * \brief Prints out passed ``USB_SetupPacket_t`` struct using debug IO + */ +void USB_PrintSetupPacket(USB_SetupPacket_t sp); + +void USB_ComposeSetupBuffer(USB_SetupPacket_t sp, unsigned char buffer[]); + +void USB_ParseSetupPacket(unsigned char b[], REFERENCE_PARAM(USB_SetupPacket_t, p)); +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h new file mode 100644 index 00000000..6f0082ef --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h @@ -0,0 +1,70 @@ +// Copyright 2015-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +/** + * @brief Defines from the Universal Serial Bus Specification Revision 2.0 + **/ + +#ifndef _USB_DEFS_H_ +#define _USB_DEFS_H_ + +/* Table 8-1. PID Types */ +#define USB_PID_OUT 0x1 /* Tokens */ +#define USB_PID_IN 0x9 +#define USB_PID_SOF 0x5 +#define USB_PID_SETUP 0xD +#define USB_PID_DATA0 0x3 /* Data packet PID even */ +#define USB_PID_DATA1 0xB /* Data packet PID odd */ +#define USB_PID_DATA2 0x7 /* Data packet PID high-speed, high bandwidth isoc transaction in a microframe */ +#define USB_PID_MDATA 0xF /* Data packet PID high-speed and high bandwidth isoc transactions */ +#define USB_PID_ACK 0x2 /* Receiver accepts error-free data packet */ +#define USB_PID_NAK 0xA /* Receiving device cannot accept data of transmitting device cannot send data */ +#define USB_PID_STALL 0xE /* Endpoint is halted or a control pipe request is not supported */ +#define USB_PID_PRE 0xC +#define USB_PID_ERR 0xC +#define USB_PID_SPLIT 0x8 +#define USB_PID_PING 0x4 /* Hign-speed flow control probe for bulk/control endpoint */ + +/* PID with error check */ +#define USB_PID_NEGATE(PID) ((PID) | (((~PID) & 0xf) << 4)) +#define USB_PIDn_OUT 0xe1 +#define USB_PIDn_IN 0x69 +#define USB_PIDn_SOF 0xa5 +#define USB_PIDn_SETUP 0x2d +#define USB_PIDn_DATA0 0xc3 +#define USB_PIDn_DATA1 USB_PID_NEGATE(USB_PID_DATA1) +#define USB_PIDn_DATA2 USB_PID_NEGATE(USB_PID_DATA2) +#define USB_PIDn_ACK 0xd2 +#define USB_PIDn_NAK 0x5a +#define USB_PIDn_STALL 0x1e + +/* Table 9-6. Standard Feature Selectors (wValue) */ +#define USB_DEVICE_REMOTE_WAKEUP 0x01 /* Recipient: Device */ +#define USB_ENDPOINT_HALT 0x00 /* Recipient: Endpoint */ +#define USB_TEST_MODE 0x02 /* Recipient: Device */ + +#define USB_STANDARD_DEVICE_REQUEST 0x00 +#define USB_STANDARD_INTERFACE_REQUEST 0x01 +#define USB_STANDARD_ENDPOINT_REQUEST 0x02 +#define USB_VENDOR_DEVICE_REQUEST 0x40 +#define USB_VENDOR_ENDPOINT_REQUEST 0x42 +#define USB_CLASS_INTERFACE_REQUEST 0x21 +#define USB_CLASS_ENDPOINT_REQUEST 0x22 + +#define USB_WVAL_EP_HALT 0 + +// Low byte values: +//#define USB_WVALUE_GETDESC_STRING_LANGIDS 0x0 +//#define USB_WVALUE_GETDESC_STRING_IPRODUCT 0x2 + +// Test selector defines for Test mode +#define USB_WINDEX_TEST_J (0x1<<8) +#define USB_WINDEX_TEST_K (0x2<<8) +#define USB_WINDEX_TEST_SE0_NAK (0x3<<8) +#define USB_WINDEX_TEST_PACKET (0x4<<8) +#define USB_WINDEX_TEST_FORCE_ENABLE (0x5<<8) + +#define USB_MAX_NUM_EP_OUT (16) +#define USB_MAX_NUM_EP_IN (16) +#define USB_MAX_NUM_EP (32) + +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h new file mode 100644 index 00000000..a6df0f11 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h @@ -0,0 +1,23 @@ +// Copyright 2015-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +/* + * @brief Defines from USB Device Class Definition for Human Interface Devices Specification + */ + +/* 7. Requests */ + +/* 7.1 Standard Requests - Class Descriptor Types - High byte of wValue + * The following defines valid types of Class descriptors */ +#define HID_HID 0x2100 +#define HID_REPORT 0x2200 +#define HID_PHYSICAL_DESCRIPTOR 0x2300 +/*0x24 - 0x2F: Reserved */ + +/* 7.2 Class-Specific Requests - bRequest values */ +#define HID_GET_REPORT 0x01 /* Mandatory */ +#define HID_GET_IDLE 0x02 +#define HID_GET_PROTOCOL 0x03 /* Required only for boot devices */ +/* Ox04 - 0x08 reserved */ +#define HID_SET_REPORT 0x09 +#define HID_SET_IDLE 0x0A +#define HID_SET_PROTOCOL 0x0B /* Required only for boot devices */ diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h new file mode 100644 index 00000000..712de4ab --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h @@ -0,0 +1,30 @@ +// Copyright 2017-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef _AUDIO10_H_ +#define _AUDIO10_H_ 1 +/** + * @brief Defines from the USB Audio 1.0 Specifications + * @author Ross Owen, XMOS Limited + */ + +/* A.9. Audio Class-Specific Request Codes */ +#define UAC_B_REQ_SET_CUR 0x01 +#define UAC_B_REQ_GET_CUR 0x81 +#define UAC_B_REQ_SET_MIN 0x02 +#define UAC_B_REQ_GET_MIN 0x82 +#define UAC_B_REQ_SET_MAX 0x03 +#define UAC_B_REQ_GET_MAX 0x83 +#define UAC_B_REQ_SET_RES 0x04 +#define UAC_B_REQ_GET_RES 0x84 +#define UAC_B_REQ_SET_MEM 0x05 +#define UAC_B_REQ_GET_MEM 0x85 +#define UAC_B_REQ_GET_STAT 0xFF + +/* A.10.5 Endpoint Control Selectors: Table A-19 */ +#define EP_CONTROL_UNDEFINED 0x00 +#define SAMPLING_FREQ_CONTROL 0x01 +#define PITCH_CONTROL 0x02 + +#endif + diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h new file mode 100644 index 00000000..ed9fec95 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h @@ -0,0 +1,357 @@ +// Copyright 2017-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. +/** + * @brief Defines from the USB Audio 2.0 Specifications + * @author Ross Owen, XMOS Limited + */ +#ifndef _USBAUDIO20_H_ +#define _USBAUDIO20_H_ + +#include "usbaudiocommon.h" + +/***********************************************************************/ +/* USB Audio 2.0 Class Specification */ + +/* A.1 Audio Function Class Code */ +#define AUDIO_FUNCTION AUDIO + +/* A.2 Audio Function Subclass Codes */ +#define FUNCTION_SUBCLASS_UNDEFINED 0x00 + +/* A.3 Audio Function Protocol Codes */ +enum USB_Audio_FuncProtocolCodes_t +{ + UAC_FUNC_PROTOCOL_UNDEFINED = 0x00, + UAC_FUNC_PROTOCOL_AF_VERSION_02_00 = 0x20 +}; + +/* A.4 Audio Interface Class Code */ +#define AUDIO 0x01 + +/* A.5 Audio Interface Subclass Codes */ +enum UAC_IntSubclassCodes_t +{ + UAC_INT_SUBCLASS_AUDIOCONTROL = 0x01, + UAC_INT_SUBCLASS_AUDIOSTREAMING = 0x02, + UAC_INT_SUBCLASS_MIDISTREAMING = 0x03 +}; + +/* A.6 Audio Interface Protocol Codes */ +enum UAC_IntProtocolCodes_t +{ + UAC_INT_PROTOCOL_UNDEFINED = 0x00, + UAC_INT_PROTOCOL_IP_VERSION_02_00 = 0x20 +}; + +/* A.7 Audio Function Category Codes */ +enum UAC_AudioFunctionCategory_t +{ + UAC_FUNCTION_SUBCLASS_UNDEFINED = 0x00, + UAC_FUNCTION_DESKTOP_SPEAKER = 0x01, + UAC_FUNCITON_HOME_THEATER = 0x02, + UAC_FUNCTION_MICROPHONE = 0x03, + UAC_FUNCITON_HEADSET = 0x04, + UAC_FUNCTION_TELEPHONE = 0x05, + UAC_FUNCTION_CONVERTER = 0x06, + UAC_FUNCTION_VOICE_SOUND_RECORDER = 0x07, + UAC_FUNCTION_IO_BOX = 0x08, + UAC_FUNCTION_MUSICAL_INTRUMENT = 0x09, + UAC_FUNCTION_PRO_AUDIO = 0x0A, + UAC_FUNCTION_AUDIO_VIDEO = 0x0B, + UAC_FUNCTION_CONTROL_PANEL = 0x0C, + UAC_FUNCITON_OTHER = 0xFF +}; + +/* A.8 Audio Class-Specific Descriptor Types */ +/* Shared with Audio Class 1.0 */ +enum UAC_CSDescriptorTypes_t +{ + UAC_CS_DESCTYPE_UNDEFINED = 0x20, + UAC_CS_DESCTYPE_DEVICE = 0x21, + UAC_CS_DESCTYPE_CONFIGURATION = 0x22, + UAC_CS_DESCTYPE_STRING = 0x23, + UAC_CS_DESCTYPE_INTERFACE = 0x24, + UAC_CS_DESCTYPE_ENDPOINT = 0x25, +}; + +/* A.9 Audio Class-Specific AC Interface Descriptor Subtypes */ +enum UAC_CS_AC_InterfaceDescriptorSubtype_t +{ + UAC_CS_AC_INTERFACE_SUBTYPE_AC_DESCRIPTOR_UNDEFINED = 0x00, + UAC_CS_AC_INTERFACE_SUBTYPE_HEADER = 0x01, + UAC_CS_AC_INTERFACE_SUBTYPE_INPUT_TERMINAL = 0x02, + UAC_CS_AC_INTERFACE_SUBTYPE_OUTPUT_TERMINAL = 0x03, + UAC_CS_AC_INTERFACE_SUBTYPE_MIXER_UNIT = 0x04, + UAC_CS_AC_INTERFACE_SUBTYPE_SELECTOR_UNIT = 0x05, + UAC_CS_AC_INTERFACE_SUBTYPE_FEATURE_UNIT = 0x06, + UAC_CS_AC_INTERFACE_SUBTYPE_EFFECT_UNIT = 0x07, + UAC_CS_AC_INTERFACE_SUBTYPE_PROCESSING_UNIT = 0x08, + UAC_CS_AC_INTERFACE_SUBTYPE_EXTENSION_UNIT = 0x09, + UAC_CS_AC_INTERFACE_SUBTYPE_CLOCK_SOURCE = 0x0A, + UAC_CS_AC_INTERFACE_SUBTYPE_CLOCK_SELECTOR = 0x0B, + UAC_CS_AC_INTERFACE_SUBSYPE_CLOCK_MULTIPLIER = 0x0C, + UAC_CS_AC_INTERFACE_SUBTYPE_SAMPLE_RATE_CONVERTER = 0x0D +}; + +/* A.10 Audio Class Specific AS Interface Descriptor Subtypes */ +enum UAC_CS_AS_InterfaceDescriptorSubtype_t +{ + UAC_CS_AS_INTERFACE_SUBTYPE_UNDEFINED = 0x00, + UAC_CS_AS_INTERFACE_SUBTYPE_AS_GENERAL = 0x01, + UAC_CS_AS_INTERFACE_SUBTYPE_FORMAT_TYPE = 0x02, + UAC_CS_AS_INTERFACE_SUBTYPE_ENCODER = 0x03, + UAC_CS_AS_INTERFACE_SUBTYPE_DECODER = 0x04 +}; + +/* A.11 Effect Unit Effect Types */ +#define EFFECT_UNDEFINED 0x00 +#define PARAM_EQ_SECTION_EFFECT 0x01 +#define REVERBERATION_EFFECT 0x02 +#define MOD_DELAY_EFFECT 0x03 +#define DYN_RANGE_COMP_EFFECT 0x04 + +/* A.12 Processing Unit Process Types */ +#define PROCESS_UNDEFINED 0x00 +#define UP_DOWNMIX_PROCESS 0x01 +#define DOLBY_PROLOGIC_PROCESS 0x02 +#define STEREO_EXTENDER_PROCESS 0x03 + +/* A.13 Audio Class-Specific Endpoint Descriptor Subtypes */ +enum UAC_CS_EndpointDescriptorSubtype_t +{ + UAC_CS_ENDPOINT_SUBTYPE_UNDEFINED = 0x00, + UAC_CS_ENDPOINT_SUBTYPE_EP_GENERAL = 0x01 +}; + +/* A.14 Audio Class-Specific Request Codes */ +#define REQUEST_CODE_UNDEFINED 0x00 +#define CUR 0x01 +#define RANGE 0x02 +#define MEM 0x03 + +/* A.15 Encoder Type Codes */ +#define ENCODER_UNDEFINED 0x00 +#define OTHER_ENCODER 0x01 +#define MPEG_ENCODER 0x02 +#define AC_3_ENCODER 0x03 +#define WMA_ENCODER 0x04 +#define DTS_ENCODER 0x05 + +/* A.17 Control Selector Codes */ +/* A.17.1 Clock Source Control Selectors */ +#define CS_CONTROL_UNDEFINED 0x00 +#define CS_SAM_FREQ_CONTROL 0x01 +#define CS_CLOCK_VALID_CONTROL 0x02 + +/* A.17.2 Clock Selector Control Selectors */ +#define CX_CONTROL_UNDEFINED 0x00 +#define CX_CLOCK_SELECTOR_CONTROL 0x01 + +/* A.17.7 Feature Unit Control Selectors */ +#define FU_CONTROL_UNDEFINED 0x00 +#define FU_MUTE_CONTROL 0x01 +#define FU_VOLUME_CONTROL 0x02 + +/* A.17.11 Audio Streaming Interface Control Selectors */ +#define AS_CONTROL_UNDEFINED 0x00 +#define AS_ACT_ALT_SETTING_CONTROL 0x01 +#define AS_VAL_ALT_SETTINGS_CONTROL 0x02 +#define AS_AUDIO_DATA_FORMAT_CONTROL 0x03 + +#ifdef __STDC__ +/* Clock Source Descriptor (Table 4-6) */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bClockID; + unsigned char bmAttributes; + unsigned char bmControls; + unsigned char bAssocTerminal; + unsigned char iClockSource; +} __attribute__((packed)) USB_Descriptor_Audio_ClockSource_t; + +/* Clock Selector Descriptor (Table 4-7) */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bClockID; + unsigned char bNrPins; + unsigned char baCSourceId[1]; + unsigned char bmControl; /* bmControls + * D[1:0] : Clock Selector Control + * D[7:4] : Reserved (0) */ + unsigned char iClockSelector; + +} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_1_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bClockID; + unsigned char bNrPins; + unsigned char baCSourceId[2]; + unsigned char bmControl; /* bmControls + * D[1:0] : Clock Selector Control + * D[7:4] : Reserved (0) */ + unsigned char iClockSelector; + +} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_2_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bClockID; + unsigned char bNrPins; + unsigned char baCSourceId[3]; + unsigned char bmControl; /* bmControls + * D[1:0] : Clock Selector Control + * D[7:4] : Reserved (0) */ + unsigned char iClockSelector; + +} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_3_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bFormatType; /* Format of the audio stream, see Audio Device Formats specification */ + unsigned char bSubslotSize; /* Number of bytes for each channels subslot */ + unsigned char bBitResolution; /* Number of bits used in the above slot for sample */ +} __attribute__((packed)) USB_Descriptor_Audio_Format_Type1_t; + +/* Table 4-11: Mixer Unit Descriptor */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bUnitID; + unsigned char bNrInPins; + unsigned char baSourceID; + unsigned char bNrChannels; + unsigned bmChannelConfig; + unsigned char iChannelNames; + unsigned char bmMixerControls[18]; /* FIXME */ + unsigned char bmControls; + unsigned char iMixer; +} __attribute__((packed)) USB_Descriptor_Audio_MixerUnit_t; + +/* Table 4-24: Extension Unit Descriptor */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bUnitID; + unsigned short wExtensionCode; + unsigned char bNrInPins; + unsigned char baSourceID[1]; + unsigned char bNrChannels; + unsigned bmChannelConfig; + unsigned char iChannelNames; + unsigned char bmControls; + unsigned char iExtension; +} __attribute__((packed)) USB_Descriptor_Audio_ExtensionUnit_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bUnitID; + unsigned short wExtensionCode; + unsigned char bNrInPins; + unsigned char baSourceID[2]; + unsigned char bNrChannels; + unsigned bmChannelConfig; + unsigned char iChannelNames; + unsigned char bmControls; + unsigned char iExtension; +} __attribute__((packed)) USB_Descriptor_Audio_ExtensionUnit2_t; +#endif + +/***********************************************************************/ +/** USB Device Class Definition for Audio Data Formats **/ + +/* A.1 Format Type Codes */ +enum USB_audio_Fmt_FormatType_t +{ + UAC_FORMAT_TYPE_UNDEFINED = 0x00, + UAC_FORMAT_TYPE_I = 0x01, + UAC_FORMAT_TYPE_II = 0x02, + UAC_FORMAT_TYPE_III = 0x03, + UAC_FORMAT_TYPE_IV = 0x04, + UAC_EXT_FORMAT_TYPE_I = 0x81, + UAC_EXT_FORMAT_TYPE_II = 0x82, + UAC_EXT_FORMAT_TYPE_III = 0x83 +}; + +/* A.2 AudioData Format Bit Allocation in the bmFormats field */ +/* A.2.1 Audio Data Format Type I Bit Allocations */ +enum USB_Audio_Fmt_DataFormat_TypeI_t +{ + UAC_FORMAT_TYPEI_PCM = 0x00000001, + UAC_FORMAT_TYPEI_PCM8 = 0x00000002, + UAC_FORMAT_TYPEI_IEEE_FLOAT = 0x00000004, + UAC_FORMAT_TYPEI_RAW_DATA = 0x80000000, +}; + +/* A.2.2 Audio Data Format Type II Bit Allocations */ +enum USB_Audio_Fmt_DataFormat_TypeII_t +{ + UAC_FORMAT_TYPEII_MPEG = 0x00000001, + UAC_FORMAT_TYPEII_AC3 = 0x00000002, + UAC_FORMAT_TYPEII_WMA = 0x00000004, + UAC_FORMAT_TYPEII_DTS = 0x00000008, + UAC_FORMAT_TYPEII_RAW_DATA = 0x80000000 +}; + +/* A.3 Side Band Protocol Codes */ +#define PROTOCOL_UNDEFINED 0x00 +#define PRESS_TIMESTAMP_PROTOCOL 0x01 + +/***********************************************************************/ +/* Univeral Serial Bus Device Class Definition for Terminal Types */ + +/* 2.1 USB Terminal Types */ +/* Terminal Types that describe Terminals that handle signals carried over USB */ +#define USB_TERMTYPE_UNDEFINED 0x0100 +#define USB_TERMTYPE_USB_STREAMING 0x0101 +#define USB_TERMTYPE_VENDOR_SPECIFIC 0x01FF + +/* 2.2 Input Terminal Types */ +/* Terminal Types that describe Terminals that are designed to record sounds */ +enum USB_Audio_TT_InputTermType_t +{ + UAC_TT_INPUT_TERMTYPE_INPUT_UNDEFINED = 0x0200, + UAC_TT_INPUT_TERMTYPE_MICROPHONE = 0x0201, + UAC_TT_INPUT_TERMTYPE_DESKTOP_MICROPHONE = 0x0202, + UAC_TT_INPUT_TERMTYPE_PERSONAL_MICROPHONE = 0x0203, + UAC_TT_INPUT_TERMTYPE_OMNIDIRECTIONAL_MICROPHONE = 0x0204, + UAC_TT_INPUT_TERMTYPE_MICROPHONE_ARRAY = 0x0205, + UAC_TT_INPUT_TERMTYPE_PROCESSING_MICROPHONE_ARRAY = 0x0206 +}; + +/* 2.3 Output Terminal Types */ +/* These Terminal Types describe Terminals that produce audible signals that are intended to + * be heard by the user of the audio function */ +enum USB_Audio_TT_OutputTermType_t +{ + UAC_TT_OUTPUT_TERMTYPE_SPEAKER = 0x0301, + UAC_TT_OUTPUT_TERMTYPE_HEADPHONES = 0x0302, + UAC_TT_OUTPUT_TERMTYPE_HEAD_MOUNTED_DISPLAY = 0x0303, + UAC_TT_OUTPUT_TERMTYPE_DESKTOP_SPEAKER = 0x0304, + UAC_TT_OUTPUT_TERMTYPE_ROOM_SPEAKER = 0x0305, + UAC_TT_OUTPUT_TERMTYPE_COMMUNICATION_SPEAKER = 0x0306, + UAC_TT_OUTPUT_TERMTYPE_LOW_FREQ_EFFECTS_SPEAKER = 0x0307 +}; +#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h new file mode 100644 index 00000000..87f20615 --- /dev/null +++ b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h @@ -0,0 +1,168 @@ +// Copyright 2017-2021 XMOS LIMITED. +// This Software is subject to the terms of the XMOS Public Licence: Version 1. + +#ifndef _USBAUDIOCOMMON_H_ +#define _USBAUDIOCOMMON_H_ + +#include "xud_conf_default.h" + +#ifdef __STDC__ +typedef struct +{ + unsigned char bLength; /* Size of descriptor (bytes) */ + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned short bcdADC; /* Binary coded decimal indicating the supported Audio Class version */ + unsigned char bCatagory; /* Primary use of this audio function. See Audio Function Category Codes */ + unsigned short wTotalLength; /* Total length of the Audio class-specific descriptors, including this descriptor */ + unsigned char bmControls; /* D[1:0]: Latency control. D[7:2]: Reserved. Must be set to 0 */ +} __attribute__((packed)) UAC_Descriptor_Interface_AC_t; + +/* Table 4-9: Input Terminal Descriptor */ +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bTerminalID; /* Unique ID of this terminal unit */ + unsigned short wTerminalType; + unsigned char bAssocTerminal; /* ID of associated output terminal, for physically grouped terminals + * such as the speaker and microphone of a phone handset */ + unsigned char bCSourceID; /* ID of the clock entity to which this Input Terminal is connected */ + unsigned char bNrChannels; /* Number of logicial output channels in the Terminal's + output audio channel cluster */ + unsigned bmChannelConfig; /* Channel layout supported by this terminal */ + unsigned char iChannelNames; /* Index in string table describing channels (points to first channel) */ + unsigned short bmControls; /* Bitmap */ + unsigned char iTerminal; /* Index of string descriptor describing this terminal */ +} __attribute__((packed))USB_Descriptor_Audio_InputTerminal_t; + +#if 0 +//UAC 1.0 version +typedef struct +{ + unsigned char bLength; /* Size of the descriptor (bytes) */ + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bTerminalID; /* Unique ID of this terminal unit */ + unsigned short wTerminalType; + unsigned char bAssocTerminal; /* ID of associated output terminal, for physically grouped terminals + * such as the speaker and microphone of a phone handset */ + unsigned char bNrChannels; /* Total number of separate audio channels within this interface */ + unsigned short wChannelConfig; /* Channel layout supported by this terminal */ + unsigned char iChannelNames; /* Index in string table describing channels (points to first channel */ + unsigned char iTerminal; /* Index of string descriptor describing this terminal */ +} USB_Descriptor_Audio_InputTerminal_t; +#endif + +/* Table 4-10: Output Terminal Descriptor */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bTerminalID; + unsigned short wTerminalType; + unsigned char bAssocTerminal; + unsigned char bSourceID; + unsigned char bCSourceID; + unsigned short bmControls; + unsigned char iTerminal; +} __attribute__((packed)) USB_Descriptor_Audio_OutputTerminal_t; + +#if 0 +/* UAC 1.0 Version */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bTerminalID; + unsigned short wTerminalType; + unsigned char bAssocTerminal; + unsigned char bSourceID; + unsigned char iTerminal; +} USB_Descriptor_Audio_OutputTerminal_t; +#endif + +/* Note, we need seperate _out and _in structs due to varying channel count */ +/* Table 4-13: Feature Unit Descriptor */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bUnitID; /* Unique ID for this feature unit */ + unsigned char bSourceID; /* Source ID value of the audio source input into this feature unit */ + unsigned bmaControls[NUM_USB_CHAN_OUT+1]; /* Feature masks for the control channel, and each separate audio channel */ + unsigned char iFeature; /* String table index describing this feature unit */ +} __attribute__((packed)) USB_Descriptor_Audio_FeatureUnit_Out_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bUnitID; /* Unique ID for this feature unit */ + unsigned char bSourceID; /* Source ID value of the audio source input into this feature unit */ + unsigned bmaControls[NUM_USB_CHAN_IN+1]; /* Feature masks for the control channel, and each separate audio channel */ + unsigned char iFeature; /* String table index describing this feature unit */ +} __attribute__((packed)) USB_Descriptor_Audio_FeatureUnit_In_t; + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bTerminalLink; + unsigned char bmControls; + unsigned char bFormatType; + unsigned bmFormats; + unsigned char bNrChannels; + unsigned bmChannelConfig; + unsigned char iChannelNames; +} __attribute__((packed)) USB_Descriptor_Audio_Interface_AS_t; + +#if 0 +/* UAC1 Version */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubType; + unsigned char bTerminalLink; + unsigned char bmControls; + unsigned char bFormatType; + unsigned bmFormats; + unsigned char bNrChannels; + unsigned char bmChannelConfig; +} __attribute__((packed)) USB_Descriptor_Audio_Interface_AS_t; +#endif + +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bmAttributes; + unsigned char bmControls; + unsigned char bLockDelayUnits; + unsigned short wLockDelay; +} __attribute__((packed)) USB_Descriptor_Audio_Class_AS_Endpoint_t; + +#if 0 +/* UAC1.0 Version */ +typedef struct +{ + unsigned char bLength; + unsigned char bDescriptorType; + unsigned char bDescriptorSubtype; + unsigned char bmAttributes; + unsigned char bLockDelayUnits; + unsigned short wLockDelay; +} __attribute__((packed)) USB_Descriptor_Audio_Class_AS_Endpoint_t; +#endif +#endif +#endif + + diff --git a/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h new file mode 100644 index 00000000..c79273ea --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ +#define SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ + +extern const int g_gen_data_size_3_delay; +extern const unsigned char g_gen_data_3_delay[]; + +extern const int g_gen_data_size_5_delay; +extern const unsigned char g_gen_data_5_delay[]; + +#endif // SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h new file mode 100644 index 00000000..f2840f66 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h @@ -0,0 +1,28 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_start_index_2_end_index_4; +extern const unsigned char g_gen_data_start_index_2_end_index_4[]; + +extern const int g_gen_data_size_start_index_0_end_index_4; +extern const unsigned char g_gen_data_start_index_0_end_index_4[]; + +extern const int g_gen_data_size_start_index_4_end_index_8; +extern const unsigned char g_gen_data_start_index_4_end_index_8[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h new file mode 100644 index 00000000..9471b836 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h @@ -0,0 +1,37 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_fft_length_64_float; +extern const unsigned char g_gen_data_fft_length_64_float[]; + +extern const int g_gen_data_size_fft_length_64_int16; +extern const unsigned char g_gen_data_fft_length_64_int16[]; + +extern const int g_gen_data_size_fft_length_64_int32; +extern const unsigned char g_gen_data_fft_length_64_int32[]; + +extern const int g_gen_data_size_fft_length_512_float; +extern const unsigned char g_gen_data_fft_length_512_float[]; + +extern const int g_gen_data_size_fft_length_512_int16; +extern const unsigned char g_gen_data_fft_length_512_int16[]; + +extern const int g_gen_data_size_fft_length_512_int32; +extern const unsigned char g_gen_data_fft_length_512_int32[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h new file mode 100644 index 00000000..59e74e7f --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_filter_bank_32_channel; +extern const unsigned char g_gen_data_filter_bank_32_channel[]; + +extern const int g_gen_data_size_filter_bank_16_channel; +extern const unsigned char g_gen_data_filter_bank_16_channel[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h new file mode 100644 index 00000000..dbc3bd92 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h @@ -0,0 +1,27 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_filter_bank_log_scale_1600_correction_bits_3; +extern const unsigned char + g_gen_data_filter_bank_log_scale_1600_correction_bits_3[]; + +extern const int g_gen_data_size_filter_bank_log_scale_32768_correction_bits_5; +extern const unsigned char + g_gen_data_filter_bank_log_scale_32768_correction_bits_5[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h new file mode 100644 index 00000000..175a14e2 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h @@ -0,0 +1,26 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_filter_bank_spectral_subtraction_32_channel; +extern const unsigned char + g_gen_data_filter_bank_spectral_subtraction_32_channel[]; +extern const int g_gen_data_size_filter_bank_spectral_subtraction_16_channel; +extern const unsigned char + g_gen_data_filter_bank_spectral_subtraction_16_channel[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h new file mode 100644 index 00000000..655bfa6a --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_3_1_0_framer; +extern const unsigned char g_gen_data_3_1_0_framer[]; + +extern const int g_gen_data_size_5_2_1_framer; +extern const unsigned char g_gen_data_5_2_1_framer[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/irfft.h b/new_ai_tools/include/signal/micro/kernels/irfft.h new file mode 100644 index 00000000..380bc3e7 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/irfft.h @@ -0,0 +1,31 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef SIGNAL_MICRO_KERNELS_IRFFT_H_ +#define SIGNAL_MICRO_KERNELS_IRFFT_H_ + +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite { +namespace tflm_signal { + +TFLMRegistration* Register_IRFFT(); +TFLMRegistration* Register_IRFFT_FLOAT(); +TFLMRegistration* Register_IRFFT_INT16(); +TFLMRegistration* Register_IRFFT_INT32(); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_MICRO_KERNELS_IRFFT_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h new file mode 100644 index 00000000..adb4fbab --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_overlap_add_float; +extern const unsigned char g_gen_data_overlap_add_float[]; + +extern const int g_gen_data_size_overlap_add_int16; +extern const unsigned char g_gen_data_overlap_add_int16[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h new file mode 100644 index 00000000..32b4cd72 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h @@ -0,0 +1,7 @@ +#ifndef SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ +#define SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ + +extern const int g_gen_data_size_snr_shift_6_test; +extern const unsigned char g_gen_data_snr_shift_6_test[]; + +#endif // SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/rfft.h b/new_ai_tools/include/signal/micro/kernels/rfft.h new file mode 100644 index 00000000..f732c6fd --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/rfft.h @@ -0,0 +1,31 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef SIGNAL_MICRO_KERNELS_RFFT_H_ +#define SIGNAL_MICRO_KERNELS_RFFT_H_ + +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite { +namespace tflm_signal { + +TFLMRegistration* Register_RFFT(); +TFLMRegistration* Register_RFFT_FLOAT(); +TFLMRegistration* Register_RFFT_INT16(); +TFLMRegistration* Register_RFFT_INT32(); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_MICRO_KERNELS_RFFT_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h new file mode 100644 index 00000000..47a38277 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_stacker_3_channels_step_1; +extern const unsigned char g_gen_data_stacker_3_channels_step_1[]; + +extern const int g_gen_data_size_stacker_10_channels_step_2; +extern const unsigned char g_gen_data_stacker_10_channels_step_2[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h new file mode 100644 index 00000000..cd26fc00 --- /dev/null +++ b/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ +#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ + +extern const int g_gen_data_size_window_shift_12; +extern const unsigned char g_gen_data_window_shift_12[]; + +extern const int g_gen_data_size_window_shift_8; +extern const unsigned char g_gen_data_window_shift_8[]; + +#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/src/circular_buffer.h b/new_ai_tools/include/signal/src/circular_buffer.h new file mode 100644 index 00000000..d175a9b9 --- /dev/null +++ b/new_ai_tools/include/signal/src/circular_buffer.h @@ -0,0 +1,118 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_CIRCULAR_BUFFER_H_ +#define SIGNAL_SRC_CIRCULAR_BUFFER_H_ + +#include +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above +struct CircularBuffer { + // Max number of elements, value passed-in to CircularBufferAlloc. + size_t capacity; + // Next position to read. + size_t read; + // Next position to write. + size_t write; + // Flag to indicate emptiness. + int32_t empty; + // Auto-generated size variable + int32_t buffer_size; + // Array of the circular buffer elements (integers). + int16_t* buffer; +}; + +// Returns the size of the memory that the circular buffer needs +// in order to hold `capacity` items. +size_t CircularBufferGetNeededMemory(size_t capacity); + +// Initialize an instance of the circular buffer that holds `capacity` items. +// `state` points to a memory allocation of size `state_size`. The size +// should be greater or equal to the value returned by +// CircularBufferGetNeededMemory(capacity). Fails if it isn't. +// On success, returns a pointer to the circular buffer's object. +CircularBuffer* CircularBufferInit(size_t capacity, void* state, + size_t state_size); + +// Reset a circular buffer to its initial empty state +void CircularBufferReset(CircularBuffer* cb); + +size_t CircularBufferCapacity(const CircularBuffer* cb); + +bool CircularBufferFull(const CircularBuffer* cb); + +bool CircularBufferEmpty(const CircularBuffer* cb); + +// Returns the number of elements ready to read +size_t CircularBufferAvailable(const CircularBuffer* cb); + +// Returns the number of elements available to write. +size_t CircularBufferCanWrite(const CircularBuffer* cb); + +// Adds a single `value` to the buffer and advances the write pointer. +void CircularBufferAdd(CircularBuffer* cb, int16_t value); + +// Writes `n` `values` into the buffer and advances the write pointer. +void CircularBufferWrite(CircularBuffer* cb, const int16_t* values, size_t n); + +// Writes `n` zeros into the buffer and advances the write pointer. +void CircularBufferWriteZeros(CircularBuffer* cb, size_t n); + +// Returns a pointer to a buffer where elements can be written, and +// advances the write pointer as though they have already been written. +// Fails if `n` elements are not available contiguously at the current +// write position. +int16_t* CircularBufferReserveForWrite(CircularBuffer* cb, size_t n); + +// Copies the final region (`count` elements) of the buffer `n` times, to +// the end of the buffer. +void CircularBufferExtend(CircularBuffer* cb, size_t count, int32_t n); + +// Reads a single value from the buffer and advances the read pointer +int16_t CircularBufferRemove(CircularBuffer* cb); + +// Reads the value at the given `index`, does not modify the read pointer. +int16_t CircularBufferPeek(const CircularBuffer* cb, size_t index); + +// Rewinds to restore the previous `n` values read +void CircularBufferRewind(CircularBuffer* cb, size_t n); + +// Returns a pointer directly into the circular buffer at the given `index`. +// Caller is responsible for not reading past the end. +const int16_t* CircularBufferPeekDirect(const CircularBuffer* cb, size_t index); + +// Returns a pointer into the circular buffer at the current read pointer, +// setting `n` to the number of values available to be read from here. +const int16_t* CircularBufferPeekMax(const CircularBuffer* cb, size_t* n); + +// Copies `n` `values` from the buffer and does not advance the read +// pointer and does not update the empty flag. +void CircularBufferGet(CircularBuffer* cb, size_t n, int16_t* values); + +// Discards the next `n` values by advancing the read index. +// Valid for n > 0. +void CircularBufferDiscard(CircularBuffer* cb, size_t n); + +// Shifts the buffer with `n` values (`n` can be negative) by moving +// the read index. +void CircularBufferShift(CircularBuffer* cb, int n); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_CIRCULAR_BUFFER_H_ diff --git a/new_ai_tools/include/signal/src/complex.h b/new_ai_tools/include/signal/src/complex.h new file mode 100644 index 00000000..6f203034 --- /dev/null +++ b/new_ai_tools/include/signal/src/complex.h @@ -0,0 +1,29 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_COMPLEX_H_ +#define SIGNAL_SRC_COMPLEX_H_ + +#include + +// We would use the standard complex type in complex.h, but there's +// no guarantee that all architectures will support it. +template +struct Complex { + T real; + T imag; +}; + +#endif // SIGNAL_SRC_COMPLEX_H_ diff --git a/new_ai_tools/include/signal/src/energy.h b/new_ai_tools/include/signal/src/energy.h new file mode 100644 index 00000000..5a1cf37e --- /dev/null +++ b/new_ai_tools/include/signal/src/energy.h @@ -0,0 +1,38 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_ENERGY_H_ +#define SIGNAL_ENERGY_H_ + +#include + +#include "signal/src/complex.h" + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Calculates the power spectrum from a DFT output between start and end indices +// +// * `start_index` and `end_index` must valid indices into `input` +// * `output` must be the same size as `input`. Only the values at indices +// `start_index` and `end_index` inclusive should be considered valid. +void SpectrumToEnergy(const Complex* input, int start_index, + int end_index, uint32_t* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_ENERGY_H_ diff --git a/new_ai_tools/include/signal/src/fft_auto_scale.h b/new_ai_tools/include/signal/src/fft_auto_scale.h new file mode 100644 index 00000000..c566a0e9 --- /dev/null +++ b/new_ai_tools/include/signal/src/fft_auto_scale.h @@ -0,0 +1,35 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_FFT_AUTO_SCALE_H_ +#define SIGNAL_SRC_FFT_AUTO_SCALE_H_ + +#include +#include + +// TODO(b/286250473): remove namespace once de-duped libraries +namespace tflite { +namespace tflm_signal { + +// Auto scales `input` and write the result to `output` +// Elements in `input` are left shifted to maximize the amplitude without +// clipping, +// * both `input` and `output` must be of size `size` +int FftAutoScale(const int16_t* input, int size, int16_t* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_FFT_AUTO_SCALE_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank.h b/new_ai_tools/include/signal/src/filter_bank.h new file mode 100644 index 00000000..95b2168a --- /dev/null +++ b/new_ai_tools/include/signal/src/filter_bank.h @@ -0,0 +1,69 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_FILTER_BANK_H_ +#define SIGNAL_SRC_FILTER_BANK_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +struct FilterbankConfig { + // Number of filterbank channels + int32_t num_channels; + + // Each of the following three arrays is of size num_channels + 1 + // An extra channel is needed for scratch. See implementation of + // FilterbankAccumulateChannels() for more details + + // For each channel, the index in the input (spectrum) where its band starts + const int16_t* channel_frequency_starts; + // For each channel, the index in the weights/unweights arrays where + // it filter weights start + const int16_t* channel_weight_starts; + // For each channel, the number of bins in the input (spectrum) that span + // its band + const int16_t* channel_widths; + + // The weights array holds the triangular filter weights of all the filters + // in the bank. The output of each filter in the bank is caluclated by + // multiplying the elements in the input spectrum that are in its band + // (see above: channel_frequency_starts, channel_widths) by the filter weights + // then accumulating. Each element in the unweights array holds the 1 minus + // corresponding elements in the weights array and is used to make this + // operation more efficient. For more details, see documnetation in + // FilterbankAccumulateChannels() + const int16_t* weights; + const int16_t* unweights; + int32_t output_scale; + + int32_t input_correction_bits; +}; + +// Accumulate the energy spectrum bins in `input` into filter bank channels +// contained in `output`. +// * `input` - Spectral energy array +// * `output` - of size `config.num_channels` + 1. +// Elements [1:num_channels] contain the filter bank channels. +// Element 0 is used as scratch and should be ignored +void FilterbankAccumulateChannels(const FilterbankConfig* config, + const uint32_t* input, uint64_t* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_FILTER_BANK_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_log.h b/new_ai_tools/include/signal/src/filter_bank_log.h new file mode 100644 index 00000000..e8514c73 --- /dev/null +++ b/new_ai_tools/include/signal/src/filter_bank_log.h @@ -0,0 +1,38 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_FILTER_BANK_LOG_H_ +#define SIGNAL_SRC_FILTER_BANK_LOG_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Apply natural log to each element in array `input` of size `num_channels` +// with pre-shift and post scaling. +// The operation is roughly equivalent to: +// `output` = min(Log(`input` << `correction_bits`) * `output_scale`, INT16_MAX) +// Where: +// If (input << `correction_bits`) is 1 or 0, the function returns 0 +void FilterbankLog(const uint32_t* input, int num_channels, + int32_t output_scale, uint32_t correction_bits, + int16_t* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_FILTER_BANK_LOG_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h b/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h new file mode 100644 index 00000000..e862d773 --- /dev/null +++ b/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h @@ -0,0 +1,73 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ +#define SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +struct SpectralSubtractionConfig { + // Number of filterbank channels in input and output + int32_t num_channels; + // The constant used for the lowpass filter for finding the noise. + // Higher values correspond to more aggressively adapting estimates + // of the noise. + // Scale is 1 << spectral_subtraction_bits + uint32_t smoothing; + // One minus smoothing constant for low pass filter. + // Scale is 1 << spectral_subtraction_bits + uint32_t one_minus_smoothing; + // The maximum cap to subtract away from the signal (ie, if this is + // 0.2, then the result of spectral subtraction will not go below + // 0.2 * signal). + // Scale is 1 << spectral_subtraction_bits + uint32_t min_signal_remaining; + // If positive, specifies the filter coefficient for odd-index + // channels, while 'smoothing' is used as the coefficient for even- + // index channels. Otherwise, the same filter coefficient is + // used on all channels. + // Scale is 1 << spectral_subtraction_bits + uint32_t alternate_smoothing; + // Alternate One minus smoothing constant for low pass filter. + // Scale is 1 << spectral_subtraction_bits + uint32_t alternate_one_minus_smoothing; + // Extra fractional bits for the noise_estimate smoothing filter. + uint32_t smoothing_bits; + // Scaling bits for some members of this struct + uint32_t spectral_subtraction_bits; + // If true, when the filterbank level drops below the output, + // the noise estimate will be forced down to the new noise level. + // If false, the noise estimate will remain above the current + // filterbank output (but the subtraction will still keep the + // output non negative). + bool clamping; +}; + +// Apply spectral subtraction to each element in `input`, then write the result +// to `output` and `noise_estimate`. `input`, `output` and `noise estimate` +// must all be of size `config.num_channels`. `config` holds the +// parameters of the spectral subtraction algorithm. +void FilterbankSpectralSubtraction(const SpectralSubtractionConfig* config, + const uint32_t* input, uint32_t* output, + uint32_t* noise_estimate); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_square_root.h b/new_ai_tools/include/signal/src/filter_bank_square_root.h new file mode 100644 index 00000000..7d484b9f --- /dev/null +++ b/new_ai_tools/include/signal/src/filter_bank_square_root.h @@ -0,0 +1,34 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ +#define SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Apply square root to each element in `input`, then shift right by +// `scale_down_bits` before writing the result to `output`, +// `input` and `output` must both be of size `num_channels` +void FilterbankSqrt(const uint64_t* input, int num_channels, + int scale_down_bits, uint32_t* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ diff --git a/new_ai_tools/include/signal/src/irfft.h b/new_ai_tools/include/signal/src/irfft.h new file mode 100644 index 00000000..c2b54d78 --- /dev/null +++ b/new_ai_tools/include/signal/src/irfft.h @@ -0,0 +1,84 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_IRFFT_H_ +#define SIGNAL_SRC_IRFFT_H_ + +#include +#include + +#include "signal/src/complex.h" + +// TODO(b/286250473): remove namespace once de-duped libraries +namespace tflite { +namespace tflm_signal { + +// IRFFT (Inverse Real Fast Fourier Transform) +// IFFT for real valued time domain outputs. + +// 16-bit Integer input/output + +// Returns the size of the memory that an IRFFT of `fft_length` needs +size_t IrfftInt16GetNeededMemory(int32_t fft_length); + +// Initialize the state of an IRFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// IrfftGetNeededMemory(fft_length). Fails if it isn't. +void* IrfftInt16Init(int32_t fft_length, void* state, size_t state_size); + +// Applies IRFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see IRfftInit) +// * `output` must be of size output +void IrfftInt16Apply(void* state, const Complex* input, + int16_t* output); + +// 32-bit Integer input/output + +// Returns the size of the memory that an IRFFT of `fft_length` needs +size_t IrfftInt32GetNeededMemory(int32_t fft_length); + +// Initialize the state of an IRFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// IrfftGetNeededMemory(fft_length). Fails if it isn't. +void* IrfftInt32Init(int32_t fft_length, void* state, size_t state_size); + +// Applies IRFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see IRfftInit) +// * `output` must be of size output +void IrfftInt32Apply(void* state, const Complex* input, + int32_t* output); + +// Floating point input/output + +// Returns the size of the memory that an IRFFT of `fft_length` needs +size_t IrfftFloatGetNeededMemory(int32_t fft_length); + +// Initialize the state of an IRFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// IrfftGetNeededMemory(fft_length). Fails if it isn't. +void* IrfftFloatInit(int32_t fft_length, void* state, size_t state_size); + +// Applies IRFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see IRfftInit) +// * `output` must be of size output +void IrfftFloatApply(void* state, const Complex* input, float* output); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_IRFFT_H_ \ No newline at end of file diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h new file mode 100644 index 00000000..75f3dcda --- /dev/null +++ b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h @@ -0,0 +1,49 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ +#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ + +// This header file should be included in all variants of kiss_fft_$type.{h,cc} +// so that their sub-included source files do not mistakenly wrap libc header +// files within their kissfft_$type namespaces. +// E.g., This header avoids kissfft_int16.h containing: +// namespace kiss_fft_int16 { +// #include "kiss_fft.h" +// } +// where kiss_fft_.h contains: +// #include +// +// TRICK: By including the following header files here, their preprocessor +// header guards prevent them being re-defined inside of the kiss_fft_$type +// namespaces declared within the kiss_fft_$type.{h,cc} sources. +// Note that the original kiss_fft*.h files are untouched since they +// may be used in libraries that include them directly. + +#include +#include +#include +#include +#include + +#ifdef FIXED_POINT +#include +#endif + +#ifdef USE_SIMD +#include +#endif + +#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h new file mode 100644 index 00000000..c795f463 --- /dev/null +++ b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h @@ -0,0 +1,31 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ +#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ + +#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" + +// Wrap floating point kiss fft in its own namespace. Enables us to link an +// application with different kiss fft resolutions +// (16/32 bit integer, float, double) without getting a linker error. +#undef FIXED_POINT +namespace kiss_fft_float { +#include "kiss_fft.h" +#include "tools/kiss_fftr.h" +} // namespace kiss_fft_float +#undef FIXED_POINT + +#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h new file mode 100644 index 00000000..7e0c9bae --- /dev/null +++ b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h @@ -0,0 +1,30 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ +#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ + +#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" +// Wrap floating point kiss fft in its own namespace. Enables us to link an +// application with different kiss fft resolutions +// (16/32 bit integer, float, double) without getting a linker error. +#define FIXED_POINT 16 +namespace kiss_fft_fixed16 { +#include "kiss_fft.h" +#include "tools/kiss_fftr.h" +} // namespace kiss_fft_fixed16 +#undef FIXED_POINT + +#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h new file mode 100644 index 00000000..47c7103d --- /dev/null +++ b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h @@ -0,0 +1,31 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ +#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ + +#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" + +// Wrap floating point kiss fft in its own namespace. Enables us to link an +// application with different kiss fft resolutions +// (16/32 bit integer, float, double) without getting a linker error. +#define FIXED_POINT 32 +namespace kiss_fft_fixed32 { +#include "kiss_fft.h" +#include "tools/kiss_fftr.h" +} // namespace kiss_fft_fixed32 +#undef FIXED_POINT + +#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ diff --git a/new_ai_tools/include/signal/src/log.h b/new_ai_tools/include/signal/src/log.h new file mode 100644 index 00000000..13045f46 --- /dev/null +++ b/new_ai_tools/include/signal/src/log.h @@ -0,0 +1,30 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_LOG_H_ +#define SIGNAL_SRC_LOG_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Natural logarithm of an integer. The result is multiplied by out_scale +uint32_t Log32(uint32_t x, uint32_t out_scale); + +} // namespace tflm_signal +} // namespace tflite +#endif // SIGNAL_SRC_LOG_H_ diff --git a/new_ai_tools/include/signal/src/max_abs.h b/new_ai_tools/include/signal/src/max_abs.h new file mode 100644 index 00000000..538f7965 --- /dev/null +++ b/new_ai_tools/include/signal/src/max_abs.h @@ -0,0 +1,31 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_MAX_ABS_H_ +#define SIGNAL_SRC_MAX_ABS_H_ + +#include + +// TODO(b/286250473): remove namespace once de-duped libraries +namespace tflite { +namespace tflm_signal { + +// Returns the maximum absolute value of the `size` elements in `input` +int16_t MaxAbs16(const int16_t* input, int size); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_MAX_ABS_H_ diff --git a/new_ai_tools/include/signal/src/msb.h b/new_ai_tools/include/signal/src/msb.h new file mode 100644 index 00000000..2bdcb47b --- /dev/null +++ b/new_ai_tools/include/signal/src/msb.h @@ -0,0 +1,32 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_MSB_H_ +#define SIGNAL_SRC_MSB_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Index of the most significant bit +uint32_t MostSignificantBit32(uint32_t x); +uint32_t MostSignificantBit64(uint64_t x); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_MSB_H_ diff --git a/new_ai_tools/include/signal/src/overlap_add.h b/new_ai_tools/include/signal/src/overlap_add.h new file mode 100644 index 00000000..61898094 --- /dev/null +++ b/new_ai_tools/include/signal/src/overlap_add.h @@ -0,0 +1,46 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef SIGNAL_SRC_OVERLAP_ADD_H_ +#define SIGNAL_SRC_OVERLAP_ADD_H_ + +#include +#include + +namespace tflm_signal { +// Adds (with saturation) the contents of `input` to the contents of `buffer`, +// both of size `input_size`, then copies the first `output_size` elements of +// `buffer` to `output`, shifts the last `input_size`-`output_size` elements of +// `buffer` to the beginning of `buffer` and fills the trailing `output_size` +// samples in `buffer` with zeros. +// input: {input[0] ... input[input_size-1]} +// buffer: {buffer[0] ... buffer[input_size-1]} +// After invocation: +// output: {saturate(input[0] + buffer[0]), +// ... , +// saturate(input[output_size-1] + buffer[output_size-1])} +// buffer: {saturate(input[output_size] + buffer[output_size]), +// ... +// saturate( input[input_size-output_size-1] +// + buffer[input_size-output_size-1]), +// zeros(output_size)} +void OverlapAdd(const int16_t* input, int16_t* buffer, int input_size, + int16_t* output, int output_size); + +// The same as the int16_t variant above, but without saturation +void OverlapAdd(const float* input, float* buffer, int input_size, + float* output, int output_size); + +} // namespace tflm_signal +#endif // SIGNAL_SRC_OVERLAP_ADD_H_ diff --git a/new_ai_tools/include/signal/src/pcan_argc_fixed.h b/new_ai_tools/include/signal/src/pcan_argc_fixed.h new file mode 100644 index 00000000..36eaf3d4 --- /dev/null +++ b/new_ai_tools/include/signal/src/pcan_argc_fixed.h @@ -0,0 +1,41 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H +#define SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H +#include + +#include "msb.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" + +namespace tflite { +namespace tflm_signal { + +#define kPcanSnrBits 12 +#define kPcanOutputBits 6 + +int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut); + +uint32_t PcanShrink(const uint32_t x); + +void ApplyPcanAutoGainControlFixed(const int16_t* gain_lut, int32_t snr_shift, + const uint32_t* noise_estimate, + uint32_t* filterbank_output, + int num_channels); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_MICRO_KERNELS__PCAN_AGC_FIXED_H diff --git a/new_ai_tools/include/signal/src/rfft.h b/new_ai_tools/include/signal/src/rfft.h new file mode 100644 index 00000000..3305af60 --- /dev/null +++ b/new_ai_tools/include/signal/src/rfft.h @@ -0,0 +1,85 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_RFFT_H_ +#define SIGNAL_SRC_RFFT_H_ + +#include +#include + +#include "signal/src/complex.h" + +// TODO(b/286250473): remove namespace once de-duped libraries +namespace tflm_signal { + +// RFFT (Real Fast Fourier Transform) +// FFT for real valued time domain inputs. + +// 16-bit Integer input/output + +// Returns the size of the memory that an RFFT of `fft_length` needs +size_t RfftInt16GetNeededMemory(int32_t fft_length); + +// Initialize the state of an RFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// RfftGetNeededMemory(fft_length). +// Return the value of `state` on success or nullptr on failure +void* RfftInt16Init(int32_t fft_length, void* state, size_t state_size); + +// Applies RFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see RfftInit) +// * `output` must be of size (`fft_length` * 2) + 1 elements +void RfftInt16Apply(void* state, const int16_t* input, + Complex* output); + +// 32-bit Integer input/output + +// Returns the size of the memory that an RFFT of `fft_length` needs +size_t RfftInt32GetNeededMemory(int32_t fft_length); + +// Initialize the state of an RFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// RfftGetNeededMemory(fft_length). +// Return the value of `state` on success or nullptr on failure +void* RfftInt32Init(int32_t fft_length, void* state, size_t state_size); + +// Applies RFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see RfftInit) +// * `output` must be of size (`fft_length` * 2) + 1 elements +void RfftInt32Apply(void* state, const int32_t* input, + Complex* output); + +// Floating point input/output + +// Returns the size of the memory that an RFFT of `fft_length` needs +size_t RfftFloatGetNeededMemory(int32_t fft_length); + +// Initialize the state of an RFFT of `fft_length` +// `state` points to an opaque state of size `state_size`, which +// must be greater or equal to the value returned by +// RfftGetNeededMemory(fft_length). +// Return the value of `state` on success or nullptr on failure +void* RfftFloatInit(int32_t fft_length, void* state, size_t state_size); + +// Applies RFFT to `input` and writes the result to `output` +// * `input` must be of size `fft_length` elements (see RfftInit) +// * `output` must be of size (`fft_length` * 2) + 1 elements +void RfftFloatApply(void* state, const float* input, Complex* output); + +} // namespace tflm_signal + +#endif // SIGNAL_SRC_RFFT_H_ diff --git a/new_ai_tools/include/signal/src/square_root.h b/new_ai_tools/include/signal/src/square_root.h new file mode 100644 index 00000000..b32855c5 --- /dev/null +++ b/new_ai_tools/include/signal/src/square_root.h @@ -0,0 +1,32 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_SQUARE_ROOT_H_ +#define SIGNAL_SRC_SQUARE_ROOT_H_ + +#include + +namespace tflite { +namespace tflm_signal { +// TODO(b/286250473): remove namespace once de-duped libraries above + +// Square root +uint16_t Sqrt32(uint32_t num); +uint32_t Sqrt64(uint64_t num); + +} // namespace tflm_signal +} // namespace tflite + +#endif // SIGNAL_SRC_SQUARE_ROOT_H_ diff --git a/new_ai_tools/include/signal/src/window.h b/new_ai_tools/include/signal/src/window.h new file mode 100644 index 00000000..88e8d119 --- /dev/null +++ b/new_ai_tools/include/signal/src/window.h @@ -0,0 +1,31 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef SIGNAL_SRC_WINDOW_H_ +#define SIGNAL_SRC_WINDOW_H_ + +#include + +namespace tflm_signal { + +// Applies a window function to an input signal +// +// * `input` and `window` must be both of size `size` elements and are +// multiplied element-by element. +// * `shift` is a right shift to apply before writing the result to `output`. +void ApplyWindow(const int16_t* input, const int16_t* window, int size, + int shift, int16_t* output); +} // namespace tflm_signal +#endif // SIGNAL_SRC_WINDOW_H_ diff --git a/new_ai_tools/include/signal/testdata/fft_test_data.h b/new_ai_tools/include/signal/testdata/fft_test_data.h new file mode 100644 index 00000000..35a0b46a --- /dev/null +++ b/new_ai_tools/include/signal/testdata/fft_test_data.h @@ -0,0 +1,48 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef SIGNAL_TESTDATA_FFT_TEST_DATA_H_ +#define SIGNAL_TESTDATA_FFT_TEST_DATA_H_ + +#include + +namespace tflite { + +/* These arrays are generated using random data. They serve to detect changes + * in the kernels. They do not test correctness. + */ +extern const int16_t kRfftInt16Length512Input[]; +extern const int16_t kRfftInt16Length512Golden[]; + +extern const int32_t kRfftInt32Length512Input[]; +extern const int32_t kRfftInt32Length512Golden[]; + +extern const float kRfftFloatLength512Input[]; +extern const float kRfftFloatLength512Golden[]; + +extern const int16_t kIrfftInt16Length512Input[]; +extern const int16_t kIrfftInt16Length512Golden[]; + +extern const int32_t kIrfftInt32Length512Input[]; +extern const int32_t kIrfftInt32Length512Golden[]; + +extern const float kIrfftFloatLength512Input[]; +extern const float kIrfftFloatLength512Golden[]; + +extern const int16_t kFftAutoScaleLength512Input[]; +extern const int16_t kFftAutoScaleLength512Golden[]; + +} // namespace tflite + +#endif // SIGNAL_TESTDATA_FFT_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/array.h b/new_ai_tools/include/tensorflow/lite/array.h new file mode 100644 index 00000000..4b4390d7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/array.h @@ -0,0 +1,156 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_ARRAY_H_ +#define TENSORFLOW_LITE_ARRAY_H_ + +#include +#include +#include +#include +#include + +#include "tensorflow/lite/core/c/common.h" + +namespace tflite_micro { + +/// TfLite*Array helpers + +namespace array_internal { + +// Function object used as a deleter for unique_ptr holding TFLite*Array +// objects. +struct TfLiteArrayDeleter { + void operator()(TfLiteIntArray* a); + void operator()(TfLiteFloatArray* a); +}; + +// Maps T to the corresponding TfLiteArray type. +template +struct TfLiteArrayInfo; + +template <> +struct TfLiteArrayInfo { + using Type = TfLiteIntArray; +}; + +template <> +struct TfLiteArrayInfo { + using Type = TfLiteFloatArray; +}; + +} // namespace array_internal + +template +using TfLiteArrayUniquePtr = + std::unique_ptr::Type, + array_internal::TfLiteArrayDeleter>; + +// `unique_ptr` wrapper for `TfLiteIntArray`s. +using IntArrayUniquePtr = TfLiteArrayUniquePtr; + +// `unique_ptr` wrapper for `TfLiteFloatArray`s. +using FloatArrayUniquePtr = TfLiteArrayUniquePtr; + +// Allocates a TfLiteArray of given size using malloc. +// +// This builds an int array by default as this is the overwhelming part of the +// use cases. +template +TfLiteArrayUniquePtr BuildTfLiteArray(int size); + +// Allocates a TfLiteIntArray of given size using malloc. +template <> +inline IntArrayUniquePtr BuildTfLiteArray(const int size) { + return IntArrayUniquePtr(TfLiteMicroIntArrayCreate(size)); +} + +// Allocates a TfLiteFloatArray of given size using malloc. +template <> +inline FloatArrayUniquePtr BuildTfLiteArray(const int size) { + return FloatArrayUniquePtr(TfLiteMicroFloatArrayCreate(size)); +} + +// Allocates a TFLiteArray of given size and initializes it with the given +// values. +// +// `values` is expected to holds `size` elements. +// +// If T is explicitely specified and the type of values is not the same as T, +// then a static_cast is performed. +template ::value, U, T>> +TfLiteArrayUniquePtr BuildTfLiteArray(const int size, + const U* const values) { + TfLiteArrayUniquePtr array = BuildTfLiteArray(size); + // If size is 0, the array pointer may be null. + if (array && values) { + if (std::is_same::value) { + memcpy(array->data, values, size * sizeof(Type)); + } else { + for (int i = 0; i < size; ++i) { + array->data[i] = static_cast(values[i]); + } + } + } + return array; +} + +// Allocates a TFLiteArray and initializes it with the given array. +// +// `values` is expected to holds `size` elements. +template +TfLiteArrayUniquePtr BuildTfLiteArray(const T (&values)[N]) { + return BuildTfLiteArray(static_cast(N), values); +} + +// Allocates a TFLiteArray and initializes it with the given values. +// +// This uses SFINAE to only be picked up by for types that implement `data()` +// and `size()` member functions. We cannot reuse detection facilities provided +// by Abseil in this code. +// +// To conform with the other overloads, we allow specifying the type of the +// array as well as deducing it from the container. +template < + class T = void, class Container, + class ElementType = + std::decay_t().data())>, + class SizeType = std::decay_t().size())>, + class Type = + std::conditional_t::value, ElementType, T>> +TfLiteArrayUniquePtr BuildTfLiteArray(const Container& values) { + return BuildTfLiteArray(static_cast(values.size()), values.data()); +} + +// Allocates a TFLiteArray and initializes it with the given values. +template +TfLiteArrayUniquePtr BuildTfLiteArray( + const std::initializer_list& values) { + return BuildTfLiteArray(static_cast(values.size()), values.begin()); +} + +// Allocates a TFLiteArray and initializes it with the given array. +inline IntArrayUniquePtr BuildTfLiteArray(const TfLiteIntArray& other) { + return BuildTfLiteArray(other.size, other.data); +} + +// Allocates a TFLiteArray and initializes it with the given array. +inline FloatArrayUniquePtr BuildTfLiteArray(const TfLiteFloatArray& other) { + return BuildTfLiteArray(other.size, other.data); +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_ARRAY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/builtin_op_data.h new file mode 100644 index 00000000..161801cb --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/builtin_op_data.h @@ -0,0 +1,22 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// Compatibility shim for new location of interface definitions. + +#ifndef TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ +#define TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ + +#include "tensorflow/lite/core/c/builtin_op_data.h" + +#endif // TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/builtin_ops.h b/new_ai_tools/include/tensorflow/lite/builtin_ops.h new file mode 100644 index 00000000..a8d68368 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/builtin_ops.h @@ -0,0 +1,241 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_BUILTIN_OPS_H_ +#define TENSORFLOW_LITE_BUILTIN_OPS_H_ + +// DO NOT EDIT MANUALLY: This file is automatically generated by +// `schema/builtin_ops_header/generator.cc`. + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// The enum for builtin operators. +// Note: CUSTOM, DELEGATE, and PLACEHOLDER_FOR_GREATER_OP_CODES are 3 special +// ops which are not real built-in ops. +typedef enum { + kTfLiteBuiltinAdd = 0, + kTfLiteBuiltinAveragePool2d = 1, + kTfLiteBuiltinConcatenation = 2, + kTfLiteBuiltinConv2d = 3, + kTfLiteBuiltinDepthwiseConv2d = 4, + kTfLiteBuiltinDepthToSpace = 5, + kTfLiteBuiltinDequantize = 6, + kTfLiteBuiltinEmbeddingLookup = 7, + kTfLiteBuiltinFloor = 8, + kTfLiteBuiltinFullyConnected = 9, + kTfLiteBuiltinHashtableLookup = 10, + kTfLiteBuiltinL2Normalization = 11, + kTfLiteBuiltinL2Pool2d = 12, + kTfLiteBuiltinLocalResponseNormalization = 13, + kTfLiteBuiltinLogistic = 14, + kTfLiteBuiltinLshProjection = 15, + kTfLiteBuiltinLstm = 16, + kTfLiteBuiltinMaxPool2d = 17, + kTfLiteBuiltinMul = 18, + kTfLiteBuiltinRelu = 19, + kTfLiteBuiltinReluN1To1 = 20, + kTfLiteBuiltinRelu6 = 21, + kTfLiteBuiltinReshape = 22, + kTfLiteBuiltinResizeBilinear = 23, + kTfLiteBuiltinRnn = 24, + kTfLiteBuiltinSoftmax = 25, + kTfLiteBuiltinSpaceToDepth = 26, + kTfLiteBuiltinSvdf = 27, + kTfLiteBuiltinTanh = 28, + kTfLiteBuiltinConcatEmbeddings = 29, + kTfLiteBuiltinSkipGram = 30, + kTfLiteBuiltinCall = 31, + kTfLiteBuiltinCustom = 32, + kTfLiteBuiltinEmbeddingLookupSparse = 33, + kTfLiteBuiltinPad = 34, + kTfLiteBuiltinUnidirectionalSequenceRnn = 35, + kTfLiteBuiltinGather = 36, + kTfLiteBuiltinBatchToSpaceNd = 37, + kTfLiteBuiltinSpaceToBatchNd = 38, + kTfLiteBuiltinTranspose = 39, + kTfLiteBuiltinMean = 40, + kTfLiteBuiltinSub = 41, + kTfLiteBuiltinDiv = 42, + kTfLiteBuiltinSqueeze = 43, + kTfLiteBuiltinUnidirectionalSequenceLstm = 44, + kTfLiteBuiltinStridedSlice = 45, + kTfLiteBuiltinBidirectionalSequenceRnn = 46, + kTfLiteBuiltinExp = 47, + kTfLiteBuiltinTopkV2 = 48, + kTfLiteBuiltinSplit = 49, + kTfLiteBuiltinLogSoftmax = 50, + kTfLiteBuiltinDelegate = 51, + kTfLiteBuiltinBidirectionalSequenceLstm = 52, + kTfLiteBuiltinCast = 53, + kTfLiteBuiltinPrelu = 54, + kTfLiteBuiltinMaximum = 55, + kTfLiteBuiltinArgMax = 56, + kTfLiteBuiltinMinimum = 57, + kTfLiteBuiltinLess = 58, + kTfLiteBuiltinNeg = 59, + kTfLiteBuiltinPadv2 = 60, + kTfLiteBuiltinGreater = 61, + kTfLiteBuiltinGreaterEqual = 62, + kTfLiteBuiltinLessEqual = 63, + kTfLiteBuiltinSelect = 64, + kTfLiteBuiltinSlice = 65, + kTfLiteBuiltinSin = 66, + kTfLiteBuiltinTransposeConv = 67, + kTfLiteBuiltinSparseToDense = 68, + kTfLiteBuiltinTile = 69, + kTfLiteBuiltinExpandDims = 70, + kTfLiteBuiltinEqual = 71, + kTfLiteBuiltinNotEqual = 72, + kTfLiteBuiltinLog = 73, + kTfLiteBuiltinSum = 74, + kTfLiteBuiltinSqrt = 75, + kTfLiteBuiltinRsqrt = 76, + kTfLiteBuiltinShape = 77, + kTfLiteBuiltinPow = 78, + kTfLiteBuiltinArgMin = 79, + kTfLiteBuiltinFakeQuant = 80, + kTfLiteBuiltinReduceProd = 81, + kTfLiteBuiltinReduceMax = 82, + kTfLiteBuiltinPack = 83, + kTfLiteBuiltinLogicalOr = 84, + kTfLiteBuiltinOneHot = 85, + kTfLiteBuiltinLogicalAnd = 86, + kTfLiteBuiltinLogicalNot = 87, + kTfLiteBuiltinUnpack = 88, + kTfLiteBuiltinReduceMin = 89, + kTfLiteBuiltinFloorDiv = 90, + kTfLiteBuiltinReduceAny = 91, + kTfLiteBuiltinSquare = 92, + kTfLiteBuiltinZerosLike = 93, + kTfLiteBuiltinFill = 94, + kTfLiteBuiltinFloorMod = 95, + kTfLiteBuiltinRange = 96, + kTfLiteBuiltinResizeNearestNeighbor = 97, + kTfLiteBuiltinLeakyRelu = 98, + kTfLiteBuiltinSquaredDifference = 99, + kTfLiteBuiltinMirrorPad = 100, + kTfLiteBuiltinAbs = 101, + kTfLiteBuiltinSplitV = 102, + kTfLiteBuiltinUnique = 103, + kTfLiteBuiltinCeil = 104, + kTfLiteBuiltinReverseV2 = 105, + kTfLiteBuiltinAddN = 106, + kTfLiteBuiltinGatherNd = 107, + kTfLiteBuiltinCos = 108, + kTfLiteBuiltinWhere = 109, + kTfLiteBuiltinRank = 110, + kTfLiteBuiltinElu = 111, + kTfLiteBuiltinReverseSequence = 112, + kTfLiteBuiltinMatrixDiag = 113, + kTfLiteBuiltinQuantize = 114, + kTfLiteBuiltinMatrixSetDiag = 115, + kTfLiteBuiltinRound = 116, + kTfLiteBuiltinHardSwish = 117, + kTfLiteBuiltinIf = 118, + kTfLiteBuiltinWhile = 119, + kTfLiteBuiltinNonMaxSuppressionV4 = 120, + kTfLiteBuiltinNonMaxSuppressionV5 = 121, + kTfLiteBuiltinScatterNd = 122, + kTfLiteBuiltinSelectV2 = 123, + kTfLiteBuiltinDensify = 124, + kTfLiteBuiltinSegmentSum = 125, + kTfLiteBuiltinBatchMatmul = 126, + kTfLiteBuiltinPlaceholderForGreaterOpCodes = 127, + kTfLiteBuiltinCumsum = 128, + kTfLiteBuiltinCallOnce = 129, + kTfLiteBuiltinBroadcastTo = 130, + kTfLiteBuiltinRfft2d = 131, + kTfLiteBuiltinConv3d = 132, + kTfLiteBuiltinImag = 133, + kTfLiteBuiltinReal = 134, + kTfLiteBuiltinComplexAbs = 135, + kTfLiteBuiltinHashtable = 136, + kTfLiteBuiltinHashtableFind = 137, + kTfLiteBuiltinHashtableImport = 138, + kTfLiteBuiltinHashtableSize = 139, + kTfLiteBuiltinReduceAll = 140, + kTfLiteBuiltinConv3dTranspose = 141, + kTfLiteBuiltinVarHandle = 142, + kTfLiteBuiltinReadVariable = 143, + kTfLiteBuiltinAssignVariable = 144, + kTfLiteBuiltinBroadcastArgs = 145, + kTfLiteBuiltinRandomStandardNormal = 146, + kTfLiteBuiltinBucketize = 147, + kTfLiteBuiltinRandomUniform = 148, + kTfLiteBuiltinMultinomial = 149, + kTfLiteBuiltinGelu = 150, + kTfLiteBuiltinDynamicUpdateSlice = 151, + kTfLiteBuiltinRelu0To1 = 152, + kTfLiteBuiltinUnsortedSegmentProd = 153, + kTfLiteBuiltinUnsortedSegmentMax = 154, + kTfLiteBuiltinUnsortedSegmentSum = 155, + kTfLiteBuiltinAtan2 = 156, + kTfLiteBuiltinUnsortedSegmentMin = 157, + kTfLiteBuiltinSign = 158, + kTfLiteBuiltinBitcast = 159, + kTfLiteBuiltinBitwiseXor = 160, + kTfLiteBuiltinRightShift = 161, + kTfLiteBuiltinStablehloLogistic = 162, + kTfLiteBuiltinStablehloAdd = 163, + kTfLiteBuiltinStablehloDivide = 164, + kTfLiteBuiltinStablehloMultiply = 165, + kTfLiteBuiltinStablehloMaximum = 166, + kTfLiteBuiltinStablehloReshape = 167, + kTfLiteBuiltinStablehloClamp = 168, + kTfLiteBuiltinStablehloConcatenate = 169, + kTfLiteBuiltinStablehloBroadcastInDim = 170, + kTfLiteBuiltinStablehloConvolution = 171, + kTfLiteBuiltinStablehloSlice = 172, + kTfLiteBuiltinStablehloCustomCall = 173, + kTfLiteBuiltinStablehloReduce = 174, + kTfLiteBuiltinStablehloAbs = 175, + kTfLiteBuiltinStablehloAnd = 176, + kTfLiteBuiltinStablehloCosine = 177, + kTfLiteBuiltinStablehloExponential = 178, + kTfLiteBuiltinStablehloFloor = 179, + kTfLiteBuiltinStablehloLog = 180, + kTfLiteBuiltinStablehloMinimum = 181, + kTfLiteBuiltinStablehloNegate = 182, + kTfLiteBuiltinStablehloOr = 183, + kTfLiteBuiltinStablehloPower = 184, + kTfLiteBuiltinStablehloRemainder = 185, + kTfLiteBuiltinStablehloRsqrt = 186, + kTfLiteBuiltinStablehloSelect = 187, + kTfLiteBuiltinStablehloSubtract = 188, + kTfLiteBuiltinStablehloTanh = 189, + kTfLiteBuiltinStablehloScatter = 190, + kTfLiteBuiltinStablehloCompare = 191, + kTfLiteBuiltinStablehloConvert = 192, + kTfLiteBuiltinStablehloDynamicSlice = 193, + kTfLiteBuiltinStablehloDynamicUpdateSlice = 194, + kTfLiteBuiltinStablehloPad = 195, + kTfLiteBuiltinStablehloIota = 196, + kTfLiteBuiltinStablehloDotGeneral = 197, + kTfLiteBuiltinStablehloReduceWindow = 198, + kTfLiteBuiltinStablehloSort = 199, + kTfLiteBuiltinStablehloWhile = 200, + kTfLiteBuiltinStablehloGather = 201, + kTfLiteBuiltinStablehloTranspose = 202, + kTfLiteBuiltinDilate = 203, + kTfLiteBuiltinStablehloRngBitGenerator = 204, + kTfLiteBuiltinReduceWindow = 205, +} TfLiteBuiltinOperator; + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus +#endif // TENSORFLOW_LITE_BUILTIN_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h new file mode 100644 index 00000000..7628e5ad --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h @@ -0,0 +1,20 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ +#define TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ + +#include "tensorflow/lite/core/c/builtin_op_data.h" + +#endif // TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/c_api_types.h b/new_ai_tools/include/tensorflow/lite/c/c_api_types.h new file mode 100644 index 00000000..05cda07e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/c/c_api_types.h @@ -0,0 +1,26 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_C_C_API_TYPES_H_ +#define TENSORFLOW_LITE_C_C_API_TYPES_H_ + +/// \file +/// +/// C API types for TensorFlow Lite. +/// +/// For documentation, see tensorflow/lite/core/c/c_api_types.h + +#include "tensorflow/lite/core/c/c_api_types.h" + +#endif // TENSORFLOW_LITE_C_C_API_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/common.h b/new_ai_tools/include/tensorflow/lite/c/common.h new file mode 100644 index 00000000..f5a31d3f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/c/common.h @@ -0,0 +1,30 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/// \file +/// +/// This file defines common C types and APIs for implementing operations, +/// delegates and other constructs in TensorFlow Lite. The actual operations and +/// delegates can be defined using C++, but the interface between the +/// interpreter and the operations are C. +/// +/// For documentation, see tensorflow/lite/core/c/common.h. + +#ifndef TENSORFLOW_LITE_C_COMMON_H_ +#define TENSORFLOW_LITE_C_COMMON_H_ + +#include "tensorflow/lite/core/c/common.h" + +#endif // TENSORFLOW_LITE_C_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/context_util.h b/new_ai_tools/include/tensorflow/lite/context_util.h new file mode 100644 index 00000000..1698f281 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/context_util.h @@ -0,0 +1,54 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +/// \file +/// +/// This provides a few C++ helpers that are useful for manipulating C +/// structures in C++. +#ifndef TENSORFLOW_LITE_CONTEXT_UTIL_H_ +#define TENSORFLOW_LITE_CONTEXT_UTIL_H_ + +#include + +#include "tensorflow/lite/core/c/common.h" + +namespace tflite_micro { + +/// Provides a range iterable wrapper for TfLiteIntArray* (C lists) that TfLite +/// C api uses. +// Can't use the google array_view, since we can't depend on even +// absl for embedded device reasons. +class TfLiteIntArrayView { + public: + /// Construct a view of a TfLiteIntArray*. Note, `int_array` should be + /// non-null and this view does not take ownership of it. + explicit TfLiteIntArrayView(const TfLiteIntArray* int_array) + : int_array_(int_array) {} + + TfLiteIntArrayView(const TfLiteIntArrayView&) = default; + TfLiteIntArrayView& operator=(const TfLiteIntArrayView& rhs) = default; + + typedef const int* const_iterator; + const_iterator begin() const { return int_array_->data; } + const_iterator end() const { return &int_array_->data[int_array_->size]; } + size_t size() const { return end() - begin(); } + int operator[](size_t pos) const { return int_array_->data[pos]; } + + private: + const TfLiteIntArray* int_array_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_CONTEXT_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h b/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h new file mode 100644 index 00000000..7a71995e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h @@ -0,0 +1,72 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ +#define TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ + +#include + +namespace tflite_micro { + +/// A functor that reports error to supporting system. Invoked similar to +/// printf. +/// +/// Usage: +/// ErrorReporter foo; +/// foo.Report("test %d", 5); +/// or +/// va_list args; +/// foo.Report("test %d", args); // where args is va_list +/// +/// Subclass ErrorReporter to provide another reporting destination. +/// For example, if you have a GUI program, you might redirect to a buffer +/// that drives a GUI error log box. +class ErrorReporter { + public: + virtual ~ErrorReporter() = default; + /// Converts `args` to character equivalents according to `format` string, + /// constructs the error string and report it. + /// Returns number of characters written or zero on success, and negative + /// number on error. + virtual int Report(const char* format, va_list args) = 0; + + /// Converts arguments to character equivalents according to `format` string, + /// constructs the error string and report it. + /// Returns number of characters written or zero on success, and negative + /// number on error. + int Report(const char* format, ...); + + /// Equivalent to `Report` above. The additional `void*` parameter is unused. + /// This method is for compatibility with macros that takes `TfLiteContext`, + /// like TF_LITE_ENSURE and related macros. + int ReportError(void*, const char* format, ...); +}; + +} // namespace tflite_micro + +// You should not make bare calls to the error reporter, instead use the +// TF_LITE_REPORT_ERROR macro, since this allows message strings to be +// stripped when the binary size has to be optimized. If you are looking to +// reduce binary size, define TF_LITE_STRIP_ERROR_STRINGS when compiling and +// every call will be stubbed out, taking no memory. +#ifndef TF_LITE_STRIP_ERROR_STRINGS +#define TF_LITE_REPORT_ERROR(reporter, ...) \ + do { \ + static_cast<::tflite_micro::ErrorReporter*>(reporter)->Report(__VA_ARGS__); \ + } while (false) +#else // TF_LITE_STRIP_ERROR_STRINGS +#define TF_LITE_REPORT_ERROR(reporter, ...) +#endif // TF_LITE_STRIP_ERROR_STRINGS + +#endif // TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h b/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h new file mode 100644 index 00000000..cee68ccd --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h @@ -0,0 +1,440 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ +#define TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ + +// These functions transform codes and data structures that are defined in the +// flatbuffer serialization format into in-memory values that are used by the +// runtime API and interpreter. + +#include +#include +#include + +#include "tensorflow/lite/core/api/error_reporter.h" +#include "tensorflow/lite/core/c/common.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// Interface class for builtin data allocations. +class BuiltinDataAllocator { + public: + virtual void* Allocate(size_t size, size_t alignment_hint) = 0; + virtual void Deallocate(void* data) = 0; + + // Allocate a structure, but make sure it is a POD structure that doesn't + // require constructors to run. The reason we do this, is that Interpreter's C + // extension part will take ownership so destructors will not be run during + // deallocation. + template + T* AllocatePOD() { + // TODO(b/154346074): Change this to is_trivially_destructible when all + // platform targets support that properly. + static_assert(std::is_pod::value, "Builtin data structure must be POD."); + void* allocated_memory = this->Allocate(sizeof(T), alignof(T)); + return new (allocated_memory) T(); + } + + virtual ~BuiltinDataAllocator() {} +}; + +// Parse the appropriate data out of the op. +// +// This handles builtin data explicitly as there are flatbuffer schemas. +// If it returns kTfLiteOk, it passes the data out with `builtin_data`. The +// calling function has to pass in an allocator object, and this allocator +// will be called to reserve space for the output data. If the calling +// function's allocator reserves memory on the heap, then it's the calling +// function's responsibility to free it. +// If it returns kTfLiteError, `builtin_data` will be `nullptr`. +TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +// Converts the tensor data type used in the flat buffer to the representation +// used by the runtime. +TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, + ErrorReporter* error_reporter); + +TfLiteStatus ParseAbs(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseAddN(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseAssignVariable(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseBatchMatMul(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseBatchToSpaceNd(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseBroadcastArgs(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseBroadcastTo(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseCallOnce(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseCeil(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseCast(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseConcatenation(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseCos(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseCumsum(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseDepthToSpace(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseDepthwiseConv2D(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseDequantize(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseDiv(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseElu(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseEmbeddingLookup(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseEqual(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseExp(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseExpandDims(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseFill(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseFloor(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseFloorDiv(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseFloorMod(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseFullyConnected(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseGather(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseGatherNd(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseGreater(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseGreaterEqual(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseHardSwish(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseIf(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseL2Normalization(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLeakyRelu(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLess(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseLessEqual(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLog(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseLogicalAnd(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLogicalNot(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLogicalOr(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLogistic(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLogSoftmax(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseLSTM(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseMaximum(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseMinimum(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseMirrorPad(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseNeg(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseNotEqual(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParsePad(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParsePadV2(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParsePow(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParsePrelu(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseQuantize(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseReadVariable(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseRelu(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseRelu6(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseResizeBilinear(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseResizeNearestNeighbor(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseRound(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseRsqrt(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSelectV2(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseShape(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSin(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSlice(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSpaceToBatchNd(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseSpaceToDepth(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSplitV(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSqueeze(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSquaredDifference(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseStridedSlice(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseTanh(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseTranspose(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseTransposeConv(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseUnidirectionalSequenceLSTM(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseVarHandle(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseWhile(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +TfLiteStatus ParseZerosLike(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseBitwiseXor(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseRightShift(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseStablehloScatter(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseStablehloRngBitGenerator(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +TfLiteStatus ParseStablehloGather(const Operator* op, + ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, + void** builtin_data); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h b/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h new file mode 100644 index 00000000..7c49135a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h @@ -0,0 +1,28 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ +#define TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ + +#include "tensorflow/lite/core/c/common.h" + +namespace tflite_micro { + +// Resets a variable tensor to the default value. +TfLiteStatus ResetVariableTensor(TfLiteContext* context, TfLiteTensor* tensor); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h new file mode 100644 index 00000000..8464a26b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h @@ -0,0 +1,626 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +/// WARNING: Users of TensorFlow Lite should not include this file directly, +/// but should instead include +/// "third_party/tensorflow/lite/c/builtin_op_data.h". +/// Only the TensorFlow Lite implementation itself should include this +/// file directly. +#ifndef TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ +#define TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ + +#include +#include + +#include "tensorflow/lite/core/c/common.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// TfLiteReshapeParams can't have dynamic data so we fix the maximum possible +// number of dimensions. +#define TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT 8 +#define TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT 8 +#define TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT 8 + +// TODO(aselle): Consider using "if this then that" for testing. + +// Useful placeholder to put in otherwise empty structs to avoid size warnings. +typedef struct { + char dummy; +} EmptyStructPlaceholder; + +// IMPORTANT: All new members of structs must be added at the end to ensure +// backwards compatibility. + +// Possible padding types (for convolutions) +typedef enum { + kTfLitePaddingUnknown = 0, + kTfLitePaddingSame, + kTfLitePaddingValid, +} TfLitePadding; + +typedef enum { + kTfLiteMirrorPaddingUnknown = 0, + kTfLiteMirrorPaddingReflect, + kTfLiteMirrorPaddingSymmetric, +} TfLiteMirrorPaddingMode; + +// TODO(b/130259536): We should move this out of builtin_op_data. +typedef struct { + int width; + int height; + int width_offset; + int height_offset; +} TfLitePaddingValues; + +typedef struct { + TfLiteMirrorPaddingMode mode; +} TfLiteMirrorPaddingParams; + +// Possible fused activation functions. +typedef enum { + kTfLiteActNone = 0, + kTfLiteActRelu, + kTfLiteActReluN1To1, // min(max(-1, x), 1) + kTfLiteActRelu6, // min(max(0, x), 6) + kTfLiteActTanh, + kTfLiteActSignBit, + kTfLiteActSigmoid, +} TfLiteFusedActivation; + +typedef struct { + // Parameters for CONV_2D version 1. + TfLitePadding padding; + int stride_width; + int stride_height; + TfLiteFusedActivation activation; + + // Parameters for CONV_2D version 2. + // Note: Version 2 supports dilation values not equal to 1. + int dilation_width_factor; + int dilation_height_factor; + + // Parameters for CONV_2D version 7 or above. + // Used to determine the default value for the quantized bias. + TfLiteType quantized_bias_type; +} TfLiteConvParams; + +typedef struct { + TfLitePadding padding; + int stride_width; + int stride_height; + int stride_depth; + int dilation_width_factor; + int dilation_height_factor; + int dilation_depth_factor; + TfLiteFusedActivation activation; +} TfLiteConv3DParams; + +typedef TfLiteConv3DParams TfLiteConv3DTransposeParams; + +typedef struct { + TfLitePadding padding; + int stride_width; + int stride_height; + int filter_width; + int filter_height; + TfLiteFusedActivation activation; + struct { + TfLitePaddingValues padding; + } computed; +} TfLitePoolParams; + +typedef struct { + // Parameters for DepthwiseConv version 1 or above. + TfLitePadding padding; + int stride_width; + int stride_height; + // `depth_multiplier` is redundant. It's used by CPU kernels in + // TensorFlow 2.0 or below, but ignored in versions above. + // + // The information can be deduced from the shape of input and the shape of + // weights. Since the TFLiteConverter toolchain doesn't support partially + // specified shapes, relying on `depth_multiplier` stops us from supporting + // graphs with dynamic shape tensors. + // + // Note: Some of the delegates (e.g. NNAPI, GPU) are still relying on this + // field. + int depth_multiplier; + TfLiteFusedActivation activation; + // Parameters for DepthwiseConv version 2 or above. + int dilation_width_factor; + int dilation_height_factor; +} TfLiteDepthwiseConvParams; + +typedef struct { + int rank; + TfLiteFusedActivation activation; + + // Parameter for SVDF version 4. + bool asymmetric_quantize_inputs; +} TfLiteSVDFParams; + +typedef struct { + TfLiteFusedActivation activation; + + // Parameter for RNN version 3. + bool asymmetric_quantize_inputs; +} TfLiteRNNParams; + +typedef struct { + bool time_major; + TfLiteFusedActivation activation; + + // Parameter for Sequence RNN version 3. + bool asymmetric_quantize_inputs; +} TfLiteSequenceRNNParams; + +typedef struct { + bool time_major; + TfLiteFusedActivation activation; + bool merge_outputs; + + // Parameter for Bidirectional RNN version 3. + bool asymmetric_quantize_inputs; +} TfLiteBidirectionalSequenceRNNParams; + +typedef enum { + kTfLiteFullyConnectedWeightsFormatDefault = 0, + kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8 = 1, +} TfLiteFullyConnectedWeightsFormat; + +typedef struct { + // Parameters for FullyConnected version 1 or above. + TfLiteFusedActivation activation; + + // Parameters for FullyConnected version 2 or above. + TfLiteFullyConnectedWeightsFormat weights_format; + + // Parameters for FullyConnected version 5 or above. + // If set to true, then the number of dimensions in the input and the output + // tensors are the same. Furthermore, all but the last dimension of the input + // and output shapes will be equal. + bool keep_num_dims; + + // Parameters for FullyConnected version 7 or above. + // If set to true and the weights are quantized, then non constant inputs + // are quantized at evaluation time with asymmetric quantization. + bool asymmetric_quantize_inputs; + + // Parameters for FullyConnected version 10 or above. + // Used to determine the default value for the quantized bias. + TfLiteType quantized_bias_type; +} TfLiteFullyConnectedParams; + +typedef enum { + kTfLiteLshProjectionUnknown = 0, + kTfLiteLshProjectionSparse = 1, + kTfLiteLshProjectionDense = 2, +} TfLiteLSHProjectionType; + +typedef struct { + TfLiteLSHProjectionType type; +} TfLiteLSHProjectionParams; + +typedef struct { + float beta; +} TfLiteSoftmaxParams; + +typedef struct { + int axis; + TfLiteFusedActivation activation; +} TfLiteConcatenationParams; + +typedef struct { + TfLiteFusedActivation activation; + // Parameter added for the version 4. + bool pot_scale_int16; +} TfLiteAddParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteSpaceToBatchNDParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteBatchToSpaceNDParams; + +typedef struct { + bool adj_x; + bool adj_y; + // Parameters for BatchMatMul version 4 or above. + // If set to true and the weights are quantized, then non constant inputs + // are quantized at evaluation time with asymmetric quantization. + bool asymmetric_quantize_inputs; +} TfLiteBatchMatMulParams; + +typedef struct { + TfLiteFusedActivation activation; +} TfLiteMulParams; + +typedef struct { + TfLiteFusedActivation activation; + // Parameter added for the version 5. + bool pot_scale_int16; +} TfLiteSubParams; + +typedef struct { + TfLiteFusedActivation activation; +} TfLiteDivParams; + +typedef struct { + TfLiteFusedActivation activation; +} TfLiteL2NormParams; + +typedef struct { + int radius; + float bias; + float alpha; + float beta; +} TfLiteLocalResponseNormParams; + +typedef enum { + kTfLiteLSTMFullKernel = 0, + kTfLiteLSTMBasicKernel +} TfLiteLSTMKernelType; + +typedef struct { + // Parameters for LSTM version 1. + TfLiteFusedActivation activation; + float cell_clip; + float proj_clip; + + // Parameters for LSTM version 2. + // kTfLiteLSTMBasicKernel is only supported in version 2 or above. + TfLiteLSTMKernelType kernel_type; + + // Parameters for LSTM version 4. + bool asymmetric_quantize_inputs; +} TfLiteLSTMParams; + +typedef struct { + // Parameters needed for the underlying LSTM. + TfLiteFusedActivation activation; + float cell_clip; + float proj_clip; + + // If set to true then the first dimension is time, otherwise batch. + bool time_major; + + // Parameter for unidirectional sequence RNN version 3. + bool asymmetric_quantize_inputs; + + // Parameter for unidirectional sequence RNN version 4. + bool diagonal_recurrent_tensors; +} TfLiteUnidirectionalSequenceLSTMParams; + +typedef struct { + // Parameters supported by version 1: + // Parameters inherited for the LSTM kernel. + TfLiteFusedActivation activation; + float cell_clip; + float proj_clip; + + // If true, store the outputs of both directions in the first output. + bool merge_outputs; + + // Parameters supported by version 2: + // If set to true then the first dimension is time, otherwise batch. + bool time_major; + + // Parameters supported by version 3: + // If set to true, then hybrid ops use asymmetric quantization for inputs. + bool asymmetric_quantize_inputs; +} TfLiteBidirectionalSequenceLSTMParams; + +typedef struct { + bool align_corners; + // half_pixel_centers assumes pixels are of half the actual dimensions, and + // yields more accurate resizes. Corresponds to the same argument for the + // original TensorFlow op in TF2.0. + bool half_pixel_centers; +} TfLiteResizeBilinearParams; + +typedef struct { + bool align_corners; + bool half_pixel_centers; +} TfLiteResizeNearestNeighborParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLitePadParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLitePadV2Params; + +typedef struct { + // These fields are only used in old models for backward compatibility. + // In the current implementation, we use the 2nd input of the op as the shape, + // and these fields are unused. + int32_t shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT]; + int num_dimensions; +} TfLiteReshapeParams; + +typedef struct { + int ngram_size; + int max_skip_size; + bool include_all_ngrams; +} TfLiteSkipGramParams; + +typedef struct { + int block_size; +} TfLiteSpaceToDepthParams; + +typedef struct { + int block_size; +} TfLiteDepthToSpaceParams; + +typedef struct { + TfLiteType in_data_type; + TfLiteType out_data_type; +} TfLiteCastParams; + +typedef enum { + kTfLiteCombinerTypeSum = 0, + kTfLiteCombinerTypeMean = 1, + kTfLiteCombinerTypeSqrtn = 2, +} TfLiteCombinerType; + +typedef struct { + TfLiteCombinerType combiner; +} TfLiteEmbeddingLookupSparseParams; + +typedef struct { + int axis; + int batch_dims; +} TfLiteGatherParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteTransposeParams; + +typedef struct { + bool keep_dims; +} TfLiteReducerParams; + +typedef struct { + int num_splits; +} TfLiteSplitParams; + +typedef struct { + int num_splits; +} TfLiteSplitVParams; + +typedef struct { + // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. + // For now we will fix the maximum possible number of dimensions. + int32_t squeeze_dims[8]; + int num_squeeze_dims; +} TfLiteSqueezeParams; + +typedef struct { + int begin_mask; + int end_mask; + int ellipsis_mask; + int new_axis_mask; + int shrink_axis_mask; + + // Parameters supported by version 8: + // If true, then the end tensor is an offset of the begin tensor. + bool offset; +} TfLiteStridedSliceParams; + +typedef struct { + TfLiteType output_type; +} TfLiteArgMaxParams; + +typedef struct { + TfLiteType output_type; +} TfLiteArgMinParams; + +typedef struct { + // Parameters supported by version 1: + TfLitePadding padding; + int stride_width; + int stride_height; + + // Parameters supported by version 4: + TfLiteFusedActivation activation; + + // Parameters for TransposeConv version 5 or above. + // Used to determine the default value for the quantized bias. + TfLiteType quantized_bias_type; +} TfLiteTransposeConvParams; + +typedef struct { + bool validate_indices; +} TfLiteSparseToDenseParams; + +typedef struct { + TfLiteType out_type; +} TfLiteShapeParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteRankParams; + +typedef struct { + // Parameters supported by version 1: + float min; + float max; + int num_bits; + + // Parameters supported by version 2: + bool narrow_range; +} TfLiteFakeQuantParams; + +typedef struct { + int values_count; + int axis; +} TfLitePackParams; + +typedef struct { + int axis; +} TfLiteOneHotParams; + +typedef struct { + int num; + int axis; +} TfLiteUnpackParams; + +typedef struct { + float alpha; +} TfLiteLeakyReluParams; + +typedef struct { + TfLiteType index_out_type; +} TfLiteUniqueParams; + +typedef struct { + int seq_dim; + int batch_dim; +} TfLiteReverseSequenceParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteMatrixDiagParams; + +typedef struct { + EmptyStructPlaceholder placeholder; +} TfLiteMatrixSetDiagParams; + +typedef struct { + int then_subgraph_index; + int else_subgraph_index; +} TfLiteIfParams; + +typedef struct { + int cond_subgraph_index; + int body_subgraph_index; +} TfLiteWhileParams; + +typedef struct { + bool exclusive; + bool reverse; +} TfLiteCumsumParams; + +typedef struct { + int init_subgraph_index; +} TfLiteCallOnceParams; + +typedef struct { + int table_id; + TfLiteType key_dtype; + TfLiteType value_dtype; +} TfLiteHashtableParams; + +typedef struct { + const char* container; + const char* shared_name; +} TfLiteVarHandleParams; + +typedef struct { + int seed; + int seed2; +} TfLiteRandomParams; + +typedef struct { + int num_boundaries; + // This points to the memory stored in the model (flatbuffer), + // and is not owned. + const float* boundaries; +} TfLiteBucketizeParams; + +typedef struct { + bool approximate; +} TfLiteGeluParams; + +typedef struct { + int64_t dimension; +} TfLiteStablehloConcatenateParams; + +typedef struct { + // See the stablehlo spec for the explanation of the attributes: + // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter + bool indices_are_sorted; + int64_t + update_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; + int num_update_window_dims; + int64_t + inserted_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; + int num_inserted_window_dims; + int64_t scatter_dims_to_operand_dims + [TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; + int num_scatter_dims_to_operand_dims; + int64_t index_vector_dim; + bool unique_indices; + int update_computation_subgraph_index; +} TfLiteStablehloScatterParams; + +typedef enum { + kTfLiteRngAlgorithmUnknown = 0, + // An algorithm auto-selected by the system according to device type. + kTfLiteRngAlgorithmDefault, + // The Philox algorithm, as described in paper + // ['Parallel Random Numbers: As Easy as 1, 2, 3'] + // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf) + kTfLiteRngAlgorithmPhilox, + // The ThreeFry algorithm, as described in paper + // ['Parallel Random Numbers: As Easy as 1, 2, 3'] + // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf) + kTfLiteRngAlgorithmThreefry, +} TfLiteRngAlgorithm; + +typedef struct { + TfLiteRngAlgorithm algorithm; +} TfLiteStablehloRngBitGeneratorParams; + +typedef struct { + // See the stablehlo spec for the explanation of the attributes: + // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather + int64_t offset_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; + int num_offset_dims; + int64_t + collapsed_slice_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; + int num_collapsed_slice_dims; + int64_t start_index_map[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; + int num_start_index_map; + int64_t index_vector_dim; + int64_t slice_sizes[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; + int num_slice_sizes; + bool indices_are_sorted; +} TfLiteStablehloGatherParams; + +enum TfLiteReduceWindowFunction { + TfLiteReduceWindowFunctionUnsupported, + TfLiteReduceWindowFunctionAdd, + TfLiteReduceWindowFunctionMul, + TfLiteReduceWindowFunctionMin, + TfLiteReduceWindowFunctionMax, + TfLiteReduceWindowFunctionAll, + TfLiteReduceWindowFunctionAny +}; + +typedef struct { + enum TfLiteReduceWindowFunction reduce_function; +} TfLiteReduceWindowParams; + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h b/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h new file mode 100644 index 00000000..a26f482d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h @@ -0,0 +1,178 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/// This file declares types used by the pure C inference API defined in +/// c_api.h, some of which are also used in the C++ and C kernel and interpreter +/// APIs. + +// WARNING: Users of TensorFlow Lite should not include this file directly, +// but should instead include +// "third_party/tensorflow/lite/c/c_api_types.h". +// Only the TensorFlow Lite implementation itself should include this +// file directly. + +// IWYU pragma: private, include "third_party/tensorflow/lite/c/c_api_types.h" + +#ifndef TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ +#define TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +/** \addtogroup c_api_types tensorflow/lite/c/c_api_types.h + * @{ + */ + +// Define TFL_CAPI_EXPORT macro to export a function properly with a shared +// library. +#ifdef SWIG +#define TFL_CAPI_EXPORT +#elif defined(TFL_STATIC_LIBRARY_BUILD) +#define TFL_CAPI_EXPORT +#else // not definded TFL_STATIC_LIBRARY_BUILD +#if defined(_WIN32) +#ifdef TFL_COMPILE_LIBRARY +#define TFL_CAPI_EXPORT __declspec(dllexport) +#else +#define TFL_CAPI_EXPORT __declspec(dllimport) +#endif // TFL_COMPILE_LIBRARY +#else +#define TFL_CAPI_EXPORT __attribute__((visibility("default"))) +#endif // _WIN32 +#endif // SWIG + +/// Note that new error status values may be added in future in order to +/// indicate more fine-grained internal states, therefore, applications should +/// not rely on status values being members of the enum. +typedef enum TfLiteStatus { + /// Success + kTfLiteOk = 0, + + /// Generally referring to an error in the runtime (i.e. interpreter) + kTfLiteError = 1, + + /// Generally referring to an error from a TfLiteDelegate itself. + kTfLiteDelegateError = 2, + + /// Generally referring to an error in applying a delegate due to + /// incompatibility between runtime and delegate, e.g., this error is returned + /// when trying to apply a TF Lite delegate onto a model graph that's already + /// immutable. + kTfLiteApplicationError = 3, + + /// Generally referring to serialized delegate data not being found. + /// See tflite_micro::delegates::Serialization. + kTfLiteDelegateDataNotFound = 4, + + /// Generally referring to data-writing issues in delegate serialization. + /// See tflite_micro::delegates::Serialization. + kTfLiteDelegateDataWriteError = 5, + + /// Generally referring to data-reading issues in delegate serialization. + /// See tflite_micro::delegates::Serialization. + kTfLiteDelegateDataReadError = 6, + + /// Generally referring to issues when the TF Lite model has ops that cannot + /// be resolved at runtime. This could happen when the specific op is not + /// registered or built with the TF Lite framework. + kTfLiteUnresolvedOps = 7, + + /// Generally referring to invocation cancelled by the user. + /// See `interpreter::Cancel`. + // TODO(b/194915839): Implement `interpreter::Cancel`. + // TODO(b/250636993): Cancellation triggered by `SetCancellationFunction` + // should also return this status code. + kTfLiteCancelled = 8, +} TfLiteStatus; + +/// Types supported by tensor +typedef enum { + kTfLiteNoType = 0, + kTfLiteFloat32 = 1, + kTfLiteInt32 = 2, + kTfLiteUInt8 = 3, + kTfLiteInt64 = 4, + kTfLiteString = 5, + kTfLiteBool = 6, + kTfLiteInt16 = 7, + kTfLiteComplex64 = 8, + kTfLiteInt8 = 9, + kTfLiteFloat16 = 10, + kTfLiteFloat64 = 11, + kTfLiteComplex128 = 12, + kTfLiteUInt64 = 13, + kTfLiteResource = 14, + kTfLiteVariant = 15, + kTfLiteUInt32 = 16, + kTfLiteUInt16 = 17, + kTfLiteInt4 = 18, +} TfLiteType; + +/// Legacy. Will be deprecated in favor of TfLiteAffineQuantization. +/// If per-layer quantization is specified this field will still be populated in +/// addition to TfLiteAffineQuantization. +/// Parameters for asymmetric quantization. Quantized values can be converted +/// back to float using: +/// real_value = scale * (quantized_value - zero_point) +typedef struct TfLiteQuantizationParams { + float scale; + int32_t zero_point; +} TfLiteQuantizationParams; + +// -------------------------------------------------------------------------- +// Opaque types used by c_api.h, c_api_opaque.h and common.h. + +/// TfLiteOpaqueContext is an opaque version of TfLiteContext; +typedef struct TfLiteOpaqueContext TfLiteOpaqueContext; + +/// TfLiteOpaqueNode is an opaque version of TfLiteNode; +typedef struct TfLiteOpaqueNode TfLiteOpaqueNode; + +/// TfLiteOpaqueTensor is an opaque version of TfLiteTensor; +typedef struct TfLiteOpaqueTensor TfLiteOpaqueTensor; + +/// TfLiteDelegate: allows delegation of nodes to alternative backends. +/// Forward declaration of concrete type declared in common.h. +typedef struct TfLiteDelegate TfLiteDelegate; + +/// TfLiteOpaqueDelegateStruct: unconditionally opaque version of +/// TfLiteDelegate; allows delegation of nodes to alternative backends. +/// +/// This is an abstract type that is intended to have the same +/// role as TfLiteDelegate, but without exposing the implementation +/// details of how delegates are implemented. +/// WARNING: This is an experimental type and subject to change. +typedef struct TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegateStruct; + +/// TfLiteOpaqueDelegate: conditionally opaque version of +/// TfLiteDelegate; allows delegation of nodes to alternative backends. +/// For TF Lite in Play Services, this is an opaque type, +/// but for regular TF Lite, this is just a typedef for TfLiteDelegate. +/// WARNING: This is an experimental type and subject to change. +#if TFLITE_WITH_STABLE_ABI || TFLITE_USE_OPAQUE_DELEGATE +typedef TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegate; +#else +typedef TfLiteDelegate TfLiteOpaqueDelegate; +#endif + +/** @} */ + +#ifdef __cplusplus +} // extern C +#endif +#endif // TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/common.h b/new_ai_tools/include/tensorflow/lite/core/c/common.h new file mode 100644 index 00000000..9efdc6ba --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/c/common.h @@ -0,0 +1,1494 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This file defines common C types and APIs for implementing operations, +// delegates and other constructs in TensorFlow Lite. The actual operations and +// delegates can be defined using C++, but the interface between the interpreter +// and the operations are C. +// +// Summary of abstractions +// TF_LITE_ENSURE - Self-sufficient error checking +// TfLiteStatus - Status reporting +// TfLiteIntArray - stores tensor shapes (dims), +// TfLiteContext - allows an op to access the tensors +// TfLiteTensor - tensor (a multidimensional array) +// TfLiteNode - a single node or operation +// TfLiteRegistration - the implementation of a conceptual operation. +// TfLiteDelegate - allows delegation of nodes to alternative backends. +// +// Some abstractions in this file are created and managed by Interpreter. +// +// NOTE: The order of values in these structs are "semi-ABI stable". New values +// should be added only to the end of structs and never reordered. + +/// WARNING: Users of TensorFlow Lite should not include this file directly, +/// but should instead include +/// "third_party/tensorflow/lite/c/common.h". +/// Only the TensorFlow Lite implementation itself should include this +/// file directly. +// IWYU pragma: private, include "third_party/tensorflow/lite/c/common.h" + +#ifndef TENSORFLOW_LITE_CORE_C_COMMON_H_ +#define TENSORFLOW_LITE_CORE_C_COMMON_H_ + +#include +#include +#include +#include +#include + +#include "tensorflow/lite/core/c/c_api_types.h" // IWYU pragma: export + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// The list of external context types known to TF Lite. This list exists solely +// to avoid conflicts and to ensure ops can share the external contexts they +// need. Access to the external contexts is controlled by one of the +// corresponding support files. +typedef enum TfLiteExternalContextType { + kTfLiteEigenContext = 0, // include eigen_support.h to use. + kTfLiteGemmLowpContext = 1, // include gemm_support.h to use. + kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support. + kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use. + kTfLiteMaxExternalContexts = 4 +} TfLiteExternalContextType; + +// Forward declare so dependent structs and methods can reference these types +// prior to the struct definitions. +struct TfLiteContext; +struct TfLiteDelegate; +struct TfLiteRegistration; +struct TfLiteOpaqueDelegateBuilder; + +// An external context is a collection of information unrelated to the TF Lite +// framework, but useful to a subset of the ops. TF Lite knows very little +// about the actual contexts, but it keeps a list of them, and is able to +// refresh them if configurations like the number of recommended threads +// change. +typedef struct TfLiteExternalContext { + TfLiteExternalContextType type; + TfLiteStatus (*Refresh)(struct TfLiteContext* context); +} TfLiteExternalContext; + +#define kTfLiteOptionalTensor (-1) + +// Fixed size list of integers. Used for dimensions and inputs/outputs tensor +// indices +typedef struct TfLiteIntArray { + int size; + +#if defined(_MSC_VER) + // Context for why this is needed is in http://b/189926408#comment21 + int data[1]; +#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ + __GNUC_MINOR__ >= 1) || \ + defined(HEXAGON) || \ + (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1) + // gcc 6.1+ have a bug where flexible members aren't properly handled + // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c + int data[0]; +#else + int data[]; +#endif +} TfLiteIntArray; + +// Given the size (number of elements) in a TfLiteIntArray, calculate its size +// in bytes. +size_t TfLiteMicroIntArrayGetSizeInBytes(int size); + +#ifndef TF_LITE_STATIC_MEMORY +// Create a array of a given `size` (uninitialized entries). +// This returns a pointer, that you must free using TfLiteIntArrayFree(). +TfLiteIntArray* TfLiteMicroIntArrayCreate(int size); +#endif + +// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise. +int TfLiteMicroIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b); + +// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise. +int TfLiteMicroIntArrayEqualsArray(const TfLiteIntArray* a, int b_size, + const int b_data[]); + +#ifndef TF_LITE_STATIC_MEMORY +// Create a copy of an array passed as `src`. +// You are expected to free memory with TfLiteIntArrayFree +TfLiteIntArray* TfLiteMicroIntArrayCopy(const TfLiteIntArray* src); + +// Free memory of array `a`. +void TfLiteMicroIntArrayFree(TfLiteIntArray* a); +#endif // TF_LITE_STATIC_MEMORY + +// Fixed size list of floats. Used for per-channel quantization. +typedef struct TfLiteFloatArray { + int size; +#if defined(_MSC_VER) + // Context for why this is needed is in http://b/189926408#comment21 + float data[1]; +#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ + __GNUC_MINOR__ >= 1) || \ + defined(HEXAGON) || \ + (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1) + // gcc 6.1+ have a bug where flexible members aren't properly handled + // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c + float data[0]; +#else + float data[]; +#endif +} TfLiteFloatArray; + +// Given the size (number of elements) in a TfLiteFloatArray, calculate its size +// in bytes. +int TfLiteFloatArrayGetSizeInBytes(int size); + +#ifndef TF_LITE_STATIC_MEMORY +// Create a array of a given `size` (uninitialized entries). +// This returns a pointer, that you must free using TfLiteFloatArrayFree(). +TfLiteFloatArray* TfLiteMicroFloatArrayCreate(int size); + +// Create a copy of an array passed as `src`. +// You are expected to free memory with TfLiteFloatArrayFree. +TfLiteFloatArray* TfLiteMicroFloatArrayCopy(const TfLiteFloatArray* src); + +// Free memory of array `a`. +void TfLiteMicroFloatArrayFree(TfLiteFloatArray* a); +#endif // TF_LITE_STATIC_MEMORY + +// Since we must not depend on any libraries, define a minimal subset of +// error macros while avoiding names that have pre-conceived meanings like +// assert and check. + +// Try to make all reporting calls through TF_LITE_KERNEL_LOG rather than +// calling the context->ReportError function directly, so that message strings +// can be stripped out if the binary size needs to be severely optimized. +#ifndef TF_LITE_STRIP_ERROR_STRINGS +#define TF_LITE_KERNEL_LOG(context, ...) \ + do { \ + (context)->ReportError((context), __VA_ARGS__); \ + } while (false) + +#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) \ + do { \ + if ((context) != nullptr) { \ + (context)->ReportError((context), __VA_ARGS__); \ + } \ + } while (false) +#else // TF_LITE_STRIP_ERROR_STRINGS +#define ARGS_UNUSED(...) (void)sizeof(#__VA_ARGS__) +#define TF_LITE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) +#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) +#endif // TF_LITE_STRIP_ERROR_STRINGS + +// Check whether value is true, and if not return kTfLiteError from +// the current function (and report the error string msg). +#define TF_LITE_ENSURE_MSG(context, value, msg) \ + do { \ + if (!(value)) { \ + TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \ + return kTfLiteError; \ + } \ + } while (0) + +// Check whether the value `a` is true, and if not return kTfLiteError from +// the current function, while also reporting the location of the error. +#define TF_LITE_ENSURE(context, a) \ + do { \ + if (!(a)) { \ + TF_LITE_KERNEL_LOG((context), "%s:%d %s was not true.", __FILE__, \ + __LINE__, #a); \ + return kTfLiteError; \ + } \ + } while (0) + +#define TF_LITE_ENSURE_STATUS(a) \ + do { \ + const TfLiteStatus s = (a); \ + if (s != kTfLiteOk) { \ + return s; \ + } \ + } while (0) + +// Check whether the value `a == b` is true, and if not return kTfLiteError from +// the current function, while also reporting the location of the error. +// `a` and `b` may be evaluated more than once, so no side effects or +// extremely expensive computations should be done. +// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes. +#define TF_LITE_ENSURE_EQ(context, a, b) \ + do { \ + if ((a) != (b)) { \ + TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%d != %d)", __FILE__, \ + __LINE__, #a, #b, (a), (b)); \ + return kTfLiteError; \ + } \ + } while (0) + +#define TF_LITE_ENSURE_TYPES_EQ(context, a, b) \ + do { \ + if ((a) != (b)) { \ + TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%s != %s)", __FILE__, \ + __LINE__, #a, #b, TfLiteMicroTypeGetName(a), \ + TfLiteMicroTypeGetName(b)); \ + return kTfLiteError; \ + } \ + } while (0) + +#define TF_LITE_ENSURE_NEAR(context, a, b, epsilon) \ + do { \ + auto delta = ((a) > (b)) ? ((a) - (b)) : ((b) - (a)); \ + if (delta > epsilon) { \ + TF_LITE_KERNEL_LOG((context), "%s:%d %s not near %s (%f != %f)", \ + __FILE__, __LINE__, #a, #b, static_cast(a), \ + static_cast(b)); \ + return kTfLiteError; \ + } \ + } while (0) + +#define TF_LITE_ENSURE_OK(context, status) \ + do { \ + const TfLiteStatus s = (status); \ + if ((s) != kTfLiteOk) { \ + return s; \ + } \ + } while (0) + +// Single-precision complex data type compatible with the C99 definition. +typedef struct TfLiteComplex64 { + float re, im; // real and imaginary parts, respectively. +} TfLiteComplex64; + +// Double-precision complex data type compatible with the C99 definition. +typedef struct TfLiteComplex128 { + double re, im; // real and imaginary parts, respectively. +} TfLiteComplex128; + +// Half precision data type compatible with the C99 definition. +typedef struct TfLiteFloat16 { + uint16_t data; +} TfLiteFloat16; + +// Return the name of a given type, for error reporting purposes. +const char* TfLiteMicroTypeGetName(TfLiteType type); + +// SupportedQuantizationTypes. +typedef enum TfLiteQuantizationType { + // No quantization. + kTfLiteNoQuantization = 0, + // Affine quantization (with support for per-channel quantization). + // Corresponds to TfLiteAffineQuantization. + kTfLiteAffineQuantization = 1, +} TfLiteQuantizationType; + +// Structure specifying the quantization used by the tensor, if-any. +typedef struct TfLiteQuantization { + // The type of quantization held by params. + TfLiteQuantizationType type; + // Holds an optional reference to a quantization param structure. The actual + // type depends on the value of the `type` field (see the comment there for + // the values and corresponding types). + void* params; +} TfLiteQuantization; + +// Parameters for asymmetric quantization across a dimension (i.e per output +// channel quantization). +// quantized_dimension specifies which dimension the scales and zero_points +// correspond to. +// For a particular value in quantized_dimension, quantized values can be +// converted back to float using: +// real_value = scale * (quantized_value - zero_point) +typedef struct TfLiteAffineQuantization { + TfLiteFloatArray* scale; + TfLiteIntArray* zero_point; + int32_t quantized_dimension; +} TfLiteAffineQuantization; + +/* A union of pointers that points to memory for a given tensor. */ +typedef union TfLitePtrUnion { + /* Do not access these members directly, if possible, use + * GetTensorData(tensor) instead, otherwise only access .data, as other + * members are deprecated. */ + int32_t* i32; + uint32_t* u32; + int64_t* i64; + uint64_t* u64; + float* f; + TfLiteFloat16* f16; + double* f64; + char* raw; + const char* raw_const; + uint8_t* uint8; + bool* b; + int16_t* i16; + uint16_t* ui16; + TfLiteComplex64* c64; + TfLiteComplex128* c128; + int8_t* int8; + /* Only use this member. */ + void* data; +} TfLitePtrUnion; + +// Memory allocation strategies. +// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated. +// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence, +// and available during eval. +// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and +// only available during eval. +// * kTfLiteDynamic: Allocated during eval, or for string tensors. +// * kTfLitePersistentRo: Allocated and populated during prepare. This is +// useful for tensors that can be computed during prepare and treated +// as constant inputs for downstream ops (also in prepare). +// * kTfLiteCustom: Custom memory allocation provided by the user. See +// TfLiteCustomAllocation below. +// * kTfLiteVariantObject: Allocation is an arbitrary type-erased C++ object. +// Allocation and deallocation are done through `new` and `delete`. +typedef enum TfLiteAllocationType { + kTfLiteMemNone = 0, + kTfLiteMmapRo, + kTfLiteArenaRw, + kTfLiteArenaRwPersistent, + kTfLiteDynamic, + kTfLitePersistentRo, + kTfLiteCustom, + kTfLiteVariantObject, +} TfLiteAllocationType; + +// Memory allocation strategies. +// +// TfLiteAllocationType values have been overloaded to mean more than their +// original intent. This enum should only be used to document the allocation +// strategy used by a tensor for it data. +typedef enum TfLiteAllocationStrategy { + kTfLiteAllocationStrategyUnknown, + kTfLiteAllocationStrategyNone, // No data is allocated. + kTfLiteAllocationStrategyMMap, // Data is mmaped. + kTfLiteAllocationStrategyArena, // Handled by the arena. + kTfLiteAllocationStrategyMalloc, // Uses `malloc`/`free`. + kTfLiteAllocationStrategyNew // Uses `new[]`/`delete[]`. +} TfLiteAllocationStrategy; + +// Describes how stable a tensor attribute is with regards to an interpreter +// runs. +typedef enum TfLiteRunStability { + kTfLiteRunStabilityUnknown, + kTfLiteRunStabilityUnstable, // May change at any time. + kTfLiteRunStabilitySingleRun, // Will stay the same for one run. + kTfLiteRunStabilityAcrossRuns // Will stay the same across all runs. +} TfLiteRunStability; + +// Describes the steps of a TFLite operation life cycle. +typedef enum TfLiteRunStep { + kTfLiteRunStepUnknown, + kTfLiteRunStepInit, + kTfLiteRunStepPrepare, + kTfLiteRunStepEval +} TfLiteRunStep; + +// The delegates should use zero or positive integers to represent handles. +// -1 is reserved from unallocated status. +typedef int TfLiteBufferHandle; +enum { + kTfLiteNullBufferHandle = -1, +}; + +// Storage format of each dimension in a sparse tensor. +typedef enum TfLiteDimensionType { + kTfLiteDimDense = 0, + kTfLiteDimSparseCSR, +} TfLiteDimensionType; + +// Metadata to encode each dimension in a sparse tensor. +typedef struct TfLiteDimensionMetadata { + TfLiteDimensionType format; + int dense_size; + TfLiteIntArray* array_segments; + TfLiteIntArray* array_indices; +} TfLiteDimensionMetadata; + +// Parameters used to encode a sparse tensor. For detailed explanation of each +// field please refer to lite/schema/schema.fbs. +typedef struct TfLiteSparsity { + TfLiteIntArray* traversal_order; + TfLiteIntArray* block_map; + TfLiteDimensionMetadata* dim_metadata; + int dim_metadata_size; +} TfLiteSparsity; + +// Defines a custom memory allocation not owned by the runtime. +// `data` should be aligned to kDefaultTensorAlignment defined in +// lite/util.h. (Currently 64 bytes) +// NOTE: See Interpreter.SetCustomAllocationForTensor for details on usage. +typedef struct TfLiteCustomAllocation { + void* data; + size_t bytes; +} TfLiteCustomAllocation; + +// The flags used in `Interpreter::SetCustomAllocationForTensor`. +// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc. +typedef enum TfLiteCustomAllocationFlags { + kTfLiteCustomAllocationFlagsNone = 0, + // Skips checking whether allocation.data points to an aligned buffer as + // expected by the TFLite runtime. + // NOTE: Setting this flag can cause crashes when calling Invoke(). + // Use with caution. + kTfLiteCustomAllocationFlagsSkipAlignCheck = 1, +} TfLiteCustomAllocationFlags; + +// A tensor in the interpreter system which is a wrapper around a buffer of +// data including a dimensionality (or NULL if not currently defined). +#ifndef TF_LITE_STATIC_MEMORY +typedef struct TfLiteTensor { + // The data type specification for data stored in `data`. This affects + // what member of `data` union should be used. + TfLiteType type; + // A union of data pointers. The appropriate type should be used for a typed + // tensor based on `type`. + TfLitePtrUnion data; + // A pointer to a structure representing the dimensionality interpretation + // that the buffer should have. NOTE: the product of elements of `dims` + // and the element datatype size should be equal to `bytes` below. + TfLiteIntArray* dims; + // Quantization information. + TfLiteQuantizationParams params; + // How memory is mapped + // kTfLiteMmapRo: Memory mapped read only. + // i.e. weights + // kTfLiteArenaRw: Arena allocated read write memory + // (i.e. temporaries, outputs). + TfLiteAllocationType allocation_type; + // The number of bytes required to store the data of this Tensor. I.e. + // (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if + // type is kTfLiteFloat32 and dims = {3, 2} then + // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24. + size_t bytes; + + // An opaque pointer to a tflite_micro::MMapAllocation + const void* allocation; + + // Null-terminated name of this tensor. + const char* name; + + // The delegate which knows how to handle `buffer_handle`. + // WARNING: This is an experimental interface that is subject to change. + struct TfLiteDelegate* delegate; + + // An integer buffer handle that can be handled by `delegate`. + // The value is valid only when delegate is not null. + // WARNING: This is an experimental interface that is subject to change. + TfLiteBufferHandle buffer_handle; + + // If the delegate uses its own buffer (e.g. GPU memory), the delegate is + // responsible to set data_is_stale to true. + // `delegate->CopyFromBufferHandle` can be called to copy the data from + // delegate buffer. + // WARNING: This is an // experimental interface that is subject to change. + bool data_is_stale; + + // True if the tensor is a variable. + bool is_variable; + + // Quantization information. Replaces params field above. + TfLiteQuantization quantization; + + // Parameters used to encode a sparse tensor. + // This is optional. The field is NULL if a tensor is dense. + // WARNING: This is an experimental interface that is subject to change. + TfLiteSparsity* sparsity; + + // Optional. Encodes shapes with unknown dimensions with -1. This field is + // only populated when unknown dimensions exist in a read-write tensor (i.e. + // an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and + // `dims_signature` contains [1, -1, -1, 3]). If no unknown dimensions exist + // then `dims_signature` is either null, or set to an empty array. Note that + // this field only exists when TF_LITE_STATIC_MEMORY is not defined. + const TfLiteIntArray* dims_signature; +} TfLiteTensor; + +// A structure representing an instance of a node. +// This structure only exhibits the inputs, outputs, user defined data and some +// node properties (like statefulness), not other features like the type. +typedef struct TfLiteNode { + // Inputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* inputs; + + // Outputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* outputs; + + // intermediate tensors to this node expressed as indices into the simulator's + // tensors. + TfLiteIntArray* intermediates; + + // Temporary tensors uses during the computations. This usually contains no + // tensors, but ops are allowed to change that if they need scratch space of + // any sort. + TfLiteIntArray* temporaries; + + // Opaque data provided by the node implementer through `Registration.init`. + void* user_data; + + // Opaque data provided to the node if the node is a builtin. This is usually + // a structure defined in builtin_op_data.h + void* builtin_data; + + // Custom initial data. This is the opaque data provided in the flatbuffer. + // WARNING: This is an experimental interface that is subject to change. + const void* custom_initial_data; + int custom_initial_data_size; + + // The pointer to the delegate. This is non-null only when the node is + // created by calling `interpreter.ModifyGraphWithDelegate`. + // WARNING: This is an experimental interface that is subject to change. + struct TfLiteDelegate* delegate; + + // Whether this op might have side effect (e.g. stateful op). + bool might_have_side_effect; +} TfLiteNode; +#else // defined(TF_LITE_STATIC_MEMORY)? +// NOTE: This flag is opt-in only at compile time. +// +// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct +// contains only the minimum fields required to initialize and prepare a micro +// inference graph. The fields in this struct have been ordered from +// largest-to-smallest for optimal struct sizeof. +// +// This struct does not use: +// - allocation +// - buffer_handle +// - data_is_stale +// - delegate +// - dims_signature +// - name +// - sparsity +typedef struct TfLiteTensor { + // A union of data pointers. The appropriate type should be used for a typed + // tensor based on `type`. + TfLitePtrUnion data; + + // A pointer to a structure representing the dimensionality interpretation + // that the buffer should have. NOTE: the product of elements of `dims` + // and the element datatype size should be equal to `bytes` below. + TfLiteIntArray* dims; + + // The data type specification for data stored in `data`. This affects + // what member of `data` union should be used. + TfLiteType type; + + // TODO(b/155784997): Consider consolidating these quantization fields: + // Quantization information. Replaces params field above. + TfLiteQuantization quantization; + + // Quantization information. + TfLiteQuantizationParams params; + +} TfLiteTensor; + +// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains +// only the minimum fields required to represent a node. +// +// This struct does not use: +// - delegate +// - intermediates +// - temporaries +typedef struct TfLiteNode { + // Inputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* inputs; + + // Outputs to this node expressed as indices into the simulator's tensors. + TfLiteIntArray* outputs; + + // Opaque data provided by the node implementer through `Registration.init`. + void* user_data; + + // Opaque data provided to the node if the node is a builtin. This is usually + // a structure defined in builtin_op_data.h + void* builtin_data; + + // Custom initial data. This is the opaque data provided in the flatbuffer. + // WARNING: This is an experimental interface that is subject to change. + int custom_initial_data_size; +} TfLiteNode; +#endif // TF_LITE_STATIC_MEMORY + +// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount +// of information required for a kernel to run during TfLiteRegistration::Eval. +// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM +// builds with this flag by default internally. +typedef struct TfLiteEvalTensor { + // A union of data pointers. The appropriate type should be used for a typed + // tensor based on `type`. + TfLitePtrUnion data; + + // A pointer to a structure representing the dimensionality interpretation + // that the buffer should have. + TfLiteIntArray* dims; + + // The data type specification for data stored in `data`. This affects + // what member of `data` union should be used. + TfLiteType type; +} TfLiteEvalTensor; + +#ifndef TF_LITE_STATIC_MEMORY +// Free data memory of tensor `t`. +void TfLiteTensorDataFree(TfLiteTensor* t); + +// Free quantization data. +void TfLiteMicroQuantizationFree(TfLiteQuantization* quantization); + +// Free sparsity parameters. +void TfLiteMicroSparsityFree(TfLiteSparsity* sparsity); + +// Free memory of tensor `t`. +void TfLiteTensorFree(TfLiteTensor* t); + +// Set all of a tensor's fields (and free any previously allocated data). +void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, + TfLiteQuantizationParams quantization, char* buffer, + size_t size, TfLiteAllocationType allocation_type, + const void* allocation, bool is_variable, + TfLiteTensor* tensor); + +// Copies the contents of 'src' in 'dst'. +// Function does nothing if either 'src' or 'dst' is passed as nullptr and +// return kTfLiteOk. +// Returns kTfLiteError if 'src' and 'dst' doesn't have matching data size. +// Note function copies contents, so it won't create new data pointer +// or change allocation type. +// All Tensor related properties will be copied from 'src' to 'dst' like +// quantization, sparsity, ... +TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst); + +// Change the size of the memory block owned by `tensor` to `num_bytes`. +// Tensors with allocation types other than `kTfLiteDynamic` will be ignored and +// a kTfLiteOk will be returned. +// `tensor`'s internal data buffer will be assigned a pointer +// which can safely be passed to free or realloc if `num_bytes` is zero. +// If `preserve_data` is true, tensor data will be unchanged in the range from +// the start of the region up to the minimum of the old and new sizes. In the +// case of NULL tensor, or an error allocating new memory, returns +// `kTfLiteError`. +TfLiteStatus TfLiteTensorResizeMaybeCopy(size_t num_bytes, TfLiteTensor* tensor, + bool preserve_data); + +// Change the size of the memory block owned by `tensor` to `num_bytes`. +// Tensors with allocation types other than kTfLiteDynamic will be ignored and +// a kTfLiteOk will be returned. +// `tensor`'s internal data buffer will be assigned a pointer +// which can safely be passed to free or realloc if `num_bytes` is zero. +// Tensor data will be unchanged in the range from the start of the region up to +// the minimum of the old and new sizes. In the case +// of NULL tensor, or an error allocating new memory, returns `kTfLiteError`. +TfLiteStatus TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); +#endif // TF_LITE_STATIC_MEMORY + +// WARNING: This is an experimental interface that is subject to change. +// +// Currently, TfLiteDelegateParams has to be allocated in a way that it's +// trivially destructable. It will be stored as `builtin_data` field in +// `TfLiteNode` of the delegate node. +// +// See also the `CreateDelegateParams` function in `interpreter.cc` details. +typedef struct TfLiteDelegateParams { + struct TfLiteDelegate* delegate; + TfLiteIntArray* nodes_to_replace; + TfLiteIntArray* input_tensors; + TfLiteIntArray* output_tensors; +} TfLiteDelegateParams; + +// WARNING: This is an experimental interface that is subject to change. +// +// Currently, TfLiteOpaqueDelegateParams has to be allocated in a way that it's +// trivially destructable. It will be stored as `builtin_data` field in +// `TfLiteNode` of the delegate node. +// +// See also the `CreateOpaqueDelegateParams` function in `subgraph.cc` +// details. +typedef struct TfLiteOpaqueDelegateParams { + TfLiteOpaqueDelegate* delegate; + void* delegate_data; + TfLiteIntArray* nodes_to_replace; + TfLiteIntArray* input_tensors; + TfLiteIntArray* output_tensors; +} TfLiteOpaqueDelegateParams; + +typedef struct TfLiteContext { + // Number of tensors in the context. + size_t tensors_size; + + // The execution plan contains a list of the node indices in execution + // order. execution_plan->size is the current number of nodes. And, + // execution_plan->data[0] is the first node that needs to be run. + // TfLiteDelegates can traverse the current execution plan by iterating + // through each member of this array and using GetNodeAndRegistration() to + // access details about a node. i.e. + // + // TfLiteIntArray* execution_plan; + // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan)); + // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) { + // int node_index = execution_plan->data[exec_index]; + // TfLiteNode* node; + // TfLiteRegistration* reg; + // context->GetNodeAndRegistration(context, node_index, &node, ®); + // } + // Note: the memory pointed by '`*execution_plan` is OWNED by TfLite runtime. + // Future calls to GetExecutionPlan invalidates earlier outputs. The following + // code snippet shows the issue of such an invocation pattern. After calling + // CheckNode, subsequent access to `plan_1st` is undefined. + // + // void CheckNode(const TfLiteNode* node) { + // ... + // TfLiteIntArray* plan_2nd; + // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_2nd)); + // ... + // } + // + // TfLiteIntArray* plan_1st; + // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_1st)); + // for (int exec_index = 0; exec_index < plan_1st->size; exec_index++) { + // int node_index = plan_1st->data[exec_index]; + // TfLiteNode* node; + // TfLiteRegistration* reg; + // context->GetNodeAndRegistration(context, node_index, &node, ®); + // CheckNode(node); + // } + // + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context, + TfLiteIntArray** execution_plan); + + // An array of tensors in the interpreter context (of length `tensors_size`) + TfLiteTensor* tensors; + + // opaque full context ptr (an opaque c++ data structure) + void* impl_; + + // Request memory pointer be resized. Updates dimensions on the tensor. + // NOTE: ResizeTensor takes ownership of newSize. + TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor, + TfLiteIntArray* new_size); + // Request that an error be reported with format string msg. + void (*ReportError)(struct TfLiteContext*, const char* msg, ...); + + // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If + // non-null, the value pointed to by `first_new_tensor_index` will be set to + // the index of the first new tensor. + TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add, + int* first_new_tensor_index); + + // Get a Tensor node by node_index. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*GetNodeAndRegistration)( + struct TfLiteContext*, int node_index, TfLiteNode** node, + struct TfLiteRegistration** registration); + + // Replace ops with one or more stub delegate operations. This function + // does not take ownership of `nodes_to_replace`. + TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)( + struct TfLiteContext*, struct TfLiteRegistration registration, + const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate); + + // Number of threads that are recommended to subsystems like gemmlowp and + // eigen. + int recommended_num_threads; + + // Access external contexts by type. + // WARNING: This is an experimental interface that is subject to change. + TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*, + TfLiteExternalContextType); + // Set the value of a external context. Does not take ownership of the + // pointer. + // WARNING: This is an experimental interface that is subject to change. + void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType, + TfLiteExternalContext*); + + // Flag for allowing float16 precision for FP32 calculation. + // default: false. + // WARNING: This is an experimental API and subject to change. + bool allow_fp32_relax_to_fp16; + + // Pointer to the op-level profiler, if set; nullptr otherwise. + void* profiler; + + // Allocate persistent buffer which has the same life time as the interpreter. + // Returns nullptr on failure. + // The memory is allocated from heap for TFL, and from tail in TFLM. + // This method is only available in Init or Prepare stage. + // WARNING: This is an experimental interface that is subject to change. + void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes); + + // Allocate a buffer which will be deallocated right after invoke phase. + // The memory is allocated from heap in TFL, and from volatile arena in TFLM. + // This method is only available in invoke stage. + // NOTE: If possible use RequestScratchBufferInArena method to avoid memory + // allocation during inference time. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes, + void** ptr); + + // Request a scratch buffer in the arena through static memory planning. + // This method is only available in Prepare stage and the buffer is allocated + // by the interpreter between Prepare and Eval stage. In Eval stage, + // GetScratchBuffer API can be used to fetch the address. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx, + size_t bytes, int* buffer_idx); + + // Get the scratch buffer pointer. + // This method is only available in Eval stage. + // WARNING: This is an experimental interface that is subject to change. + void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx); + + // Resize the memory pointer of the `tensor`. This method behaves the same as + // `ResizeTensor`, except that it makes a copy of the shape array internally + // so the shape array could be deallocated right afterwards. + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx, + TfLiteTensor* tensor, int dims, + const int* shape); + + // This method provides a preview of post-delegation partitioning. Each + // TfLiteDelegateParams in the referenced array corresponds to one instance of + // the delegate kernel. + // Example usage: + // + // TfLiteIntArray* nodes_to_replace = ...; + // TfLiteDelegateParams* params_array; + // int num_partitions = 0; + // TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning( + // context, delegate, nodes_to_replace, ¶ms_array, &num_partitions)); + // for (int idx = 0; idx < num_partitions; idx++) { + // const auto& partition_params = params_array[idx]; + // ... + // } + // + // NOTE: The context owns the memory referenced by partition_params_array. It + // will be cleared with another call to PreviewDelegatePartitioning, or after + // TfLiteDelegateParams::Prepare returns. + // + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*PreviewDelegatePartitioning)( + struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace, + TfLiteDelegateParams** partition_params_array, int* num_partitions); + + // Returns a TfLiteTensor struct for a given index. + // WARNING: This is an experimental interface that is subject to change. + // WARNING: This method may not be available on all platforms. + TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context, + int tensor_idx); + + // Returns a TfLiteEvalTensor struct for a given index. + // WARNING: This is an experimental interface that is subject to change. + // WARNING: This method may not be available on all platforms. + TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context, + int tensor_idx); + + // TODO + // New interface to avoid storing an is_variable bool + bool (*IsVariableTensor)(struct TfLiteContext* context, + TfLiteTensor* tensor); + + // TODO + // New interface to avoid storing allocation type in TfLiteTensor + bool (*IsConstantTensor)(struct TfLiteContext* context, + TfLiteTensor* tensor); + + // Retrieves named metadata buffer from the TFLite model. + // Returns kTfLiteOk if metadata is successfully obtained from the flatbuffer + // Model: that is, there exists a `metadata` entry with given `name` string. + // (see TFLite's schema.fbs). + // The corresponding `buffer` information is populated in `ptr` & `bytes`. + // The data from `ptr` is valid for the lifetime of the Interpreter. + // + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*GetModelMetadata)(const struct TfLiteContext* context, + const char* name, const char** ptr, + size_t* bytes); + + // Retrieves the corresponding TfLiteContext of a subgraph that the given + // subgraph_index points to and switches to the delegate context for that + // subgraph. If an invalid subgraph index is given, returns kTfLiteError. + // NOTE: This function is expected to be paired with ReleaseSubgraphContext() + // once the delegate preparation is done and/or the delegate context functions + // are no longer needed. + // + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*AcquireSubgraphContext)( + struct TfLiteContext* context, int subgraph_index, + struct TfLiteContext** acquired_context); + // Releases the subgraph context by switching back to the TFLite kernel + // context for the subgraph that the given subgraph_index points to. + // NOTE: This function is expected to be used after AcquireSubgraphContext() + // once the delegate preparation is done and/or the delegate context functions + // are no longer needed. + // + // WARNING: This is an experimental interface that is subject to change. + TfLiteStatus (*ReleaseSubgraphContext)(struct TfLiteContext* context, + int subgraph_index); +} TfLiteContext; + +// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration` +// for C API which doesn't use internal types (such as `TfLiteContext`) but only +// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each +// field is the exactly the same as with `TfLiteRegistration`. +typedef struct TfLiteRegistrationExternal TfLiteRegistrationExternal; + +// The valid values of the `inplace_operator` field in `TfLiteRegistration`. +// This allow an op to signal to the runtime that the same data pointer +// may be passed as an input and output without impacting the result. +// This does not mean that the memory can safely be reused, it is up to the +// runtime to determine this, e.g. if another op consumes the same input or not +// or if an input tensor has sufficient memory allocated to store the output +// data. +// +// Setting these flags authorizes the runtime to set the data pointers of an +// input and output tensor to the same value. In such cases, the memory required +// by the output must be less than or equal to that required by the shared +// input, never greater. If kTfLiteInplaceOpDataUnmodified is set, then the +// runtime can share the same input tensor with multiple operator's outputs, +// provided that kTfLiteInplaceOpDataUnmodified is set for all of them. +// Otherwise, if an input tensor is consumed by multiple operators, it may only +// be shared with the operator which is the last to consume it. +// +// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc. +typedef enum { + // The default value. This indicates that the same data pointer cannot safely + // be passed as an op's input and output. + kTfLiteInplaceOpNone = 0, + // This indicates that an op's first output's data is identical to its first + // input's data, for example Reshape. + kTfLiteInplaceOpDataUnmodified = 1, + // Setting kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput means + // that InputN may be shared with OutputN instead of with the first output. + // This flag requires one or more of kTfLiteInplaceOpInputNShared to be set. + kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput = 2, + // kTfLiteInplaceOpInputNShared indicates that it is safe for an op to share + // InputN's data pointer with an output tensor. If + // kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is set then + // kTfLiteInplaceOpInputNShared indicates that InputN may be shared + // with OutputN, otherwise kTfLiteInplaceOpInputNShared indicates that InputN + // may be shared with the first output. + // + // Indicates that an op's first input may be shared with the first output + // tensor. kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput has + // no impact on the behavior allowed by this flag. + kTfLiteInplaceOpInput0Shared = 4, + // Indicates that an op's second input may be shared with the first output + // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set + // or second output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput + // is set. + kTfLiteInplaceOpInput1Shared = 8, + // Indicates that an op's third input may be shared with the first output + // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set + // or third output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is + // set. + kTfLiteInplaceOpInput2Shared = 16, + // Placeholder to ensure that enum can hold 64 bit values to accommodate + // future fields. + kTfLiteInplaceOpMaxValue = UINT64_MAX, +} TfLiteInPlaceOp; + +// The number of shareable inputs supported. +static const int kTfLiteMaxSharableOpInputs = 3; + +typedef struct TfLiteRegistration { + // Initializes the op from serialized data. + // Called only *once* for the lifetime of the op, so any one-time allocations + // should be made here (unless they depend on tensor sizes). + // + // If a built-in op: + // `buffer` is the op's params data (TfLiteLSTMParams*). + // `length` is zero. + // If custom op: + // `buffer` is the op's `custom_options`. + // `length` is the size of the buffer. + // + // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer + // or an instance of a struct). + // + // The returned pointer will be stored with the node in the `user_data` field, + // accessible within prepare and invoke functions below. + // NOTE: if the data is already in the desired format, simply implement this + // function to return `nullptr` and implement the free function to be a no-op. + void* (*init)(TfLiteContext* context, const char* buffer, size_t length); + + // The pointer `buffer` is the data previously returned by an init invocation. + void (*free)(TfLiteContext* context, void* buffer); + + // prepare is called when the inputs this node depends on have been resized. + // context->ResizeTensor() can be called to request output tensors to be + // resized. + // Can be called multiple times for the lifetime of the op. + // + // Returns kTfLiteOk on success. + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); + + // Execute the node (should read node->inputs and output to node->outputs). + // Returns kTfLiteOk on success. + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + + // profiling_string is called during summarization of profiling information + // in order to group executions together. Providing a value here will cause a + // given op to appear multiple times is the profiling report. This is + // particularly useful for custom ops that can perform significantly + // different calculations depending on their `user-data`. + const char* (*profiling_string)(const TfLiteContext* context, + const TfLiteNode* node); + + // Builtin codes. If this kernel refers to a builtin this is the code + // of the builtin. This is so we can do marshaling to other frameworks like + // NN API. + // Note: It is the responsibility of the registration binder to set this + // properly. + int32_t builtin_code; + + // Custom op name. If the op is a builtin, this will be null. + // Note: It is the responsibility of the registration binder to set this + // properly. + // WARNING: This is an experimental interface that is subject to change. + const char* custom_name; + + // The version of the op. + // Note: It is the responsibility of the registration binder to set this + // properly. + int version; + + // The external version of `TfLiteRegistration`. Since we can't use internal + // types (such as `TfLiteContext`) for C API to maintain ABI stability. + // C API user will provide `TfLiteRegistrationExternal` to implement custom + // ops. We keep it inside of `TfLiteRegistration` and use it to route + // callbacks properly. + TfLiteRegistrationExternal* registration_external; + + // Retrieves asynchronous kernel. + // + // If the `async_kernel` field is nullptr, it means the operation described by + // this TfLiteRegistration object does not support asynchronous execution. + // Otherwise, the function that the field points to should only be called for + // delegate kernel nodes, i.e. `node` should be a delegate kernel node created + // by applying a delegate. + // If the function returns nullptr, that means that the underlying delegate + // does not support asynchronous execution for this `node`. + struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context, + TfLiteNode* node); + + // Indicates if an operator's output may safely overwrite its inputs. + // See the comments in `TfLiteInPlaceOp`. + uint64_t inplace_operator; +} TfLiteRegistration; + +/// \private +// Old version of `TfLiteRegistration` to maintain binary backward +// compatibility. +// The legacy registration type must be a POD struct type whose field types must +// be a prefix of the field types in TfLiteRegistration, and offset of the first +// field in TfLiteRegistration that is not present in the legacy registration +// type must be greater than or equal to the size of the legacy registration +// type. +// WARNING: This structure is deprecated / not an official part of the +// API. It should be only used for binary backward compatibility. +typedef struct TfLiteRegistration_V3 { + void* (*init)(TfLiteContext* context, const char* buffer, size_t length); + void (*free)(TfLiteContext* context, void* buffer); + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + const char* (*profiling_string)(const TfLiteContext* context, + const TfLiteNode* node); + int32_t builtin_code; + const char* custom_name; + int version; + TfLiteRegistrationExternal* registration_external; + struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context, + TfLiteNode* node); +} TfLiteRegistration_V3; + +/// \private +// Old version of `TfLiteRegistration` to maintain binary backward +// compatibility. +// The legacy registration type must be a POD struct type whose field types must +// be a prefix of the field types in TfLiteRegistration, and offset of the first +// field in TfLiteRegistration that is not present in the legacy registration +// type must be greater than or equal to the size of the legacy registration +// type. +// WARNING: This structure is deprecated / not an official part of the +// API. It should be only used for binary backward compatibility. +typedef struct TfLiteRegistration_V2 { + void* (*init)(TfLiteContext* context, const char* buffer, size_t length); + void (*free)(TfLiteContext* context, void* buffer); + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + const char* (*profiling_string)(const TfLiteContext* context, + const TfLiteNode* node); + int32_t builtin_code; + const char* custom_name; + int version; + TfLiteRegistrationExternal* registration_external; +} TfLiteRegistration_V2; + +/// \private +// Old version of `TfLiteRegistration` to maintain binary backward +// compatibility. +// The legacy registration type must be a POD struct type whose field types must +// be a prefix of the field types in TfLiteRegistration, and offset of the first +// field in TfLiteRegistration that is not present in the legacy registration +// type must be greater than or equal to the size of the legacy registration +// type. +// WARNING: This structure is deprecated / not an official part of the +// API. It should be only used for binary backward compatibility. +typedef struct TfLiteRegistration_V1 { + void* (*init)(TfLiteContext* context, const char* buffer, size_t length); + void (*free)(TfLiteContext* context, void* buffer); + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + const char* (*profiling_string)(const TfLiteContext* context, + const TfLiteNode* node); + int32_t builtin_code; + const char* custom_name; + int version; +} TfLiteRegistration_V1; + +// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the +// values should be 1, 2, 4, 8, ...etc. +typedef enum TfLiteDelegateFlags { + kTfLiteDelegateFlagsNone = 0, + // The flag is set if the delegate can handle dynamic sized tensors. + // For example, the output shape of a `Resize` op with non-constant shape + // can only be inferred when the op is invoked. + // In this case, the Delegate is responsible for calling + // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling + // `ResizeTensor` when invoking the op. + // + // If the delegate isn't capable to handle dynamic tensors, this flag need + // to be set to false. + kTfLiteDelegateFlagsAllowDynamicTensors = 1, + + // This flag can be used by delegates (that allow dynamic tensors) to ensure + // applicable tensor shapes are automatically propagated in the case of tensor + // resizing. + // This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors + // of a delegate kernel will have correct shapes before its Prepare() method + // is called. The runtime leverages TFLite builtin ops in the original + // execution plan to propagate shapes. + // + // A few points to note: + // 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is + // false, this one is redundant since the delegate kernels are re-initialized + // every time tensors are resized. + // 2. Enabling this flag adds some overhead to AllocateTensors(), since extra + // work is required to prepare the original execution plan. + // 3. This flag requires that the original execution plan only have ops with + // valid registrations (and not 'dummy' custom ops like with Flex). + // WARNING: This feature is experimental and subject to change. + kTfLiteDelegateFlagsRequirePropagatedShapes = 2, + + // This flag can be used by delegates to request per-operator profiling. If a + // node is a delegate node, this flag will be checked before profiling. If + // set, then the node will not be profiled. The delegate will then add per + // operator information using Profiler::EventType::OPERATOR_INVOKE_EVENT and + // the results will appear in the operator-wise Profiling section and not in + // the Delegate internal section. + kTfLiteDelegateFlagsPerOperatorProfiling = 4 +} TfLiteDelegateFlags; + +// WARNING: This is an experimental interface that is subject to change. +typedef struct TfLiteDelegate { + // Data that delegate needs to identify itself. This data is owned by the + // delegate. The delegate is owned in the user code, so the delegate is + // responsible for deallocating this when it is destroyed. + void* data_; + + // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the + // delegate a view of the current graph through TfLiteContext*. It typically + // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels() + // to ask the TensorFlow lite runtime to create macro-nodes to represent + // delegated subgraphs of the original graph. + TfLiteStatus (*Prepare)(TfLiteContext* context, + struct TfLiteDelegate* delegate); + + // Copy the data from delegate buffer handle into raw memory of the given + // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as + // long as it follows the rules for kTfLiteDynamic tensors, in which case this + // cannot be null. + TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context, + struct TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + TfLiteTensor* tensor); + + // Copy the data from raw memory of the given 'tensor' to delegate buffer + // handle. This can be null if the delegate doesn't use its own buffer. + TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context, + struct TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + TfLiteTensor* tensor); + + // Free the Delegate Buffer Handle. Note: This only frees the handle, but + // this doesn't release the underlying resource (e.g. textures). The + // resources are either owned by application layer or the delegate. + // This can be null if the delegate doesn't use its own buffer. + void (*FreeBufferHandle)(TfLiteContext* context, + struct TfLiteDelegate* delegate, + TfLiteBufferHandle* handle); + + // Bitmask flags. See the comments in `TfLiteDelegateFlags`. + int64_t flags; + + // The opaque delegate builder associated with this object. If set then the + // TF Lite runtime will give precedence to this field. E.g. instead of + // invoking 'Prepare' via the function pointer inside the 'TfLiteDelegate' + // object, the runtime will first check if the corresponding function + // pointer inside 'opaque_delegate_builder' is set and if so invoke that. + // + // If this field is non-null, then the 'Prepare' field (of the + // 'TfLiteDelegate') should be null. + struct TfLiteOpaqueDelegateBuilder* opaque_delegate_builder; +} TfLiteDelegate; + +// Build a 'null' delegate, with all the fields properly set to their default +// values. +TfLiteDelegate TfLiteDelegateCreate(void); + +// `TfLiteOpaqueDelegateBuilder` is used for constructing +// `TfLiteOpaqueDelegate`, see `TfLiteOpaqueDelegateCreate` below. Note: +// This struct is not ABI stable. +// +// For forward source compatibility `TfLiteOpaqueDelegateBuilder` objects should +// be brace-initialized, so that all fields (including any that might be added +// in the future) get zero-initialized. The purpose of each field is exactly +// the same as with `TfLiteDelegate`. +// +// WARNING: This is an experimental interface that is subject to change. +typedef struct TfLiteOpaqueDelegateBuilder { + // Data that delegate needs to identify itself. This data is owned by the + // delegate. The delegate is owned in the user code, so the delegate is + // responsible for deallocating this when it is destroyed. + void* data; + // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the + // delegate a view of the current graph through TfLiteContext*. It typically + // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels() + // to ask the TensorFlow lite runtime to create macro-nodes to represent + // delegated subgraphs of the original graph. + TfLiteStatus (*Prepare)(TfLiteOpaqueContext* context, // NOLINT + TfLiteOpaqueDelegate* delegate, void* data); + // Copies the data from delegate buffer handle into raw memory of the given + // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as + // long as it follows the rules for kTfLiteDynamic tensors, in which case this + // cannot be null. + TfLiteStatus (*CopyFromBufferHandle)( // NOLINT + TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data, + TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor); + // Copies the data from raw memory of the given 'tensor' to delegate buffer + // handle. This can be null if the delegate doesn't use its own buffer. + TfLiteStatus (*CopyToBufferHandle)( // NOLINT + TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data, + TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor); + // Frees the Delegate Buffer Handle. Note: This only frees the handle, but + // this doesn't release the underlying resource (e.g. textures). The + // resources are either owned by application layer or the delegate. + // This can be null if the delegate doesn't use its own buffer. + void (*FreeBufferHandle)(TfLiteOpaqueContext* context, // NOLINT + TfLiteOpaqueDelegate* delegate, void* data, + TfLiteBufferHandle* handle); + // Bitmask flags. See the comments in `TfLiteDelegateFlags`. + int64_t flags; +} TfLiteOpaqueDelegateBuilder; + +#ifndef TF_LITE_STATIC_MEMORY +// Creates an opaque delegate and returns its address. The opaque delegate will +// behave according to the provided 'opaque_delegate_builder'. The lifetime of +// the objects pointed to by any of the fields within the +// 'opaque_delegate_builder' must outlive the returned +// 'TfLiteOpaqueDelegate' and any 'TfLiteInterpreter', +// 'TfLiteInterpreterOptions', 'tflite_micro::Interpreter', or +// 'tflite_micro::InterpreterBuilder' that the delegate is added to. The returned +// address should be passed to 'TfLiteOpaqueDelegateDelete' for deletion. If +// 'opaque_delegate_builder' is a null pointer, then a null pointer will be +// returned. +TfLiteOpaqueDelegate* TfLiteOpaqueDelegateCreate( + const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder); + +// Deletes the provided opaque 'delegate'. This function has no effect if the +// 'delegate' is a null pointer. +void TfLiteOpaqueDelegateDelete(TfLiteOpaqueDelegate* delegate); +#endif // TF_LITE_STATIC_MEMORY + +// Returns a pointer to the data associated with the provided opaque 'delegate'. +// +// A null pointer will be returned when: +// - The 'delegate' is null. +// - The 'data' field of the 'TfLiteOpaqueDelegateBuilder' used to construct the +// 'delegate' was null. +// - Or in case of any other error. +// - The 'delegate' has been constructed via a 'TfLiteOpaqueDelegateBuilder', +// but the 'data' field of the 'TfLiteOpaqueDelegateBuilder' is null. +// +// The data_ field of 'delegate' will be returned if the +// 'opaque_delegate_builder' field is null. +void* TfLiteOpaqueDelegateGetData(const TfLiteOpaqueDelegate* delegate); + +// Returns a tensor data allocation strategy. +TfLiteAllocationStrategy TfLiteTensorGetAllocationStrategy( + const TfLiteTensor* t); + +// Returns how stable a tensor data buffer address is across runs. +TfLiteRunStability TfLiteTensorGetBufferAddressStability(const TfLiteTensor* t); + +// Returns how stable a tensor data values are across runs. +TfLiteRunStability TfLiteTensorGetDataStability(const TfLiteTensor* t); + +// Returns the operation step when the data of a tensor is populated. +// +// Some operations can precompute their results before the evaluation step. This +// makes the data available earlier for subsequent operations. +TfLiteRunStep TfLiteTensorGetDataKnownStep(const TfLiteTensor* t); + +// Returns the operation steop when the shape of a tensor is computed. +// +// Some operations can precompute the shape of their results before the +// evaluation step. This makes the shape available earlier for subsequent +// operations. +TfLiteRunStep TfLiteTensorGetShapeKnownStep(const TfLiteTensor* t); + +#ifdef __cplusplus +} // extern "C" + +#include + +// --- TFLITE VARIANT TENSORS ---- +// Programming languges usually define "variant" as a type that can hold an +// unbounded set of types. See std::any +// (https://en.cppreference.com/w/cpp/utility/any) for a related standard +// library construct. In tensorflow, variant tensors have a data member which is +// an Object that is destructible and copy constructible. +// Variant tensors are commonly used to represent non trivial data +// semantics that don't fit into simple primitives, such as lists of tensors and +// datasets. Additionally, they can facilitate containers for optimizing +// memory movement of tensor data. +// +// The following set of classes define the variant tensor member for tflite. +// They implement a type-erased container intended to be used behind the +// `data.data : void*` member of `TfLiteTensor`s. Runtime functions interact +// the variant member at the level of a `VariantData`, whereas kernels +// operate with the full knowledge of the un-erased type. The `VariantData` +// class provides abstract methods for destroying and copying `VariantData`. +// Invoking these methods will dispatch to the erased type opaquely. +// The contents of any object of type derived from `AbstractVariant` can be +// written to `TfLiteTensor::data::data : void*` from kernels. If the runtime +// were to copy such a tensor through `TfLiteTensorCopy`, the destination data +// member will contain the result of invoking the erased type's copy +// constructor. Similar for the runtime releasing tensors from memory, the +// erased type's destructor will be invoked. There are a few caveats to consider +// to use these safely, which we discuss below. +// +// EXAMPLE: READING VARIANT TENSORS +// ``` +// // retrieve input with `type == kTfLiteVariant` +// TfLiteTensor* input = ... +// // must first static cast to `VariantData`, more on this below. +// VariantData* vd_input = static_cast(t->data.data); +// CustomType* typed_input = +// static_cast(vd_input); +// // do custom work on `typed_input`... +// ``` +// +// EXAMPLE: WRITING VARIANT TENSORS +// ``` +// TfLiteTensor* output = ... +// // construct a new variant object behind the target tensor +// TfLiteVariantRealloc(output, args...); +// // again must static cast to `VariantData*` before writing to `void*`. +// output->data.data = static_cast(typed_output); +// ``` +// +// WHY STATIC CAST TO `VariantData*` +// The Standard defines a `reinterpret_cast` from a derived type to its +// parents as undefined behavior when the parent is a non-standard layout. +// https://en.cppreference.com/w/cpp/language/reinterpret_cast (see bullet 5). +// Due to the `VariantData` having virtual members it is indeed non-standard +// layout, and any type derived from `VariantData` fails to be +// "transparently-replaceable". I.e. implicit cast from derived to base in this +// case may adjust the pointer and by definition `reinterpret_cast` will not +// the adjust the pointer. +// Thus, dereferencing a pointer of type `VariantData` which addresses +// the first byte of an object of said derived type is UB unless it was first +// implicitly or statically casted to a `VariantData`. Writing the object of +// derived type directly to `void*` which is dereferenced as a `VariantData` is +// then UB, and so the intermediate cast through `VariantData` must be enforced. +// A good example of this issue is ellucidate in the bottom code snippet +// here: https://en.cppreference.com/w/cpp/utility/launder. +class VariantData { + public: + // All variant objects must be able to be destroyed and copied. + virtual ~VariantData() = default; + // A "virtual copy-constructor". Often the destination tensor of a variant + // copy may have been previously allocated in a prior call to inference. We + // allow the copy to target the destinations buffer (`maybe_alloc`), + // for potential reuse and optimizations. `maybe_alloc` must be of the same + // underlying derived type. References to whatever object is at + // `maybe_alloc` may be invalidated. + virtual VariantData* CloneTo(VariantData* maybe_alloc) const = 0; +}; + +// Concrete implementations extend `AbstractVariantData` with CRPT. +template +class AbstractVariantData : public VariantData { + public: + VariantData* CloneTo(VariantData* maybe_alloc) const override { + if (maybe_alloc != nullptr) { + // If the output is still allocated, then its object may still be + // in its life time and the destructor must be called before re-using the + // buffer. + // This may actual have a non-negligible effect on performance if the + // destructor is complex. A future iteration may + // introduce copy or move assignment semantics, allowing for the + // underlying implementation to optimize for this case. + auto* derived = static_cast(maybe_alloc); + derived->~ErasedDerived(); + return new (derived) + ErasedDerived(static_cast(*this)); + } + return new ErasedDerived(static_cast(*this)); + } + + protected: + AbstractVariantData() = default; + AbstractVariantData(const AbstractVariantData&) = default; + AbstractVariantData(AbstractVariantData&&) = delete; +}; + +// Analogous to `TfLiteTensorRealloc` for allocation of tensors whose +// data member points to an arbitrary C++ object. `VariantType` refers +// to the erased type of said object and `VariantArgs` refers to +// a list of argument types with which to construct a new `VariantType`. +// `VariantArgs` must match a constructor of `VariantType`. +template +TfLiteStatus TfLiteTensorVariantRealloc(TfLiteTensor* t, + VariantArgs&&... args) { + if (t->type != kTfLiteVariant) return kTfLiteError; + VariantType* new_vd; + if (t->data.raw != nullptr) { + auto* target_vd = static_cast(t->data.data); + target_vd->~VariantData(); + // As above, we assume if `t` is already allocated then it was allocated + // with the same `VariantType` as templated. + new_vd = new (t->data.raw) VariantType(std::forward(args)...); + } else { + new_vd = new VariantType(std::forward(args)...); + } + t->data.data = static_cast(new_vd); + + assert(false && "Disabled function!"); + // t->allocation_type = kTfLiteVariantObject; + return kTfLiteOk; +} + +#endif // __cplusplus +#endif // TENSORFLOW_LITE_CORE_C_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/macros.h b/new_ai_tools/include/tensorflow/lite/core/macros.h new file mode 100644 index 00000000..d329ded7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/core/macros.h @@ -0,0 +1,78 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +// This provides utility macros and functions that are inherently platform +// specific or shared across runtime & converter. +#ifndef TENSORFLOW_LITE_CORE_MACROS_H_ +#define TENSORFLOW_LITE_CORE_MACROS_H_ + +#ifdef __has_builtin +#define TFLITE_HAS_BUILTIN(x) __has_builtin(x) +#else +#define TFLITE_HAS_BUILTIN(x) 0 +#endif + +#if (!defined(__NVCC__)) && (TFLITE_HAS_BUILTIN(__builtin_expect) || \ + (defined(__GNUC__) && __GNUC__ >= 3)) +#define TFLITE_EXPECT_FALSE(cond) __builtin_expect(cond, false) +#define TFLITE_EXPECT_TRUE(cond) __builtin_expect(!!(cond), true) +#else +#define TFLITE_EXPECT_FALSE(cond) (cond) +#define TFLITE_EXPECT_TRUE(cond) (cond) +#endif + +#ifdef _WIN32 +#define TFLITE_NOINLINE __declspec(noinline) +#else +#ifdef __has_attribute +#if __has_attribute(noinline) +#define TFLITE_NOINLINE __attribute__((noinline)) +#else +#define TFLITE_NOINLINE +#endif // __has_attribute(noinline) +#else +#define TFLITE_NOINLINE +#endif // __has_attribute +#endif // _WIN32 + +// Normally we'd use ABSL_HAVE_ATTRIBUTE_WEAK and ABSL_ATTRIBUTE_WEAK, but +// we avoid the absl dependency for binary size reasons. +#ifdef __has_attribute +#define TFLITE_HAS_ATTRIBUTE(x) __has_attribute(x) +#else +#define TFLITE_HAS_ATTRIBUTE(x) 0 +#endif + +#if (TFLITE_HAS_ATTRIBUTE(weak) || \ + (defined(__GNUC__) && !defined(__clang__))) && \ + !(defined(__llvm__) && defined(_WIN32)) && !defined(__MINGW32__) +#undef TFLITE_ATTRIBUTE_WEAK +#define TFLITE_ATTRIBUTE_WEAK __attribute__((weak)) +#define TFLITE_HAS_ATTRIBUTE_WEAK 1 +#else +#define TFLITE_ATTRIBUTE_WEAK +#define TFLITE_HAS_ATTRIBUTE_WEAK 0 +#endif + +#ifndef TF_LITE_STATIC_MEMORY +// maximum size of a valid flatbuffer +inline constexpr unsigned int flatbuffer_size_max = 2147483648; +// If none zero then the buffer is stored outside of the flatbuffers, string +inline constexpr char tflite_metadata_buffer_location[] = "buffer_location"; +// field for minimum runtime version, string +inline constexpr char tflite_metadata_min_runtime_version[] = + "min_runtime_version"; +#endif + +#endif // TENSORFLOW_LITE_CORE_MACROS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h new file mode 100644 index 00000000..04b3ba6f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h @@ -0,0 +1,102 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ + +#ifdef __cplusplus +#include + +extern "C" { +#endif + +static inline int CountLeadingZeros32Slow(uint64_t n) { + int zeroes = 28; + if (n >> 16) zeroes -= 16, n >>= 16; + if (n >> 8) zeroes -= 8, n >>= 8; + if (n >> 4) zeroes -= 4, n >>= 4; + return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[n] + zeroes; +} + +static inline int CountLeadingZeros32(uint32_t n) { +#if defined(_MSC_VER) + unsigned long result = 0; // NOLINT(runtime/int) + if (_BitScanReverse(&result, n)) { + return 31 - result; + } + return 32; +#elif defined(__GNUC__) + + // Handle 0 as a special case because __builtin_clz(0) is undefined. + if (n == 0) { + return 32; + } + return __builtin_clz(n); +#else + return CountLeadingZeros32Slow(n); +#endif +} + +static inline int MostSignificantBit32(uint32_t n) { + return 32 - CountLeadingZeros32(n); +} + +static inline int CountLeadingZeros64Slow(uint64_t n) { + int zeroes = 60; + if (n >> 32) zeroes -= 32, n >>= 32; + if (n >> 16) zeroes -= 16, n >>= 16; + if (n >> 8) zeroes -= 8, n >>= 8; + if (n >> 4) zeroes -= 4, n >>= 4; + return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[n] + zeroes; +} + +static inline int CountLeadingZeros64(uint64_t n) { +#if defined(_MSC_VER) && defined(_M_X64) + // MSVC does not have __builtin_clzll. Use _BitScanReverse64. + unsigned long result = 0; // NOLINT(runtime/int) + if (_BitScanReverse64(&result, n)) { + return 63 - result; + } + return 64; +#elif defined(_MSC_VER) + // MSVC does not have __builtin_clzll. Compose two calls to _BitScanReverse + unsigned long result = 0; // NOLINT(runtime/int) + if ((n >> 32) && _BitScanReverse(&result, n >> 32)) { + return 31 - result; + } + if (_BitScanReverse(&result, n)) { + return 63 - result; + } + return 64; +#elif defined(__GNUC__) + + // Handle 0 as a special case because __builtin_clzll(0) is undefined. + if (n == 0) { + return 64; + } + return __builtin_clzll(n); +#else + return CountLeadingZeros64Slow(n); +#endif +} + +static inline int MostSignificantBit64(uint64_t n) { + return 64 - CountLeadingZeros64(n); +} + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h new file mode 100644 index 00000000..aaffa69d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +struct complex_int16_t { + int16_t real; + int16_t imag; +}; + +struct FftState { + int16_t* input; + struct complex_int16_t* output; + size_t fft_size; + size_t input_size; + void* scratch; + size_t scratch_size; +}; + +void FftCompute(struct FftState* state, const int16_t* input, + int input_scale_shift); + +void FftInit(struct FftState* state); + +void FftReset(struct FftState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h new file mode 100644 index 00000000..7a59af68 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ + +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" + +#ifdef __cplusplus +extern "C" { +#endif + +void FftWriteMemmapPreamble(FILE* fp, const struct FftState* state); +void FftWriteMemmap(FILE* fp, const struct FftState* state, + const char* variable); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h new file mode 100644 index 00000000..6a471301 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" + +#ifdef __cplusplus +extern "C" { +#endif + +// Prepares and FFT for the given input size. +int FftPopulateState(struct FftState* state, size_t input_size); + +// Frees any allocated buffers. +void FftFreeStateContents(struct FftState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h new file mode 100644 index 00000000..1e6d3885 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h @@ -0,0 +1,63 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ + +#include +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" + +#define kFilterbankBits 12 + +#ifdef __cplusplus +extern "C" { +#endif + +struct FilterbankState { + int num_channels; + int start_index; + int end_index; + int16_t* channel_frequency_starts; + int16_t* channel_weight_starts; + int16_t* channel_widths; + int16_t* weights; + int16_t* unweights; + uint64_t* work; +}; + +// Converts the relevant complex values of an FFT output into energy (the +// square magnitude). +void FilterbankConvertFftComplexToEnergy(struct FilterbankState* state, + struct complex_int16_t* fft_output, + int32_t* energy); + +// Computes the mel-scale filterbank on the given energy array. Output is cached +// internally - to fetch it, you need to call FilterbankSqrt. +void FilterbankAccumulateChannels(struct FilterbankState* state, + const int32_t* energy); + +// Applies an integer square root to the 64 bit intermediate values of the +// filterbank, and returns a pointer to them. Memory will be invalidated the +// next time FilterbankAccumulateChannels is called. +uint32_t* FilterbankSqrt(struct FilterbankState* state, int scale_down_shift); + +void FilterbankReset(struct FilterbankState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h new file mode 100644 index 00000000..5fc96845 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h @@ -0,0 +1,35 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ + +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" + +#ifdef __cplusplus +extern "C" { +#endif + +void FilterbankWriteMemmapPreamble(FILE* fp, + const struct FilterbankState* state); +void FilterbankWriteMemmap(FILE* fp, const struct FilterbankState* state, + const char* variable); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h new file mode 100644 index 00000000..781d1024 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct FilterbankConfig { + // number of frequency channel buckets for filterbank + int num_channels; + // maximum frequency to include + float upper_band_limit; + // minimum frequency to include + float lower_band_limit; + // unused + int output_scale_shift; +}; + +// Fills the frontendConfig with "sane" defaults. +void FilterbankFillConfigWithDefaults(struct FilterbankConfig* config); + +// Allocates any buffers. +int FilterbankPopulateState(const struct FilterbankConfig* config, + struct FilterbankState* state, int sample_rate, + int spectrum_size); + +// Frees any allocated buffers. +void FilterbankFreeStateContents(struct FilterbankState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h new file mode 100644 index 00000000..883df5fd --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h @@ -0,0 +1,64 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ + +#include +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" +#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" +#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" +#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" +#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h" +#include "tensorflow/lite/experimental/microfrontend/lib/window.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct FrontendState { + struct WindowState window; + struct FftState fft; + struct FilterbankState filterbank; + struct NoiseReductionState noise_reduction; + struct PcanGainControlState pcan_gain_control; + struct LogScaleState log_scale; +}; + +struct FrontendOutput { + const uint16_t* values; + size_t size; +}; + +// Main entry point to processing frontend samples. Updates num_samples_read to +// contain the number of samples that have been consumed from the input array. +// Returns a struct containing the generated output. If not enough samples were +// added to generate a feature vector, the returned size will be 0 and the +// values pointer will be NULL. Note that the output pointer will be invalidated +// as soon as FrontendProcessSamples is called again, so copy the contents +// elsewhere if you need to use them later. +struct FrontendOutput FrontendProcessSamples(struct FrontendState* state, + const int16_t* samples, + size_t num_samples, + size_t* num_samples_read); + +void FrontendReset(struct FrontendState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h new file mode 100644 index 00000000..0d59eda7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h @@ -0,0 +1,31 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +int WriteFrontendStateMemmap(const char* header, const char* source, + const struct FrontendState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h new file mode 100644 index 00000000..895ce6cd --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/fft_util.h" +#include "tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h" +#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h" +#include "tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h" +#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h" +#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h" +#include "tensorflow/lite/experimental/microfrontend/lib/window_util.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct FrontendConfig { + struct WindowConfig window; + struct FilterbankConfig filterbank; + struct NoiseReductionConfig noise_reduction; + struct PcanGainControlConfig pcan_gain_control; + struct LogScaleConfig log_scale; +}; + +// Fills the frontendConfig with "sane" defaults. +void FrontendFillConfigWithDefaults(struct FrontendConfig* config); + +// Allocates any buffers. +int FrontendPopulateState(const struct FrontendConfig* config, + struct FrontendState* state, int sample_rate); + +// Frees any allocated buffers. +void FrontendFreeStateContents(struct FrontendState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h new file mode 100644 index 00000000..33556dab --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h @@ -0,0 +1,48 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ + +// This header file should be included in all variants of kiss_fft_$type.{h,cc} +// so that their sub-included source files do not mistakenly wrap libc header +// files within their kissfft_$type namespaces. +// E.g, This header avoids kissfft_int16.h containing: +// namespace kiss_fft_int16 { +// #include "kiss_fft.h" +// } +// where kiss_fft_.h contains: +// #include +// +// TRICK: By including the following header files here, their preprocessor +// header guards prevent them being re-defined inside of the kiss_fft_$type +// namespaces declared within the kiss_fft_$type.{h,cc} sources. +// Note that the original kiss_fft*.h files are untouched since they +// may be used in libraries that include them directly. + +#include +#include +#include +#include +#include + +#ifdef FIXED_POINT +#include +#endif + +#ifdef USE_SIMD +#include +#endif +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h new file mode 100644 index 00000000..beee99aa --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h @@ -0,0 +1,33 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h" + +// Wrap 16-bit kiss fft in its own namespace. Enables us to link an application +// with different kiss fft resultions (16/32 bit interger, float, double) +// without getting a linker error. +#define FIXED_POINT 16 +namespace kissfft_fixed16 { +#include "kiss_fft.h" +#include "tools/kiss_fftr.h" +} // namespace kissfft_fixed16 +#undef FIXED_POINT +#undef kiss_fft_scalar +#undef KISS_FFT_H + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h new file mode 100644 index 00000000..b2448a32 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h @@ -0,0 +1,40 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +// Number of segments in the log lookup table. The table will be kLogSegments+1 +// in length (with some padding). +#define kLogSegments 128 +#define kLogSegmentsLog2 7 + +// Scale used by lookup table. +#define kLogScale 65536 +#define kLogScaleLog2 16 +#define kLogCoeff 45426 + +extern const uint16_t kLogLut[]; + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h new file mode 100644 index 00000000..a383f32f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h @@ -0,0 +1,39 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +struct LogScaleState { + int enable_log; + int scale_shift; +}; + +// Applies a fixed point logarithm to the signal and converts it to 16 bit. Note +// that the signal array will be modified. +uint16_t* LogScaleApply(struct LogScaleState* state, uint32_t* signal, + int signal_size, int correction_bits); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h new file mode 100644 index 00000000..9d447ac9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ + +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" + +#ifdef __cplusplus +extern "C" { +#endif + +void LogScaleWriteMemmap(FILE* fp, const struct LogScaleState* state, + const char* variable); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h new file mode 100644 index 00000000..11f7d9ee --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h @@ -0,0 +1,45 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ + +#include +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct LogScaleConfig { + // set to false (0) to disable this module + int enable_log; + // scale results by 2^(scale_shift) + int scale_shift; +}; + +// Populates the LogScaleConfig with "sane" default values. +void LogScaleFillConfigWithDefaults(struct LogScaleConfig* config); + +// Allocates any buffers. +int LogScalePopulateState(const struct LogScaleConfig* config, + struct LogScaleState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h new file mode 100644 index 00000000..46d3f52e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h @@ -0,0 +1,46 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ + +#define kNoiseReductionBits 14 + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +struct NoiseReductionState { + int smoothing_bits; + uint16_t even_smoothing; + uint16_t odd_smoothing; + uint16_t min_signal_remaining; + int num_channels; + uint32_t* estimate; +}; + +// Removes stationary noise from each channel of the signal using a low pass +// filter. +void NoiseReductionApply(struct NoiseReductionState* state, uint32_t* signal); + +void NoiseReductionReset(struct NoiseReductionState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h new file mode 100644 index 00000000..ded52118 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h @@ -0,0 +1,36 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ + +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" + +#ifdef __cplusplus +extern "C" { +#endif + +void NoiseReductionWriteMemmapPreamble(FILE* fp, + const struct NoiseReductionState* state); +void NoiseReductionWriteMemmap(FILE* fp, + const struct NoiseReductionState* state, + const char* variable); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h new file mode 100644 index 00000000..fa555391 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h @@ -0,0 +1,50 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct NoiseReductionConfig { + // scale the signal up by 2^(smoothing_bits) before reduction + int smoothing_bits; + // smoothing coefficient for even-numbered channels + float even_smoothing; + // smoothing coefficient for odd-numbered channels + float odd_smoothing; + // fraction of signal to preserve (1.0 disables this module) + float min_signal_remaining; +}; + +// Populates the NoiseReductionConfig with "sane" default values. +void NoiseReductionFillConfigWithDefaults(struct NoiseReductionConfig* config); + +// Allocates any buffers. +int NoiseReductionPopulateState(const struct NoiseReductionConfig* config, + struct NoiseReductionState* state, + int num_channels); + +// Frees any allocated buffers. +void NoiseReductionFreeStateContents(struct NoiseReductionState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h new file mode 100644 index 00000000..3f6222be --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h @@ -0,0 +1,47 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ + +#include +#include + +#define kPcanSnrBits 12 +#define kPcanOutputBits 6 + +#ifdef __cplusplus +extern "C" { +#endif + +// Details at https://research.google/pubs/pub45911.pdf +struct PcanGainControlState { + int enable_pcan; + uint32_t* noise_estimate; + int num_channels; + int16_t* gain_lut; + int32_t snr_shift; +}; + +int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut); + +uint32_t PcanShrink(const uint32_t x); + +void PcanGainControlApply(struct PcanGainControlState* state, uint32_t* signal); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h new file mode 100644 index 00000000..d4bfaa2e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h" + +#define kWideDynamicFunctionBits 32 +#define kWideDynamicFunctionLUTSize (4 * kWideDynamicFunctionBits - 3) + +#ifdef __cplusplus +extern "C" { +#endif + +struct PcanGainControlConfig { + // set to false (0) to disable this module + int enable_pcan; + // gain normalization exponent (0.0 disables, 1.0 full strength) + float strength; + // positive value added in the normalization denominator + float offset; + // number of fractional bits in the gain + int gain_bits; +}; + +void PcanGainControlFillConfigWithDefaults( + struct PcanGainControlConfig* config); + +int16_t PcanGainLookupFunction(const struct PcanGainControlConfig* config, + int32_t input_bits, uint32_t x); + +int PcanGainControlPopulateState(const struct PcanGainControlConfig* config, + struct PcanGainControlState* state, + uint32_t* noise_estimate, + const int num_channels, + const uint16_t smoothing_bits, + const int32_t input_correction_bits); + +void PcanGainControlFreeStateContents(struct PcanGainControlState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h new file mode 100644 index 00000000..bad81514 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h @@ -0,0 +1,49 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ + +#include +#include + +#define kFrontendWindowBits 12 + +#ifdef __cplusplus +extern "C" { +#endif + +struct WindowState { + size_t size; + int16_t* coefficients; + size_t step; + + int16_t* input; + size_t input_used; + int16_t* output; + int16_t max_abs_output_value; +}; + +// Applies a window to the samples coming in, stepping forward at the given +// rate. +int WindowProcessSamples(struct WindowState* state, const int16_t* samples, + size_t num_samples, size_t* num_samples_read); + +void WindowReset(struct WindowState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h new file mode 100644 index 00000000..a76b2dc3 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h @@ -0,0 +1,34 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ + +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/window.h" + +#ifdef __cplusplus +extern "C" { +#endif + +void WindowWriteMemmapPreamble(FILE* fp, const struct WindowState* state); +void WindowWriteMemmap(FILE* fp, const struct WindowState* state, + const char* variable); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h new file mode 100644 index 00000000..68e4de9e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h @@ -0,0 +1,45 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ +#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ + +#include "tensorflow/lite/experimental/microfrontend/lib/window.h" + +#ifdef __cplusplus +extern "C" { +#endif + +struct WindowConfig { + // length of window frame in milliseconds + size_t size_ms; + // length of step for next frame in milliseconds + size_t step_size_ms; +}; + +// Populates the WindowConfig with "sane" default values. +void WindowFillConfigWithDefaults(struct WindowConfig* config); + +// Allocates any buffers. +int WindowPopulateState(const struct WindowConfig* config, + struct WindowState* state, int sample_rate); + +// Frees any allocated buffers. +void WindowFreeStateContents(struct WindowState* state); + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h new file mode 100644 index 00000000..747ae42d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h @@ -0,0 +1,1358 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/runtime_shape.h" +#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK +#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK +#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK +#endif +#endif + +#include +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/core/macros.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/optimized/neon_check.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +constexpr int kReverseShift = -1; + +// Reduces and compresses dimensions so that broadcast handling becomes more +// efficient. Returns true if the output shape is broadcastable; it doesn't +// contain any degenerate dimension, i.e. shape dimension = 0. False otherwise. +template +bool ReduceDimensionsForBroadcast(const RuntimeShape& input1_shape, + const RuntimeShape& input2_shape, + size_t* compressed_input1_stride, + size_t* compressed_input2_stride, + size_t* compressed_output_shape) { + size_t num_compressed_dims = 0; + size_t compressed_input1_shape[MAX_DIM]; + size_t compressed_input2_shape[MAX_DIM]; + std::fill(compressed_input1_shape, compressed_input1_shape + MAX_DIM, 1); + std::fill(compressed_input2_shape, compressed_input2_shape + MAX_DIM, 1); + std::fill(compressed_output_shape, compressed_output_shape + MAX_DIM, 1); + bool broadcast_input1 = false; + bool broadcast_input2 = false; + bool first_nonunit = true; + const size_t num_input1_dims = input1_shape.DimensionsCount(); + const size_t num_input2_dims = input2_shape.DimensionsCount(); + const int32_t* input1_dims = input1_shape.DimsData(); + const int32_t* input2_dims = input2_shape.DimsData(); + const size_t num_common_dims = std::min(num_input1_dims, num_input2_dims); + for (size_t i = 1; i <= num_common_dims; i++) { + const size_t input1_dim = input1_dims[num_input1_dims - i]; + const size_t input2_dim = input2_dims[num_input2_dims - i]; + if (input1_dim == 0 || input2_dim == 0) { + return false; + } + if (input1_dim == 1 && input2_dim == 1) { + continue; + } + assert(!broadcast_input1 || !broadcast_input2); + + if (input1_dim == 1) { + if (!broadcast_input1) { + broadcast_input1 = true; + broadcast_input2 = false; + num_compressed_dims++; + } + compressed_input2_shape[num_compressed_dims - 1] *= input2_dim; + compressed_output_shape[num_compressed_dims - 1] *= input2_dim; + } else if (input2_dim == 1) { + if (!broadcast_input2) { + broadcast_input1 = false; + broadcast_input2 = true; + num_compressed_dims++; + } + compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; + compressed_output_shape[num_compressed_dims - 1] *= input1_dim; + } else { + TFLITE_DCHECK(input1_dim == input2_dim); + if (broadcast_input1 || broadcast_input2 || first_nonunit) { + broadcast_input1 = false; + broadcast_input2 = false; + num_compressed_dims++; + } + compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; + compressed_input2_shape[num_compressed_dims - 1] *= input1_dim; + compressed_output_shape[num_compressed_dims - 1] *= input1_dim; + } + first_nonunit = false; + } + if (num_input1_dims > num_input2_dims) { + if (!broadcast_input2) { + num_compressed_dims++; + } + for (size_t i = 0; i < num_input1_dims - num_input2_dims; i++) { + const size_t input1_dim = input1_dims[i]; + if (input1_dim == 0) { + return false; + } + compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; + compressed_output_shape[num_compressed_dims - 1] *= input1_dim; + } + } else if (num_input2_dims > num_input1_dims) { + if (!broadcast_input1) { + num_compressed_dims++; + } + for (size_t i = 0; i < num_input2_dims - num_input1_dims; i++) { + const size_t input2_dim = input2_dims[i]; + if (input2_dim == 0) { + return false; + } + compressed_input2_shape[num_compressed_dims - 1] *= input2_dim; + compressed_output_shape[num_compressed_dims - 1] *= input2_dim; + } + } + num_compressed_dims = (num_compressed_dims > 1) ? num_compressed_dims : 1; + + int input1_stride = 1; + int input2_stride = 1; + for (int i = 0; i < MAX_DIM; ++i) { + compressed_input1_stride[i] = input1_stride; + input1_stride *= compressed_input1_shape[i]; + compressed_input2_stride[i] = input2_stride; + input2_stride *= compressed_input2_shape[i]; + } + for (int i = 0; i < MAX_DIM; ++i) { + if (compressed_input1_shape[i] != compressed_input2_shape[i]) { + if (compressed_input1_shape[i] == 1) { + compressed_input1_stride[i] = 0; + } else { + TFLITE_DCHECK_EQ(compressed_input2_shape[i], 1); + compressed_input2_stride[i] = 0; + } + } + } + return true; +} + +inline void GetActivationMinMax(FusedActivationFunctionType ac, + float* output_activation_min, + float* output_activation_max) { + switch (ac) { + case FusedActivationFunctionType::kNone: + *output_activation_min = std::numeric_limits::lowest(); + *output_activation_max = std::numeric_limits::max(); + break; + case FusedActivationFunctionType::kRelu: + *output_activation_min = 0.f; + *output_activation_max = std::numeric_limits::max(); + break; + case FusedActivationFunctionType::kRelu1: + *output_activation_min = -1.f; + *output_activation_max = 1.f; + break; + case FusedActivationFunctionType::kRelu6: + *output_activation_min = 0.f; + *output_activation_max = 6.f; + break; + } +} + +template +inline T ActivationFunctionWithMinMax(T x, T output_activation_min, + T output_activation_max) { + using std::max; + using std::min; + return min(max(x, output_activation_min), output_activation_max); +} + +// Legacy function, left for compatibility only. +template +float ActivationFunction(float x) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + return ActivationFunctionWithMinMax(x, output_activation_min, + output_activation_max); +} + +inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, + const float* bias_data, int array_size, + float* array_data) { + if (bias_size == 0) return; + // Note: see b/132215220: in May 2019 we thought it would be OK to replace + // this with the Eigen one-liner: + // return (array.colwise() + bias).cwiseMin(clamp_max).cwiseMin(clamp_max). + // This turned out to severely regress performance: +4ms (i.e. 8%) on + // MobileNet v2 / 1.0 / 224. So we keep custom NEON code for now. + TFLITE_DCHECK_EQ((array_size % bias_size), 0); +#ifdef USE_NEON + float* array_ptr = array_data; + float* array_end_ptr = array_ptr + array_size; + const auto clamp_min_vec = vdupq_n_f32(clamp_min); + const auto clamp_max_vec = vdupq_n_f32(clamp_max); + for (; array_ptr != array_end_ptr; array_ptr += bias_size) { + int i = 0; + for (; i <= bias_size - 16; i += 16) { + auto b0 = vld1q_f32(bias_data + i); + auto b1 = vld1q_f32(bias_data + i + 4); + auto b2 = vld1q_f32(bias_data + i + 8); + auto b3 = vld1q_f32(bias_data + i + 12); + auto a0 = vld1q_f32(array_ptr + i); + auto a1 = vld1q_f32(array_ptr + i + 4); + auto a2 = vld1q_f32(array_ptr + i + 8); + auto a3 = vld1q_f32(array_ptr + i + 12); + auto x0 = vaddq_f32(a0, b0); + auto x1 = vaddq_f32(a1, b1); + auto x2 = vaddq_f32(a2, b2); + auto x3 = vaddq_f32(a3, b3); + x0 = vmaxq_f32(clamp_min_vec, x0); + x1 = vmaxq_f32(clamp_min_vec, x1); + x2 = vmaxq_f32(clamp_min_vec, x2); + x3 = vmaxq_f32(clamp_min_vec, x3); + x0 = vminq_f32(clamp_max_vec, x0); + x1 = vminq_f32(clamp_max_vec, x1); + x2 = vminq_f32(clamp_max_vec, x2); + x3 = vminq_f32(clamp_max_vec, x3); + vst1q_f32(array_ptr + i, x0); + vst1q_f32(array_ptr + i + 4, x1); + vst1q_f32(array_ptr + i + 8, x2); + vst1q_f32(array_ptr + i + 12, x3); + } + for (; i <= bias_size - 4; i += 4) { + auto b = vld1q_f32(bias_data + i); + auto a = vld1q_f32(array_ptr + i); + auto x = vaddq_f32(a, b); + x = vmaxq_f32(clamp_min_vec, x); + x = vminq_f32(clamp_max_vec, x); + vst1q_f32(array_ptr + i, x); + } + for (; i < bias_size; i++) { + array_ptr[i] = ActivationFunctionWithMinMax(array_ptr[i] + bias_data[i], + clamp_min, clamp_max); + } + } +#else // not NEON + for (int array_offset = 0; array_offset < array_size; + array_offset += bias_size) { + for (int i = 0; i < bias_size; i++) { + array_data[array_offset + i] = ActivationFunctionWithMinMax( + array_data[array_offset + i] + bias_data[i], clamp_min, clamp_max); + } + } +#endif +} + +// Single-rounding MultiplyByQuantizedMultiplier +#if TFLITE_SINGLE_ROUNDING +inline int32_t MultiplyByQuantizedMultiplier(int32_t x, + int32_t quantized_multiplier, + int shift) { + TFLITE_DCHECK(quantized_multiplier >= 0); + TFLITE_DCHECK(shift >= -31 && shift <= 30); + + const int64_t total_shift = 31 - shift; + const int64_t round = static_cast(1) << (total_shift - 1); + int64_t result = x * static_cast(quantized_multiplier) + round; + result = result >> total_shift; + + TFLITE_DCHECK(result >= std::numeric_limits::min() && + result <= std::numeric_limits::max()); + return static_cast(result); +} + +inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp( + int32_t x, int32_t quantized_multiplier, int shift) { + TFLITE_DCHECK_LE(shift, 0); + return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); +} + +inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne( + int32_t x, int32_t quantized_multiplier, int shift) { + TFLITE_DCHECK_GE(shift, 0); + return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); +} + +inline int32_t MultiplyByQuantizedMultiplier(int64_t x, + int32_t quantized_multiplier, + int shift) { + // Inputs: + // - quantized_multiplier has fixed point at bit 31 + // - shift is -31 to +7 (negative for right shift) + // + // Assumptions: The following input ranges are assumed + // - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1) + // - scaling is chosen so final scaled result fits in int32_t + // - input x is in the range -(1<<47) <= x < (1<<47) + TFLITE_DCHECK(quantized_multiplier >= 0); + TFLITE_DCHECK(shift >= -31 && shift < 8); + TFLITE_DCHECK(x >= -(static_cast(1) << 47) && + x < (static_cast(1) << 47)); + + const int32_t reduced_multiplier = + (quantized_multiplier < 0x7FFF0000) + ? ((quantized_multiplier + (1 << 15)) >> 16) + : 0x7FFF; + const int64_t total_shift = 15 - shift; + const int64_t round = static_cast(1) << (total_shift - 1); + int64_t result = x * static_cast(reduced_multiplier) + round; + result = result >> total_shift; + + TFLITE_DCHECK(result >= std::numeric_limits::min() && + result <= std::numeric_limits::max()); + return static_cast(result); +} + +#ifdef USE_NEON +inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows( + int32x4x4_t input_val, int32_t quantized_multiplier, int shift) { + TFLITE_DCHECK(quantized_multiplier >= 0); + + const int right_shift = std::min(-1, shift); + const int left_shift = shift - right_shift; + + const int32x4_t multiplier_dup = vdupq_n_s32(quantized_multiplier); + const int32x4_t left_shift_dup = vdupq_n_s32(left_shift); + const int32x4_t right_shift_dup = vdupq_n_s32(right_shift); + + int32x4x4_t result; + result.val[0] = vrshlq_s32( + vqdmulhq_s32(vshlq_s32(input_val.val[0], left_shift_dup), multiplier_dup), + right_shift_dup); + + result.val[1] = vrshlq_s32( + vqdmulhq_s32(vshlq_s32(input_val.val[1], left_shift_dup), multiplier_dup), + right_shift_dup); + + result.val[2] = vrshlq_s32( + vqdmulhq_s32(vshlq_s32(input_val.val[2], left_shift_dup), multiplier_dup), + right_shift_dup); + + result.val[3] = vrshlq_s32( + vqdmulhq_s32(vshlq_s32(input_val.val[3], left_shift_dup), multiplier_dup), + right_shift_dup); + + return result; +} +#endif // USE_NEON +// Double-rounding MultiplyByQuantizedMultiplier +#else +inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp( + int32_t x, int32_t quantized_multiplier, int left_shift) { + using gemmlowp::RoundingDivideByPOT; + using gemmlowp::SaturatingRoundingDoublingHighMul; + return RoundingDivideByPOT( + SaturatingRoundingDoublingHighMul(x, quantized_multiplier), -left_shift); +} + +inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne( + int32_t x, int32_t quantized_multiplier, int left_shift) { + using gemmlowp::SaturatingRoundingDoublingHighMul; + return SaturatingRoundingDoublingHighMul(x * (1 << left_shift), + quantized_multiplier); +} + +TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier( + int32_t x, int32_t quantized_multiplier, int shift); + +TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier( + int64_t x, int32_t quantized_multiplier, int shift); + +#ifdef USE_NEON +// Round uses ARM's rounding shift right. +inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows( + int32x4x4_t input_val, int32_t quantized_multiplier, int shift) { + const int left_shift = std::max(shift, 0); + const int right_shift = std::min(shift, 0); + int32x4x4_t result; + + int32x4_t multiplier_dup = vdupq_n_s32(quantized_multiplier); + int32x4_t left_shift_dup = vdupq_n_s32(left_shift); + int32x4_t right_shift_dup = vdupq_n_s32(right_shift); + + result.val[0] = + vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[0], left_shift_dup), + multiplier_dup), + right_shift_dup); + + result.val[1] = + vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[1], left_shift_dup), + multiplier_dup), + right_shift_dup); + + result.val[2] = + vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[2], left_shift_dup), + multiplier_dup), + right_shift_dup); + + result.val[3] = + vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[3], left_shift_dup), + multiplier_dup), + right_shift_dup); + + return result; +} +#endif // USE_NEON +#endif // TFLITE_SINGLE_ROUNDING + +template +int CountLeadingZeros(T integer_input) { + static_assert(std::is_unsigned::value, + "Only unsigned integer types handled."); + if (integer_input == 0) { + return std::numeric_limits::digits; + } +#if defined(__GNUC__) + if (std::is_same::value) { + return __builtin_clz(integer_input); + } else if (std::is_same::value) { + return __builtin_clzll(integer_input); + } +#endif + const T one_in_leading_positive = static_cast(1) + << (std::numeric_limits::digits - 1); + int leading_zeros = 0; + while (integer_input < one_in_leading_positive) { + integer_input <<= 1; + ++leading_zeros; + } + return leading_zeros; +} + +template +inline int CountLeadingSignBits(T integer_input) { + static_assert(std::is_signed::value, "Only signed integer types handled."); +#if defined(__GNUC__) && !defined(__clang__) + return integer_input ? __builtin_clrsb(integer_input) + : std::numeric_limits::digits; +#else + using U = typename std::make_unsigned::type; + return integer_input >= 0 + ? CountLeadingZeros(static_cast(integer_input)) - 1 + : integer_input != std::numeric_limits::min() + ? CountLeadingZeros(2 * static_cast(-integer_input) - 1) + : 0; +#endif +} + +// Use "count leading zeros" helper functions to do a fast Floor(log_2(x)). +template +inline Integer FloorLog2(Integer n) { + static_assert(std::is_integral::value, ""); + static_assert(std::is_signed::value, ""); + static_assert(sizeof(Integer) == 4 || sizeof(Integer) == 8, ""); + TFLITE_CHECK_GT(n, 0); + if (sizeof(Integer) == 4) { + return 30 - CountLeadingSignBits(n); + } else { + return 62 - CountLeadingSignBits(n); + } +} + +namespace detail { + +// LUTPopulate takes an optional type-erased transform_params to allow passing +// extra parameters to the transform function pointer. const void* is used +// instead of std::function to be compatible with TFLite Micro +template +inline typename std::enable_if::value, + FloatT>::type +LUTTransform(Func transform, const void* /*transform_params*/, FloatT value) { + static_assert(std::is_floating_point::value, + "FloatT must be a floating-point type."); + return transform(value); +} + +template +inline typename std::enable_if< + std::is_same::value, FloatT>::type +LUTTransform(Func transform, const void* transform_params, FloatT value) { + static_assert(std::is_floating_point::value, + "FloatT must be a floating-point type."); + return transform(value, transform_params); +} + +// Use the same LUT generation code for both uint8_t and int8_t. Int8_t indexes +// will be directly casted to uint8_t, the int8 LUT will thus be ordered as [0, +// 1, ..., 127, -128, ..., -2, -1] instead of [-128, -127, ..., -1, 0, 1, ..., +// 126, 127]. +template +inline void LUTPopulateInt8(float input_scale, int32_t input_zero_point, + float output_scale, int32_t output_zero_point, + Func transform, const void* transform_params, + T* lut) { + static_assert( + std::is_same::value || std::is_same::value, + "T must be an uint8 or int8 type."); + uint8_t* lut_uint8 = reinterpret_cast(lut); + const float inverse_scale = 1 / output_scale; + int32_t maxval = std::numeric_limits::max(); + int32_t minval = std::numeric_limits::min(); + for (int32_t val = minval; val <= maxval; ++val) { + const float dequantized = input_scale * (val - input_zero_point); + const float transformed = + LUTTransform(transform, transform_params, dequantized); + const float rescaled = TfLiteRound(transformed * inverse_scale); + const int32_t quantized = + static_cast(rescaled + output_zero_point); + lut_uint8[static_cast(static_cast(val))] = static_cast( + static_cast(std::max(std::min(maxval, quantized), minval))); + } +} + +// Keep floating-point type configurable for backward compatibility. float +// should be used for FloatT by default. +template +inline void LUTPopulateInt16(FloatT input_scale, int32_t input_zero_point, + FloatT output_scale, int32_t output_zero_point, + Func transform, const void* transform_params, + int16_t* lut) { + static_assert(std::is_floating_point::value, + "FloatT must be a floating-point type."); + const FloatT input_min = + input_scale * (std::numeric_limits::min() - input_zero_point); + const FloatT input_max = + input_scale * (std::numeric_limits::max() - input_zero_point); + const FloatT output_min = + output_scale * (std::numeric_limits::min() - output_zero_point); + const FloatT output_max = + output_scale * (std::numeric_limits::max() - output_zero_point); + + const int nb_steps = 512; + const FloatT step = (input_max - input_min) / nb_steps; + const FloatT half_step = step / 2; + const FloatT output_scaling_inv = + static_cast(std::numeric_limits::max() - + std::numeric_limits::min() + 1) / + (output_max - output_min); + const FloatT table_min = + static_cast(std::numeric_limits::min()); + const FloatT table_max = + static_cast(std::numeric_limits::max()); + + for (int i = 0; i < nb_steps; i++) { + const FloatT val = + LUTTransform(transform, transform_params, input_min + i * step); + const FloatT val_midpoint = LUTTransform( + transform, transform_params, input_min + i * step + half_step); + const FloatT val_next = LUTTransform(transform, transform_params, + input_min + (i + 1) * step); + + const FloatT sample_val = TfLiteRound(val * output_scaling_inv); + const FloatT midpoint_interp_val = + TfLiteRound((val_next * output_scaling_inv + + TfLiteRound(val * output_scaling_inv)) / + 2); + const FloatT midpoint_val = TfLiteRound(val_midpoint * output_scaling_inv); + const FloatT midpoint_err = midpoint_interp_val - midpoint_val; + const FloatT bias = TfLiteRound(midpoint_err / 2); + + lut[i] = static_cast(std::min( + std::max(sample_val - bias, table_min), table_max)); + } + + lut[nb_steps] = static_cast(std::min( + std::max(TfLiteRound(LUTTransform( + transform, transform_params, input_max) * + output_scaling_inv), + table_min), + table_max)); +} + +} // namespace detail + +template +inline typename std::enable_if::value || + std::is_same::value, + void>::type +LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, + int32_t output_zero_point, float (*transform)(float), T* lut) { + detail::LUTPopulateInt8(input_scale, input_zero_point, output_scale, + output_zero_point, transform, nullptr, lut); +} + +template +inline typename std::enable_if::value || + std::is_same::value, + void>::type +LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, + int32_t output_zero_point, float (*transform)(float, const void*), + const void* transform_params, T* lut) { + detail::LUTPopulateInt8(input_scale, input_zero_point, output_scale, + output_zero_point, transform, transform_params, lut); +} + +template +inline typename std::enable_if::value, void>::type +LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, + int32_t output_zero_point, float (*transform)(float), T* lut) { + detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, + output_zero_point, transform, nullptr, lut); +} + +template +inline typename std::enable_if::value, void>::type +LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, + int32_t output_zero_point, float (*transform)(float, const void*), + const void* transform_params, T* lut) { + detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, + output_zero_point, transform, + transform_params, lut); +} + +// Deprecated, avoid usage and prefer the float version. Kept for +// backward-compatiblity. +template +inline typename std::enable_if::value, void>::type +LUTPopulate(double input_scale, int32_t input_zero_point, double output_scale, + int32_t output_zero_point, double (*transform)(double), T* lut) { + detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, + output_zero_point, transform, nullptr, lut); +} + +// The size of the LUT depends on the type of input. For uint8 and int8 inputs a +// simple 256 entries LUT is used. For int16 inputs the high 9 bits are used for +// indexing and the 7 remaining bits are used for interpolation. We thus use a +// 513-entries LUT for int16 cases, 512 for the 9-bit indexing and 1 extra entry +// to interpolate the last value. +template +constexpr int LUTSize() { + static_assert(std::is_same::value || + std::is_same::value || + std::is_same::value, + "Only LUTs with uint8, int8 or int16 inputs are supported."); + // As per c++11: constexpr methods cannot have more than one return statement. + return (std::is_same::value || std::is_same::value) + ? 256 + : 513; +} + +// int16_t -> int16_t table lookup with interpolation +// LUT must have 513 values +inline int16_t LUTLookup(int16_t value, const int16_t* lut) { + // 512 base values, lut[513] is only used to calculate the slope + const uint16_t index = static_cast(256 + (value >> 7)); + assert(index < 512 && "LUT index out of range."); + const int16_t offset = value & 0x7f; + + // Base and slope are Q0.x + const int16_t base = lut[index]; + const int16_t slope = lut[index + 1] - lut[index]; + + // Q0.x * Q0.7 = Q0.(x + 7) + // Round and convert from Q0.(x + 7) to Q0.x + const int delta = (slope * offset + 64) >> 7; + + // Q0.15 + Q0.15 + return static_cast(base + delta); +} + +// int8_t -> int8_t table lookup without interpolation +// LUT must have 256 values +// LUTPopulate has ordered the LUT so that indexing it with an +// int8_t is just done by casting it to an uint8_t. +inline int8_t LUTLookup(int8_t value, const int8_t* lut) { + return lut[static_cast(value)]; +} + +// uint8_t -> uint8_t table lookup without interpolation +// LUT must have 256 values +inline uint8_t LUTLookup(uint8_t value, const uint8_t* lut) { + return lut[value]; +} + +// Table of sigmoid(i/24) at 0.16 format - 256 elements. + +// We use combined sigmoid and tanh look-up table, since +// tanh(x) = 2*sigmoid(2*x) -1. +// Both functions are symmetric, so the LUT table is only needed +// for the absolute value of the input. +static const uint16_t sigmoid_table_uint16[256] = { + 32768, 33451, 34133, 34813, 35493, 36169, 36843, 37513, 38180, 38841, 39498, + 40149, 40794, 41432, 42064, 42688, 43304, 43912, 44511, 45102, 45683, 46255, + 46817, 47369, 47911, 48443, 48964, 49475, 49975, 50464, 50942, 51409, 51865, + 52311, 52745, 53169, 53581, 53983, 54374, 54755, 55125, 55485, 55834, 56174, + 56503, 56823, 57133, 57433, 57724, 58007, 58280, 58544, 58800, 59048, 59288, + 59519, 59743, 59959, 60168, 60370, 60565, 60753, 60935, 61110, 61279, 61441, + 61599, 61750, 61896, 62036, 62172, 62302, 62428, 62549, 62666, 62778, 62886, + 62990, 63090, 63186, 63279, 63368, 63454, 63536, 63615, 63691, 63765, 63835, + 63903, 63968, 64030, 64090, 64148, 64204, 64257, 64308, 64357, 64405, 64450, + 64494, 64536, 64576, 64614, 64652, 64687, 64721, 64754, 64786, 64816, 64845, + 64873, 64900, 64926, 64950, 64974, 64997, 65019, 65039, 65060, 65079, 65097, + 65115, 65132, 65149, 65164, 65179, 65194, 65208, 65221, 65234, 65246, 65258, + 65269, 65280, 65291, 65301, 65310, 65319, 65328, 65337, 65345, 65352, 65360, + 65367, 65374, 65381, 65387, 65393, 65399, 65404, 65410, 65415, 65420, 65425, + 65429, 65433, 65438, 65442, 65445, 65449, 65453, 65456, 65459, 65462, 65465, + 65468, 65471, 65474, 65476, 65479, 65481, 65483, 65485, 65488, 65489, 65491, + 65493, 65495, 65497, 65498, 65500, 65501, 65503, 65504, 65505, 65507, 65508, + 65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517, 65517, 65518, + 65519, 65520, 65520, 65521, 65522, 65522, 65523, 65523, 65524, 65524, 65525, + 65525, 65526, 65526, 65526, 65527, 65527, 65528, 65528, 65528, 65529, 65529, + 65529, 65529, 65530, 65530, 65530, 65530, 65531, 65531, 65531, 65531, 65531, + 65532, 65532, 65532, 65532, 65532, 65532, 65533, 65533, 65533, 65533, 65533, + 65533, 65533, 65533, 65534, 65534, 65534, 65534, 65534, 65534, 65534, 65534, + 65534, 65534, 65535}; + +// TODO(b/77858996): Add these to gemmlowp. +template +IntegerType SaturatingAddNonGemmlowp(IntegerType a, IntegerType b) { + static_assert(std::is_same::value, "unimplemented"); + return a; +} + +template <> +inline std::int32_t SaturatingAddNonGemmlowp(std::int32_t a, std::int32_t b) { + std::int64_t a64 = a; + std::int64_t b64 = b; + std::int64_t sum = a64 + b64; + return static_cast(std::min( + static_cast(std::numeric_limits::max()), + std::max( + static_cast(std::numeric_limits::min()), + sum))); +} + +template +gemmlowp::FixedPoint SaturatingAddNonGemmlowp( + gemmlowp::FixedPoint a, + gemmlowp::FixedPoint b) { + return gemmlowp::FixedPoint::FromRaw( + SaturatingAddNonGemmlowp(a.raw(), b.raw())); +} + +template +IntegerType SaturatingSub(IntegerType a, IntegerType b) { + static_assert(std::is_same::value, "unimplemented"); + return a; +} + +template <> +inline std::int16_t SaturatingSub(std::int16_t a, std::int16_t b) { + std::int32_t a32 = a; + std::int32_t b32 = b; + std::int32_t diff = a32 - b32; + return static_cast( + std::min(static_cast(32767), + std::max(static_cast(-32768), diff))); +} + +template <> +inline std::int32_t SaturatingSub(std::int32_t a, std::int32_t b) { + std::int64_t a64 = a; + std::int64_t b64 = b; + std::int64_t diff = a64 - b64; + return static_cast(std::min( + static_cast(std::numeric_limits::max()), + std::max( + static_cast(std::numeric_limits::min()), + diff))); +} + +template +gemmlowp::FixedPoint SaturatingSub( + gemmlowp::FixedPoint a, + gemmlowp::FixedPoint b) { + return gemmlowp::FixedPoint::FromRaw( + SaturatingSub(a.raw(), b.raw())); +} +// End section to be moved to gemmlowp. + +template +IntegerType SaturatingRoundingMultiplyByPOTParam(IntegerType x, int exponent) { + if (exponent == 0) { + return x; + } + using ScalarIntegerType = + typename gemmlowp::FixedPointRawTypeTraits::ScalarRawType; + const IntegerType min = + gemmlowp::Dup(std::numeric_limits::min()); + const IntegerType max = + gemmlowp::Dup(std::numeric_limits::max()); + const int ScalarIntegerTypeBits = 8 * sizeof(ScalarIntegerType); + + const std::int32_t threshold = + ((1 << (ScalarIntegerTypeBits - 1 - exponent)) - 1); + const IntegerType positive_mask = + gemmlowp::MaskIfGreaterThan(x, gemmlowp::Dup(threshold)); + const IntegerType negative_mask = + gemmlowp::MaskIfLessThan(x, gemmlowp::Dup(-threshold)); + + IntegerType result = gemmlowp::ShiftLeft(x, exponent); + result = gemmlowp::SelectUsingMask(positive_mask, max, result); + result = gemmlowp::SelectUsingMask(negative_mask, min, result); + return result; +} + +// If we want to leave IntegerBits fixed, then multiplication +// by a power of two has to be saturating/rounding, not exact anymore. +template +gemmlowp::FixedPoint +SaturatingRoundingMultiplyByPOTParam( + gemmlowp::FixedPoint a, int exponent) { + return gemmlowp::FixedPoint::FromRaw( + SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); +} + +// Convert int32_t multiplier to int16_t with rounding. +inline void DownScaleInt32ToInt16Multiplier(int32_t multiplier_int32_t, + int16_t* multiplier_int16_t) { + TFLITE_DCHECK_GE(multiplier_int32_t, 0); + static constexpr int32_t kRoundingOffset = 1 << 15; + if (multiplier_int32_t >= + std::numeric_limits::max() - kRoundingOffset) { + *multiplier_int16_t = std::numeric_limits::max(); + return; + } + const int32_t result = (multiplier_int32_t + kRoundingOffset) >> 16; + TFLITE_DCHECK_LE(result << 16, multiplier_int32_t + kRoundingOffset); + TFLITE_DCHECK_GT(result << 16, multiplier_int32_t - kRoundingOffset); + *multiplier_int16_t = result; + TFLITE_DCHECK_EQ(*multiplier_int16_t, result); +} + +// Minimum output bits to accommodate log of maximum input range. It actually +// does not matter if one considers, say, [-64,64] or [-64,64). +// +// For example, run this through Octave: +// [0:127; ... +// ceil(log(abs( log(2.^(0:127))+1 ))/log(2)); ... +// ceil(log(abs( log(2.^(0:127))+1 ))/log(2))] +constexpr int min_log_x_output_bits(int input_bits) { + return input_bits > 90 ? 7 + : input_bits > 44 ? 6 + : input_bits > 21 ? 5 + : input_bits > 10 ? 4 + : input_bits > 4 ? 3 + : input_bits > 1 ? 2 + : 1; +} + +// Although currently the name of this function says that it cannot handle +// values less than 1, in practice it can handle as low as 1/x_max, where +// x_max is the largest representable input. In other words, the output range +// is symmetric. +template +inline gemmlowp::FixedPoint +log_x_for_x_greater_than_or_equal_to_1_impl( + gemmlowp::FixedPoint input_val) { + // assert(__builtin_clz(0u) >= std::numeric_limits::digits - 1); + // assert(__builtin_clz(0u) <= std::numeric_limits::digits); + using FixedPoint0 = gemmlowp::FixedPoint; + // The reason for accumulating the result with an extra bit of headroom is + // that z_pow_2_adj * log_2 might be saturated, and adding num_scaled * + // recip_denom will otherwise introduce an error. + static constexpr int kAccumIntegerBits = OutputIntegerBits + 1; + using FixedPointAccum = gemmlowp::FixedPoint; + + const FixedPoint0 log_2 = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 1488522236, std::log(2.0)); + const FixedPoint0 sqrt_sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 1805811301, std::sqrt(std::sqrt(0.5))); + const FixedPoint0 sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 1518500250, std::sqrt(0.5)); + const FixedPoint0 one_quarter = + GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(FixedPoint0, 536870912, 1.0 / 4.0); + + const FixedPoint0 alpha_n = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 117049297, 11.0 / 240.0 * std::sqrt(std::sqrt(2.0))); + const FixedPoint0 alpha_d = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 127690142, 1.0 / 20.0 * std::sqrt(std::sqrt(2.0))); + const FixedPoint0 alpha_i = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 1057819769, + 2.0 / std::sqrt(std::sqrt(2.0)) - std::sqrt(std::sqrt(2.0))); + const FixedPoint0 alpha_f = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( + FixedPoint0, 638450708, 1.0 / 4.0 * std::sqrt(std::sqrt(2.0))); + + const FixedPointAccum shifted_quarter = + gemmlowp::Rescale(one_quarter); + + // Reinterpret the input value as Q0.31, because we will figure out the + // required shift "ourselves" instead of using, say, Rescale. + FixedPoint0 z_a = FixedPoint0::FromRaw(input_val.raw()); + // z_a_pow_2 = input_integer_bits - z_a_headroom; + int z_a_headroom_plus_1 = CountLeadingZeros(static_cast(z_a.raw())); + FixedPoint0 r_a_tmp = + SaturatingRoundingMultiplyByPOTParam(z_a, (z_a_headroom_plus_1 - 1)); + const int32_t r_a_raw = + SaturatingRoundingMultiplyByPOTParam((r_a_tmp * sqrt_half).raw(), 1); + // z_pow_2_adj = max(z_pow_2_a - 0.75, z_pow_2_b - 0.25); + // z_pow_2_adj = max(InputIntegerBits - z_a_headroom_plus_1 + 0.25, + // InputIntegerBits - z_b_headroom - 0.25); + const FixedPointAccum z_a_pow_2_adj = SaturatingAddNonGemmlowp( + FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( + static_cast(InputIntegerBits - z_a_headroom_plus_1), + 31 - kAccumIntegerBits)), + shifted_quarter); + + // z_b is treated like z_a, but premultiplying by sqrt(0.5). + FixedPoint0 z_b = z_a * sqrt_half; + int z_b_headroom = CountLeadingZeros(static_cast(z_b.raw())) - 1; + const int32_t r_b_raw = + SaturatingRoundingMultiplyByPOTParam(z_a.raw(), z_b_headroom); + const FixedPointAccum z_b_pow_2_adj = SaturatingSub( + FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( + static_cast(InputIntegerBits - z_b_headroom), + 31 - kAccumIntegerBits)), + shifted_quarter); + + const FixedPoint0 r = FixedPoint0::FromRaw(std::min(r_a_raw, r_b_raw)); + const FixedPointAccum z_pow_2_adj = FixedPointAccum::FromRaw( + std::max(z_a_pow_2_adj.raw(), z_b_pow_2_adj.raw())); + + const FixedPoint0 p = gemmlowp::RoundingHalfSum(r, sqrt_sqrt_half); + FixedPoint0 q = r - sqrt_sqrt_half; + q = q + q; + + const FixedPoint0 common_sq = q * q; + const FixedPoint0 num = q * r + q * common_sq * alpha_n; + const FixedPoint0 denom_minus_one_0 = + p * (alpha_i + q + alpha_d * common_sq) + alpha_f * q; + const FixedPoint0 recip_denom = + one_over_one_plus_x_for_x_in_0_1(denom_minus_one_0); + + const FixedPointAccum num_scaled = gemmlowp::Rescale(num); + return gemmlowp::Rescale(z_pow_2_adj * log_2 + + num_scaled * recip_denom); +} + +template +inline gemmlowp::FixedPoint +log_x_for_x_greater_than_or_equal_to_1( + gemmlowp::FixedPoint input_val) { + static_assert( + OutputIntegerBits >= min_log_x_output_bits(InputIntegerBits), + "Output integer bits must be sufficient to accommodate logs of inputs."); + return log_x_for_x_greater_than_or_equal_to_1_impl( + input_val); +} + +inline int32_t GetReciprocal(int32_t x, int x_integer_digits, + int* num_bits_over_unit) { + int headroom_plus_one = CountLeadingZeros(static_cast(x)); + // This is the number of bits to the left of the binary point above 1.0. + // Consider x=1.25. In that case shifted_scale=0.8 and + // no later adjustment will be needed. + *num_bits_over_unit = x_integer_digits - headroom_plus_one; + const int32_t shifted_sum_minus_one = + static_cast((static_cast(x) << headroom_plus_one) - + (static_cast(1) << 31)); + + gemmlowp::FixedPoint shifted_scale = + gemmlowp::one_over_one_plus_x_for_x_in_0_1( + gemmlowp::FixedPoint::FromRaw(shifted_sum_minus_one)); + return shifted_scale.raw(); +} + +inline void GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift, + int32_t* output_inv_sqrt, + int* output_shift) { + TFLITE_DCHECK_GE(input, 0); + if (input <= 1) { + // Handle the input value 1 separately to avoid overflow in that case + // in the general computation below (b/143972021). Also handle 0 as if it + // were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid + // but rare/unrealistic input value. We can expect both to occur in some + // incompletely trained models, but probably not in fully trained models. + *output_inv_sqrt = std::numeric_limits::max(); + *output_shift = 0; + return; + } + TFLITE_DCHECK_GT(input, 1); + *output_shift = 11; + while (input >= (1 << 29)) { + input /= 4; + ++*output_shift; + } + const unsigned max_left_shift_bits = + CountLeadingZeros(static_cast(input)) - 1; + const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2; + const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1; + *output_shift -= left_shift_bit_pairs; + input <<= 2 * left_shift_bit_pairs; + TFLITE_DCHECK_GE(input, (1 << 27)); + TFLITE_DCHECK_LT(input, (1 << 29)); + using gemmlowp::FixedPoint; + using gemmlowp::Rescale; + using gemmlowp::SaturatingRoundingMultiplyByPOT; + // Using 3 integer bits gives us enough room for the internal arithmetic in + // this Newton-Raphson iteration. + using F3 = FixedPoint; + using F0 = FixedPoint; + const F3 fixedpoint_input = F3::FromRaw(input >> 1); + const F3 fixedpoint_half_input = + SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input); + const F3 fixedpoint_half_three = + GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5); + // Newton-Raphson iteration + // Naive unoptimized starting guess: x = 1 + F3 x = F3::One(); + // Naive unoptimized number of iterations: 5 + for (int i = 0; i < 5; i++) { + const F3 x3 = Rescale<3>(x * x * x); + x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3); + } + const F0 fixedpoint_half_sqrt_2 = + GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.); + x = x * fixedpoint_half_sqrt_2; + *output_inv_sqrt = x.raw(); + if (*output_shift < 0) { + *output_inv_sqrt <<= -*output_shift; + *output_shift = 0; + } + // Convert right shift (right is positive) to left shift. + *output_shift *= reverse_shift; +} + +// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// NdArrayDesc describes the shape and memory layout of an N-dimensional +// rectangular array of numbers. +// +// NdArrayDesc is basically identical to Dims defined in types.h. +// However, as Dims is to be deprecated, this class exists as an adaptor +// to enable simple unoptimized implementations of element-wise broadcasting +// operations. +template +struct NdArrayDesc { + // The "extent" of each dimension. Indices along dimension d must be in the + // half-open interval [0, extents[d]). + int extents[N]; + + // The number of *elements* (not bytes) between consecutive indices of each + // dimension. + int strides[N]; +}; + +// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// Same as Offset(), except takes as NdArrayDesc instead of Dims. +inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, + int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); + TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); + TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); + TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); + return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + + i3 * desc.strides[3]; +} + +inline int SubscriptToIndex(const NdArrayDesc<5>& desc, int indexes[5]) { + return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] + + indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] + + indexes[4] * desc.strides[4]; +} + +inline int SubscriptToIndex(const NdArrayDesc<8>& desc, int indexes[8]) { + return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] + + indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] + + indexes[4] * desc.strides[4] + indexes[5] * desc.strides[5] + + indexes[6] * desc.strides[6] + indexes[7] * desc.strides[7]; +} + +// Given the dimensions of the operands for an element-wise binary broadcast, +// adjusts them so that they can be directly iterated over with simple loops. +// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and +// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. +// +// This function assumes that the two input shapes are compatible up to +// broadcasting and the shorter one has already been prepended with 1s to be the +// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), +// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that +// Dims refer to shapes in reverse order. In this case, input0_dims will be +// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). +// +// When two shapes are compatible up to broadcasting, for each dimension d, +// the input extents are either equal, or one of them is 1. +// +// This function performs the following for each dimension d: +// - If the extents are equal, then do nothing since the loop that walks over +// both of the input arrays is correct. +// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 +// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows +// array0 to be referenced *at any index* in dimension d and still access the +// same slice. +template +inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, + const Dims& input1_dims, + NdArrayDesc* desc0_out, + NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + // Copy dims to desc. + for (int i = 0; i < N; ++i) { + desc0_out->extents[i] = input0_dims.sizes[i]; + desc0_out->strides[i] = input0_dims.strides[i]; + desc1_out->extents[i] = input1_dims.sizes[i]; + desc1_out->strides[i] = input1_dims.strides[i]; + } + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = ArraySize(input0_dims, i); + const int extent1 = ArraySize(input1_dims, i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + +// Copies dims to desc, calculating strides. +template +TFLITE_NOINLINE void CopyDimsToDesc(const RuntimeShape& input_shape, + NdArrayDesc* desc_out) { + int desc_stride = 1; + for (int i = N - 1; i >= 0; --i) { + desc_out->extents[i] = input_shape.Dims(i); + desc_out->strides[i] = desc_stride; + desc_stride *= input_shape.Dims(i); + } +} + +template +inline void NdArrayDescsForElementwiseBroadcast( + const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, + NdArrayDesc* desc0_out, NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); + auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); + + // Copy dims to desc, calculating strides. + CopyDimsToDesc(extended_input0_shape, desc0_out); + CopyDimsToDesc(extended_input1_shape, desc1_out); + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = extended_input0_shape.Dims(i); + const int extent1 = extended_input1_shape.Dims(i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + +template +inline void NdArrayDescsForElementwiseBroadcast( + const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, + const RuntimeShape& input2_shape, NdArrayDesc* desc0_out, + NdArrayDesc* desc1_out, NdArrayDesc* desc2_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + TFLITE_DCHECK(desc2_out != nullptr); + + auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); + auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); + auto extended_input2_shape = RuntimeShape::ExtendedShape(N, input2_shape); + + // Copy dims to desc, calculating strides. + CopyDimsToDesc(extended_input0_shape, desc0_out); + CopyDimsToDesc(extended_input1_shape, desc1_out); + CopyDimsToDesc(extended_input2_shape, desc2_out); + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = extended_input0_shape.Dims(i); + const int extent1 = extended_input1_shape.Dims(i); + const int extent2 = extended_input2_shape.Dims(i); + + int extent = extent0; + if (extent1 != 1) extent = extent1; + if (extent2 != 1) extent = extent2; + + TFLITE_DCHECK(extent0 == 1 || extent0 == extent); + TFLITE_DCHECK(extent1 == 1 || extent1 == extent); + TFLITE_DCHECK(extent2 == 1 || extent2 == extent); + + if (!(extent0 == extent1 && extent1 == extent2)) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent; + } + if (extent1 == 1) { + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent; + } + if (extent2 == 1) { + desc2_out->strides[i] = 0; + desc2_out->extents[i] = extent; + } + } + } +} + +// Detailed implementation of NDOpsHelper, the indexes must be a zero array. +// This implementation is equivalent to N nested loops. Ex, if N=4, it can be +// re-writen as: +// for (int b = 0; b < output.extents[0]; ++b) { +// for (int y = 0; y < output.extents[1]; ++y) { +// for (int x = 0; x < output.extents[2]; ++x) { +// for (int c = 0; c < output.extents[3]; ++c) { +// calc({b,y,x,c}); +// } +// } +// } +// } +template +typename std::enable_if::type NDOpsHelperImpl( + const NdArrayDesc& output, const Calc& calc, int indexes[N]) { + for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { + NDOpsHelperImpl(output, calc, indexes); + } +} + +template +typename std::enable_if::type NDOpsHelperImpl( + const NdArrayDesc& output, const Calc& calc, int indexes[N]) { + for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { + calc(indexes); + } +} + +// Execute the calc function in the innermost iteration based on the shape of +// the output. The calc function should take a single argument of type int[N]. +template +inline void NDOpsHelper(const NdArrayDesc& output, const Calc& calc) { + int indexes[N] = {0}; + NDOpsHelperImpl(output, calc, indexes); +} +// Copied from gemmlowp::RoundDown when we dropped direct dependency on +// gemmlowp. +// +// Returns the runtime argument rounded down to the nearest multiple of +// the fixed Modulus. +template +Integer RoundDown(Integer i) { + return i - (i % Modulus); +} + +// Copied from gemmlowp::RoundUp when we dropped direct dependency on +// gemmlowp. +// +// Returns the runtime argument rounded up to the nearest multiple of +// the fixed Modulus. +template +Integer RoundUp(Integer i) { + return RoundDown(i + Modulus - 1); +} + +// Copied from gemmlowp::CeilQuotient when we dropped direct dependency on +// gemmlowp. +// +// Returns the quotient a / b rounded up ('ceil') to the nearest integer. +template +Integer CeilQuotient(Integer a, Integer b) { + return (a + b - 1) / b; +} + +// This function is a copy of gemmlowp::HowManyThreads, copied when we dropped +// the direct dependency of internal/optimized/ on gemmlowp. +// +// It computes a reasonable number of threads to use for a GEMM of shape +// (rows, cols, depth). +// +// TODO(b/131910176): get rid of this function by switching each call site +// to its own more sensible logic for its own workload. +template +inline int LegacyHowManyThreads(int max_num_threads, int rows, int cols, + int depth) { + // Early-exit in the default case where multi-threading is disabled. + if (max_num_threads == 1) { + return 1; + } + + // Ensure that each thread has KernelRows rows to process, if at all possible. + int thread_count = std::min(max_num_threads, rows / KernelRows); + + // Limit the number of threads according to the overall size of the problem. + if (thread_count > 1) { + // Empirically determined value. + static constexpr std::uint64_t min_cubic_size_per_thread = 64 * 1024; + + // We can only multiply two out of three sizes without risking overflow + const std::uint64_t cubic_size = + std::uint64_t(rows) * std::uint64_t(cols) * std::uint64_t(depth); + + thread_count = std::min( + thread_count, static_cast(cubic_size / min_cubic_size_per_thread)); + } + + if (thread_count < 1) { + thread_count = 1; + } + + assert(thread_count > 0 && thread_count <= max_num_threads); + return thread_count; +} + +template +void optimized_ops_preload_l1_stream(const T* ptr) { +#ifdef __GNUC__ + // builtin offered by GCC-compatible compilers including clang + __builtin_prefetch(ptr, /* 0 means read */ 0, /* 0 means no locality */ 0); +#else + (void)ptr; +#endif +} + +template +void optimized_ops_preload_l1_keep(const T* ptr) { +#ifdef __GNUC__ + // builtin offered by GCC-compatible compilers including clang + __builtin_prefetch(ptr, /* 0 means read */ 0, /* 3 means high locality */ 3); +#else + (void)ptr; +#endif +} + +template +void optimized_ops_prefetch_write_l1_keep(const T* ptr) { +#ifdef __GNUC__ + // builtin offered by GCC-compatible compilers including clang + __builtin_prefetch(ptr, /* 1 means write */ 1, /* 3 means high locality */ 3); +#else + (void)ptr; +#endif +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h new file mode 100644 index 00000000..7ba66ed8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h @@ -0,0 +1,122 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ + +#include + +#include "tensorflow/lite/kernels/op_macros.h" + +#ifndef TFLITE_DCHECK +#define TFLITE_DCHECK(condition) (condition) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_EQ +#define TFLITE_DCHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_NE +#define TFLITE_DCHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_GE +#define TFLITE_DCHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_GT +#define TFLITE_DCHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_LE +#define TFLITE_DCHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +#ifndef TFLITE_DCHECK_LT +#define TFLITE_DCHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ASSERT_FALSE +#endif + +// TODO(ahentz): Clean up: We should stick to the DCHECK versions. +#ifndef TFLITE_CHECK +#define TFLITE_CHECK(condition) (condition) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_EQ +#define TFLITE_CHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_NE +#define TFLITE_CHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_GE +#define TFLITE_CHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_GT +#define TFLITE_CHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_LE +#define TFLITE_CHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TFLITE_CHECK_LT +#define TFLITE_CHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ABORT +#endif + +#ifndef TF_LITE_STATIC_MEMORY +// TODO(b/162019032): Consider removing these type-aliases. +using int8 = std::int8_t; +using uint8 = std::uint8_t; +using int16 = std::int16_t; +using uint16 = std::uint16_t; +using int32 = std::int32_t; +using uint32 = std::uint32_t; +#endif // !defined(TF_LITE_STATIC_MEMORY) + +// Allow for cross-compiler usage of function signatures - currently used for +// specifying named RUY profiler regions in templated methods. +#if defined(_MSC_VER) +#define TFLITE_PRETTY_FUNCTION __FUNCSIG__ +#elif defined(__GNUC__) +#define TFLITE_PRETTY_FUNCTION __PRETTY_FUNCTION__ +#else +#define TFLITE_PRETTY_FUNCTION __func__ +#endif + +// TFLITE_DEPRECATED() +// +// Duplicated from absl/base/macros.h to avoid pulling in that library. +// Marks a deprecated class, struct, enum, function, method and variable +// declarations. The macro argument is used as a custom diagnostic message (e.g. +// suggestion of a better alternative). +// +// Example: +// +// class TFLITE_DEPRECATED("Use Bar instead") Foo {...}; +// TFLITE_DEPRECATED("Use Baz instead") void Bar() {...} +// +// Every usage of a deprecated entity will trigger a warning when compiled with +// clang's `-Wdeprecated-declarations` option. This option is turned off by +// default, but the warnings will be reported by clang-tidy. +#if defined(__clang__) && __cplusplus >= 201103L +#define TFLITE_DEPRECATED(message) __attribute__((deprecated(message))) +#endif + +#ifndef TFLITE_DEPRECATED +#define TFLITE_DEPRECATED(message) +#endif + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h new file mode 100644 index 00000000..ee891753 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h @@ -0,0 +1,40 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ + +#include + +namespace tflite_micro { + +#if defined(TF_LITE_USE_GLOBAL_CMATH_FUNCTIONS) || \ + (defined(__ANDROID__) && !defined(__NDK_MAJOR__)) || defined(__ZEPHYR__) +#define TF_LITE_GLOBAL_STD_PREFIX +#else +#define TF_LITE_GLOBAL_STD_PREFIX std +#endif + +#define DECLARE_STD_GLOBAL_SWITCH1(tf_name, std_name) \ + template \ + inline T tf_name(const T x) { \ + return TF_LITE_GLOBAL_STD_PREFIX::std_name(x); \ + } + +DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round) +DECLARE_STD_GLOBAL_SWITCH1(TfLiteExpm1, expm1) + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h new file mode 100644 index 00000000..b8d8fc60 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h @@ -0,0 +1,35 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ + +#include + +namespace tflite_micro { + +#if defined(TF_LITE_USE_GLOBAL_MAX) || defined(__ZEPHYR__) +inline float TfLiteMax(const float& x, const float& y) { + return std::max(x, y); +} +#else +template +inline T TfLiteMax(const T& x, const T& y) { + return std::fmax(x, y); +} +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h new file mode 100644 index 00000000..76353545 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h @@ -0,0 +1,35 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ + +#include + +namespace tflite_micro { + +#if defined(TF_LITE_USE_GLOBAL_MIN) || defined(__ZEPHYR__) +inline float TfLiteMin(const float& x, const float& y) { + return std::min(x, y); +} +#else +template +inline T TfLiteMin(const T& x, const T& y) { + return std::fmin(x, y); +} +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h new file mode 100644 index 00000000..7df1129d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h @@ -0,0 +1,20 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ + +// TFLM does not need to utilize any Neon optimizations. + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h new file mode 100644 index 00000000..12fdc732 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h @@ -0,0 +1,141 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ + +#include +#include + +#include "tensorflow/lite/core/c/common.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +// A list of tensors in a format that can be used by kernels like split and +// concatenation. +template +class VectorOfTensors { + public: + // Build with the tensors in 'tensor_list'. + VectorOfTensors(const TfLiteContext& context, + const TfLiteIntArray& tensor_list) { + int num_tensors = tensor_list.size; + + all_data_.reserve(num_tensors); + all_shape_.reserve(num_tensors); + all_shape_ptr_.reserve(num_tensors); + + for (int i = 0; i < num_tensors; ++i) { + TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; + all_data_.push_back(GetTensorData(t)); + all_shape_.push_back(GetTensorShape(t)); + } + + // Taking the pointer from inside a std::vector is only OK if the vector is + // never modified, so we populate all_shape in the previous loop and then we + // are free to grab iterators here. + for (int i = 0; i < num_tensors; ++i) { + all_shape_ptr_.push_back(&all_shape_[i]); + } + } + + explicit VectorOfTensors(const std::vector& tensors) { + int num_tensors = tensors.size(); + + all_data_.reserve(num_tensors); + all_shape_.reserve(num_tensors); + all_shape_ptr_.reserve(num_tensors); + + for (auto* t : tensors) { + all_data_.push_back(GetTensorData(t)); + all_shape_.push_back(GetTensorShape(t)); + } + + // Taking the pointer from inside a std::vector is only OK if the vector is + // never modified, so we populate all_shape in the previous loop and then we + // are free to grab iterators here. + for (int i = 0; i < num_tensors; ++i) { + all_shape_ptr_.push_back(&all_shape_[i]); + } + } + // Return a pointer to the data pointers of all tensors in the list. For + // example: + // float* const* f = v.data(); + // f[0][1] is the second element of the first tensor. + T* const* data() const { return all_data_.data(); } + + // Return a pointer the shape pointers of all tensors in the list. For + // example: + // const RuntimeShape* const* d = v.dims(); + // dims[1] are the dimensions of the second tensor in the list. + const RuntimeShape* const* shapes() const { return all_shape_ptr_.data(); } + + size_t size() const { return all_data_.size(); } + + private: + std::vector all_data_; + std::vector all_shape_; + std::vector all_shape_ptr_; +}; + +// A list of quantized tensors in a format that can be used by kernels like +// split and concatenation. +class VectorOfQuantizedTensors : public VectorOfTensors { + public: + // Build with the tensors in 'tensor_list'. + VectorOfQuantizedTensors(const TfLiteContext& context, + const TfLiteIntArray& tensor_list) + : VectorOfTensors(context, tensor_list) { + for (int i = 0; i < tensor_list.size; ++i) { + TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; + zero_point_.push_back(t->params.zero_point); + scale_.push_back(t->params.scale); + } + } + + const float* scale() const { return scale_.data(); } + const int32_t* zero_point() const { return zero_point_.data(); } + + private: + std::vector zero_point_; + std::vector scale_; +}; + +// Writes randomly accessed values from `input` sequentially into `output`. +template +class SequentialTensorWriter { + public: + SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output) { + input_data_ = GetTensorData(input); + output_ptr_ = GetTensorData(output); + } + SequentialTensorWriter(const T* input_data, T* output_data) + : input_data_(input_data), output_ptr_(output_data) {} + + void Write(int position) { *output_ptr_++ = input_data_[position]; } + void WriteN(int position, int len) { + memcpy(output_ptr_, &input_data_[position], sizeof(T) * len); + output_ptr_ += len; + } + + private: + const T* input_data_; + T* output_ptr_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h new file mode 100644 index 00000000..4a27c146 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h @@ -0,0 +1,623 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ + +#include +#include +#include + +#include "tensorflow/lite/core/c/builtin_op_data.h" +#include "tensorflow/lite/core/c/common.h" + +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + +namespace tflite_micro { + +// Not all backends support CpuBackendContext usage, so forward declare to avoid +// pulling in its implementation. Use of CpuBackendContext in method +// implementations is purely optional. +class CpuBackendContext; + +namespace tensor_utils { + +// Multiplies a matrix with a scalar and reduce the result on each row to a +// scalar. +// Parameters: +// - matrix: matrix of size n_row * n_col +// - scalar: the scalar that is multiplied to each element in the matrix +// - n_row: the row count of the matrix +// - n_col: the column count of the matrix +// - output: the 32bit output +// Note: We do not need saturation because the int8 * int8 is safe from overflow +// in (2^31-1) / (2^14) = 131072, which is bigger than the n_row. Non-zero +// initial output value is not exceptionally large. +void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, + int32_t n_row, int32_t n_col, + int32_t* output); + +// Add another vector for each batch in the batch vector. +template +void VectorBatchVectorAdd(const T* vector, int v_size, int n_batch, + T* batch_vector) { + for (int b = 0; b < n_batch; b++) { + for (int i = 0; i < v_size; ++i) { + batch_vector[i] += vector[i]; + } + batch_vector += v_size; + } +} + +// Cwise product of two vectors. +template +inline void VectorVectorCwiseProduct(const T* vector1, const T* vector2, + int v_size, T* result) { + for (int v = 0; v < v_size; v++) { + *result++ = *vector1++ * *vector2++; + } +} + +// Cwise product of a vector and a batch-vector. +template +inline void VectorBatchVectorCwiseProduct(const T* vector, int v_size, + const T* batch_vector, int n_batch, + T* result) { + for (int b = 0; b < n_batch; b++) { + VectorVectorCwiseProduct(vector, batch_vector, v_size, result); + // Update the pointers. + result += v_size; + batch_vector += v_size; + } +} + +// Cwise product and accumulate of two vectors. Since it's a MAC operation, the +// assumption here is that result array is initialized to valid values. +template +inline void VectorVectorCwiseProductAccumulate(const T* __restrict__ vector1, + const T* __restrict__ vector2, + int v_size, + T* __restrict__ result) { + for (int v = 0; v < v_size; v++) { + *result++ += *vector1++ * *vector2++; + } +} + +// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC +// operation, the assumption here is that result array is initialized to valid +// values. +template +inline void VectorBatchVectorCwiseProductAccumulate(const T* vector, int v_size, + const T* batch_vector, + int n_batch, T* result) { + for (int b = 0; b < n_batch; b++) { + VectorVectorCwiseProductAccumulate(vector, batch_vector, v_size, result); + // Update the pointers. + result += v_size; + batch_vector += v_size; + } +} + +// Batch vector initialization with another vector. +template +void VectorBatchVectorAssign(const T* vector, int v_size, int n_batch, + T* batch_vector) { + for (int b = 0; b < n_batch; b++) { + std::copy_n(vector, v_size, batch_vector + b * v_size); + } +} + +// Checks if all entries of vector are zero for float. +bool IsZeroVector(const float* vector, int v_size); + +// Checks if all entries of vector are zero for int8. +bool IsZeroVector(const int8_t* vector, int v_size); + +// Quantizes a buffer of floating point values using a symmetric quantization +// (i.e. linear quantization without an offset) to 8-bit signed integers. +// It also outputs the range (min, max) of the floating point buffer, and the +// scaling factor used to quantize the values. +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); + +// Quantizes a buffer of floating point values using a symmetric quantization +// (i.e. linear quantization without an offset) to 8-bit signed integers. +// It uses the range (min, max) provided to the function to calculate the +// appropriate scaling factor to quantize the values. +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float min_value, + float max_value, float* scaling_factor); + +void AsymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* scaling_factor, + int32_t* offset); + +// Helper function to quantize floats. +// float_data_ptr input float vectors +// n_batch number of input vectors +// n_data size of a single input vector +// quantized_data_ptr (out) vector with quantized data +// scaling_factors (out) scaling factors (one per vector) +// zero_points (out) zero points (one per vector) +// do_asymmetric controls if the quantization should be asymmetric. +inline void BatchQuantizeFloats(const float* float_data_ptr, int n_batch, + int n_data, int8_t* quantized_data_ptr, + float* scaling_factors, int32_t* zero_points, + bool do_asymmetric) { + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_data; + if (do_asymmetric) { + tensor_utils::AsymmetricQuantizeFloats( + float_data_ptr + offset, n_data, quantized_data_ptr + offset, + &scaling_factors[b], &zero_points[b]); + } else { + float unused_min, unused_max; + tensor_utils::SymmetricQuantizeFloats( + float_data_ptr + offset, n_data, quantized_data_ptr + offset, + &unused_min, &unused_max, &scaling_factors[b]); + } + } +} + +// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch +// dimension composed by input vectors independent from each other). The result +// of the multiplication is accumulated to the passed result buffer. +// More specifically, for a matrix M of shape [n, i] and a batched-vector +// of shape [i, batch] it will first compute the product of shape [n, batch]. +// This product will be accumulated to the result buffer. +void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, + int m_cols, const float* vector, + int n_batch, float* result); + +// Same as the function above, but the matrix is a sparse tensor with block +// pattern 1x4. +// This function assumes that m_cols is a multiple of the block size (4 in this +// case) so that there's no incomplete block. +void SparseMatrixBatchVectorMultiplyAccumulate1x4( + const float* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const float* __restrict__ vector, int n_batch, float* __restrict__ result); + +// Same as the function above, but the matrix is stored in block compressed +// sparse row format with block pattern 1x16 which consists of two arrays: +// 1. A matrix array stores non-zero blocks of the matrix in row major. +// 2. A ledger array stores nrows groups, one group per row. Each group starts +// with an integer representing the number of non-zero blocks for the +// corresponding row and follows with column indexes of the first element +// of each non-zero block. +// This function assumes that +// 1. m_cols is a multiple of 16 so that all blocks are full blocks. +// 2. m_cols < 254 * 16 so that block index can be represented by uint8. +void SparseMatrixBatchVectorMultiplyAccumulate( + const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, + int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, + float* __restrict__ result); + +// Same as the function above, but for values quantized using symmetric +// quantization (e.g. by calling SymmetricQuantizeFloats). +// The passed scaling factors is a buffer of the quantization scaling factors +// that will be used to dequentize the products into the final result buffer. +// These scaling factors are the multiplication of the matrix scaling factor +// by the vector's scaling factor, one per batch (i.e. this allows quantizing +// each batch in the batch-vector matrix independently). +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, + const float* __restrict__ scaling_factors, int n_batch, + float* __restrict__ result); + +// Same as the function above except that vector values +// are quantized with asymmetric quantization per-batch and the matrix +// is quantized per row. +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, + const float* __restrict__ scaling_factors, int n_batch, + float* __restrict__ result, const float* __restrict__ per_channel_scale, + const int32_t* __restrict__ input_offset); + +// Same as the function above, but the matrix is a sparse tensor with block +// pattern 1x16. +// This function assumes that m_cols is a multiple of the block size (16 in this +// case) so that there's no incomplete block. Also, it assumes all offsets of +// input, output and filter are zero. +void SparseMatrixBatchVectorMultiplyAccumulate1x16( + const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, + int n_batch, const int32_t input_offset, const int32_t output_multiplier, + const int32_t output_shift, const int32_t output_offset, + const int32_t output_activation_min, const int32_t output_activation_max, + int8_t* __restrict__ result); + +// Same as the function above, but the matrix is stored in block compressed +// sparse row format with block pattern 1x16 which consists of two arrays: +// 1. A matrix array stores non-zero blocks of the matrix in row major. +// 2. A ledger array stores nrows groups, one group per row. Each group starts +// with an integer representing the number of non-zero blocks for the +// corresponding row followed by column index of the first element of +// each non-zero block. +// This function assumes that +// 1. m_cols is a multiple of 16 so that all blocks are full blocks. +// 2. m_cols < 254 * 16 so that block index can be represented by uint8. +void SparseMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const uint8_t* __restrict__ ledger, + const int m_rows, const int m_cols, const int8_t* __restrict__ vectors, + const float* __restrict__ scaling_factors, int n_batch, + float* __restrict__ result); + +// Same as the above 8, 8, 8 integer matmul except for the presence of zero +// point and non-accumulative. +// TODO(b/148688698): remove this function by folding zero point calculation in +// prepare() function. +void MatrixBatchVectorMultiply(const int8_t* input, int32_t input_zeropoint, + const int8_t* input_to_gate_weights, + int32_t input_to_gate_effective_scale_a, + int32_t input_to_gate_effective_scale_b, + int32_t n_batch, int32_t n_input, int32_t n_cell, + int8_t* gate_output, int8_t gate_output_zp); + +// Same as above but has 16 bit and 8 bit input and 8 bit output. +// Used in projection when hidden is 16bit. +void MatrixBatchVectorMultiply(const int16_t* hidden, + const int8_t* hidden_to_output_weights, + int32_t proj_effective_scale_a, + int32_t proj_effective_scale_b, + const int32_t* gate_bias, int32_t n_batch, + int32_t n_hidden, int32_t n_output, + int32_t output_zp, int8_t* proj_output); + +// Apply Layer Normalization (https://arxiv.org/abs/1607.06450) to a Quantized +// vector. +// Parameters: +// - input: batch vector of size n_batch * n_input; 16 bit. +// - layer_norm_weights: the quantized layer normalization weights. +// - bias: the bias for the layer normalization. +// - layer_norm_scale_a: multiplier for scale factor. +// - layer_norm_scale_b: shift for scale factor. +// - variance_limit: the guard to make sure the inverse does not overflow. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - output: the 16 bit output +void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, + const int32_t* bias, int32_t layer_norm_scale_a, + int32_t layer_norm_scale_b, int32_t variance_limit, + int n_batch, int n_input, int16_t* output); + +// Same as above but the internal calculation is done in float. +void ApplyLayerNormFloat(const int16_t* input, + const int16_t* layer_norm_weights, + int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, + const int32_t* bias, int n_batch, int n_input, + int16_t* output); + +// Apply Sigmoid to a quantized vector. +// Parameters: +// - input: batch vector of size n_batch * n_input; 16 bit. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - output: the 16 bit output +// The input is in Q3.12 format and the output is in Q0.15 format. +void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, + int16_t* output); + +// Same as above but the internal calcualtion is float. +void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input, + int16_t* output); + +// Apply Tanh to a quantized vector. +// Parameters: +// - integer_bits: the integer bits of the input. +// Currently supports 0, 1, 2, 3, 4, 5, 6. +// - input: batch vector of size n_batch * n_input; 16 bit. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - output: the 16 bit output +// The input is in Qm.15-m format and the output is in Q0.15 format. +void ApplyTanh(int32_t intger_bits, const int16_t* input, int32_t n_batch, + int32_t n_input, int16_t* output); + +// Apply Tanh to a quantized vector. Tbe internal calculation is in float. +// - Input has 2^(integer_bits) as scale. +// - Output has Q0.15 as scale. +void ApplyTanhFloat(const int16_t* input, int32_t n_batch, int32_t n_input, + int32_t integer_bits, int16_t* output); + +// Element-wise multiplication of two quantized vectors. +// Parameters: +// - input_1: batch vector of size n_batch * n_input; 16 bit. +// - input_2: batch vector of size n_batch * n_input; 16 bit. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - shift: the shift needed to produce the output. +// - output: the 16 bit output of size n_batch * n_input. +// Output does not need to be initialized. +void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int shift, int16_t* output); + +// Element-wise multiplication of two quantized vectors. +// Parameters: +// - input_1: batch vector of size n_batch * n_input; 16 bit. +// - input_2: batch vector of size n_batch * n_input; 16 bit. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - shift: the shift needed to produce the output. +// - output: the 8 bit output of size n_batch * n_input. +// Output does not need to be initialized. +void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int shift, int8_t* output); + +// Element-wise multiplication of two quantized vectors with rescaling. +// Parameters: +// - input_1: batch vector of size n_batch * n_input; 16 bit. +// - input_2: batch vector of size n_batch * n_input; 16 bit. +// - multiplier: the multiplier part of scale. +// - shift: the shift part of scale. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - output: the 8 bit output of size n_batch * n_input. +// - output_zp: the zero point of output. +// Output does not need to be initialized. +// Multiplier ("m") and shift ("s") are connected to scale ("s") with s = m * +// 2^(s - 31). +void CwiseMul(const int16_t* input_1, const int16_t* input_2, + int32_t multiplier, int32_t shift, int32_t n_batch, + int32_t n_input, int32_t output_zp, int8_t* output); + +// Element-wise saturating addition of two quantized vectors without rescaling. +// Parameters: +// - input_1: batch vector of size n_batch * n_input; 16 bit. +// - input_2: batch vector of size n_batch * n_input; 16 bit. +// - n_batch: the number of batches. +// - n_input: the size for input and output. +// - output: the 8 bit output of size n_batch * n_input. +// Output does not need to be initialized. +void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int16_t* output); + +// Element-wise in-place clipping of a vector. Overloaded for float, int16_t, +// int8_t. Parameters: +// - vector: vector of size v_size. +// - v_size: the size of the vector. +// - clipping_value: the value used for clipping. +void CwiseClipping(float* vector, const int v_size, const float clipping_value); +void CwiseClipping(int16_t* vector, const int v_size, + const int16_t clipping_value); +void CwiseClipping(int8_t* vector, const int v_size, + const int8_t clipping_value); + +// Dot product of two vectors. +float VectorVectorDotProduct(const float* vector1, const float* vector2, + int v_size); + +// Dot product of two batch vectors of size n_batch * v_size: +// vector1 = [x_1_1, x_1_2, ..., x_1_vsize, +// x_2_1, x_2_2, ..., x_2_vsize, +// ... +// x_nbatch_1,..., x_nbatch_vsize] +// vector2 = [y_1_1, y_1_2, ..., y_1_vsize, +// y_2_1, y_2_2, ..., y_2_vsize, +// ... +// y_nbatch_1,..., y_nbatch_vsize] +// Then result will be a vector of n_batch size starting from 'result': +// [x_1_1 * y_1_1 + x_1_2 * y_1_2 + ... + x_1_vsize * y_1_vsize, +// x_2_1 * y_2_1 + x_2_2 * y_2_2 + ... + x_2_vsize * y_2_vsize, +// ... +// x_nbatch_1 * y_nbatch_1 + ... + x_nbatch_vsize * y_nbatch_vsize] +template +inline void BatchVectorBatchVectorDotProduct(const T* vector1, const T* vector2, + int v_size, int n_batch, + T* result) { + for (int b = 0; b < n_batch; b++) { + result[b] = VectorVectorDotProduct(vector1, vector2, v_size); + vector1 += v_size; + vector2 += v_size; + } +} + +// Same as above but input is 16bit and output is 32bit. +void BatchVectorBatchVectorDotProduct(const int16_t* vector1, + const int16_t* vector2, int v_size, + int n_batch, int32_t* result); + +// Same as above, but inputs are 16bit integer and output is 16bit integer. +void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size, + const int16_t* batch_vector, + int n_batch, int32_t multiplier, + int shift, int16_t* result); + +// Compute "1.0f - elements of vector" (used in CIFG). +void Sub1Vector(const float* vector, int v_size, float* result); + +// Compute "1.0f - elements of vector" (used in CIFG) for int16 input. +// "vector" has range [0, 32767] because it is the output of sigmoid function. +void Sub1Vector(const int16_t* vector, int v_size, int16_t* result); + +// Reduce-sum on a float input vector: +// input_vector: float pointer to input vector. +// output_vector: float pointer to vector. +// output_size: output vector size. +// reduction_size: number of consecutive elements from input vector which are +// added to get one element of output. +void ReductionSumVector(const float* input_vector, float* output_vector, + int output_size, int reduction_size); + +// Same as above but input/output is 32 bit integer. +void ReductionSumVector(const int32_t* input_vector, int32_t* output_vector, + int output_size, int reduction_size); + +// Same as above but input is 8 bit integer. +void ReductionSumVector(const int8_t* input_vector, int32_t* output_vector, + int output_size, int reduction_size); + +// Multiply all elements of vector with a scalar. +void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); + +// Layer norm for each batch. +void MeanStddevNormalization(const float* input_vector, float* output_vector, + int v_size, int n_batch); + +// Saturate Add with rescale on both inputs. +void TwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, + const int8_t* recurrent, int8_t recurrent_zp, + int32_t input_effective_scale_a, + int32_t input_effective_scale_b, + int32_t recurrent_effective_scale_a, + int32_t recurrent_effective_scale_b, int32_t n_batch, + int32_t n_cell, int16_t* output); + +// Same as the function above, but provide a scratch buffer for the +// int8 x int8 -> int32 and a CpuBackendContext for the accumulator +// computation. +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, + const float* __restrict__ scaling_factors, int n_batch, + int32_t* __restrict__ scratch, float* __restrict__ result, + CpuBackendContext* __restrict__ context); + +// Same as the function above except that can make use of cached row sums. +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, const float* per_channel_scale, + const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, + bool* compute_row_sums, CpuBackendContext* context); + +// Same as the function above, but provides separate scaling factor for the +// matrix and the vectors. The scaling factors are multiplied in the +// scaling_factor_scratch buffer. +inline void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float matrix_scaling_factor, + const float* vector_scaling_factors, int n_batch, + float* __restrict__ result, const float* per_channel_scale, + const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, + bool* compute_row_sums, float* scaling_factor_scratch, + CpuBackendContext* context) { + for (int b = 0; b < n_batch; ++b) { + scaling_factor_scratch[b] = + vector_scaling_factors[b] * matrix_scaling_factor; + } + MatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, + scaling_factor_scratch, n_batch, result, + per_channel_scale, input_offset, scratch, + row_sums, compute_row_sums, context); +} + +// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch +// dimension composed by input vectors independent from each other). The result +// of the multiplication is accumulated to the passed result buffer. +// More specifically, for a matrix M of shape [n, i] and a batched-vector +// of shape [i, batch] it will first compute the product of shape [n, batch]. +// This product will be accumulated to the result buffer, +// Parameters: +// - input: batch vector of size n_batch * n_input +// - bias: vector of size b_input +// - input_to_gate_weights: matrix of size n_input * n_output +// - multiplier: scalar +// - shift: scalar +// - n_batch: the batch size +// - n_input: the input size +// - n_output: the output size +// - output_zp: the zero point of the output. +// - scratch: batch vector of size n_batch * n_output +// - output: the 16 bit output +// Notes: +// - this is used for gate matmul: for non-cifg it is for input, forget, +// cell, output gates; for cifg, it is for forget, cell, output gates. +// - multiplier and shift combined gives the scale. +// - assumes input zero point is 0. +// - scratch is created for optimization purpose only. +// TODO(b/152066492): this can be removed if some future optimization +// work makes it unnecessary. +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int16_t* output, CpuBackendContext* context); + +// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch +// dimension composed by input vectors independent from each other). The result +// of the multiplication is accumulated to the passed result buffer. +// More specifically, for a matrix M of shape [n, i] and a batched-vector +// of shape [i, batch] it will first compute the product of shape [n, batch]. +// This product will be accumulated to the result buffer, +// Parameters: +// - input: batch vector of size n_batch * n_input +// - bias: vector of size b_input +// - input_to_gate_weights: matrix of size n_input * n_output +// - multiplier: scalar +// - shift: scalar +// - n_batch: the batch size +// - n_input: the input size +// - n_output: the output size +// - output_zp: the zero point of the output. +// - scratch: batch vector of size n_batch * n_output +// - output: the 8 bit output +// Notes: +// - this is used for projection matmul. +// - multiplier and shift combined gives the scale. +// - assumes input zero point is 0. +// - scratch is created for optimization purpose only. +// TODO(b/152066492): this can be removed if some future optimization +// work makes it unnecessary. +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int8_t* output, CpuBackendContext* context); + +// Apply Rectified Linear to elements of a vector. +void ApplyReluToVector(const float* __restrict__ vector, int v_size, + float* __restrict__ result); + +// Apply Rectified Linear 1 (cap to [-1;1]) to elements of a vector +void ApplyRelu1ToVector(const float* __restrict__ vector, int v_size, + float* __restrict__ result); + +// Apply Rectified Linear 6 (cap to [0;6]) to elements of a vector +void ApplyRelu6ToVector(const float* __restrict__ vector, int v_size, + float* __restrict__ result); + +// Apply signbit to elements of a vector +void ApplySignbitToVector(const float* __restrict__ vector, int v_size, + float* __restrict__ result); + +// Unpack or inflate `src_buffer` by taking each element and splitting it as +// two elements into `dst_buffer`. +// Parameters: +// src_buffer : Densely packed buffer containing int4 values +// num_elements : Number of elements stored in the buffer. Note that this can +// be smaller than the size of `src_buffer` by 1 if it's odd, +// in which case the last nibble in `src_buffer` is ignored. +// This should be equal to the size of `dst_buffer`. +// dst_buffer : Buffer to unpack into. Should be allocated by the caller. +// Size should be at least `num_elements`. +// Notes: +// For example, given `src_buffer = {0x12, 0x34};`, calling this function +// will return `dst_buffer = {0x02, 0x01, 0x04, 0x03}`. +void UnpackDenseInt4IntoInt8(const int8_t* src_buffer, int num_elements, + int8_t* dst_buffer); + +} // namespace tensor_utils + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h new file mode 100644 index 00000000..902efbc0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h @@ -0,0 +1,292 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +// Given the min and max values of a float array, return +// reasonable quantization parameters to use for this array. +template +QuantizationParams ChooseQuantizationParams(double rmin, double rmax, + bool narrow_range) { + const T qmin = std::numeric_limits::min() + (narrow_range ? 1 : 0); + const T qmax = std::numeric_limits::max(); + const double qmin_double = qmin; + const double qmax_double = qmax; + // 0 should always be a representable value. Let's assume that the initial + // min,max range contains 0. + TFLITE_CHECK_LE(rmin, 0.); + TFLITE_CHECK_GE(rmax, 0.); + if (rmin == rmax) { + // Special case where the min,max range is a point. Should be {0}. + TFLITE_CHECK_EQ(rmin, 0.); + TFLITE_CHECK_EQ(rmax, 0.); + QuantizationParams quantization_params; + quantization_params.zero_point = 0; + quantization_params.scale = 0.; + return quantization_params; + } + + // General case. + // + // First determine the scale. + const double scale = (rmax - rmin) / (qmax_double - qmin_double); + + // Zero-point computation. + // First the initial floating-point computation. The zero-point can be + // determined from solving an affine equation for any known pair + // (real value, corresponding quantized value). + // We know two such pairs: (rmin, qmin) and (rmax, qmax). + // The arithmetic error on the zero point computed from either pair + // will be roughly machine_epsilon * (sum of absolute values of terms) + // so we want to use the variant that adds the smaller terms. + const double zero_point_from_min = qmin_double - rmin / scale; + const double zero_point_from_max = qmax_double - rmax / scale; + const double zero_point_from_min_error = + std::abs(qmin_double) + std::abs(rmin / scale); + const double zero_point_from_max_error = + std::abs(qmax_double) + std::abs(rmax / scale); + + const double zero_point_double = + zero_point_from_min_error < zero_point_from_max_error + ? zero_point_from_min + : zero_point_from_max; + + // Now we need to nudge the zero point to be an integer + // (our zero points are integer, and this is motivated by the requirement + // to be able to represent the real value "0" exactly as a quantized value, + // which is required in multiple places, for example in Im2col with SAME + // padding). + T nudged_zero_point = 0; + if (zero_point_double < qmin_double) { + nudged_zero_point = qmin; + } else if (zero_point_double > qmax_double) { + nudged_zero_point = qmax; + } else { + nudged_zero_point = static_cast(round(zero_point_double)); + } + // The zero point should always be in the range of quantized value, + // [qmin, qmax]. + TFLITE_CHECK_GE(nudged_zero_point, qmin); + TFLITE_CHECK_LE(nudged_zero_point, qmax); + + // Finally, store the result nudged quantization params. + QuantizationParams quantization_params; + quantization_params.zero_point = nudged_zero_point; + quantization_params.scale = scale; + return quantization_params; +} + +template +QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { + return ChooseQuantizationParams(rmin, rmax, false); +} + +// Converts a floating-point number to an integer. For all inputs x where +// static_cast(x) is legal according to the C++ standard, the result +// is identical to that cast (i.e. the result is x with its fractional part +// truncated whenever that is representable as IntOut). +// +// static_cast would cause undefined behavior for the following cases, which +// have well-defined behavior for this function: +// +// 1. If x is NaN, the result is zero. +// +// 2. If the truncated form of x is above the representable range of IntOut, +// the result is std::numeric_limits::max(). +// +// 3. If the truncated form of x is below the representable range of IntOut, +// the result is std::numeric_limits::min(). +// +// Note that cases #2 and #3 cover infinities as well as finite numbers. +// +// The range of FloatIn must include the range of IntOut, otherwise +// the results are undefined. +// TODO(sfeuz): Replace by absl::SafeCast once available. +template +IntOut SafeCast(FloatIn x) { + static_assert(!std::numeric_limits::is_integer, + "FloatIn is integer"); + static_assert(std::numeric_limits::is_integer, + "IntOut is not integer"); + static_assert(std::numeric_limits::radix == 2, "IntOut is base 2"); + + // Special case NaN, for which the logic below doesn't work. + if (std::isnan(x)) { + return 0; + } + + // Negative values all clip to zero for unsigned results. + if (!std::numeric_limits::is_signed && x < 0) { + return 0; + } + + // Handle infinities. + if (std::isinf(x)) { + return x < 0 ? std::numeric_limits::min() + : std::numeric_limits::max(); + } + + // Set exp such that x == f * 2^exp for some f with |f| in [0.5, 1.0), + // unless x is zero in which case exp == 0. Note that this implies that the + // magnitude of x is strictly less than 2^exp. + int exp = 0; + std::frexp(x, &exp); + + // Let N be the number of non-sign bits in the representation of IntOut. If + // the magnitude of x is strictly less than 2^N, the truncated version of x + // is representable as IntOut. The only representable integer for which this + // is not the case is kMin for signed types (i.e. -2^N), but that is covered + // by the fall-through below. + if (exp <= std::numeric_limits::digits) { + return x; + } + + // Handle numbers with magnitude >= 2^N. + return x < 0 ? std::numeric_limits::min() + : std::numeric_limits::max(); +} + +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of NEGATIVE its exponent --- +// this is intended as a RIGHT-shift. +// +// Restricted to the case where the multiplier < 1 (and non-negative). +void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, + int32_t* quantized_multiplier, + int* left_shift); + +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of its exponent. +// +// Restricted to the case where the multiplier > 1. +void QuantizeMultiplierGreaterThanOne(double double_multiplier, + int32_t* quantized_multiplier, + int* left_shift); + +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of its exponent. +// +// Handles an arbitrary positive multiplier. The 'shift' output-value is +// basically the 'floating-point exponent' of the multiplier: +// Negative for a right-shift (when the multiplier is <1), positive for a +// left-shift (when the multiplier is >1) +void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, + int* shift); + +// Splits a double input value into a returned fraction, and a shift value from +// the exponent, using only bitwise and integer operations to support +// microcontrollers and other environments without floating-point support. +// +// This is designed to be a replacement for how std::frexp() is used within the +// QuantizeMultiplier() function, and so has a different signature than the +// standard version, returning a 64-bit integer rather than a double. This +// result has a maximum value of 1<<31, with the fraction expressed as a +// proportion of that maximum. +// +// std::frexp() returns NaNs and infinities unmodified, but since we're +// returning integers that can't represent those values, instead we return +// a shift of std::numeric_limits::max() for all bad numbers, with an int64 +// result of 0 for NaNs, std:numeric_limits::max() for +INFINITY, and +// std::numeric_limits::min() for -INFINITY. Denormalized inputs will +// result in return values that end up truncating some bits at the end, +// reflecting the loss of precision inherent in denormalization. +int64_t IntegerFrExp(double input, int* shift); + +// Converts an integer fraction in the format produced by IntegerFrExp (where +// 0x40000000 is 1.0) and an exponent shift (between -1022 and +1022) into an +// IEEE binary64 double format result. The implementation uses only integer and +// bitwise operators, so no floating point hardware support or emulation is +// needed. This is here so quantized operations can run non-time-critical +// preparation calculations on microcontrollers and other platforms without +// float support. +double DoubleFromFractionAndShift(int64_t fraction, int shift); + +// Performs a multiplication of two numbers in double format, using only integer +// and bitwise instructions. This is aimed at supporting housekeeping functions +// for quantized operations on microcontrollers without floating-point hardware. +double IntegerDoubleMultiply(double a, double b); + +// Returns -1 if a is less than b, 0 if a and b are equal, and +1 if a is +// greater than b. It is implemented using only integer and logical instructions +// so that it can be easily run on microcontrollers for quantized operations. +int IntegerDoubleCompare(double a, double b); + +// This first creates a multiplier in a double equivalent of +// Q(input_integer_bits).(31-input_integer_bits) representation, with extra +// precision in the double's fractional bits. It then splits the result into +// significand and exponent. +void PreprocessSoftmaxScaling(double beta, double input_scale, + int input_integer_bits, + int32_t* quantized_multiplier, int* left_shift); +// Like PreprocessSoftmaxScaling, but inverse scaling factors also calculated. +void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, + int input_integer_bits, + int32_t* quantized_multiplier, + int* left_shift, + int32_t* reverse_scaling_divisor, + int* reverse_scaling_left_shift); +// Calculate the largest input that will result in a within-bounds intermediate +// result within MultiplyByQuantizedMultiplierGreaterThanOne. In other words, +// it must not overflow before we reduce the value by multiplication by the +// input multiplier. The negative radius is used as the minimum difference in +// Softmax. +int CalculateInputRadius(int input_integer_bits, int input_left_shift, + int total_signed_bits = 31); + +// Nudges a min/max quantization range to ensure zero is zero. +// Gymnastics with nudged zero point is to ensure that real zero maps to +// an integer, which is required for e.g. zero-padding in convolutional layers. +// Outputs nudged_min, nudged_max, nudged_scale. +void NudgeQuantizationRange(const float min, const float max, + const int quant_min, const int quant_max, + float* nudged_min, float* nudged_max, + float* nudged_scale); + +// Fake quantizes (quantizes and dequantizes) input_data using the scale, +// nudged_min, and nudged_max from NudgeQuantizationRange. This matches the code +// in TensorFlow's FakeQuantizeWithMinMaxVarsFunctor. +void FakeQuantizeArray(const float nudged_scale, const float nudged_min, + const float nudged_max, const float* input_data, + float* output_data, const float size); + +// If x is approximately a power of two (with any positive or negative +// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise +// returns false. +bool CheckedLog2(const float x, int* log2_result); + +// Decomposes an array of double multipliers into a Q0.31 int32 representation +// of its significand, and shift representation of its exponent. +// +// Handles an arbitrary multiplier. The 'shift' output-value is +// basically the 'floating-point exponent' of the multiplier: +// Negative for a right-shift (when the multiplier is <1), positive for a +// left-shift (when the multiplier is >1) +void QuantizeMultiplierArray(const double* effective_scales, size_t size, + int32_t* effective_scale_significand, + int* effective_shift); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h new file mode 100644 index 00000000..63a6c47b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h @@ -0,0 +1,561 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ + +#include +#include +#include +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + T activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] + input2_data[i], activation_min, activation_max); + } +} + +// Element-wise add that can often be used for inner loop of broadcast add as +// well as the non-broadcast add. + +// This function is used for 8-bit as well as for 16-bit, but the accumulator +// is 32-bit for both cases. The overflow does not happen due to the +// choice of the shift (20 or 15, accordingly - see add.cc for more comments). +template +inline void AddElementwise(int size, const ArithmeticParams& params, + const T* input1_data, const T* input2_data, + T* output_data) { + TFLITE_DCHECK_GT(params.input1_offset, -std::numeric_limits::max()); + TFLITE_DCHECK_GT(params.input2_offset, -std::numeric_limits::max()); + TFLITE_DCHECK_LT(params.input1_offset, std::numeric_limits::max()); + TFLITE_DCHECK_LT(params.input2_offset, std::numeric_limits::max()); + + for (int i = 0; i < size; ++i) { + const int32_t input1_val = params.input1_offset + input1_data[i]; + const int32_t input2_val = params.input2_offset + input2_data[i]; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32_t raw_sum = scaled_input1_val + scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); + } +} + +// Scalar-broadcast add that can be used for inner loop of more general +// broadcast add, so that, for example, scalar-broadcast with batch will still +// be fast. +inline void AddScalarBroadcast(int size, const ArithmeticParams& params, + uint8_t input1_data, const uint8_t* input2_data, + uint8_t* output_data) { + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + + const int32_t input1_val = params.input1_offset + input1_data; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + for (int i = 0; i < size; ++i) { + const int32_t input2_val = params.input2_offset + input2_data[i]; + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32_t raw_sum = scaled_input1_val + scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); + } +} + +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8_t* input1_data, + const RuntimeShape& input2_shape, const uint8_t* input2_data, + const RuntimeShape& output_shape, uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void AddGeneralParamScale(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int16_t* input1_data, + const RuntimeShape& input2_shape, + const int16_t* input2_data, + const RuntimeShape& output_shape, + int16_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + int max_value = std::numeric_limits::max(); + + TFLITE_DCHECK_GT(params.input1_offset, -max_value); + TFLITE_DCHECK_GT(params.input2_offset, -max_value); + TFLITE_DCHECK_LT(params.input1_offset, max_value); + TFLITE_DCHECK_LT(params.input2_offset, max_value); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16_t* input1_data, + const RuntimeShape& input2_shape, const int16_t* input2_data, + const RuntimeShape& output_shape, int16_t* output_data, + bool pot_scale = true) { + if (!pot_scale) { + AddGeneralParamScale(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); + return; + } + + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int input1_shift = params.input1_shift; + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + const int16_t output_activation_min = params.quantized_activation_min; + const int16_t output_activation_max = params.quantized_activation_max; + + TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); + TFLITE_DCHECK_LE(input1_shift, 0); + TFLITE_DCHECK_LE(params.input2_shift, 0); + const int16_t* not_shift_input = + input1_shift == 0 ? input1_data : input2_data; + const int16_t* shift_input = input1_shift == 0 ? input2_data : input1_data; + const int input_right_shift = + input1_shift == 0 ? -params.input2_shift : -input1_shift; + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); + F0 scaled_input = F0::FromRaw( + gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); + F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); + const int16_t raw_output = result.raw(); + const int16_t clamped_output = std::min( + output_activation_max, std::max(output_activation_min, raw_output)); + output_data[i] = clamped_output; + } +} + +template +inline void AddBroadcast(const T* input_data, const T* broadcast_data, + T* output_data, size_t size, T activation_min, + T activation_max) { + for (size_t c = 0; c < size; ++c) { + output_data[c] = ActivationFunctionWithMinMax( + input_data[c] + broadcast_data[0], activation_min, activation_max); + } +} + +template <> +inline void AddBroadcast(const int32_t* input_data, + const int32_t* broadcast_data, + int32_t* output_data, size_t size, + int32_t activation_min, + int32_t activation_max) { + size_t c = 0; +#ifdef USE_NEON + const int32x4_t vmax = vdupq_n_s32(activation_max); + const int32x4_t vmin = vdupq_n_s32(activation_min); + const int32x4_t vb = vdupq_n_s32(broadcast_data[0]); + for (; c + 4 <= size; c += 4) { + const int32x4_t va = vld1q_s32(&input_data[c]); + int32x4_t vres = vaddq_s32(va, vb); + vres = vmaxq_s32(vmin, vres); + vres = vminq_s32(vmax, vres); + vst1q_s32(&output_data[c], vres); + } +#endif + for (; c < size; ++c) { + output_data[c] = ActivationFunctionWithMinMax( + input_data[c] + broadcast_data[0], activation_min, activation_max); + } +} + +template +void AddElementwise(const T* input1_data, const T* input2_data, T* output_data, + size_t size, T activation_min, T activation_max) { + for (size_t c = 0; c < size; ++c) { + output_data[c] = ActivationFunctionWithMinMax( + input1_data[c] + input2_data[c], activation_min, activation_max); + } +} + +template <> +inline void AddElementwise(const int32_t* input1_data, + const int32_t* input2_data, + int32_t* output_data, size_t size, + int32_t activation_min, + int32_t activation_max) { + size_t c = 0; +#ifdef USE_NEON + const int32x4_t vmax = vdupq_n_s32(activation_max); + const int32x4_t vmin = vdupq_n_s32(activation_min); + for (; c + 4 <= size; c += 4) { + const int32x4_t va = vld1q_s32(&input1_data[c]); + const int32x4_t vb = vld1q_s32(&input2_data[c]); + int32x4_t vres = vaddq_s32(va, vb); + vres = vmaxq_s32(vmin, vres); + vres = vminq_s32(vmax, vres); + vst1q_s32(&output_data[c], vres); + } +#endif + for (; c < size; ++c) { + output_data[c] = ActivationFunctionWithMinMax( + input1_data[c] + input2_data[c], activation_min, activation_max); + } +} + +template +inline void BroadcastAddRecursiveDimensions( + int dimension, size_t* input1_offset_p, size_t* input2_offset_p, + size_t* output_offset, size_t* compressed_input1_stride, + size_t* compressed_input2_stride, size_t* compressed_output_shape, + T activation_min, T activation_max, const T* input1_data, + const T* input2_data, T* output_data) { + if (dimension > 0) { + for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { + size_t input1_offset_c = *input1_offset_p; + size_t input2_offset_c = *input2_offset_p; + BroadcastAddRecursiveDimensions( + dimension - 1, &input1_offset_c, &input2_offset_c, output_offset, + compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, activation_min, activation_max, input1_data, + input2_data, output_data); + *input1_offset_p += compressed_input1_stride[dimension]; + *input2_offset_p += compressed_input2_stride[dimension]; + } + } else { + TFLITE_DCHECK(dimension == 0); + bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; + bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; + TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); + const T* input1_data_ptr = input1_data + *input1_offset_p; + const T* input2_data_ptr = input2_data + *input2_offset_p; + T* output_data_ptr = output_data + *output_offset; + if (input1_is_broadcast) { + // input1 is broadcast. + AddBroadcast(input2_data_ptr, input1_data_ptr, output_data_ptr, + compressed_output_shape[dimension], activation_min, + activation_max); + *input2_offset_p += compressed_output_shape[dimension]; + } else if (input2_is_broadcast) { + // input2 is broadcast. + AddBroadcast(input1_data_ptr, input2_data_ptr, output_data_ptr, + compressed_output_shape[dimension], activation_min, + activation_max); + *input1_offset_p += compressed_output_shape[dimension]; + } else { + // Add element-wise. + AddElementwise(input1_data_ptr, input2_data_ptr, output_data_ptr, + compressed_output_shape[dimension], activation_min, + activation_max); + *input1_offset_p += compressed_output_shape[dimension]; + *input2_offset_p += compressed_output_shape[dimension]; + } + *output_offset += compressed_output_shape[dimension]; + } +} + +template +inline typename std::enable_if::value || dummy, void>::type +BroadcastAdd6DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + constexpr int kMaxBroadcastDim = 6; + T activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + size_t compressed_input1_stride[kMaxBroadcastDim]; + size_t compressed_input2_stride[kMaxBroadcastDim]; + size_t compressed_output_shape[kMaxBroadcastDim]; + bool broadcastable_shape = ReduceDimensionsForBroadcast( + input1_shape, input2_shape, compressed_input1_stride, + compressed_input2_stride, compressed_output_shape); + // Skip broadcasting for degenerate shapes. + if (!broadcastable_shape) { + return; + } + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastAddRecursiveDimensions( + kMaxBroadcastDim - 1, &input1_offset, &input2_offset, &output_offset, + compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, activation_min, activation_max, input1_data, + input2_data, output_data); +} + +// This function is used for 8-bit as well as for 16-bit, but the accumulator +// is 32-bit for both cases. The overflow does not happen due to the +// choice of the shift (20 or 15, accordingly - see add.cc for more comments). +template +inline void BroadcastAddRecursiveDimensions( + const ArithmeticParams& params, int dimension, size_t* input1_offset_p, + size_t* input2_offset_p, size_t* output_offset, + size_t* compressed_input1_stride, size_t* compressed_input2_stride, + size_t* compressed_output_shape, const T* input1_data, const T* input2_data, + T* output_data) { + for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { + if (dimension > 0) { + size_t input1_offset_c = *input1_offset_p; + size_t input2_offset_c = *input2_offset_p; + BroadcastAddRecursiveDimensions( + params, dimension - 1, &input1_offset_c, &input2_offset_c, + output_offset, compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, input1_data, input2_data, output_data); + } else { + TFLITE_DCHECK(dimension == 0); + const int32_t input1_val = + params.input1_offset + input1_data[*input1_offset_p]; + const int32_t input2_val = + params.input2_offset + input2_data[*input2_offset_p]; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, + params.input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, + params.input2_shift); + const int32_t raw_sum = scaled_input1_val + scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[*output_offset] = static_cast(clamped_output); + ++(*output_offset); + } + *input1_offset_p += compressed_input1_stride[dimension]; + *input2_offset_p += compressed_input2_stride[dimension]; + } +} + +// This function is used for 8-bit as well as for 16-bit, but the accumulator +// is 32-bit for both cases. The overflow does not happen due to the +// choice of the shift (20 or 15, accordingly - see add.cc for more comments). +template +inline typename std::enable_if::value, void>::type +BroadcastAdd6DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + constexpr int kMaxBroadcastDim = 6; + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + size_t compressed_input1_stride[kMaxBroadcastDim]; + size_t compressed_input2_stride[kMaxBroadcastDim]; + size_t compressed_output_shape[kMaxBroadcastDim]; + bool broadcastable_shape = ReduceDimensionsForBroadcast( + input1_shape, input2_shape, compressed_input1_stride, + compressed_input2_stride, compressed_output_shape); + // Skip broadcasting for degenerate shapes. + if (!broadcastable_shape) { + return; + } + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastAddRecursiveDimensions( + params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset, + &output_offset, compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, input1_data, input2_data, output_data); +} + +template +inline void BroadcastAdd4DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data) { + return BroadcastAdd6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); +} + +inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, + const RuntimeShape& unswitched_input1_shape, + const uint8_t* unswitched_input1_data, + const RuntimeShape& unswitched_input2_shape, + const uint8_t* unswitched_input2_data, + const RuntimeShape& output_shape, + uint8_t* output_data) { + ArithmeticParams switched_params = unswitched_params; + switched_params.input1_offset = unswitched_params.input2_offset; + switched_params.input1_multiplier = unswitched_params.input2_multiplier; + switched_params.input1_shift = unswitched_params.input2_shift; + switched_params.input2_offset = unswitched_params.input1_offset; + switched_params.input2_multiplier = unswitched_params.input1_multiplier; + switched_params.input2_shift = unswitched_params.input1_shift; + + const bool use_unswitched = + unswitched_params.broadcast_category == + tflite_micro::BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const ArithmeticParams& params = + use_unswitched ? unswitched_params : switched_params; + const uint8_t* input1_data = + use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const uint8_t* input2_data = + use_unswitched ? unswitched_input2_data : unswitched_input1_data; + + // Fivefold nested loops. The second input resets its position for each + // iteration of the second loop. The first input resets its position at the + // beginning of the fourth loop. The innermost loop is an elementwise add of + // sections of the arrays. + uint8_t* output_data_ptr = output_data; + const uint8_t* input1_data_ptr = input1_data; + const uint8_t* input2_data_reset = input2_data; + // In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared + // between input shapes. y3 for input 1 is always broadcast, and so the + // dimension there is 1, whereas optionally y1 might be broadcast for input 2. + // Put another way, + // input1.shape.FlatSize = y0 * y1 * y2 * y4, + // input2.shape.FlatSize = y0 * y2 * y3 * y4. + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + if (y4 > 1) { + // General fivefold pattern, with y4 > 1 so there is a non-broadcast inner + // dimension. + for (int i0 = 0; i0 < y0; ++i0) { + const uint8_t* input2_data_ptr; + for (int i1 = 0; i1 < y1; ++i1) { + input2_data_ptr = input2_data_reset; + for (int i2 = 0; i2 < y2; ++i2) { + for (int i3 = 0; i3 < y3; ++i3) { + AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; + } + // We have broadcast y4 of input1 data y3 times, and now move on. + input1_data_ptr += y4; + } + } + // We have broadcast y2*y3*y4 of input2 data y1 times, and now move on. + input2_data_reset = input2_data_ptr; + } + } else { + // Special case of y4 == 1, in which the innermost loop is a single element + // and can be combined with the next (y3) as an inner broadcast. + // + // Note that this handles the case of pure scalar broadcast when + // y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar + // broadcast with batch (as y2 > 1). + // + // NOTE The process is the same as the above general case except simplified + // for y4 == 1 and the loop over y3 is contained within the + // AddScalarBroadcast function. + for (int i0 = 0; i0 < y0; ++i0) { + const uint8_t* input2_data_ptr; + for (int i1 = 0; i1 < y1; ++i1) { + input2_data_ptr = input2_data_reset; + for (int i2 = 0; i2 < y2; ++i2) { + AddScalarBroadcast(y3, params, *input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y3; + output_data_ptr += y3; + input1_data_ptr += 1; + } + } + input2_data_reset = input2_data_ptr; + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h new file mode 100644 index 00000000..da40d766 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h @@ -0,0 +1,86 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_ops { + +// T is expected to be either float or int. +template +inline void AddN(const RuntimeShape& input_shape, const size_t num_inputs, + const T* const* input_data, T* output_data) { + // All inputs and output should have the same shape, this is checked during + // Prepare stage. + const size_t size = input_shape.FlatSize(); + for (size_t i = 0; i < size; ++i) { + T x = 0; + for (size_t j = 0; j < num_inputs; ++j) { + x += input_data[j][i]; + } + output_data[i] = x; + } +} + +inline void AddN(const ArithmeticParams& params, + const RuntimeShape& input_shape, const size_t num_inputs, + const int8_t* const* input_data, int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + // Input offset is negative input zero point. Activation tensors are + // asymmetric quantized so they span the full int8 range. + // All inputs should have same zero-point and scale, this is checked during + // Prepare stage. + TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); + TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); + + // All inputs and output should have the same shape, this is checked during + // Prepare stage. + const size_t size = input_shape.FlatSize(); + for (size_t i = 0; i < size; ++i) { + // accumulate in scaled_x before clamping to avoid overflow + const int32_t x = params.input1_offset; // x = 0 + const int32_t shifted_x = x * (1 << params.left_shift); + int32_t scaled_x = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_x, params.input1_multiplier, params.input1_shift); + + for (size_t j = 0; j < num_inputs; ++j) { + const int32_t y = params.input1_offset + input_data[j][i]; + const int32_t shifted_y = y * (1 << params.left_shift); + int32_t scaled_y = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_y, params.input1_multiplier, params.input1_shift); + scaled_x += scaled_y; + } + + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + scaled_x, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h new file mode 100644 index 00000000..beee76b4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h @@ -0,0 +1,88 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +std::function GetComparefunction(bool is_arg_max) { + if (is_arg_max) { + return std::greater(); + } else { + return std::less(); + } +} + +template +void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, + const T3* input2_data, const RuntimeShape& output_shape, + T2* output_data, const Cmp& cmp) { + TFLITE_DCHECK_GT(input1_shape.DimensionsCount(), 0); + TFLITE_DCHECK_EQ(input1_shape.DimensionsCount() - 1, + output_shape.DimensionsCount()); + int axis = input2_data[0]; + if (axis < 0) { + axis += input1_shape.DimensionsCount(); + } + const int axis_size = input1_shape.Dims(axis); + + int outer_size = 1; + for (int i = 0; i < axis; ++i) { + TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i)); + outer_size *= input1_shape.Dims(i); + } + + int inner_size = 1; + const int dims_count = input1_shape.DimensionsCount(); + for (int i = axis + 1; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1)); + inner_size *= input1_shape.Dims(i); + } + for (int outer = 0; outer < outer_size; ++outer) { + for (int inner = 0; inner < inner_size; ++inner) { + auto min_max_value = input1_data[outer * axis_size * inner_size + inner]; + T2 min_max_index = 0; + for (int i = 1; i < axis_size; ++i) { + const auto& curr_value = + input1_data[(outer * axis_size + i) * inner_size + inner]; + if (cmp(curr_value, min_max_value)) { + min_max_value = curr_value; + min_max_index = static_cast(i); + } + } + output_data[outer * inner_size + inner] = min_max_index; + } + } +} + +template +void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, + const T3* input2_data, const RuntimeShape& output_shape, + T2* output_data, const bool is_arg_max) { + ArgMinMax(input1_shape, input1_data, input2_data, output_shape, output_data, + GetComparefunction(is_arg_max)); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h new file mode 100644 index 00000000..9b19f60c --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h @@ -0,0 +1,275 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { +namespace batch_matmul { + +// Determine which dimension is the broadcast dimension. +inline int broadcast_dim(int lhs_dim, int rhs_dim) { + if (lhs_dim == rhs_dim) return lhs_dim; + if (lhs_dim == 1) return rhs_dim; + TFLITE_DCHECK_EQ(rhs_dim, 1); + return lhs_dim; +} + +// Compute the "extent" for iterating on this dimension. +// If we are broadcasting, then don't advance (i.e return 0). +inline int extent(const RuntimeShape& shape, int x) { + if (shape.Dims(x) == 1) { + return 0; + } + int prod = 1; + for (int i = x + 1; i < shape.DimensionsCount(); ++i) { + prod *= shape.Dims(i); + } + return prod; +} + +} // namespace batch_matmul + +template +inline void BatchMatMul(const RuntimeShape& lhs_shape, const Ta* lhs_data, + const RuntimeShape& rhs_shape, const Tb* rhs_data, + const RuntimeShape& output_shape, Tout* output_data) { + const RuntimeShape extended_lhs_shape = + RuntimeShape::ExtendedShape(5, lhs_shape); + const RuntimeShape extended_rhs_shape = + RuntimeShape::ExtendedShape(5, rhs_shape); + + const int batch_dim0 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); + const int batch_dim1 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); + const int batch_dim2 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); + + const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); + const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); + const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); + const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); + const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); + const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); + + // Set params for each matrix multiply. + const int lhs_rows = extended_lhs_shape.Dims(3); + const int rhs_cols = extended_rhs_shape.Dims(4); + const int accum_depth = extended_lhs_shape.Dims(4); + + for (int b0 = 0; b0 < batch_dim0; ++b0) { + const Ta* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); + const Tb* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); + for (int b1 = 0; b1 < batch_dim1; ++b1) { + const Ta* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; + const Tb* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; + for (int b2 = 0; b2 < batch_dim2; ++b2) { + const Ta* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; + const Tb* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; + Tout* out_ptr = output_data + ((b0 * batch_dim1 * batch_dim2) + + b1 * batch_dim2 + b2) * + lhs_rows * rhs_cols; + for (int j = 0; j < rhs_cols; ++j) { + for (int i = 0; i < lhs_rows; ++i) { + Tout total = 0; + for (int k = 0; k < accum_depth; ++k) { + total += static_cast(lhs_ptr2[accum_depth * i + k]) * + static_cast(rhs_ptr2[j * accum_depth + k]); + } + int idx = lhs_rows * j + i; + out_ptr[idx] = total; + } + } + } + } + } +} + +inline void BatchMatMul(const RuntimeShape& lhs_shape, const int8_t* lhs_data, + const RuntimeShape& rhs_shape, const int8_t* rhs_data, + const float* scaling_factors, + const int32_t* input_offset, int32_t* row_sums, + const RuntimeShape& output_shape, float* output_data, + bool* compute_row_sums) { + const RuntimeShape extended_lhs_shape = + RuntimeShape::ExtendedShape(5, lhs_shape); + const RuntimeShape extended_rhs_shape = + RuntimeShape::ExtendedShape(5, rhs_shape); + + const int batch_dim0 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); + const int batch_dim1 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); + const int batch_dim2 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); + + const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); + const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); + const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); + const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); + const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); + const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); + + // Set params for each matrix multiply. + const int lhs_rows = extended_lhs_shape.Dims(3); + const int rhs_cols = extended_rhs_shape.Dims(4); + const int accum_depth = extended_lhs_shape.Dims(4); + + const int ioff_ext0 = rhs_ext0 == 0 ? 0 : rhs_cols; + const int ioff_ext1 = rhs_ext1 == 0 ? 0 : rhs_cols; + const int ioff_ext2 = rhs_ext2 == 0 ? 0 : rhs_cols; + const int woff_ext0 = lhs_ext0 == 0 ? 0 : lhs_rows; + const int woff_ext1 = lhs_ext1 == 0 ? 0 : lhs_rows; + const int woff_ext2 = lhs_ext2 == 0 ? 0 : lhs_rows; + + if (!compute_row_sums || *compute_row_sums) { + int num_weights_matrices = 1; + for (int i = 1; i < extended_lhs_shape.DimensionsCount() - 2; ++i) { + num_weights_matrices *= extended_lhs_shape.Dims(i); + } + tensor_utils::ReductionSumVector( + lhs_data, row_sums, num_weights_matrices * lhs_rows, accum_depth); + if (compute_row_sums) { + *compute_row_sums = false; + } + } + + for (int b0 = 0; b0 < batch_dim0; ++b0) { + const int8_t* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); + const int8_t* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); + const int32_t* ioff_ptr0 = input_offset + (b0 * ioff_ext0); + const float* scale_ptr0 = scaling_factors + (b0 * ioff_ext0); + const int32_t* woff_ptr0 = row_sums + (b0 * woff_ext0); + for (int b1 = 0; b1 < batch_dim1; ++b1) { + const int8_t* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; + const int8_t* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; + const int32_t* ioff_ptr1 = ioff_ptr0 + (b1 * ioff_ext1); + const float* scale_ptr1 = scale_ptr0 + (b1 * ioff_ext1); + const int32_t* woff_ptr1 = woff_ptr0 + (b1 * woff_ext1); + for (int b2 = 0; b2 < batch_dim2; ++b2) { + const int8_t* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; + const int8_t* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; + const int32_t* ioff_ptr2 = ioff_ptr1 + (b2 * ioff_ext2); + const float* scale_ptr2 = scale_ptr1 + (b2 * ioff_ext2); + const int32_t* woff_ptr2 = woff_ptr1 + (b2 * woff_ext2); + float* out_ptr = output_data + ((b0 * batch_dim1 * batch_dim2) + + b1 * batch_dim2 + b2) * + lhs_rows * rhs_cols; + for (int j = 0; j < rhs_cols; ++j) { + const float batch_scaling_factor = scale_ptr2[j]; + const float batch_offset = static_cast(ioff_ptr2[j]); + for (int i = 0; i < lhs_rows; ++i) { + int32_t total = 0; + for (int k = 0; k < accum_depth; ++k) { + total += + lhs_ptr2[accum_depth * i + k] * rhs_ptr2[j * accum_depth + k]; + } + int32_t row_sum = woff_ptr2[i]; + total -= row_sum * batch_offset; + int idx = lhs_rows * j + i; + out_ptr[idx] += batch_scaling_factor * total; + } + } + } + } + } +} + +template +inline void BatchMatMul(const FullyConnectedParams& params, + const RuntimeShape& lhs_shape, const T* lhs_data, + const RuntimeShape& rhs_shape, const T* rhs_data, + const RuntimeShape& output_shape, T* output_data) { + const RuntimeShape extended_lhs_shape = + RuntimeShape::ExtendedShape(5, lhs_shape); + const RuntimeShape extended_rhs_shape = + RuntimeShape::ExtendedShape(5, rhs_shape); + + const int batch_dim0 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); + const int batch_dim1 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); + const int batch_dim2 = batch_matmul::broadcast_dim( + extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); + + const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); + const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); + const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); + const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); + const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); + const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); + + // Set params for each matrix multiply. + const int lhs_rows = extended_lhs_shape.Dims(3); + const int rhs_cols = extended_rhs_shape.Dims(4); + const int accum_depth = extended_lhs_shape.Dims(4); + + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + for (int b0 = 0; b0 < batch_dim0; ++b0) { + const T* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); + const T* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); + for (int b1 = 0; b1 < batch_dim1; ++b1) { + const T* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; + const T* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; + for (int b2 = 0; b2 < batch_dim2; ++b2) { + const T* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; + const T* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; + T* out_ptr = output_data + + ((b0 * batch_dim1 * batch_dim2) + b1 * batch_dim2 + b2) * + lhs_rows * rhs_cols; + + for (int j = 0; j < rhs_cols; ++j) { + for (int i = 0; i < lhs_rows; ++i) { + AccumT total = 0; + for (int k = 0; k < accum_depth; ++k) { + AccumT lhs_val = lhs_ptr2[accum_depth * i + k]; + AccumT rhs_val = rhs_ptr2[accum_depth * j + k]; + total += (lhs_val + filter_offset) * (rhs_val + input_offset); + } + int32_t total_scaled = MultiplyByQuantizedMultiplier( + total, output_multiplier, output_shift); + total_scaled += output_offset; + total_scaled = std::max(total_scaled, output_activation_min); + total_scaled = std::min(total_scaled, output_activation_max); + const int idx = lhs_rows * j + i; + out_ptr[idx] = static_cast(total_scaled); + } + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h new file mode 100644 index 00000000..0788e971 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h @@ -0,0 +1,101 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +// TODO(b/135760455): Move this method anonymous namespace in a cc file. +inline RuntimeShape ExtendShapeBatchToSpace(const RuntimeShape& shape) { + if (shape.DimensionsCount() == 4) { + return shape; + } + RuntimeShape new_shape(4, 1); + new_shape.SetDim(0, shape.Dims(0)); + new_shape.SetDim(1, shape.Dims(1)); + new_shape.SetDim(3, shape.Dims(2)); + return new_shape; +} + +template +inline void BatchToSpaceND(const RuntimeShape& unextended_input1_shape, + const T* input1_data, + const RuntimeShape& unextended_input2_shape, + const int32_t* block_shape_data, + const RuntimeShape& unextended_input3_shape, + const int32_t* crops_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + ruy::profiler::ScopeLabel label("BatchToSpaceND"); + TFLITE_DCHECK_GE(unextended_input1_shape.DimensionsCount(), 3); + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(unextended_input1_shape.DimensionsCount(), + unextended_output_shape.DimensionsCount()); + + const RuntimeShape input1_shape = + ExtendShapeBatchToSpace(unextended_input1_shape); + const RuntimeShape output_shape = + ExtendShapeBatchToSpace(unextended_output_shape); + + const int output_width = output_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_batch_size = output_shape.Dims(0); + + const int depth = input1_shape.Dims(3); + const int input_width = input1_shape.Dims(2); + const int input_height = input1_shape.Dims(1); + const int input_batch_size = input1_shape.Dims(0); + + const int block_shape_height = block_shape_data[0]; + const int block_shape_width = + unextended_input1_shape.DimensionsCount() == 4 ? block_shape_data[1] : 1; + const int crops_top = crops_data[0]; + const int crops_left = + unextended_input1_shape.DimensionsCount() == 4 ? crops_data[2] : 0; + for (int in_batch = 0; in_batch < input_batch_size; ++in_batch) { + const int out_batch = in_batch % output_batch_size; + const int spatial_offset = in_batch / output_batch_size; + for (int in_h = 0; in_h < input_height; ++in_h) { + const int out_h = in_h * block_shape_height + + spatial_offset / block_shape_width - crops_top; + if (out_h < 0 || out_h >= output_height) { + continue; + } + for (int in_w = 0; in_w < input_width; ++in_w) { + const int out_w = in_w * block_shape_width + + spatial_offset % block_shape_width - crops_left; + + if (out_w < 0 || out_w >= output_width) { + continue; + } + T* out = output_data + Offset(output_shape, out_batch, out_h, out_w, 0); + const T* in = + input1_data + Offset(input1_shape, in_batch, in_h, in_w, 0); + memcpy(out, in, depth * sizeof(T)); + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h new file mode 100644 index 00000000..c66d39b8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h @@ -0,0 +1,91 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +// Also appears to duplicate MinimumMaximum. +// +// R: Result type. T1: Input 1 type. T2: Input 2 type. +template +inline void BroadcastBinaryFunction4DSlow( + const RuntimeShape& unextended_input1_shape, const T1* input1_data, + const RuntimeShape& unextended_input2_shape, const T2* input2_data, + const RuntimeShape& unextended_output_shape, R* output_data, + R (*func)(T1, T2)) { + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, + unextended_input2_shape, &desc1, &desc2); + + const int* dims_data = + reinterpret_cast(output_shape.DimsDataUpTo5D()); + for (int b = 0; b < output_shape.Dims(0); ++b) { + int out_idx_b = b * dims_data[1]; + int in_idx1_b = desc1.strides[0] * b; + int in_idx2_b = desc2.strides[0] * b; + for (int y = 0; y < output_shape.Dims(1); ++y) { + int out_idx_y = (out_idx_b + y) * dims_data[2]; + int in_idx1_y = in_idx1_b + desc1.strides[1] * y; + int in_idx2_y = in_idx2_b + desc2.strides[1] * y; + for (int x = 0; x < output_shape.Dims(2); ++x) { + int out_idx_x = (out_idx_y + x) * dims_data[3]; + int in1_idx = in_idx1_y + desc1.strides[2] * x; + int in2_idx = in_idx2_y + desc2.strides[2] * x; + for (int c = 0; c < output_shape.Dims(3); ++c) { + auto out_idx = out_idx_x + c; + auto in1_val = input1_data[in1_idx]; + auto in2_val = input2_data[in2_idx]; + output_data[out_idx] = func(in1_val, in2_val); + in1_idx += desc1.strides[3]; + in2_idx += desc2.strides[3]; + } + } + } + } +} + +// R: Result type. T1: Input 1 type. T2: Input 2 type. +template +inline void BinaryFunction(const RuntimeShape& input1_shape, + const T1* input1_data, + const RuntimeShape& input2_shape, + const T2* input2_data, + const RuntimeShape& output_shape, R* output_data, + R (*func)(T1, T2)) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = func(input1_data[i], input2_data[i]); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h new file mode 100644 index 00000000..532aaf59 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h @@ -0,0 +1,56 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ + +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +void BroadcastArgs(const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + // Gets data at the backward index i of the shape tensor. Returns 1 if the + // index is out of range. + auto get_shape_data = [](const RuntimeShape& shape, const T* data, + int backward_idx) -> T { + int forward_idx = shape.FlatSize() - 1 - backward_idx; + if (forward_idx < 0) return 1; + return data[forward_idx]; + }; + + int output_num_elements = output_shape.FlatSize(); + for (int i = 0; i < output_num_elements; ++i) { + int backward_i = output_num_elements - 1 - i; + int shape1_i = get_shape_data(input1_shape, input1_data, i); + int shape2_i = get_shape_data(input2_shape, input2_data, i); + if (shape1_i == 1) { + output_data[backward_i] = shape2_i; + } else if (shape2_i == 1) { + output_data[backward_i] = shape1_i; + } else { + TFLITE_CHECK_EQ(shape1_i, shape2_i); + output_data[backward_i] = shape1_i; + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h new file mode 100644 index 00000000..60a4549e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h @@ -0,0 +1,97 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/kernel_util.h" + +namespace tflite_micro { +namespace reference_ops { +template +void BroadcastImpl(const NdArrayDesc& input_desc, const char* input_data, + const NdArrayDesc& output_desc, char* output_data, + int indexes[N], int dim, const int last_broadcasting_dim, + const int type_size) { + // Copy data from input to output. + if (dim == last_broadcasting_dim) { + int copy_size = output_desc.strides[dim] * type_size; + const char* data_src = + input_data + SubscriptToIndex(input_desc, indexes) * type_size; + char* data_dst = + output_data + SubscriptToIndex(output_desc, indexes) * type_size; + for (int i = 0; i < output_desc.extents[dim]; ++i, data_dst += copy_size) { + memcpy(data_dst, data_src, copy_size); + } + return; + } + + // Recursive call to find the next broadcasting. + for (indexes[dim] = 0; indexes[dim] < input_desc.extents[dim]; + ++indexes[dim]) { + BroadcastImpl(input_desc, input_data, output_desc, output_data, indexes, + dim + 1, last_broadcasting_dim, type_size); + } + + // Duplicate data in output tensor. + indexes[dim] = 0; + if (input_desc.extents[dim] != output_desc.extents[dim]) { + int copy_size = output_desc.strides[dim] * type_size; + char* data_src = + output_data + SubscriptToIndex(output_desc, indexes) * type_size; + char* data_dst = data_src + copy_size; + for (int i = 1; i < output_desc.extents[dim]; ++i, data_dst += copy_size) { + memcpy(data_dst, data_src, copy_size); + } + } +} + +template +inline void BroadcastTo(const RuntimeShape& unextended_input_shape, + const char* input_data, + const RuntimeShape& unextended_output_shape, + char* output_data, TfLiteType data_type) { + NdArrayDesc input_desc; + NdArrayDesc output_desc; + CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_input_shape), + &input_desc); + CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), + &output_desc); + + // Get the last dimension has broadcasting. At this dimension, the data is + // copied from input tensor to output tensor. + int last_broadcast_dim = -1; + for (int i = N - 1; i >= 0; --i) { + if (input_desc.extents[i] != output_desc.extents[i]) { + last_broadcast_dim = i; + break; + } + } + + // If non-broadcasting, just copy data from input to output tensor. + if (last_broadcast_dim == -1) { + memcpy(output_data, input_data, + unextended_input_shape.FlatSize() * TfLiteTypeGetSize(data_type)); + return; + } + + // Broadcasting using memcpy. + int indexes[N] = {0}; + BroadcastImpl(input_desc, input_data, output_desc, output_data, indexes, 0, + last_broadcast_dim, TfLiteTypeGetSize(data_type)); +} +} // namespace reference_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h new file mode 100644 index 00000000..565f3584 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h @@ -0,0 +1,37 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void Ceil(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; ++i) { + output_data[i] = std::ceil(input_data[i]); + } +} + +} // namespace reference_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h new file mode 100644 index 00000000..72b53bda --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h @@ -0,0 +1,271 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ + +#include "tensorflow/lite/core/c/common.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline bool EqualFn(T lhs, T rhs) { + return lhs == rhs; +} + +template +inline bool NotEqualFn(T lhs, T rhs) { + return lhs != rhs; +} + +template +inline bool GreaterFn(T lhs, T rhs) { + return lhs > rhs; +} +template +inline bool GreaterEqualFn(T lhs, T rhs) { + return lhs >= rhs; +} +template +inline bool LessFn(T lhs, T rhs) { + return lhs < rhs; +} +template +inline bool LessEqualFn(T lhs, T rhs) { + return lhs <= rhs; +} + +template +using ComparisonFn = bool (*)(T, T); + +template F> +inline void ComparisonImpl( + const ComparisonParams& op_params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { + const int64_t flatsize = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int64_t i = 0; i < flatsize; ++i) { + output_data[i] = F(input1_data[i], input2_data[i]); + } +} + +template F> +inline void Comparison(const ComparisonParams& op_params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, bool* output_data) { + ComparisonImpl(op_params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); +} + +template F> +inline void ComparisonWithScaling( + const ComparisonParams& op_params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { + int left_shift = op_params.left_shift; + int32_t input1_offset = op_params.input1_offset; + int32_t input1_multiplier = op_params.input1_multiplier; + int input1_shift = op_params.input1_shift; + int32_t input2_offset = op_params.input2_offset; + int32_t input2_multiplier = op_params.input2_multiplier; + int input2_shift = op_params.input2_shift; + + const int64_t flatsize = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int64_t i = 0; i < flatsize; ++i) { + const int32_t input1_val = input1_offset + input1_data[i]; + const int32_t input2_val = input2_offset + input2_data[i]; + const int32_t shifted_input1_val = input1_val * (1 << left_shift); + const int32_t shifted_input2_val = input2_val * (1 << left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, input1_multiplier, input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, input2_multiplier, input2_shift); + output_data[i] = F(scaled_input1_val, scaled_input2_val); + } +} + +struct BroadcastComparison4DSlowCommon { + const RuntimeShape output_shape; + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; +}; + +TFLITE_NOINLINE +BroadcastComparison4DSlowCommon BroadcastComparison4DSlowPreprocess( + const RuntimeShape& unextended_input1_shape, + const RuntimeShape& unextended_input2_shape, + const RuntimeShape& unextended_output_shape); + +template F> +inline void BroadcastComparison4DSlowImpl( + const ComparisonParams& op_params, + const RuntimeShape& unextended_input1_shape, const T* input1_data, + const RuntimeShape& unextended_input2_shape, const T* input2_data, + const RuntimeShape& unextended_output_shape, bool* output_data) { + const BroadcastComparison4DSlowCommon dims = + BroadcastComparison4DSlowPreprocess(unextended_input1_shape, + unextended_input2_shape, + unextended_output_shape); + + for (int b = 0; b < dims.output_shape.Dims(0); ++b) { + for (int y = 0; y < dims.output_shape.Dims(1); ++y) { + for (int x = 0; x < dims.output_shape.Dims(2); ++x) { + for (int c = 0; c < dims.output_shape.Dims(3); ++c) { + output_data[Offset(dims.output_shape, b, y, x, c)] = + F(input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)], + input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]); + } + } + } + } +} + +template F> +inline void BroadcastComparison4DSlow(const ComparisonParams& op_params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + bool* output_data) { + BroadcastComparison4DSlowImpl(op_params, input1_shape, input1_data, + input2_shape, input2_data, + output_shape, output_data); +} + +template F> +inline void BroadcastComparison4DSlowWithScaling( + const ComparisonParams& op_params, + const RuntimeShape& unextended_input1_shape, const T* input1_data, + const RuntimeShape& unextended_input2_shape, const T* input2_data, + const RuntimeShape& unextended_output_shape, bool* output_data) { + const BroadcastComparison4DSlowCommon dims = + BroadcastComparison4DSlowPreprocess(unextended_input1_shape, + unextended_input2_shape, + unextended_output_shape); + + int left_shift = op_params.left_shift; + int32_t input1_offset = op_params.input1_offset; + int32_t input1_multiplier = op_params.input1_multiplier; + int input1_shift = op_params.input1_shift; + int32_t input2_offset = op_params.input2_offset; + int32_t input2_multiplier = op_params.input2_multiplier; + int input2_shift = op_params.input2_shift; + + for (int b = 0; b < dims.output_shape.Dims(0); ++b) { + for (int y = 0; y < dims.output_shape.Dims(1); ++y) { + for (int x = 0; x < dims.output_shape.Dims(2); ++x) { + for (int c = 0; c < dims.output_shape.Dims(3); ++c) { + const int32_t input1_val = + input1_offset + + input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)]; + const int32_t input2_val = + input2_offset + + input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]; + const int32_t shifted_input1_val = input1_val * (1 << left_shift); + const int32_t shifted_input2_val = input2_val * (1 << left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, input1_multiplier, input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, input2_multiplier, input2_shift); + output_data[Offset(dims.output_shape, b, y, x, c)] = + F(scaled_input1_val, scaled_input2_val); + } + } + } + } +} + +#define TFLITE_COMPARISON_OP(name) \ + inline void name(const ComparisonParams& op_params, \ + const RuntimeShape& input1_shape, const float* input1_data, \ + const RuntimeShape& input2_shape, const float* input2_data, \ + const RuntimeShape& output_shape, bool* output_data) { \ + Comparison(op_params, input1_shape, input1_data, input2_shape, \ + input2_data, output_shape, output_data); \ + } \ + template \ + inline void name##NoScaling( \ + const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ + const T* input1_data, const RuntimeShape& input2_shape, \ + const T* input2_data, const RuntimeShape& output_shape, \ + bool* output_data) { \ + ComparisonImpl(op_params, input1_shape, input1_data, \ + input2_shape, input2_data, output_shape, \ + output_data); \ + } \ + template \ + inline void name##WithScaling( \ + const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ + const T* input1_data, const RuntimeShape& input2_shape, \ + const T* input2_data, const RuntimeShape& output_shape, \ + bool* output_data) { \ + ComparisonWithScaling(op_params, input1_shape, input1_data, \ + input2_shape, input2_data, \ + output_shape, output_data); \ + } \ + template \ + inline void Broadcast4DSlow##name##NoScaling( \ + const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ + const T* input1_data, const RuntimeShape& input2_shape, \ + const T* input2_data, const RuntimeShape& output_shape, \ + bool* output_data) { \ + BroadcastComparison4DSlowImpl( \ + op_params, input1_shape, input1_data, input2_shape, input2_data, \ + output_shape, output_data); \ + } \ + inline void Broadcast4DSlow##name( \ + const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ + const float* input1_data, const RuntimeShape& input2_shape, \ + const float* input2_data, const RuntimeShape& output_shape, \ + bool* output_data) { \ + BroadcastComparison4DSlow(op_params, input1_shape, input1_data, \ + input2_shape, input2_data, \ + output_shape, output_data); \ + } \ + template \ + inline void Broadcast4DSlow##name##WithScaling( \ + const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ + const T* input1_data, const RuntimeShape& input2_shape, \ + const T* input2_data, const RuntimeShape& output_shape, \ + bool* output_data) { \ + BroadcastComparison4DSlowWithScaling( \ + op_params, input1_shape, input1_data, input2_shape, input2_data, \ + output_shape, output_data); \ + } +TFLITE_COMPARISON_OP(Equal) +TFLITE_COMPARISON_OP(NotEqual) +TFLITE_COMPARISON_OP(Greater) +TFLITE_COMPARISON_OP(GreaterEqual) +TFLITE_COMPARISON_OP(Less) +TFLITE_COMPARISON_OP(LessEqual) +#undef TFLITE_COMPARISON_OP + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h new file mode 100644 index 00000000..8f062f86 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h @@ -0,0 +1,141 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void Concatenation(const ConcatenationParams& params, + const RuntimeShape* const* input_shapes, + const Scalar* const* input_data, + const RuntimeShape& output_shape, + Scalar* output_data) { + int axis = params.axis; + int inputs_count = params.inputs_count; + const int concat_dimensions = output_shape.DimensionsCount(); + TFLITE_DCHECK_LT(axis, concat_dimensions); + + int64_t concat_size = 0; + for (int i = 0; i < inputs_count; i++) { + TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); + for (int j = 0; j < concat_dimensions; j++) { + if (j != axis) { + MatchingDim(*input_shapes[i], j, output_shape, j); + } + } + concat_size += input_shapes[i]->Dims(axis); + } + TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); + int64_t outer_size = 1; + for (int i = 0; i < axis; ++i) { + outer_size *= output_shape.Dims(i); + } + // For all input arrays, + // FlatSize() = outer_size * Dims(axis) * base_inner_size; + int64_t base_inner_size = 1; + for (int i = axis + 1; i < concat_dimensions; ++i) { + base_inner_size *= output_shape.Dims(i); + } + + Scalar* output_ptr = output_data; + for (int k = 0; k < outer_size; k++) { + for (int i = 0; i < inputs_count; ++i) { + const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; + const Scalar* input_ptr = input_data[i] + k * copy_size; + memcpy(output_ptr, input_ptr, copy_size * sizeof(Scalar)); + output_ptr += copy_size; + } + } +} + +// TODO(b/174275780): The quantized implementation of concatentation isn't fully +// quantized as it takes scale as a floating point value. This should be fixed +// when optimizng this routine further. +inline void ConcatenationWithScaling(const ConcatenationParams& params, + const RuntimeShape* const* input_shapes, + const uint8_t* const* input_data, + const RuntimeShape& output_shape, + uint8_t* output_data) { + int axis = params.axis; + const int32_t* input_zeropoint = params.input_zeropoint; + const float* input_scale = params.input_scale; + int inputs_count = params.inputs_count; + const int32_t output_zeropoint = params.output_zeropoint; + const float output_scale = params.output_scale; + + const int concat_dimensions = output_shape.DimensionsCount(); + TFLITE_DCHECK_LT(axis, concat_dimensions); + + int64_t concat_size = 0; + for (int i = 0; i < inputs_count; i++) { + TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); + for (int j = 0; j < concat_dimensions; j++) { + if (j != axis) { + MatchingDim(*input_shapes[i], j, output_shape, j); + } + } + concat_size += input_shapes[i]->Dims(axis); + } + TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); + int64_t outer_size = 1; + for (int i = 0; i < axis; ++i) { + outer_size *= output_shape.Dims(i); + } + // For all input arrays, + // FlatSize() = outer_size * Dims(axis) * base_inner_size; + int64_t base_inner_size = 1; + for (int i = axis + 1; i < concat_dimensions; ++i) { + base_inner_size *= output_shape.Dims(i); + } + + const float inverse_output_scale = 1.f / output_scale; + uint8_t* output_ptr = output_data; + for (int k = 0; k < outer_size; k++) { + for (int i = 0; i < inputs_count; ++i) { + const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; + const uint8_t* input_ptr = input_data[i] + k * copy_size; + if (input_zeropoint[i] == output_zeropoint && + input_scale[i] == output_scale) { + memcpy(output_ptr, input_ptr, copy_size); + } else { + const float scale = input_scale[i] * inverse_output_scale; + const float bias = -input_zeropoint[i] * scale; + for (int j = 0; j < copy_size; ++j) { + const int32_t value = static_cast(tflite_micro::TfLiteRound( + input_ptr[j] * scale + bias)) + + output_zeropoint; + output_ptr[j] = static_cast( + std::max(std::min(255, value), 0)); + } + } + output_ptr += copy_size; + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h new file mode 100644 index 00000000..38443c3b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h @@ -0,0 +1,289 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& filter_shape, + const float* filter_data, const RuntimeShape& bias_shape, + const float* bias_data, const RuntimeShape& output_shape, + float* output_data, const RuntimeShape& im2col_shape, + float* im2col_data) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_NE(groups, 0); + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + TFLITE_DCHECK_NE(filters_per_group, 0); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + for (int out_x = 0; out_x < output_width; ++out_x) { + const int in_x_origin = (out_x * stride_width) - pad_width; + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + auto group = out_channel / filters_per_group; + float total = 0.f; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + + if (!is_point_inside_image) { + continue; + } + for (int in_channel = 0; in_channel < filter_input_depth; + ++in_channel) { + float input_value = + input_data[Offset(input_shape, batch, in_y, in_x, + in_channel + group * filter_input_depth)]; + float filter_value = filter_data[Offset( + filter_shape, out_channel, filter_y, filter_x, in_channel)]; + total += (input_value * filter_value); + } + } + } + float bias_value = 0.0f; + if (bias_data) { + bias_value = bias_data[out_channel]; + } + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + ActivationFunctionWithMinMax(total + bias_value, + output_activation_min, + output_activation_max); + } + } + } + } +} + +inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + uint8_t* output_data, const RuntimeShape& im2col_shape, + uint8_t* im2col_data, void* cpu_backend_context) { + (void)cpu_backend_context; // only used in optimized code. + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + for (int out_x = 0; out_x < output_width; ++out_x) { + const int in_x_origin = (out_x * stride_width) - pad_width; + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + auto group = out_channel / filters_per_group; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + + if (!is_point_inside_image) { + continue; + } + + for (int in_channel = 0; in_channel < filter_input_depth; + ++in_channel) { + int32_t input_val = + input_data[Offset(input_shape, batch, in_y, in_x, + in_channel + group * filter_input_depth)]; + int32_t filter_val = filter_data[Offset( + filter_shape, out_channel, filter_y, filter_x, in_channel)]; + acc += + (filter_val + filter_offset) * (input_val + input_offset); + } + } + } + if (bias_data) { + acc += bias_data[out_channel]; + } + acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, + output_shift); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(acc); + } + } + } + } +} + +inline void HybridConvPerChannel( + const ConvParams& params, float* scaling_factors_ptr, + const RuntimeShape& input_shape, const int8_t* input_data, + const RuntimeShape& filter_shape, const int8_t* filter_data, + const RuntimeShape& bias_shape, const float* bias_data, + const RuntimeShape& output_shape, float* output_data, + const RuntimeShape& im2col_shape, int8_t* im2col_data, + const float* per_channel_scale, int32_t* input_offset) { + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + auto group = out_channel / filters_per_group; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int in_channel = 0; in_channel < filter_input_depth; + ++in_channel) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // If the location is outside the bounds of the input image, + // use zero as a default value. + if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height)) { + int32_t input_val = input_data[Offset( + input_shape, batch, in_y, in_x, + in_channel + group * filter_input_depth)]; + int32_t filter_val = + filter_data[Offset(filter_shape, out_channel, filter_y, + filter_x, in_channel)]; + acc += filter_val * (input_val - input_offset[batch]); + } + } + } + } + float acc_float = + acc * per_channel_scale[out_channel] * scaling_factors_ptr[batch]; + if (bias_data) { + acc_float += bias_data[out_channel]; + } + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + ActivationFunctionWithMinMax(acc_float, output_activation_min, + output_activation_max); + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h new file mode 100644 index 00000000..880a2bc3 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h @@ -0,0 +1,175 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void CumSum(const T* input_data, const RuntimeShape& shape, int32_t axis, + bool exclusive, bool reverse, T* output_data) { + const int32_t rank = shape.DimensionsCount(); + TFLITE_DCHECK_GE(rank, 1); + TFLITE_DCHECK_GE(axis, 0); + TFLITE_DCHECK_LT(axis, rank); + + size_t inner = 1; + size_t outer = 1; + size_t depth = 1; + for (int32_t i = 0; i < rank; i++) { + if (i < axis) + inner *= shape.Dims(i); + else if (i > axis) + outer *= shape.Dims(i); + else + depth = shape.Dims(i); + } + + for (size_t outer_index = 0; outer_index < outer; outer_index++) { + size_t outer_index_adj; + if (reverse) + outer_index_adj = (outer - 1) - outer_index; + else + outer_index_adj = outer_index; + for (size_t inner_index = 0; inner_index < inner; inner_index++) { + T accumulator = 0; + size_t inner_index_adj; + if (reverse) + inner_index_adj = (inner - 1) - inner_index; + else + inner_index_adj = inner_index; + for (size_t depth_index = 0; depth_index < depth; depth_index++) { + size_t depth_index_adj; + if (reverse) + depth_index_adj = (depth - 1) - depth_index; + else + depth_index_adj = depth_index; + + size_t index = outer_index_adj; + index += inner_index_adj * depth * outer; + index += depth_index_adj * outer; + + if (exclusive) { + output_data[index] = accumulator; + accumulator += input_data[index]; + } else { + accumulator += input_data[index]; + output_data[index] = accumulator; + } + } + } + } +} + +// +// Quantized INT8 CUMSUM +// +inline void CumSum(const ArithmeticParams& params, const int8_t* input_data, + const RuntimeShape& shape, int32_t axis, bool exclusive, + bool reverse, int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + // Input offset is negative input zero point. Activation tensors are + // asymmetric quantized so they span the full int8 range. + // All inputs should have same zero-point and scale, this is checked during + // Prepare stage. + TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); + TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); + + const int32_t rank = shape.DimensionsCount(); + TFLITE_DCHECK_GE(rank, 1); + TFLITE_DCHECK_GE(axis, 0); + TFLITE_DCHECK_LT(axis, rank); + + size_t inner = 1; + size_t outer = 1; + size_t depth = 1; + for (int32_t i = 0; i < rank; i++) { + if (i < axis) + inner *= shape.Dims(i); + else if (i > axis) + outer *= shape.Dims(i); + else + depth = shape.Dims(i); + } + + for (size_t outer_index = 0; outer_index < outer; outer_index++) { + size_t outer_index_adj; + if (reverse) + outer_index_adj = (outer - 1) - outer_index; + else + outer_index_adj = outer_index; + for (size_t inner_index = 0; inner_index < inner; inner_index++) { + int32_t accumulator = params.input1_offset; // accumulator = 0 + accumulator *= (1 << params.left_shift); + accumulator = MultiplyByQuantizedMultiplierSmallerThanOneExp( + accumulator, params.input1_multiplier, params.input1_shift); + + size_t inner_index_adj; + if (reverse) + inner_index_adj = (inner - 1) - inner_index; + else + inner_index_adj = inner_index; + + for (size_t depth_index = 0; depth_index < depth; depth_index++) { + size_t depth_index_adj; + if (reverse) + depth_index_adj = (depth - 1) - depth_index; + else + depth_index_adj = depth_index; + + size_t index = outer_index_adj; + index += inner_index_adj * depth * outer; + index += depth_index_adj * outer; + + const int32_t y = params.input1_offset + input_data[index]; + const int32_t shifted_y = y * (1 << params.left_shift); + const int32_t scaled_y = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_y, params.input1_multiplier, params.input1_shift); + + int32_t scaled_output; + if (exclusive) { + scaled_output = accumulator; + accumulator += scaled_y; + } else { + accumulator += scaled_y; + scaled_output = accumulator; + } + + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + scaled_output, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[index] = static_cast(clamped_output); + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h new file mode 100644 index 00000000..dcc9e5fa --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h @@ -0,0 +1,79 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void DepthToSpace(const tflite_micro::DepthToSpaceParams& op_params, + const RuntimeShape& unextended_input_shape, + const T* input_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + const int input_depth = input_shape.Dims(3); + const int input_width = input_shape.Dims(2); + const int input_height = input_shape.Dims(1); + const int input_batch = input_shape.Dims(0); + + const int output_depth = output_shape.Dims(3); + const int output_width = output_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_batch = output_shape.Dims(0); + + const int32_t block_size = op_params.block_size; + + TFLITE_DCHECK_EQ(input_width * block_size, output_width); + TFLITE_DCHECK_EQ(input_height * block_size, output_height); + TFLITE_DCHECK_EQ(input_depth, output_depth * block_size * block_size); + TFLITE_DCHECK_EQ(input_batch, output_batch); + + for (int out_b = 0; out_b < output_batch; ++out_b) { + for (int out_h = 0; out_h < output_height; ++out_h) { + for (int out_w = 0; out_w < output_width; ++out_w) { + for (int out_d = 0; out_d < output_depth; ++out_d) { + const int in_d = + out_d + ((out_h % block_size) * block_size + out_w % block_size) * + output_depth; + + const int in_w = out_w / block_size; + const int in_h = out_h / block_size; + const int in_b = out_b; + + const int input_index = Offset(input_shape, in_b, in_h, in_w, in_d); + const int output_index = + Offset(output_shape, out_b, out_h, out_w, out_d); + + output_data[output_index] = input_data[input_index]; + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h new file mode 100644 index 00000000..b50e7bec --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h @@ -0,0 +1,100 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void DepthwiseConv( + const DepthwiseParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& filter_shape, + const float* filter_data, const RuntimeShape& bias_shape, + const float* bias_data, const RuntimeShape& output_shape, + float* output_data) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + for (int b = 0; b < batches; ++b) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int ic = 0; ic < input_depth; ++ic) { + for (int m = 0; m < depth_multiplier; m++) { + const int oc = m + ic * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + float total = 0.f; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // If the location is outside the bounds of the input image, + // use zero as a default value. + if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height)) { + float input_value = + input_data[Offset(input_shape, b, in_y, in_x, ic)]; + float filter_value = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, oc)]; + total += (input_value * filter_value); + } + } + } + float bias_value = 0.0f; + if (bias_data) { + bias_value = bias_data[oc]; + } + output_data[Offset(output_shape, b, out_y, out_x, oc)] = + ActivationFunctionWithMinMax(total + bias_value, + output_activation_min, + output_activation_max); + } + } + } + } + } +} + +} // end namespace reference_ops +} // end namespace tflite + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h new file mode 100644 index 00000000..aef25b7b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h @@ -0,0 +1,319 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ + +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +// Used in tests and template parameters to control which version of depthwise +// convolution is called. Primarily for reference code, and specializations +// forced in tests. +enum class DepthwiseConvImplementation { + // Run all tests against kUseStandardEntry even if also testing another + // kernel, since we need to be sure that the main DepthwiseConv() function in + // optimized_ops.h dispatches to a correctly-executing kernel. + kNone = 0, // The "default" option: use the normal + // DepthwiseConv kernel (entry) function. + kUseGenericKernel, // Forced use of generic kernel. + kUseNeon3x3, // 3x3 kernel that uses NEON when available. + kUseNeon3x3DotProduct, // 3x3 kernel that uses dot-product enabled NEON + // when available. + kUseCModel3x3DotProduct, // 3x3 kernel, reference C model that is intended + // to match overall design NEON code. + kUseUnwound3x3DotProduct, // 3x3 kernel, reference C model with unwound loops + // and some arrays. + kUseIntrinsics3x3DotProduct, // 3x3 kernel using NEON intrinsics. +}; + +// Category of depthwise convolution output rounding. +enum class DepthwiseConvOutputRounding { + kNone = 0, // Invalid: specific method must be specified. + kAwayFromZero, // Original method: exact halves rounded away from zero. + kUpward, // Halves towards +infinity: adds 0.5 before truncate. + // This is where a future kNearestEven would be placed. +}; + +// Category of depthwise convolution depth multiplication. +enum class DepthwiseConvDepthMultiplication { + kNoMultiplication = 0, // Depth multiplier = 1. + kUnitInputDepth, // Input depth = 1, output depth = depth multiplier. +}; + +namespace reference_ops { +namespace depthwise_conv { + +template +inline int32_t DepthwiseConvRound(int32_t x, int32_t quantized_multiplier, + int shift) { + TFLITE_DCHECK_NE(output_rounding, DepthwiseConvOutputRounding::kNone); + return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); +} + +// Single-rounding MultiplyByQuantizedMultiplier +#if TFLITE_SINGLE_ROUNDING +template <> +inline int32_t DepthwiseConvRound( + int32_t x, int32_t quantized_multiplier, int shift) { + using gemmlowp::RoundingDivideByPOT; + using gemmlowp::SaturatingRoundingDoublingHighMul; + int left_shift = shift > 0 ? shift : 0; + int right_shift = shift > 0 ? 0 : -shift; + return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul( + x * (1 << left_shift), quantized_multiplier), + right_shift); +} + +template <> +inline int32_t DepthwiseConvRound( + int32_t x, int32_t quantized_multiplier, int shift) { + return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); +} +// Double-rounding MultiplyByQuantizedMultiplier +#else +template <> +inline int32_t DepthwiseConvRound( + int32_t x, int32_t quantized_multiplier, int shift) { + return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); +} + +template <> +inline int32_t DepthwiseConvRound( + int32_t x, int32_t quantized_multiplier, int shift) { + using gemmlowp::SaturatingRoundingDoublingHighMul; + const int left_shift = shift > 0 ? shift : 0; + const int right_shift = shift > 0 ? 0 : -shift; + const int rounding_offset = right_shift > 0 ? 1 << (right_shift - 1) : 0; + return (SaturatingRoundingDoublingHighMul(x * (1 << left_shift), + quantized_multiplier) + + rounding_offset) >> + right_shift; +} +#endif // TFLITE_SINGLE_ROUNDING + +template +struct DepthwiseConvBasicKernel { + static inline void Run( + const DepthwiseParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + uint8_t* output_data) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + for (int b = 0; b < batches; ++b) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int ic = 0; ic < input_depth; ++ic) { + for (int m = 0; m < depth_multiplier; m++) { + const int oc = m + ic * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = + in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // If the location is outside the bounds of the input image, + // use zero as a default value. + if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height)) { + int32_t input_val = + input_data[Offset(input_shape, b, in_y, in_x, ic)]; + int32_t filter_val = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, oc)]; + acc += (filter_val + filter_offset) * + (input_val + input_offset); + } + } + } + if (bias_data) { + acc += bias_data[oc]; + } + acc = DepthwiseConvRound(acc, output_multiplier, + output_shift); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, b, out_y, out_x, oc)] = + static_cast(acc); + } + } + } + } + } + } + + // TODO(b/148596273): Reconcile reference versions, perhaps with common + // MultiplyByQuantizedMultiplier or DepthwiseConvRound function. + static inline void RunPerChannel( + const DepthwiseParams& params, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int8_t* output_data) { + // Get parameters. + // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const int32_t input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + const int32_t* output_multiplier = params.output_multiplier_per_channel; + const int32_t* output_shift = params.output_shift_per_channel; + + // Check dimensions of the tensors. + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + for (int m = 0; m < depth_multiplier; ++m) { + const int output_channel = m + in_channel * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = + in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + if (is_point_inside_image) { + int32_t input_val = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + int32_t filter_val = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, output_channel)]; + // Accumulate with 32 bits accumulator. + // In the nudging process during model quantization, we + // force real value of 0.0 be represented by a quantized + // value. This guarantees that the input_offset is a int8_t, + // even though it is represented using int32_t. int32_t += + // int8_t + // * (int8_t - int8_t) so the highest value we can get from + // each accumulation is [-127, 127] * ([-128, 127] - + // [-128, 127]), which is [-32512, 32512]. log2(32512) + // = 14.98, which means we can accumulate at least 2^16 + // multiplications without overflow. The accumulator is + // applied to a filter so the accumulation logic will hold + // as long as the filter size (filter_y * filter_x * + // in_channel) does not exceed 2^16, which is the case in + // all the models we have seen so far. + acc += filter_val * (input_val + input_offset); + } + } + } + if (bias_data) { + acc += bias_data[output_channel]; + } + acc = DepthwiseConvRound( + acc, output_multiplier[output_channel], + output_shift[output_channel]); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, + output_channel)] = static_cast(acc); + } + } + } + } + } + } +}; + +} // namespace depthwise_conv + +inline void DepthwiseConv( + const DepthwiseParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + uint8_t* output_data) { + return depthwise_conv::DepthwiseConvBasicKernel< + DepthwiseConvOutputRounding::kAwayFromZero>::Run(params, input_shape, + input_data, filter_shape, + filter_data, bias_shape, + bias_data, output_shape, + output_data); +} + +} // namespace reference_ops +} // end namespace tflite + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h new file mode 100644 index 00000000..6e1a1cab --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h @@ -0,0 +1,78 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ + +#include + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +// Dequantizes into a float without rounding. +template +inline void Dequantize(const tflite_micro::DequantizationParams& op_params, + const RuntimeShape& input_shape, + const InputT* input_data, + const RuntimeShape& output_shape, OutputT* output_data) { + int32_t zero_point = op_params.zero_point; + const double scale = op_params.scale; + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + const int32_t val = input_data[i]; + const OutputT result = static_cast(scale * (val - zero_point)); + output_data[i] = result; + } +} + +// Dequantizes per-channel quantized tensor to float. +template +inline void PerChannelDequantize( + const tflite_micro::PerChannelDequantizationParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const RuntimeShape& output_shape, float* output_data) { + // Ensure flat size is same. + MatchingFlatSize(input_shape, output_shape); + + const int32_t* zero_point = op_params.zero_point; + const float* scale = op_params.scale; + const int32_t quantized_dimension = op_params.quantized_dimension; + const int32_t num_dims = input_shape.DimensionsCount(); + const int32_t* dims_data = input_shape.DimsData(); + std::vector current_dim(num_dims, 0); + + do { + size_t offset = + ReducedOutputOffset(num_dims, reinterpret_cast(dims_data), + current_dim.data(), 0, nullptr); + const int channel = current_dim[quantized_dimension]; + const int32_t val = input_data[offset]; + const float result = + static_cast(scale[channel] * (val - zero_point[channel])); + output_data[offset] = result; + } while (NextIndex(num_dims, reinterpret_cast(dims_data), + current_dim.data())); +} + +} // namespace reference_ops + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h new file mode 100644 index 00000000..330b88b5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h @@ -0,0 +1,247 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void DivCheckArithmeticParams(const ArithmeticParams& params) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + // Input offset is negative input zero point. Activation tensors are + // asymmetric quantized so they span the full int8 range. + constexpr int32_t max_value = + static_cast(std::numeric_limits::max()); + TFLITE_DCHECK_GE(params.input1_offset, -max_value); + TFLITE_DCHECK_LE(params.input1_offset, max_value); + TFLITE_DCHECK_GE(params.input2_offset, -max_value); + TFLITE_DCHECK_LE(params.input2_offset, max_value); + TFLITE_DCHECK_GE(params.output_offset, -max_value); + TFLITE_DCHECK_LE(params.output_offset, max_value); +} + +// Element-wise div that can often be used for inner loop of broadcast Div as +// well as the non-broadcast Div. +template +inline void DivElementwise(int size, const ArithmeticParams& params, + const T* input1_data, const T* input2_data, + T* output_data) { + DivCheckArithmeticParams(params); + + for (int i = 0; i < size; ++i) { + int32_t input1_val = params.input1_offset + input1_data[i]; + int32_t input2_val = params.input2_offset + input2_data[i]; + TFLITE_DCHECK_NE(input2_val, 0); + if (input2_val < 0) { + // Invert signs to avoid a negative input2_val as input2_inv needs to be + // positive to be used as multiplier of MultiplyByQuantizedMultiplier. + input1_val = -input1_val; + input2_val = -input2_val; + } + int recip_shift; + const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift); + const int headroom = CountLeadingSignBits(input1_val); + const int32_t unscaled_quotient = + MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv, + headroom); + const int total_shift = params.output_shift - recip_shift - headroom; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplierSmallerThanOneExp( + unscaled_quotient, params.output_multiplier, total_shift); + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + output_data[i] = static_cast(clamped_output); + } +} + +inline void Div(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8_t* input1_data, + const RuntimeShape& input2_shape, const uint8_t* input2_data, + const RuntimeShape& output_shape, uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + DivElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Div(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int8_t* input1_data, + const RuntimeShape& input2_shape, const int8_t* input2_data, + const RuntimeShape& output_shape, int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + DivElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +template +inline void BroadcastDivSlowQuantized( + const ArithmeticParams& params, const RuntimeShape& unextended_input1_shape, + const T* input1_data, const RuntimeShape& unextended_input2_shape, + const T* input2_data, const RuntimeShape& unextended_output_shape, + T* output_data) { + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); + + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDesc output_desc; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, + unextended_input2_shape, &desc1, &desc2); + CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), + &output_desc); + + DivCheckArithmeticParams(params); + + auto div_func = [&](int indexes[N]) { + int32_t input1_val = + params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; + int32_t input2_val = + params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; + TFLITE_DCHECK_NE(input2_val, 0); + if (input2_val < 0) { + // Invert signs to avoid a negative input2_val as input2_inv needs to be + // positive to be used as multiplier of MultiplyByQuantizedMultiplier. + input1_val = -input1_val; + input2_val = -input2_val; + } + int recip_shift; + const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift); + const int headroom = CountLeadingSignBits(input1_val); + const int32_t unscaled_quotient = + MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv, + headroom); + const int total_shift = params.output_shift - recip_shift - headroom; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplierSmallerThanOneExp( + unscaled_quotient, params.output_multiplier, total_shift); + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + output_data[SubscriptToIndex(output_desc, indexes)] = + static_cast(clamped_output); + }; + NDOpsHelper(output_desc, div_func); +} + +template +inline void BroadcastDivSlow(const ArithmeticParams& params, + const RuntimeShape& unextended_input1_shape, + const uint8_t* input1_data, + const RuntimeShape& unextended_input2_shape, + const uint8_t* input2_data, + const RuntimeShape& unextended_output_shape, + uint8_t* output_data) { + BroadcastDivSlowQuantized( + params, unextended_input1_shape, input1_data, unextended_input2_shape, + input2_data, unextended_output_shape, output_data); +} + +template +inline void BroadcastDivSlow(const ArithmeticParams& params, + const RuntimeShape& unextended_input1_shape, + const int8_t* input1_data, + const RuntimeShape& unextended_input2_shape, + const int8_t* input2_data, + const RuntimeShape& unextended_output_shape, + int8_t* output_data) { + BroadcastDivSlowQuantized( + params, unextended_input1_shape, input1_data, unextended_input2_shape, + input2_data, unextended_output_shape, output_data); +} + +// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary +// dimensionality if the runtime code does a single loop over one dimension +// that handles broadcasting as the base case. The code generator would then +// generate max(D1, D2) nested for loops. +template +void BroadcastDivSlow(const ArithmeticParams& params, + const RuntimeShape& unextended_input1_shape, + const T* input1_data, + const RuntimeShape& unextended_input2_shape, + const T* input2_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + T output_activation_min; + T output_activation_max; + GetActivationParams(params, &output_activation_min, &output_activation_max); + + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); + + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDesc output_desc; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, + unextended_input2_shape, &desc1, &desc2); + CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), + &output_desc); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest + // stride, typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + + auto div_func = [&](int indexes[N]) { + output_data[SubscriptToIndex(output_desc, indexes)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, indexes)] / + input2_data[SubscriptToIndex(desc2, indexes)], + output_activation_min, output_activation_max); + }; + NDOpsHelper(output_desc, div_func); +} + +template +inline void Div(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + T output_activation_min; + T output_activation_max; + GetActivationParams(params, &output_activation_min, &output_activation_max); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] / input2_data[i], output_activation_min, + output_activation_max); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h new file mode 100644 index 00000000..20ef92ee --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h @@ -0,0 +1,37 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ + +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void Elu(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + const float val = input_data[i]; + output_data[i] = val < 0.0f ? TfLiteExpm1(val) : val; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h new file mode 100644 index 00000000..bd1cadc5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h @@ -0,0 +1,38 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void Exp(const T* input_data, const size_t num_elements, + T* output_data) { + ruy::profiler::ScopeLabel label("Exp"); + for (size_t idx = 0; idx < num_elements; ++idx) { + output_data[idx] = std::exp(input_data[idx]); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h new file mode 100644 index 00000000..aa412959 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h @@ -0,0 +1,38 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +void Fill(const RuntimeShape& value_shape, const T* value_data, + const RuntimeShape& output_shape, T* output_data) { + TFLITE_DCHECK_EQ(value_shape.DimensionsCount(), 0); + const int flat_size = output_shape.FlatSize(); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = *value_data; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h new file mode 100644 index 00000000..28fe190e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h @@ -0,0 +1,39 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void Floor(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + int offset = i; + output_data[offset] = std::floor(input_data[offset]); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h new file mode 100644 index 00000000..3b56e06a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h @@ -0,0 +1,35 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +T FloorDiv(T input1, T input2) { + return std::floor(std::divides()(static_cast(input1), + static_cast(input2))); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h new file mode 100644 index 00000000..9a4c7139 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h @@ -0,0 +1,44 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ + +#include +#include + +namespace tflite_micro { + +namespace reference_ops { + +template +T FloorMod(T input1, T input2) { + struct FloatMod { + float operator()(const float lhs, const float rhs) const { + return std::fmod(lhs, rhs); + } + }; + using ModFunc = typename std::conditional::value, + std::modulus, FloatMod>::type; + ModFunc mod_func; + T trunc_mod = mod_func(input1, input2); + return (trunc_mod != 0) && ((input2 < 0) != (trunc_mod < 0)) + ? (trunc_mod + input2) + : trunc_mod; +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h new file mode 100644 index 00000000..9ede8d9b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h @@ -0,0 +1,323 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/quantization_util.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void FullyConnected( + const FullyConnectedParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& weights_shape, + const float* weights_data, const RuntimeShape& bias_shape, + const float* bias_data, const RuntimeShape& output_shape, + float* output_data) { + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + // TODO(b/62193649): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int output_dims_count = output_shape.DimensionsCount(); + const int weights_dims_count = weights_shape.DimensionsCount(); + const int batches = FlatSizeSkipDim(output_shape, output_dims_count - 1); + const int output_depth = MatchingDim(weights_shape, weights_dims_count - 2, + output_shape, output_dims_count - 1); + const int accum_depth = weights_shape.Dims(weights_dims_count - 1); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + float total = 0.f; + for (int d = 0; d < accum_depth; ++d) { + total += input_data[b * accum_depth + d] * + weights_data[out_c * accum_depth + d]; + } + float bias_value = 0.0f; + if (bias_data) { + bias_value = bias_data[out_c]; + } + output_data[out_c + output_depth * b] = ActivationFunctionWithMinMax( + total + bias_value, output_activation_min, output_activation_max); + } + } +} + +inline void FullyConnected( + const FullyConnectedParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + uint8_t* output_data) { + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); + TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + // TODO(b/62193649): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int output_dim_count = output_shape.DimensionsCount(); + const int filter_dim_count = filter_shape.DimensionsCount(); + const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); + const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, + output_shape, output_dim_count - 1); + const int accum_depth = filter_shape.Dims(filter_dim_count - 1); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + int32_t acc = 0; + for (int d = 0; d < accum_depth; ++d) { + int32_t input_val = input_data[b * accum_depth + d]; + int32_t filter_val = filter_data[out_c * accum_depth + d]; + acc += (filter_val + filter_offset) * (input_val + input_offset); + } + if (bias_data) { + acc += bias_data[out_c]; + } + acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[out_c + output_depth * b] = static_cast(acc); + } + } +} + +inline void FullyConnected( + const FullyConnectedParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int16_t* output_data) { + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(output_offset, 0); + // TODO(b/62193649): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int output_dim_count = output_shape.DimensionsCount(); + const int filter_dim_count = filter_shape.DimensionsCount(); + const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); + const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, + output_shape, output_dim_count - 1); + const int accum_depth = filter_shape.Dims(filter_dim_count - 1); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32_t accum = bias_data[out_c]; + // Accumulation loop. + for (int d = 0; d < accum_depth; ++d) { + int16_t input_val = input_data[b * accum_depth + d] + input_offset; + int16_t filter_val = + filter_data[out_c * accum_depth + d] + filter_offset; + accum += filter_val * input_val; + } + // Down-scale the final int32_t accumulator to the scale used by our + // (16-bit, typically 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + accum = + MultiplyByQuantizedMultiplier(accum, output_multiplier, output_shift); + // Saturate, cast to int16_t, and store to output array. + accum = std::max(accum, output_activation_min - output_offset); + accum = std::min(accum, output_activation_max - output_offset); + accum += output_offset; + output_data[out_c + output_depth * b] = accum; + } + } +} + +inline void ShuffledFullyConnected( + const FullyConnectedParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& weights_shape, + const uint8_t* shuffled_weights_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int16_t* output_data, uint8_t* shuffled_input_workspace_data) { + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); + TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); + TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); + // TODO(b/62193649): This really should be: + // const int batches = ArraySize(output_dims, 1); + // but the current --variable_batch hack consists in overwriting the 3rd + // dimension with the runtime batch size, as we don't keep track for each + // array of which dimension is the batch dimension in it. + const int output_dim_count = output_shape.DimensionsCount(); + const int weights_dim_count = weights_shape.DimensionsCount(); + const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); + const int output_depth = MatchingDim(weights_shape, weights_dim_count - 2, + output_shape, output_dim_count - 1); + const int accum_depth = weights_shape.Dims(weights_dim_count - 1); + TFLITE_DCHECK((accum_depth % 16) == 0); + TFLITE_DCHECK((output_depth % 4) == 0); + + // Shuffling and xoring of input activations into the workspace buffer + uint8_t* shuffled_input_workspace_ptr = shuffled_input_workspace_data; + if (batches == 1) { + for (int i = 0; i < accum_depth; i++) { + shuffled_input_workspace_data[i] = input_data[i] ^ 0x80; + } + } else if (batches == 4) { + for (int c = 0; c < accum_depth; c += 16) { + for (int b = 0; b < 4; b++) { + const uint8_t* src_data_ptr = input_data + b * accum_depth + c; + for (int j = 0; j < 16; j++) { + uint8_t src_val = *src_data_ptr++; + // Flip the sign bit, so that the kernel will only need to + // reinterpret these uint8_t values as int8_t, getting for free the + // subtraction of the zero_point value 128. + uint8_t dst_val = src_val ^ 0x80; + *shuffled_input_workspace_ptr++ = dst_val; + } + } + } + } else { + TFLITE_DCHECK(false); + return; + } + + // Actual computation + if (batches == 1) { + int16_t* output_ptr = output_data; + // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) + // so that just reinterpreting them as int8_t values is equivalent to + // subtracting 128 from them, thus implementing for free the subtraction of + // the zero_point value 128. + const int8_t* shuffled_weights_ptr = + reinterpret_cast(shuffled_weights_data); + // Likewise, we preshuffled and pre-xored the input data above. + const int8_t* shuffled_input_data = + reinterpret_cast(shuffled_input_workspace_data); + for (int c = 0; c < output_depth; c += 4) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32_t accum[4] = {0}; + // Accumulation loop. + for (int d = 0; d < accum_depth; d += 16) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 16; j++) { + int8_t input_val = shuffled_input_data[d + j]; + int8_t weights_val = *shuffled_weights_ptr++; + accum[i] += weights_val * input_val; + } + } + } + for (int i = 0; i < 4; i++) { + // Add bias value + int32_t acc = accum[i] + bias_data[c + i]; + // Down-scale the final int32_t accumulator to the scale used by our + // (16-bit, typically 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + acc = + MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); + // Saturate, cast to int16_t, and store to output array. + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_ptr[c + i] = acc; + } + } + } else if (batches == 4) { + int16_t* output_ptr = output_data; + // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) + // so that just reinterpreting them as int8_t values is equivalent to + // subtracting 128 from them, thus implementing for free the subtraction of + // the zero_point value 128. + const int8_t* shuffled_weights_ptr = + reinterpret_cast(shuffled_weights_data); + // Likewise, we preshuffled and pre-xored the input data above. + const int8_t* shuffled_input_data = + reinterpret_cast(shuffled_input_workspace_data); + for (int c = 0; c < output_depth; c += 4) { + const int8_t* shuffled_input_ptr = shuffled_input_data; + // Accumulation loop. + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32_t accum[4][4]; + for (int i = 0; i < 4; i++) { + for (int b = 0; b < 4; b++) { + accum[i][b] = 0; + } + } + for (int d = 0; d < accum_depth; d += 16) { + for (int i = 0; i < 4; i++) { + for (int b = 0; b < 4; b++) { + for (int j = 0; j < 16; j++) { + int8_t input_val = shuffled_input_ptr[16 * b + j]; + int8_t weights_val = shuffled_weights_ptr[16 * i + j]; + accum[i][b] += weights_val * input_val; + } + } + } + shuffled_input_ptr += 64; + shuffled_weights_ptr += 64; + } + for (int i = 0; i < 4; i++) { + for (int b = 0; b < 4; b++) { + // Add bias value + int32_t acc = accum[i][b] + bias_data[c + i]; + // Down-scale the final int32_t accumulator to the scale used by our + // (16-bit, typically 3 integer bits) fixed-point format. The + // quantized multiplier and shift here have been pre-computed offline + // (e.g. by toco). + acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, + output_shift); + // Saturate, cast to int16_t, and store to output array. + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_ptr[b * output_depth + c + i] = acc; + } + } + } + } else { + TFLITE_DCHECK(false); + return; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h new file mode 100644 index 00000000..28867479 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h @@ -0,0 +1,168 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline int16_t SaturatingLeftShift(int16_t value, int amount) { + int64_t result = static_cast(value) * (1 << amount); + result = std::min(result, std::numeric_limits::max()); + result = std::max(result, std::numeric_limits::min()); + return result; +} + +// Similar to ARM instruction SQDMULH. +// Similar to gemmlowp::SaturatingRoundingDoublingHighMul except +// rounding to zero instead of to nearest (SQRDMULH). +inline std::int16_t SaturatingDoublingHighMul(std::int16_t a, std::int16_t b) { + bool overflow = a == b && a == std::numeric_limits::min(); + std::int32_t a_32(a); + std::int32_t b_32(b); + std::int32_t ab_32 = a_32 * b_32; + std::int16_t ab_x2_high16 = static_cast((ab_32) / (1 << 15)); + return overflow ? std::numeric_limits::max() : ab_x2_high16; +} + +template +inline void HardSwish(const RuntimeShape& input_shape, const T* input_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("ReferenceHardSwish/Float"); + auto matching_size = MatchingFlatSize(input_shape, output_shape); + const T* in_end = input_data + matching_size; + for (; input_data < in_end; input_data++, output_data++) { + const float in = *input_data; + *output_data = + in * std::min(static_cast(6), std::max(static_cast(0), in + 3)) / + 6; + } +} + +template +inline void HardSwish(const HardSwishParams& params, + const RuntimeShape& input_shape, const T* input_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("ReferenceHardSwish/Quantized"); + + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + const int16_t input_value = input_data[i] - params.input_zero_point; + // Left-shift as much as we can without overflow/saturation to put + // significant bits in the high bits of our 16-bit fixedpoint values, so + // that fixed-point approximate computations below are as accurate as + // possible. + const int16_t input_value_on_hires_input_scale = input_value * (1 << 7); + // Compute the input value on essentially the output scale, just not + // right-shifted yet. This is the value that we'll use in the (x >= +3) + // case, and that in the general case we'll multiply against the "relu-ish" + // fixed-point multiplier in [0, 1]. + const int16_t input_value_on_preshift_output_scale = + gemmlowp::SaturatingRoundingDoublingHighMul( + input_value_on_hires_input_scale, + params.output_multiplier_fixedpoint_int16); + // Now compute the "relu-ish multiplier". In the (-3 <= x <= +3) case, that + // is just an affine rescaling of x from [-3, 3] to [0, 1]. In the general + // case, it is just that plus saturation at the boundaries of [-3, 3]. + // First, we rescale from [-3, 3] to [-1, 1], saturating. + // That is done by rescaling the input value with a fixed-point multiplier + // (reluish_multiplier_fixedpoint) and bit-shift such that we represent + // that input value on the scale where the real value 3.0f is represented + // by the quantized value 32768. (+32768 is actually not representable as + // int16_t, so this saturates at +32767, and that is seen empirically to be + // a negligible contribution to numerical error/bias). + // + // This code is careful to correctly implement any magnitude of multiplier, + // involving either a right shift or a left shift, with correct saturation + // behavior in the left-shift case. This forces this code to be more + // complicated, but is necessary for real applications: a partially + // trained quantized MobileNet v3-small model that motivated this code + // exhibits some large [min, max] range boundaries, of the order of + // magnitude of 10 or 100 depending on layers. + // + // The next few lines are basically just an ordinary + // MultiplyByQuantizedMultiplier, except that we are more careful here + // about the fine details of saturation when left-shifting, because here + // overflow in left-shift is a common case, not an anomaly as + // MultiplyByQuantizedMultiplier assumes. + int16_t reluish_value = input_value_on_hires_input_scale; + // Shift left, saturating, as much as we can while ensuring that this + // saturation will not contribute to the result. That is, left shift amount + // reduced by 1. + if (params.reluish_multiplier_exponent > 0) { + reluish_value = SaturatingLeftShift( + reluish_value, params.reluish_multiplier_exponent - 1); + } + // Apply the fixed-point multiplier, dividing the value by a divisor + // ranging in [1, 2]. + reluish_value = gemmlowp::SaturatingRoundingDoublingHighMul( + reluish_value, params.reluish_multiplier_fixedpoint_int16); + // Apply the last bit of left-shift. Thus, in the left-shifting case, if + // any saturation affects the result, it is happening here --- any + // saturation having occurred above is overwritten here, not affecting the + // result. + if (params.reluish_multiplier_exponent > 0) { + reluish_value = SaturatingLeftShift(reluish_value, 1); + } + // Shift right, in the right-shifting case. + if (params.reluish_multiplier_exponent < 0) { + reluish_value = gemmlowp::RoundingDivideByPOT( + reluish_value, -params.reluish_multiplier_exponent); + } + // At this point we have rescaled the value into a 16bit fixedpoint + // reluish_value in [-1, 1]. + // We now convert that to a 16bit fixedpoint value in [0, 1]. + reluish_value = (reluish_value + (1 << 15)) >> 1; + // Use of SaturatingDoublingHighMul here is important to cancel the biases + // from the above SaturatingRoundingDoublingHighMul. + // + // On a partially trained MobileNet-v3-small, + // + // | bias on | ImageNet + // | quantized | Top-1 + // Operation used here | values | accuracy (50k) + // --------------------------------------+------------+----------- + // SaturatingDoublingHighMul | -0.0024 | 58.920 + // SaturatingRoundingDoublingHighMul | -0.0067 | 58.064 + // + // In activations_test, this is covered by this testcase: + // QuantizedActivationsOpTest.HardSwishBias + // + const int16_t preshift_output_value = SaturatingDoublingHighMul( + reluish_value, input_value_on_preshift_output_scale); + // We were so far operating on the pre-shift output scale. Now we finally + // apply that output shift, arriving at the final output scale. + int16_t output_value = gemmlowp::RoundingDivideByPOT( + preshift_output_value, -params.output_multiplier_exponent); + output_value += params.output_zero_point; + output_value = + std::min(output_value, std::numeric_limits::max()); + output_value = + std::max(output_value, std::numeric_limits::min()); + output_data[i] = output_value; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h new file mode 100644 index 00000000..113d7941 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h @@ -0,0 +1,250 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +inline void CheckArithmeticParams(const ArithmeticParams& params) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + // Input offset is negative input zero point. Activation tensors are + // asymmetric quantized so they span the full int8 range. + TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); + TFLITE_DCHECK_GE(-params.input2_offset, std::numeric_limits::min()); + TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); + TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits::max()); +} + +// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2) +template +void BroadcastInput1(int size, const ArithmeticParams& params, + const T* input1_data, const T* input2_data, T* output_data, + void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + CheckArithmeticParams(params); + for (int i = 0; i < size; ++i) { + output_data[i] = binary_func(input1_data[0], input2_data[i], params); + } +} + +template +void BroadcastInput2(int size, const ArithmeticParams& params, + const T* input1_data, const T* input2_data, T* output_data, + void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + CheckArithmeticParams(params); + for (int i = 0; i < size; ++i) { + output_data[i] = binary_func(input1_data[i], input2_data[0], params); + } +} + +// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2) +template +void ElementWise(int size, const ArithmeticParams& params, const T* input1_data, + const T* input2_data, T* output_data, + void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + CheckArithmeticParams(params); + for (int i = 0; i < size; ++i) { + output_data[i] = binary_func(input1_data[i], input2_data[i], params); + } +} + +template +inline void BroadcastAddRecursiveDimensions( + const ArithmeticParams& params, int dimension, size_t* input1_offset_p, + size_t* input2_offset_p, size_t* output_offset, + size_t* compressed_input1_stride, size_t* compressed_input2_stride, + size_t* compressed_output_shape, const T* input1_data, const T* input2_data, + T* output_data, void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + if (dimension > 0) { + for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { + size_t input1_offset_c = *input1_offset_p; + size_t input2_offset_c = *input2_offset_p; + BroadcastAddRecursiveDimensions( + params, dimension - 1, &input1_offset_c, &input2_offset_c, + output_offset, compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, input1_data, input2_data, output_data, + check_arithmetic_params, binary_func); + *input1_offset_p += compressed_input1_stride[dimension]; + *input2_offset_p += compressed_input2_stride[dimension]; + } + } else { + TFLITE_DCHECK(dimension == 0); + bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; + bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; + TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); + const T* input1_data_ptr = input1_data + *input1_offset_p; + const T* input2_data_ptr = input2_data + *input2_offset_p; + T* output_data_ptr = output_data + *output_offset; + if (input1_is_broadcast) { + // input1 is broadcast. + BroadcastInput1(compressed_output_shape[dimension], params, + input1_data_ptr, input2_data_ptr, output_data_ptr, + check_arithmetic_params, binary_func); + *input2_offset_p += compressed_output_shape[dimension]; + } else if (input2_is_broadcast) { + // input2 is broadcast. + BroadcastInput2(compressed_output_shape[dimension], params, + input1_data_ptr, input2_data_ptr, output_data_ptr, + check_arithmetic_params, binary_func); + *input1_offset_p += compressed_output_shape[dimension]; + } else { + // Add element-wise. + ElementWise(compressed_output_shape[dimension], params, + input1_data_ptr, input2_data_ptr, output_data_ptr, + check_arithmetic_params, binary_func); + *input1_offset_p += compressed_output_shape[dimension]; + *input2_offset_p += compressed_output_shape[dimension]; + } + *output_offset += compressed_output_shape[dimension]; + } +} + +// TODO: b/270589088 - move to a more appropriate file. (b/270589088#comment2) +template +void BroadcastBinaryFunction6DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data, + void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + constexpr int kMaxBroadcastDim = 6; + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + size_t compressed_input1_stride[kMaxBroadcastDim]; + size_t compressed_input2_stride[kMaxBroadcastDim]; + size_t compressed_output_shape[kMaxBroadcastDim]; + bool broadcastable_shape = ReduceDimensionsForBroadcast( + input1_shape, input2_shape, compressed_input1_stride, + compressed_input2_stride, compressed_output_shape); + // Skip broadcasting for degenerate shapes. + if (!broadcastable_shape) { + return; + } + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastAddRecursiveDimensions( + params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset, + &output_offset, compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, input1_data, input2_data, output_data, + check_arithmetic_params, binary_func); +} + +template +void BroadcastBinaryFunction4DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data, + void (*check_arithmetic_params)(const ArithmeticParams&), + T (*binary_func)(T, T, const ArithmeticParams&)) { + BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, + check_arithmetic_params, binary_func); +} + +inline int8_t AddFunc(int8_t x, int8_t y, const ArithmeticParams& params) { + const int32_t input1_val = params.input1_offset + x; + const int32_t input2_val = params.input2_offset + y; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32_t raw_sum = scaled_input1_val + scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + return static_cast(clamped_output); +} + +// Element-wise add that can often be used for inner loop of broadcast add as +// well as the non-broadcast add. +inline void AddElementwise(int size, const ArithmeticParams& params, + const int8_t* input1_data, const int8_t* input2_data, + int8_t* output_data) { + ElementWise(size, params, input1_data, input2_data, output_data, + CheckArithmeticParams, AddFunc); +} + +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int8_t* input1_data, + const RuntimeShape& input2_shape, const int8_t* input2_data, + const RuntimeShape& output_shape, int8_t* output_data) { + CheckArithmeticParams(params); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void BroadcastAdd6DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int8_t* input1_data, + const RuntimeShape& input2_shape, + const int8_t* input2_data, + const RuntimeShape& output_shape, + int8_t* output_data) { + BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, + CheckArithmeticParams, AddFunc); +} + +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int8_t* input1_data, + const RuntimeShape& input2_shape, + const int8_t* input2_data, + const RuntimeShape& output_shape, + int8_t* output_data) { + BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, + CheckArithmeticParams, AddFunc); +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h new file mode 100644 index 00000000..1bce235d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h @@ -0,0 +1,241 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +// Fixed-point per-channel-quantization convolution reference kernel. +inline void ConvPerChannel( + const ConvParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int8_t* output_data) { + // Get parameters. + const int32_t input_offset = params.input_offset; // r = s(q - Z) + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int32_t output_offset = params.output_offset; + + // Set min and max value of the output. + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Consistency check. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + + // Check dimensions of the tensors. + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_NE(groups, 0); + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + TFLITE_DCHECK_NE(filters_per_group, 0); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + for (int out_x = 0; out_x < output_width; ++out_x) { + const int in_x_origin = (out_x * stride_width) - pad_width; + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + auto group = out_channel / filters_per_group; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + + if (!is_point_inside_image) { + continue; + } + + for (int in_channel = 0; in_channel < filter_input_depth; + ++in_channel) { + int32_t input_val = + input_data[Offset(input_shape, batch, in_y, in_x, + in_channel + group * filter_input_depth)]; + int32_t filter_val = filter_data[Offset( + filter_shape, out_channel, filter_y, filter_x, in_channel)]; + // Accumulate with 32 bits accumulator. + // In the nudging process during model quantization, we force + // real value of 0.0 be represented by a quantized value. This + // guarantees that the input_offset is a int8_t, even though + // it is represented using int32_t. int32_t += int8_t * + // (int8_t - int8_t) so the highest value we can get from each + // accumulation is [-127, 127] * ([-128, 127] - + // [-128, 127]), which is [-32512, 32512]. log2(32512) + // = 14.98, which means we can accumulate at least 2^16 + // multiplications without overflow. The accumulator is + // applied to a filter so the accumulation logic will hold as + // long as the filter size (filter_y * filter_x * in_channel) + // does not exceed 2^16, which is the case in all the models + // we have seen so far. + // TODO(b/174275578): Add a check to make sure the + // accumulator depth is smaller than 2^16. + acc += filter_val * (input_val + input_offset); + } + } + } + + if (bias_data) { + acc += bias_data[out_channel]; + } + acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[out_channel], output_shift[out_channel]); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(acc); + } + } + } + } +} + + +// Fixed-point per-channel-quantization convolution reference kernel. +// 16-bit data and 8-bit filter +template +inline void ConvPerChannel( + const ConvParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const AccumScalar* bias_data, const RuntimeShape& output_shape, + int16_t* output_data) { + // Get parameters. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + + // Set min and max value of the output. + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Consistency check. + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = input_shape.Dims(3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + + // Check dimensions of the tensors. + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int filter_input_depth = filter_shape.Dims(3); + const int groups = input_depth / filter_input_depth; + TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); + const int filters_per_group = output_depth / groups; + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + const int in_y_origin = (out_y * stride_height) - pad_height; + for (int out_x = 0; out_x < output_width; ++out_x) { + const int in_x_origin = (out_x * stride_width) - pad_width; + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + auto group = out_channel / filters_per_group; + AccumScalar acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + const int in_y = in_y_origin + dilation_height_factor * filter_y; + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + + if (!is_point_inside_image) { + continue; + } + + for (int in_channel = 0; in_channel < filter_input_depth; + ++in_channel) { + int32_t input_val = + input_data[Offset(input_shape, batch, in_y, in_x, + in_channel + group * filter_input_depth)]; + int32_t filter_val = filter_data[Offset( + filter_shape, out_channel, filter_y, filter_x, in_channel)]; + // Accumulate with 64 bits accumulator. + // int64_t += int8_t * int16_t so the highest value we can + // get from each accumulation is [-127, 127] * ([-32768, + // 32767] - + // [-32768, 32767]), which is [-8322945, 8322945]. + // log2(8322945) = 22.99. + acc += filter_val * input_val; + } + } + } + if (bias_data) { + acc += bias_data[out_channel]; + } + int32_t scaled_acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[out_channel], output_shift[out_channel]); + scaled_acc = std::max(scaled_acc, output_activation_min); + scaled_acc = std::min(scaled_acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(scaled_acc); + } + } + } + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h new file mode 100644 index 00000000..b0afaeef --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h @@ -0,0 +1,291 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { +inline void DepthwiseConvPerChannel( + const DepthwiseParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int8_t* output_data) { + // Get parameters. + // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const int32_t input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Check dimensions of the tensors. + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + for (int m = 0; m < depth_multiplier; ++m) { + const int output_channel = m + in_channel * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + if (is_point_inside_image) { + int32_t input_val = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + int32_t filter_val = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, output_channel)]; + // Accumulate with 32 bits accumulator. + // In the nudging process during model quantization, we force + // real value of 0.0 be represented by a quantized value. This + // guarantees that the input_offset is a int8_t, even though + // it is represented using int32_t. int32_t += int8_t * + // (int8_t - int8_t) so the highest value we can get from each + // accumulation is [-127, 127] * ([-128, 127] - + // [-128, 127]), which is [-32512, 32512]. log2(32512) + // = 14.98, which means we can accumulate at least 2^16 + // multiplications without overflow. The accumulator is + // applied to a filter so the accumulation logic will hold as + // long as the filter size (filter_y * filter_x * in_channel) + // does not exceed 2^16, which is the case in all the models + // we have seen so far. + // TODO(b/174275578): Add a check to make sure the + // accumulator depth is smaller than 2^16. + acc += filter_val * (input_val + input_offset); + } + } + } + if (bias_data) { + acc += bias_data[output_channel]; + } + acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[output_channel], + output_shift[output_channel]); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, + output_channel)] = static_cast(acc); + } + } + } + } + } +} + +inline void DepthwiseConvPerChannel( + const DepthwiseParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const std::int64_t* bias_data, const RuntimeShape& output_shape, + int16_t* output_data) { + // Get parameters. + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + + // Check dimensions of the tensors. + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + for (int m = 0; m < depth_multiplier; ++m) { + const int output_channel = m + in_channel * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + std::int64_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + if (is_point_inside_image) { + int32_t input_val = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + int32_t filter_val = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, output_channel)]; + // Accumulate with 64 bits accumulator. + // We assume maximum of 2^16 accumulations as with the 8-bit + // case so actually the value in the accumulator should not + // exceed 40 bits + acc += static_cast(filter_val) * + static_cast(input_val); + } + } + } + if (bias_data) { + acc += bias_data[output_channel]; + } + int32_t scaled_acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[output_channel], + output_shift[output_channel]); + scaled_acc = std::max(scaled_acc, output_activation_min); + scaled_acc = std::min(scaled_acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, + output_channel)] = + static_cast(scaled_acc); + } + } + } + } + } +} + +inline void DepthwiseConvHybridPerChannel( + const DepthwiseParams& params, float* scaling_factors_ptr, + const RuntimeShape& input_shape, const int8_t* input_data, + const RuntimeShape& filter_shape, const int8_t* filter_data, + const RuntimeShape& bias_shape, const float* bias_data, + const RuntimeShape& output_shape, float* output_data, + const float* per_channel_scale, int32_t* input_offset) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int dilation_width_factor = params.dilation_width_factor; + const int dilation_height_factor = params.dilation_height_factor; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + const int depth_multiplier = params.depth_multiplier; + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + // Check dimensions of the tensors. + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int input_depth = input_shape.Dims(3); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int bias_depth = bias_shape.FlatSize(); + TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); + TFLITE_DCHECK_EQ(bias_depth, output_depth); + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + for (int m = 0; m < depth_multiplier; ++m) { + const int output_channel = m + in_channel * depth_multiplier; + const int in_x_origin = (out_x * stride_width) - pad_width; + const int in_y_origin = (out_y * stride_height) - pad_height; + int32_t acc = 0; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + const int in_x = in_x_origin + dilation_width_factor * filter_x; + const int in_y = + in_y_origin + dilation_height_factor * filter_y; + // Zero padding by omitting the areas outside the image. + const bool is_point_inside_image = + (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && + (in_y < input_height); + if (is_point_inside_image) { + int32_t input_val = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + int32_t filter_val = filter_data[Offset( + filter_shape, 0, filter_y, filter_x, output_channel)]; + acc += filter_val * (input_val - input_offset[batch]); + } + } + } + float acc_float = static_cast(acc); + acc_float *= + per_channel_scale[output_channel] * scaling_factors_ptr[batch]; + if (bias_data && output_channel < bias_depth) { + acc_float += bias_data[output_channel]; + } + output_data[Offset(output_shape, batch, out_y, out_x, + output_channel)] = + ActivationFunctionWithMinMax(acc_float, output_activation_min, + output_activation_max); + } + } + } + } + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h new file mode 100644 index 00000000..0421d531 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h @@ -0,0 +1,126 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +// For per-channel functions, since it is defined in quantization spec that +// weights are symmetric +// (https://www.tensorflow.org/lite/performance/quantization_spec#symmetric_vs_asymmetric), +// zero_point (params.weights_offset) is always 0. +// However, for per-tensor functions, params.weights_offset is still applied for +// backward compatibility. +template +void FullyConnectedPerChannel( + const FullyConnectedParams& params, const int32_t* output_multiplier, + const int* output_shift, const RuntimeShape& input_shape, + const InputType* input_data, const RuntimeShape& filter_shape, + const WeightType* filter_data, const RuntimeShape& bias_shape, + const BiasType* bias_data, const RuntimeShape& output_shape, + OutputType* output_data) { + const int32_t input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int filter_dim_count = filter_shape.DimensionsCount(); + const int batches = output_shape.Dims(0); + const int output_depth = output_shape.Dims(1); + TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); + const int accum_depth = filter_shape.Dims(filter_dim_count - 1); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + BiasType acc = 0; + for (int d = 0; d < accum_depth; ++d) { + int32_t input_val = input_data[b * accum_depth + d]; + int32_t filter_val = filter_data[out_c * accum_depth + d]; + acc += filter_val * (input_val + input_offset); + } + if (bias_data) { + acc += bias_data[out_c]; + } + int32_t acc_scaled = MultiplyByQuantizedMultiplier( + acc, output_multiplier[out_c], output_shift[out_c]); + acc_scaled += output_offset; + acc_scaled = std::max(acc_scaled, output_activation_min); + acc_scaled = std::min(acc_scaled, output_activation_max); + output_data[out_c + output_depth * b] = + static_cast(acc_scaled); + } + } +} + +template +void FullyConnected(const FullyConnectedParams& params, + const RuntimeShape& input_shape, + const InputType* input_data, + const RuntimeShape& filter_shape, + const WeightType* filter_data, + const RuntimeShape& bias_shape, const BiasType* bias_data, + const RuntimeShape& output_shape, OutputType* output_data) { + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); + TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); + + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + const int filter_dim_count = filter_shape.DimensionsCount(); + const int output_dim_count = output_shape.DimensionsCount(); + const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); + const int output_depth = output_shape.Dims(output_dim_count - 1); + TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); + const int accum_depth = filter_shape.Dims(filter_dim_count - 1); + for (int b = 0; b < batches; ++b) { + for (int out_c = 0; out_c < output_depth; ++out_c) { + BiasType acc = 0; + for (int d = 0; d < accum_depth; ++d) { + int32_t input_val = input_data[b * accum_depth + d]; + int32_t filter_val = filter_data[out_c * accum_depth + d]; + acc += (filter_val + filter_offset) * (input_val + input_offset); + } + if (bias_data) { + acc += bias_data[out_c]; + } + int32_t acc_scaled = + MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); + acc_scaled += output_offset; + acc_scaled = std::max(acc_scaled, output_activation_min); + acc_scaled = std::min(acc_scaled, output_activation_max); + output_data[out_c + output_depth * b] = + static_cast(acc_scaled); + } + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h new file mode 100644 index 00000000..adf2aacc --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h @@ -0,0 +1,67 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +inline void L2Normalization(int32_t input_zero_point, int32_t outer_size, + int32_t depth, const int8_t* input_data, + int8_t* output_data) { + static constexpr int8_t kMinInt8 = std::numeric_limits::min(); + static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); + // The output scale must be in sync with Prepare(). + // Output is in 1/128 scale so the actual output range is nudged from [-1, 1] + // to [-1, 127/128]. + static constexpr int32_t kOutputScale = 7; + for (int outer_index = 0; outer_index < outer_size; ++outer_index) { + // int32_t = (int8_t - int8_t) ^ 2. + // ([-128, 127] - [-128, 127]) ^ 2 = [0, (2^8 - 1)^2] so the accumulator is + // safe from overflowing in at least 2^16 steps. + int32_t acc = 0; + for (int inner_index = 0; inner_index < depth; ++inner_index) { + int32_t input = + input_data[depth * outer_index + inner_index] - input_zero_point; + acc += input * input; + } + int32_t inv_l2norm_multiplier; + int inv_l2norm_shift; + GetInvSqrtQuantizedMultiplierExp(acc, kReverseShift, &inv_l2norm_multiplier, + &inv_l2norm_shift); + + for (int inner_index = 0; inner_index < depth; ++inner_index) { + int32_t input = + input_data[depth * outer_index + inner_index] - input_zero_point; + + // Rescale and downcast. Rescale is folded into the division. + int32_t output_in_q24 = MultiplyByQuantizedMultiplier( + input, inv_l2norm_multiplier, inv_l2norm_shift + kOutputScale); + output_in_q24 = + std::min(static_cast(kMaxInt8), + std::max(static_cast(kMinInt8), output_in_q24)); + output_data[depth * outer_index + inner_index] = + static_cast(output_in_q24); + } + } +} +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h new file mode 100644 index 00000000..be5fb90e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h @@ -0,0 +1,121 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +inline void Logistic(int32_t input_zero_point, int32_t input_range_radius, + int32_t input_multiplier, int32_t input_left_shift, + int32_t input_size, const int8_t* input_data, + int8_t* output_data) { + // Integer bits must be in sync with Prepare() function. + static constexpr int32_t kInputIntegerBits = 4; + static constexpr int32_t kOutputIntegerBits = 8; + static constexpr int8_t kMinInt8 = std::numeric_limits::min(); + static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); + static constexpr int32_t kOutputZeroPoint = -128; + + for (int i = 0; i < input_size; ++i) { + const int32_t input = + static_cast(input_data[i]) - input_zero_point; + if (input <= -input_range_radius) { + output_data[i] = kMinInt8; + } else if (input >= input_range_radius) { + output_data[i] = kMaxInt8; + } else { + const int32_t input_in_q4 = MultiplyByQuantizedMultiplier( + input, input_multiplier, input_left_shift); + using FixedPoint4 = gemmlowp::FixedPoint; + const int32_t output_in_q0 = + gemmlowp::logistic(FixedPoint4::FromRaw(input_in_q4)).raw(); + + // Rescale and downcast. + using gemmlowp::RoundingDivideByPOT; + int32_t output_in_q23 = + RoundingDivideByPOT(output_in_q0, 31 - kOutputIntegerBits); + output_in_q23 = std::min(std::max(output_in_q23 + kOutputZeroPoint, + static_cast(kMinInt8)), + static_cast(kMaxInt8)); + output_data[i] = static_cast(output_in_q23); + } + } +} + +inline void Logistic(int32_t input_multiplier, int32_t input_left_shift, + int32_t input_size, const int16_t* ptr_input_data, + int16_t* ptr_output_data) { + // We use the LUT for sigmoid and take into account, that + // tanh(x) = 2*sigmoid(2*x) - 1 + + // We scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. + // In case of general parameter scale, multiplier 3 is taken into account + // in TanhPrepare function and it is included in + // input_multiplier already. + + TFLITE_DCHECK_GE(input_left_shift, 0); + if (input_multiplier == 0) { // power of two case + input_multiplier = 3 << input_left_shift; + input_left_shift = 0; + } + + int32_t round = (input_left_shift > 0) ? 1 << (input_left_shift - 1) : 0; + + for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) { + int32_t input_data = + ((*ptr_input_data) * input_multiplier + round) >> input_left_shift; + + // We do interpolation on unsigned values. + uint32_t abs_input_data = abs(input_data); + + // We divide by 2 power of 9, because + // we need to divide by 2 in power of 7 for + // the input conversion + 1/4 from the scale above. + + // Define uh as uint32_t type not to make this function overflow. + uint32_t uh = abs_input_data >> 9; + uint32_t result; + + if (uh >= 255) { + // Saturate to maximum. + result = 0x7FFF << 10; + } else { + uint32_t ua = sigmoid_table_uint16[uh]; + uint32_t ub = sigmoid_table_uint16[uh + 1]; + uint32_t ut = abs_input_data & 0x1ff; + // Interpolation is done using the fractional bit. + result = (ua << 9) + ut * (ub - ua); + } + + result = (input_data >= 0) ? (result + (1 << 9)) + : ((1 << (16 + 9)) - result + (1 << 9) - 1); + + // Back to 16-bit. + result >>= 10; + + *ptr_output_data = result; + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h new file mode 100644 index 00000000..7e3f690e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h @@ -0,0 +1,18 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h new file mode 100644 index 00000000..40516f58 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h @@ -0,0 +1,194 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ + +#include + +#include "fixedpoint/fixedpoint.h" +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +// Maximum dimension supported by the broadcast mul operation. +constexpr int kMaxMulBroadcastDim = 6; + +template +void MulElementwise(int size, const ArithmeticParams& params, + const InputType* input1_data, const InputType* input2_data, + OutputType* output_data) { + for (int i = 0; i < size; ++i) { + const int32_t input1_val = params.input1_offset + input1_data[i]; + const int32_t input2_val = params.input2_offset + input2_data[i]; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplier(input1_val * input2_val, + params.output_multiplier, + params.output_shift); + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + output_data[i] = static_cast(clamped_output); + } +} + +template +inline void Mul(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + ruy::profiler::ScopeLabel label("Mul/8bit"); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + MulElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +// Mul with 16 bit inputs and int8_t outputs. +inline void Mul(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16_t* input1_data, + const RuntimeShape& input2_shape, const int16_t* input2_data, + const RuntimeShape& output_shape, int8_t* output_data) { + ruy::profiler::ScopeLabel label("Mul/Int16Int8"); + int32_t output_offset = params.output_offset; + int32_t output_activation_min = params.quantized_activation_min; + int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + using F0 = gemmlowp::FixedPoint; + + F0 unclamped_result = + F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); + int16_t rescaled_result = + gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); + int16_t clamped_result = std::min( + output_activation_max - output_offset, rescaled_result); + clamped_result = std::max(output_activation_min - output_offset, + clamped_result); + output_data[i] = output_offset + clamped_result; + } +} + +template +inline void BroadcastMul6DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("BroadcastMul6DSlow"); + + NdArrayDesc desc1; + NdArrayDesc desc2; + // The input shapes are extended as part of NdArrayDesc initialization. + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape); + // Cache output shape dimensions. + int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; + std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), + sizeof(extended_output_shape_dims)); + + size_t input1_offset_a = 0; + size_t input2_offset_a = 0; + size_t output_offset_a = 0; + for (int a = 0; a < extended_output_shape_dims[0]; ++a) { + size_t input1_offset_d = input1_offset_a; + size_t input2_offset_d = input2_offset_a; + size_t output_offset_d = output_offset_a; + for (int d = 0; d < extended_output_shape_dims[1]; ++d) { + size_t input1_offset_b = input1_offset_d; + size_t input2_offset_b = input2_offset_d; + size_t output_offset_b = output_offset_d; + for (int b = 0; b < extended_output_shape_dims[2]; ++b) { + size_t input1_offset_y = input1_offset_b; + size_t input2_offset_y = input2_offset_b; + size_t output_offset_y = output_offset_b; + for (int y = 0; y < extended_output_shape_dims[3]; ++y) { + size_t input1_offset_x = input1_offset_y; + size_t input2_offset_x = input2_offset_y; + size_t output_offset_x = output_offset_y; + for (int x = 0; x < extended_output_shape_dims[4]; ++x) { + size_t input1_offset_c = input1_offset_x; + size_t input2_offset_c = input2_offset_x; + size_t output_offset_c = output_offset_x; + for (int c = 0; c < extended_output_shape_dims[5]; ++c) { + const int32_t input1_val = + params.input1_offset + input1_data[input1_offset_c]; + const int32_t input2_val = + params.input2_offset + input2_data[input2_offset_c]; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplier(input1_val * input2_val, + params.output_multiplier, + params.output_shift); + const int32_t clamped_output = std::min( + params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + output_data[output_offset_c] = static_cast(clamped_output); + input1_offset_c += desc1.strides[5]; + input2_offset_c += desc2.strides[5]; + ++output_offset_c; + } + input1_offset_x += desc1.strides[4]; + input2_offset_x += desc2.strides[4]; + output_offset_x += extended_output_shape_dims[5]; + } + input1_offset_y += desc1.strides[3]; + input2_offset_y += desc2.strides[3]; + output_offset_y += + extended_output_shape_dims[4] * extended_output_shape_dims[5]; + } + input1_offset_b += desc1.strides[2]; + input2_offset_b += desc2.strides[2]; + output_offset_b += extended_output_shape_dims[3] * + extended_output_shape_dims[4] * + extended_output_shape_dims[5]; + } + input1_offset_d += desc1.strides[1]; + input2_offset_d += desc2.strides[1]; + output_offset_d += + extended_output_shape_dims[2] * extended_output_shape_dims[3] * + extended_output_shape_dims[4] * extended_output_shape_dims[5]; + } + input1_offset_a += desc1.strides[0]; + input2_offset_a += desc2.strides[0]; + output_offset_a += + extended_output_shape_dims[1] * extended_output_shape_dims[2] * + extended_output_shape_dims[3] * extended_output_shape_dims[4] * + extended_output_shape_dims[5]; + } +} + +template +inline void BroadcastMul4DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data) { + BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); +} + +} // namespace reference_integer_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h new file mode 100644 index 00000000..b1d58176 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h @@ -0,0 +1,264 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +inline bool AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const int8_t* input_data, + const RuntimeShape& output_shape, int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + int32_t acc = 0; + int filter_count = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + filter_count++; + } + } + if (filter_count == 0) return false; + // Round to the closest integer value. + acc = acc > 0 ? (acc + filter_count / 2) / filter_count + : (acc - filter_count / 2) / filter_count; + acc = std::max(acc, params.quantized_activation_min); + acc = std::min(acc, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(acc); + } + } + } + } + return true; +} + +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& output_shape, + int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_GE(params.quantized_activation_min, + std::numeric_limits::min()); + TFLITE_DCHECK_LE(params.quantized_activation_max, + std::numeric_limits::max()); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + int8_t max = std::numeric_limits::lowest(); + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + max = std::max( + max, + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + } + } + max = std::max(max, params.quantized_activation_min); + max = std::min(max, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(max); + } + } + } + } +} + +inline bool AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const int16_t* input_data, + const RuntimeShape& output_shape, + int16_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + int32_t acc = 0; + int filter_count = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + filter_count++; + } + } + if (filter_count == 0) return false; + // Round to the closest integer value. + acc = acc > 0 ? (acc + filter_count / 2) / filter_count + : (acc - filter_count / 2) / filter_count; + acc = std::max(acc, params.quantized_activation_min); + acc = std::min(acc, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(acc); + } + } + } + } + return true; +} + +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& output_shape, + int16_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_GE(params.quantized_activation_min, + std::numeric_limits::min()); + TFLITE_DCHECK_LE(params.quantized_activation_max, + std::numeric_limits::max()); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + int16_t max = std::numeric_limits::lowest(); + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + max = std::max( + max, + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + } + } + max = std::max(max, params.quantized_activation_min); + max = std::min(max, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(max); + } + } + } + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h new file mode 100644 index 00000000..25d81fa1 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h @@ -0,0 +1,117 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ + +#include +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +inline void Tanh(int32_t input_zero_point, int32_t input_range_radius, + int32_t input_multiplier, int32_t input_shift, + const RuntimeShape& input_shape, const int8_t* input_data, + const RuntimeShape& output_shape, int8_t* output_data) { + // Integer bits must be in sync with Prepare() function. + static constexpr int32_t kInputIntegerBits = 4; + static constexpr int32_t kOutputScale = 7; + static constexpr int32_t kMinInt8 = std::numeric_limits::min(); + static constexpr int32_t kMaxInt8 = std::numeric_limits::max(); + using F4 = gemmlowp::FixedPoint; + + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; ++i) { + const int32_t input = + static_cast(input_data[i]) - input_zero_point; + if (input <= -input_range_radius) { + output_data[i] = kMinInt8; + } else if (input >= input_range_radius) { + output_data[i] = kMaxInt8; + } else { + const int32_t input_in_q4 = + MultiplyByQuantizedMultiplier(input, input_multiplier, input_shift); + const int32_t output_in_q0 = + gemmlowp::tanh(F4::FromRaw(input_in_q4)).raw(); + + // Rescale and downcast. + using gemmlowp::RoundingDivideByPOT; + int32_t output_in_q24 = + RoundingDivideByPOT(output_in_q0, 31 - kOutputScale); + output_in_q24 = std::min(std::max(output_in_q24, kMinInt8), kMaxInt8); + output_data[i] = static_cast(output_in_q24); + } + } +} + +inline void Tanh(int32_t input_multiplier, int32_t input_left_shift, + const RuntimeShape& input_shape, const int16_t* ptr_input_data, + const RuntimeShape& output_shape, int16_t* ptr_output_data) { + // We use the LUT for sigmoid and take into account, that + // tanh(x) = 2*sigmoid(2*x) - 1 + + // We scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. + // In case of general parameter scale, multiplier 3 is taken into account + // in TanhPrepare function and it is included in + // input_multiplier already. + + if (input_multiplier == 0) { // power of two case + input_multiplier = 3 << input_left_shift; + input_left_shift = 0; + } + + int32_t round = (input_left_shift > 0) ? 1 << (input_left_shift - 1) : 0; + + int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; ++i, ptr_input_data++, ptr_output_data++) { + int32_t input_data = + ((*ptr_input_data) * input_multiplier + round) >> input_left_shift; + + uint32_t abs_input_data = abs(input_data); + uint32_t uh = abs_input_data >> 8; + int32_t result; + + if (uh >= 255) { + // Saturate to maximum. + result = 0xFFFF << 8; + } else { + uint32_t ua = sigmoid_table_uint16[uh]; + uint32_t ub = sigmoid_table_uint16[uh + 1]; + + uint8_t ut = abs_input_data & 0xFF; + + result = (ua << 8) + ut * (ub - ua); + } + + result = (input_data >= 0) + ? (result - (1 << (14 + 9)) + (1 << (9 - 2))) + : (-result + (1 << (14 + 9)) + (1 << (9 - 2)) - 1); + + // Convert back to 16-bit. + result >>= (9 - 1); + + *ptr_output_data = result; + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h new file mode 100644 index 00000000..7434b2a7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h @@ -0,0 +1,224 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_integer_ops { + +// Fixed-point per-channel-quantization transpose convolution reference kernel. +inline void TransposeConv( + const ConvParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int8_t* output_data, const RuntimeShape& im2col_shape, int8_t* im2col_data, + int32_t* scratch_buffer) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int32_t input_offset = params.input_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + const int num_elements = output_shape.FlatSize(); + // We need to initialize scratch_buffer to all 0s, as we apply the same + // 'scatter' based trick as in float version. + memset(scratch_buffer, 0, num_elements * sizeof(int32_t)); + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence. + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location. + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds. + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + const int8_t input_value = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + const int8_t filter_value = + filter_data[Offset(filter_shape, out_channel, filter_y, + filter_x, in_channel)]; + scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)] += + (input_value + input_offset) * filter_value; + } + } + } + } + } + } + } + } + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + int32_t acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)]; + if (bias_data) { + acc += bias_data[out_channel]; + } + acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[out_channel], output_shift[out_channel]); + acc += output_offset; + acc = std::max(acc, output_activation_min); + acc = std::min(acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(acc); + } + } + } + } +} + +// int16_t input (zero_point=0), int8_t filter, int32 or int64 accumulator +template +inline void TransposeConv( + const ConvParams& params, const int32_t* output_multiplier, + const int32_t* output_shift, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const Scalar* bias_data, const RuntimeShape& output_shape, + int16_t* output_data, const RuntimeShape& im2col_shape, int8_t* im2col_data, + Scalar* scratch_buffer) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + + const int num_elements = output_shape.FlatSize(); + // We need to initialize scratch_buffer to all 0s, as we apply the same + // 'scatter' based trick as in float version. + memset(scratch_buffer, 0, num_elements * sizeof(Scalar)); + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence. + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location. + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds. + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + const int32_t input_value = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + const int32_t filter_value = + filter_data[Offset(filter_shape, out_channel, filter_y, + filter_x, in_channel)]; + scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)] += + input_value * filter_value; + } + } + } + } + } + } + } + } + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + Scalar acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)]; + if (bias_data) { + acc += bias_data[out_channel]; + } + int32_t scaled_acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier[out_channel], output_shift[out_channel]); + scaled_acc = std::max(scaled_acc, output_activation_min); + scaled_acc = std::min(scaled_acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(scaled_acc); + } + } + } + } +} + +} // namespace reference_integer_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h new file mode 100644 index 00000000..2744366a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h @@ -0,0 +1,90 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ + +#include +#include + +#include "tensorflow/lite/core/c/common.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void L2Normalization(const tflite_micro::L2NormalizationParams& op_params, + const RuntimeShape& input_shape, + const float* input_data, + const RuntimeShape& output_shape, + float* output_data, float epsilon = 1e-6) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + for (int i = 0; i < outer_size; ++i) { + float squared_l2_norm = 0; + for (int c = 0; c < depth; ++c) { + const float val = input_data[depth * i + c]; + squared_l2_norm += val * val; + } + float l2_norm = std::sqrt(squared_l2_norm); + l2_norm = std::max(l2_norm, epsilon); + for (int c = 0; c < depth; ++c) { + output_data[depth * i + c] = input_data[depth * i + c] / l2_norm; + } + } +} + +inline void L2Normalization(const tflite_micro::L2NormalizationParams& op_params, + const RuntimeShape& input_shape, + const uint8_t* input_data, + const RuntimeShape& output_shape, + uint8_t* output_data) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int32_t input_zero_point = op_params.input_zero_point; + + for (int i = 0; i < outer_size; ++i) { + int32_t square_l2_norm = 0; + for (int c = 0; c < depth; c++) { + int32_t diff = input_data[depth * i + c] - input_zero_point; + square_l2_norm += diff * diff; + } + int32_t inv_l2norm_multiplier; + int inv_l2norm_shift; + GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift, + &inv_l2norm_multiplier, &inv_l2norm_shift); + for (int c = 0; c < depth; c++) { + int32_t diff = input_data[depth * i + c] - input_zero_point; + int32_t rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( + 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); + int32_t unclamped_output_val = 128 + rescaled_diff; + int32_t output_val = + std::min(static_cast(255), + std::max(static_cast(0), unclamped_output_val)); + output_data[depth * i + c] = static_cast(output_val); + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h new file mode 100644 index 00000000..06e3c155 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h @@ -0,0 +1,69 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void LeakyRelu(const tflite_micro::LeakyReluParams& params, + const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + const float val = input_data[i]; + // Note that alpha might be > 1 or < 0, so we don't use std::max here. + output_data[i] = val > 0 ? val : val * params.alpha; + } +} + +template +inline void QuantizeLeakyRelu(const LeakyReluParams& params, + const RuntimeShape& input_shape, + const T* input_data, + const RuntimeShape& output_shape, + T* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + static const int32_t quantized_min = std::numeric_limits::min(); + static const int32_t quantized_max = std::numeric_limits::max(); + for (int i = 0; i < flat_size; ++i) { + const int32_t input_value = input_data[i] - params.input_offset; + int32_t unclamped_output; + if (input_value >= 0) { + unclamped_output = params.output_offset + + MultiplyByQuantizedMultiplier( + input_value, params.output_multiplier_identity, + params.output_shift_identity); + } else { + unclamped_output = params.output_offset + + MultiplyByQuantizedMultiplier( + input_value, params.output_multiplier_alpha, + params.output_shift_alpha); + } + const T clamped_output = + std::min(quantized_max, std::max(quantized_min, unclamped_output)); + output_data[i] = static_cast(clamped_output); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h new file mode 100644 index 00000000..c99f3a20 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h @@ -0,0 +1,256 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ + +#include +#include +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void LogSoftmax(const SoftmaxParams& params, + const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + + for (int i = 0; i < outer_size; ++i) { + // Find max element value which we'll use to ensure numerical stability + // taking advantage of the following equality: + // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C))) + float max = std::numeric_limits::lowest(); + for (int c = 0; c < depth; ++c) { + max = std::max(max, input_data[i * depth + c]); + } + + // Compute sum. + float sum = 0.f; + for (int c = 0; c < depth; ++c) { + sum += std::exp(input_data[i * depth + c] - max); + } + + // Compute result. + const float log_sum = std::log(sum); + for (int c = 0; c < depth; ++c) { + output_data[i * depth + c] = input_data[i * depth + c] - max - log_sum; + } + } +} + +inline void LogSoftmax(const SoftmaxParams& params, + const RuntimeShape& input_shape, + const uint8_t* input_data, + const RuntimeShape& output_shape, uint8_t* output_data) { + const int32_t input_multiplier = params.input_multiplier; + const int32_t input_left_shift = params.input_left_shift; + const int32_t reverse_scaling_divisor = params.reverse_scaling_divisor; + const int32_t reverse_scaling_right_shift = + params.reverse_scaling_right_shift; + const int diff_min = params.diff_min; + // The representation chosen for the input to the exp() function is Q5.26. + // We need to leave extra space since values that we skip might be as large + // as -32 before multiplying by input_beta_multiplier, and therefore as + // large as -16 afterwards. Note that exp(-8) is definitely not + // insignificant to accumulation, but exp(-16) definitely is. + static constexpr int kScaledDiffIntegerBits = 5; + static constexpr int kAccumulationIntegerBits = 12; + static constexpr int kOutputIntegerBits = 4; + using FixedPointScaledDiff = + gemmlowp::FixedPoint; + using FixedPointAccum = + gemmlowp::FixedPoint; + + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + + for (int i = 0; i < outer_size; ++i) { + uint8_t max_in_row = 0; + for (int c = 0; c < depth; ++c) { + max_in_row = std::max(max_in_row, input_data[i * depth + c]); + } + + FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); + for (int c = 0; c < depth; ++c) { + int32_t input_diff = + static_cast(input_data[i * depth + c]) - max_in_row; + if (input_diff >= diff_min) { + const int32_t input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_multiplier, input_left_shift); + const FixedPointScaledDiff scaled_diff_f8 = + FixedPointScaledDiff::FromRaw(input_diff_rescaled); + sum_of_exps = sum_of_exps + gemmlowp::Rescale( + exp_on_negative_values(scaled_diff_f8)); + } + } + + const int32_t fixed_log_sum_of_exps = + log_x_for_x_greater_than_or_equal_to_1( + sum_of_exps) + .raw(); + + // rescaled_diff_min is smallest representable in + // Q(kScaledDiffIntegerBits).(31-kScaledDiffIntegerBits) plus the + // log-sub-exps that will be subtracted in the loop. + // + // The thresholds diff_min, etc are negative. + const int rescaled_diff_min = + fixed_log_sum_of_exps + std::numeric_limits::lowest(); + const int adjusted_diff_min = + std::max(static_cast( + diff_min - 1), // Note use of > below instead of >= above. + MultiplyByQuantizedMultiplierSmallerThanOneExp( + rescaled_diff_min, reverse_scaling_divisor, + -reverse_scaling_right_shift)); + + for (int c = 0; c < depth; ++c) { + int32_t input_diff = + static_cast(input_data[i * depth + c]) - max_in_row; + if (input_diff > adjusted_diff_min) { + const int32_t input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_multiplier, input_left_shift); + int32_t unsat_output = + gemmlowp::RoundingDivideByPOT( + (input_diff_rescaled - fixed_log_sum_of_exps), + 31 - kScaledDiffIntegerBits - kOutputIntegerBits) + + 255; + + output_data[i * depth + c] = static_cast( + std::max(std::min(unsat_output, static_cast(255)), + static_cast(0))); + } else { + // Set output to smallest value. + output_data[i * depth + c] = 0; + } + } + } +} + +template +inline void LogSoftmaxQuantized(const SoftmaxParams& params, + const size_t outer_size, const size_t depth, + const RuntimeShape& input_shape, + const T* input_data, + const RuntimeShape& output_shape, + T* output_data) { + const int32_t input_multiplier = params.input_multiplier; + const int32_t input_left_shift = params.input_left_shift; + const int32_t reverse_scaling_divisor = params.reverse_scaling_divisor; + const int32_t reverse_scaling_right_shift = + params.reverse_scaling_right_shift; + const int diff_min = params.diff_min; + + static constexpr T kMinT8 = std::numeric_limits::min(); + static constexpr T kMaxT8 = std::numeric_limits::max(); + static constexpr int32_t kMinInt32 = std::numeric_limits::min(); + + // All IntegerBits must agree with Prepare function. + // Input is chosen as Q5.26 so exp(-1 * 2^5 * 2^-1) = exp(-16) is negligible. + static constexpr int kInputIntegerBits = 5; + static constexpr int kAccumulationIntegerBits = 12; + static constexpr int kOutputIntegerBits = 4; + using F5 = gemmlowp::FixedPoint; + using F12 = gemmlowp::FixedPoint; + + for (size_t outer_index = 0; outer_index < outer_size; ++outer_index) { + T max_in_row = kMinT8; + for (size_t inner_index = 0; inner_index < depth; ++inner_index) { + max_in_row = + std::max(max_in_row, input_data[outer_index * depth + inner_index]); + } + + // Accumulator "sum_of_exps_in_q12" is safe from overflowing in 2^12 steps. + F12 sum_of_exps_in_q12 = F12::FromRaw(0); + for (size_t inner_index = 0; inner_index < depth; ++inner_index) { + int32_t input_diff = + static_cast(input_data[outer_index * depth + inner_index]) - + max_in_row; + if (input_diff >= diff_min) { + const int32_t input_diff_in_q5 = MultiplyByQuantizedMultiplier( + input_diff, input_multiplier, input_left_shift); + sum_of_exps_in_q12 = + sum_of_exps_in_q12 + + gemmlowp::Rescale( + exp_on_negative_values(F5::FromRaw(input_diff_in_q5))); + } + } + + const int32_t log_sum_of_exps_in_q5 = + log_x_for_x_greater_than_or_equal_to_1( + sum_of_exps_in_q12) + .raw(); + + // Potentially reduced the valid range. shifted_log_sum_of_exps_in_q5 is + // smallest representable in Q5.26 plus the log_sum_of_exps. + const int32_t shifted_log_sum_of_exps_in_q5 = + log_sum_of_exps_in_q5 + kMinInt32; + const int32_t adjusted_diff_min = + std::max(static_cast(diff_min - 1), + MultiplyByQuantizedMultiplier(shifted_log_sum_of_exps_in_q5, + reverse_scaling_divisor, + -reverse_scaling_right_shift)); + + for (size_t inner_index = 0; inner_index < depth; ++inner_index) { + int32_t input_diff = + static_cast(input_data[outer_index * depth + inner_index]) - + max_in_row; + // Note use of > below instead of >= above. + if (input_diff > adjusted_diff_min) { + const int32_t input_diff_in_q5 = MultiplyByQuantizedMultiplier( + input_diff, input_multiplier, input_left_shift); + + // Rescale and downcast. + int32_t output_in_q27 = + gemmlowp::RoundingDivideByPOT( + (input_diff_in_q5 - log_sum_of_exps_in_q5), + 31 - kInputIntegerBits - kOutputIntegerBits) + + kMaxT8; + + output_in_q27 = + std::max(std::min(output_in_q27, static_cast(kMaxT8)), + static_cast(kMinT8)); + output_data[outer_index * depth + inner_index] = + static_cast(output_in_q27); + } else { + output_data[outer_index * depth + inner_index] = kMinT8; + } + } + } +} + +inline void LogSoftmax(const SoftmaxParams& params, const size_t outer_size, + const size_t depth, const RuntimeShape& input_shape, + const int8_t* input_data, + const RuntimeShape& output_shape, int8_t* output_data) { + LogSoftmaxQuantized(params, outer_size, depth, input_shape, input_data, + output_shape, output_data); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h new file mode 100644 index 00000000..6b2dcf0e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h @@ -0,0 +1,132 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ + +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/quantization_util.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/kernels/op_macros.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void Logistic(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const float cutoff_upper = 16.619047164916992188f; + const float cutoff_lower = -9.f; + + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + // Rational for using approximation in reference kernel. + // 0. This approximation gives enough precision for float. + // 1. This works around an issue on an embedded chipset where exp() does not + // return correctly as expected - exp(x) should return inf when overflown + // not 1.701417 IEEE 754 defines representation for inf. + // 2. This will speed up calculation and is matching the behavior in the + // optimized kernels. (check the definition of scalar_logistic_op) + + for (int i = 0; i < flat_size; i++) { + float val = input_data[i]; + float result; + if (val > cutoff_upper) { + result = 1.0f; + } else if (val < cutoff_lower) { + result = std::exp(val); + } else { + result = 1.f / (1.f + std::exp(-val)); + } + output_data[i] = result; + } +} + +// Convenience version that allows, for example, generated-code calls to be +// uniform between data types. +inline void Logistic(const LogisticParams&, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { + // Drop params: not needed. + Logistic(input_shape, input_data, output_shape, output_data); +} + +inline void Logistic(const LogisticParams& params, + const RuntimeShape& input_shape, const int16_t* input_data, + const RuntimeShape& output_shape, int16_t* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + const F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::logistic(input); + output_data[i] = output.raw(); + } +} + +// Quantized int8_t logistic activation. Cheats by dequantizing and +// requantizing around the floating point logistic method. This implementation +// is slow on platforms without a floating point unit. + +// TODO(b/141211002): Delete this int8_t implementation once we can reuse the +// approach used in TFLite for int8_t Logistic. +inline void Logistic(const RuntimeShape& input_shape, const int8_t* input_data, + float input_scale, int input_zero_point, + const RuntimeShape& output_shape, int8_t* output_data, + float output_scale, int output_zero_point) { + const float cutoff_upper = 16.619047164916992188f; + const float cutoff_lower = -9.f; + + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + // Rational for using approximation in reference kernel. + // 0. This approximation gives enough precision for float. + // 1. This works around an issue on an embedded chipset where exp() does not + // return correctly as expected - exp(x) should return inf when overflown + // not 1.701417 IEEE 754 defines representation for inf. + // 2. This will speed up calculation and is matching the behavior in the + // optimized kernels. (check the definition of scalar_logistic_op) + + for (int i = 0; i < flat_size; i++) { + // Dequantize. + float val = + static_cast((input_data[i] - input_zero_point) * input_scale); + float result; + if (val > cutoff_upper) { + result = 1.0f; + } else if (val < cutoff_lower) { + result = std::exp(val); + } else { + result = 1.f / (1.f + std::exp(-val)); + } + // Requantize + int8_t output = + static_cast(result / output_scale + output_zero_point); + output_data[i] = output; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h new file mode 100644 index 00000000..b580eb2d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h @@ -0,0 +1,422 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/reference/concatenation.h" +#include "tensorflow/lite/kernels/internal/reference/fully_connected.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void LstmCell( + const LstmCellParams& params, const RuntimeShape& unextended_input_shape, + const float* input_data, const RuntimeShape& unextended_prev_activ_shape, + const float* prev_activ_data, const RuntimeShape& weights_shape, + const float* weights_data, const RuntimeShape& unextended_bias_shape, + const float* bias_data, const RuntimeShape& unextended_prev_state_shape, + const float* prev_state_data, + const RuntimeShape& unextended_output_state_shape, float* output_state_data, + const RuntimeShape& unextended_output_activ_shape, float* output_activ_data, + const RuntimeShape& unextended_concat_temp_shape, float* concat_temp_data, + const RuntimeShape& unextended_activ_temp_shape, float* activ_temp_data) { + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape prev_activ_shape = + RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); + const RuntimeShape bias_shape = + RuntimeShape::ExtendedShape(4, unextended_bias_shape); + const RuntimeShape prev_state_shape = + RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); + const RuntimeShape output_state_shape = + RuntimeShape::ExtendedShape(4, unextended_output_state_shape); + const RuntimeShape output_activ_shape = + RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); + const RuntimeShape concat_temp_shape = + RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); + const RuntimeShape activ_temp_shape = + RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); + TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); + + const int weights_dim_count = weights_shape.DimensionsCount(); + const int batches = + MatchingDim(input_shape, 0, prev_activ_shape, 0, prev_state_shape, 0, + output_state_shape, 0, output_activ_shape, 0); + const int height = + MatchingDim(input_shape, 1, prev_activ_shape, 1, prev_state_shape, 1, + output_state_shape, 1, output_activ_shape, 1); + const int width = + MatchingDim(input_shape, 2, prev_activ_shape, 2, prev_state_shape, 2, + output_state_shape, 2, output_activ_shape, 2); + const int input_depth = input_shape.Dims(3); + const int prev_activ_depth = prev_activ_shape.Dims(3); + const int total_input_depth = prev_activ_depth + input_depth; + TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), + total_input_depth); + TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); + const int intern_activ_depth = + MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); + TFLITE_DCHECK_EQ(weights_shape.FlatSize(), + intern_activ_depth * total_input_depth); + TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); + const int output_depth = + MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, + 3, output_activ_shape, 3); + TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); + + // Concatenate prev_activ and input data together + float const* concat_input_arrays_data[2] = {input_data, prev_activ_data}; + const RuntimeShape* concat_input_arrays_shapes[2] = {&input_shape, + &prev_activ_shape}; + tflite_micro::ConcatenationParams concat_params; + concat_params.axis = 3; + concat_params.inputs_count = 2; + Concatenation(concat_params, concat_input_arrays_shapes, + concat_input_arrays_data, concat_temp_shape, concat_temp_data); + + // Fully connected + tflite_micro::FullyConnectedParams fc_params; + fc_params.float_activation_min = std::numeric_limits::lowest(); + fc_params.float_activation_max = std::numeric_limits::max(); + FullyConnected(fc_params, concat_temp_shape, concat_temp_data, weights_shape, + weights_data, bias_shape, bias_data, activ_temp_shape, + activ_temp_data); + + // Memory state update (the LSTM "guts") + for (int b = 0; b < batches; ++b) { + for (int w = 0; w < width; ++w) { + for (int h = 0; h < height; ++h) { + for (int c = 0; c < output_depth; ++c) { + const float input_gate = + 1.f / + (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, + 0 * output_depth + c)])); + const float new_input = std::tanh(activ_temp_data[Offset( + activ_temp_shape, b, h, w, 1 * output_depth + c)]); + const float forget_gate = + 1.f / + (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, + 2 * output_depth + c)])); + const float output_gate = + 1.f / + (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, + 3 * output_depth + c)])); + const float new_state = + input_gate * new_input + + forget_gate * + prev_state_data[Offset(prev_state_shape, b, h, w, c)]; + output_state_data[Offset(output_state_shape, b, h, w, c)] = new_state; + output_activ_data[Offset(output_activ_shape, b, h, w, c)] = + output_gate * std::tanh(new_state); + } + } + } + } +} + +// Quantized LSTM cell implementation. +// The quantization of the input, output arrays is as follows: +// - The input activations are quantized as uint8 on the interval +// [-1, 127/128]. +// The rationale for that is that is the natural interval for output +// activations (see next point) and these need to be concatenated together. +// We could accommodate different ranges by re-scaling, but we empirically +// found that setting the input activations range to be [-1, 127/128] in the +// first place, removing the need for re-scaling, greatly improves accuracy. +// - The output activations are quantized as uint8 on the interval +// [-1, 127/128]. +// The rationale for that is that the definition of a LSTM cell makes them +// intrinsically constrained in [-1, 1]; tweaking that to [-1, 127/128] +// makes for simpler, more accurate fixed-point arithmetic. +// - The output-at-previous-timestep state array is obviously quantized as +// the output activations. +// - The internal LSTM memory (not the output-at-previous-timestep, the other +// internal state array) is int16-quantized and may use any power-of-two, +// symmetric range i.e. [-2^N, 2^N * 32767/32768] for any N, which we call +// StateIntegerBits below, see the below discussion of that template +// parameter ("The StateIntegerBits template parameter"). +// - The output of the internal fully-connected node is int16-quantized +// on the interval [-8, 8 * 32767/32768], the rationale for which is +// explained just below ("Why [-8, 8] for fully-connected output?"). +// +// +// === The StateIntegerBits template parameter === +// +// The StateIntegerBits template parameter controls the fixed-point format used +// to represent the internal memory of the LSTM cell (not the +// output-at-previous-timestep, the other internal state array). It's currently +// a template parameter so that the model can control that. The most typical +// value for StateIntegerBits is 4. Other plausible values are anywhere between +// 3 and 5. We might eventually standardize on a single supported value, e.g. 4, +// and drop that template parameter. The reason why it can't be a runtime +// parameter is that this controls the fixed-point format used, i.e. we need to +// generate actually different code based on it. In particular, we generate code +// for a fixed-point tanh() implementation for that format, which internally +// uses a fixed-point exp() implementation, which internally uses a +// barrel-shifter with a number of steps that depends on StateIntegerBits. +// Another consequence of that is that a higher value of StateIntegerBits +// results in a more expensive implementation (more barrel shifter steps +// needed). +// +// +// === Why [-8, 8] for fully-connected output? === +// +// This array is only fed to Logistic and Tanh functions, for which +// the quantized implementation will want to use fixed-point arithmetic, +// requiring a power-of-two representation interval. Thus, we should right +// away quantize this array to a power-of-two interval; otherwise, +// implementation will need to rescale that, losing any benefit that a tighter +// representation interval might otherwise yield, while introducing some +// numerical error and computational overhead. +// +// Now, Logistic and Tanh +// are nearly constant (nearly equal to their horizontal asymptotes) +// outside of a small bounded interval around 0: +// +// Logistic(4) = 1 - 1.8e-2 Tanh(4) = 1 - 6.7e-4 +// Logistic(8) = 1 - 3.4e-4 Tanh(8) = 1 - 2.3e-7 +// Logistic(16) = 1 - 1.1e-7 Tanh(16) = 1 - 2.5e-14 +// +// From this, we see that clamping to [-4, 4] would be too inaccurate +// (the error of 1.8e-2 on Logistic would be felt even in 8bit precision) +// while clamping to [-16, 16] would make no difference even in float32. +// However, for a fixed-point implementation in 16-bit integers, using 5 +// integer bits to represent the [-16, 16] range would leave only 11 +// fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive +// representable values. Notice that is higher than the +// worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. +// Using [-8, 8] thus seems like the better compromise overall, enjoying +// an increment of 2.4e-4 between representable values and a worst-case +// clamping error of 3.4e-4, both better than the increment of 4.9e-4 with +// [-16, 16]. +// +// Moreover, all other things being equal, it is nice to choose the narrower +// representation range, as that makes the implementation of fixed-point +// math functions a little cheaper (each integer bit requires an additional +// barrel-shifter atep in the implementation of exp(-x)). That is further +// reason to prefer [-8, 8] over [-16, 16]. The choice of [-16, 16] would make +// sense for 32-bit float or 32-bit fixed-point quantization, but we are +// aiming for 16-bit fixed-point quantization of these internal nodes here. +// +template +inline void LstmCell(const LstmCellParams& params, + const RuntimeShape& unextended_input_shape, + const uint8_t* input_data_uint8, + const RuntimeShape& unextended_prev_activ_shape, + const uint8_t* prev_activ_data_uint8, + const RuntimeShape& weights_shape, + const uint8_t* weights_data_uint8, + const RuntimeShape& unextended_bias_shape, + const int32_t* bias_data_int32, + const RuntimeShape& unextended_prev_state_shape, + const int16_t* prev_state_data_int16, + const RuntimeShape& unextended_output_state_shape, + int16_t* output_state_data_int16, + const RuntimeShape& unextended_output_activ_shape, + uint8_t* output_activ_data_uint8, + const RuntimeShape& unextended_concat_temp_shape, + uint8_t* concat_temp_data_uint8, + const RuntimeShape& unextended_activ_temp_shape, + int16_t* activ_temp_data_int16, void* gemmlowp_context) { + (void)gemmlowp_context; // only used in optimized code. + int32_t weights_zero_point = params.weights_zero_point; + int32_t accum_multiplier = params.accum_multiplier; + int accum_shift = params.accum_shift; + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape prev_activ_shape = + RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); + const RuntimeShape bias_shape = + RuntimeShape::ExtendedShape(4, unextended_bias_shape); + const RuntimeShape prev_state_shape = + RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); + const RuntimeShape output_state_shape = + RuntimeShape::ExtendedShape(4, unextended_output_state_shape); + const RuntimeShape output_activ_shape = + RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); + const RuntimeShape concat_temp_shape = + RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); + const RuntimeShape activ_temp_shape = + RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); + TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); + + // Gather dimensions information, and perform consistency checks. + const int weights_dim_count = weights_shape.DimensionsCount(); + const int outer_size = MatchingFlatSizeSkipDim( + input_shape, 3, prev_activ_shape, prev_state_shape, output_state_shape, + output_activ_shape); + const int input_depth = input_shape.Dims(3); + const int prev_activ_depth = prev_activ_shape.Dims(3); + const int total_input_depth = prev_activ_depth + input_depth; + TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), + total_input_depth); + const int intern_activ_depth = + MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); + TFLITE_DCHECK_EQ(weights_shape.FlatSize(), + intern_activ_depth * total_input_depth); + TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); + TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); + const int output_depth = + MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, + 3, output_activ_shape, 3); + TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); + const int fc_batches = FlatSizeSkipDim(activ_temp_shape, 3); + const int fc_output_depth = + MatchingDim(weights_shape, weights_dim_count - 2, activ_temp_shape, 3); + const int fc_accum_depth = total_input_depth; + TFLITE_DCHECK_EQ(fc_output_depth, 4 * output_depth); + + // Depth-concatenate prev_activ and input data together. + uint8_t const* concat_input_arrays_data[2] = {input_data_uint8, + prev_activ_data_uint8}; + const RuntimeShape* concat_input_arrays_shapes[2] = {&input_shape, + &prev_activ_shape}; + tflite_micro::ConcatenationParams concat_params; + concat_params.axis = 3; + concat_params.inputs_count = 2; + Concatenation(concat_params, concat_input_arrays_shapes, + concat_input_arrays_data, concat_temp_shape, + concat_temp_data_uint8); + + // Implementation of the fully connected node inside the LSTM cell. + // The operands are 8-bit integers, the accumulators are internally 32bit + // integers, and the output is 16-bit fixed-point with 3 integer bits so + // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that + // is explained in the function comment above. + for (int b = 0; b < fc_batches; ++b) { + for (int out_c = 0; out_c < fc_output_depth; ++out_c) { + // Internal accumulation. + // Initialize accumulator with the bias-value. + int32_t accum = bias_data_int32[out_c]; + // Accumulation loop. + for (int d = 0; d < fc_accum_depth; ++d) { + int16_t input_val = + concat_temp_data_uint8[b * fc_accum_depth + d] - 128; + int16_t weights_val = + weights_data_uint8[out_c * fc_accum_depth + d] - weights_zero_point; + accum += input_val * weights_val; + } + // Down-scale the final int32 accumulator to the scale used by our + // (16-bit, using 3 integer bits) fixed-point format. The quantized + // multiplier and shift here have been pre-computed offline + // (e.g. by toco). + accum = + MultiplyByQuantizedMultiplier(accum, accum_multiplier, accum_shift); + // Saturate, cast to int16, and store to the temporary activations array. + accum = std::max(-32768, std::min(32767, accum)); + activ_temp_data_int16[out_c + fc_output_depth * b] = accum; + } + } + + // Rest of the LSTM cell: tanh and logistic math functions, and some adds + // and muls, all done in 16-bit fixed-point. + for (int b = 0; b < outer_size; ++b) { + for (int c = 0; c < output_depth; ++c) { + // Define the fixed-point data types that we will use here. All use + // int16 as the underlying integer type i.e. all are 16-bit fixed-point. + // They only differ by the number of integral vs. fractional bits, + // determining the range of values that they can represent. + // + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8]. + // This is the range of the previous fully-connected node's output, + // which is our input here. + using F3 = gemmlowp::FixedPoint; + // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, + // 2^StateIntegerBits]. It's used to represent the internal state, whose + // number of integer bits is currently dictated by the model. See comment + // on the StateIntegerBits template parameter above. + using FS = gemmlowp::FixedPoint; + // Implementation of input gate, using fixed-point logistic function. + F3 input_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 0 * output_depth + c]); + F0 input_gate_output = gemmlowp::logistic(input_gate_input); + // Implementation of input modulation gate, using fixed-point tanh + // function. + F3 input_modulation_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 1 * output_depth + c]); + F0 input_modulation_gate_output = + gemmlowp::tanh(input_modulation_gate_input); + // Implementation of forget gate, using fixed-point logistic function. + F3 forget_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 2 * output_depth + c]); + F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); + // Implementation of output gate, using fixed-point logistic function. + F3 output_gate_input = F3::FromRaw( + activ_temp_data_int16[b * fc_output_depth + 3 * output_depth + c]); + F0 output_gate_output = gemmlowp::logistic(output_gate_input); + // Implementation of internal multiplication nodes, still in fixed-point. + F0 input_times_input_modulation = + input_gate_output * input_modulation_gate_output; + FS prev_state = FS::FromRaw(prev_state_data_int16[b * output_depth + c]); + FS prev_state_times_forget_state = forget_gate_output * prev_state; + // Implementation of internal addition node, saturating. + FS new_state = gemmlowp::SaturatingAdd( + gemmlowp::Rescale(input_times_input_modulation), + prev_state_times_forget_state); + // Implementation of last internal Tanh node, still in fixed-point. + // Since a Tanh fixed-point implementation is specialized for a given + // number or integer bits, and each specialization can have a substantial + // code size, and we already used above a Tanh on an input with 3 integer + // bits, and per the table in the above function comment there is no + // significant accuracy to be lost by clamping to [-8, +8] for a + // 3-integer-bits representation, let us just do that. This helps people + // porting this to targets where code footprint must be minimized. + F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); + F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); + // Store the new internal state back to memory, as 16-bit integers. + // Note: here we store the original value with StateIntegerBits, not + // the rescaled 3-integer-bits value fed to tanh. + output_state_data_int16[b * output_depth + c] = new_state.raw(); + // Down-scale the output activations to 8-bit integers, saturating, + // and store back to memory. + int16_t rescaled_output_activ = + gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); + int16_t clamped_output_activ = std::max( + -128, std::min(127, rescaled_output_activ)); + output_activ_data_uint8[b * output_depth + c] = + 128 + clamped_output_activ; + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h new file mode 100644 index 00000000..52d17105 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h @@ -0,0 +1,64 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +void MaximumMinimumBroadcastSlow(const RuntimeShape& unextended_input1_shape, + const T* input1_data, + const RuntimeShape& unextended_input2_shape, + const T* input2_data, + const RuntimeShape& unextended_output_shape, + T* output_data, Op op) { + // Uses element-wise calculation if broadcast is not required. + if (unextended_input1_shape == unextended_input2_shape) { + const int flat_size = + MatchingElementsSize(unextended_input1_shape, unextended_input2_shape, + unextended_output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = op(input1_data[i], input2_data[i]); + } + } else { + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); + + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDesc output_desc; + NdArrayDescsForElementwiseBroadcast( + unextended_input1_shape, unextended_input2_shape, &desc1, &desc2); + CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), + &output_desc); + + auto maxmin_func = [&](int indexes[N]) { + output_data[SubscriptToIndex(output_desc, indexes)] = + op(input1_data[SubscriptToIndex(desc1, indexes)], + input2_data[SubscriptToIndex(desc2, indexes)]); + }; + NDOpsHelper(output_desc, maxmin_func); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h new file mode 100644 index 00000000..a53edf66 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h @@ -0,0 +1,267 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" + +namespace tflite_micro { + +namespace reference_ops { + +// Maximum dimension supported by the broadcast mul operation. +constexpr int kMaxMulBroadcastDim = 6; + +// Element-wise mul that can often be used for inner loop of broadcast Mul as +// well as the non-broadcast Mul. +inline void MulElementwise(int size, const ArithmeticParams& params, + const uint8_t* input1_data, + const uint8_t* input2_data, uint8_t* output_data) { + for (int i = 0; i < size; ++i) { + const int32_t input1_val = params.input1_offset + input1_data[i]; + const int32_t input2_val = params.input2_offset + input2_data[i]; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplier(input1_val * input2_val, + params.output_multiplier, + params.output_shift); + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + output_data[i] = static_cast(clamped_output); + } +} + +template +inline void Mul(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + T output_activation_min; + T output_activation_max; + GetActivationParams(params, &output_activation_min, &output_activation_max); + + const int flat_size = + MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] * input2_data[i], output_activation_min, + output_activation_max); + } +} + +inline void Mul(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const std::complex* input1_data, + const RuntimeShape& input2_shape, + const std::complex* input2_data, + const RuntimeShape& output_shape, + std::complex* output_data) { + const int flat_size = + MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = input1_data[i] * input2_data[i]; + } +} + +inline void Mul(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8_t* input1_data, + const RuntimeShape& input2_shape, const uint8_t* input2_data, + const RuntimeShape& output_shape, uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); + + MulElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +template +void BroadcastMulRecursiveDimensions( + const ArithmeticParams& params, int dimension, const T* input1_data, + const T* input2_data, T* output_data, size_t* input1_offset_p, + size_t* input2_offset_p, size_t* output_offset, + const NdArrayDesc& desc1, + const NdArrayDesc& desc2, + const int32_t extended_output_shape_dims[kMaxMulBroadcastDim], + F binary_func) { + if (dimension == kMaxMulBroadcastDim - 1) { + for (int c = 0; c < extended_output_shape_dims[dimension]; ++c) { + const T input1_val = input1_data[*input1_offset_p]; + const T input2_val = input2_data[*input2_offset_p]; + output_data[*output_offset] = binary_func(params, input1_val, input2_val); + *input1_offset_p += desc1.strides[dimension]; + *input2_offset_p += desc2.strides[dimension]; + ++(*output_offset); + } + } else { + for (int a = 0; a < extended_output_shape_dims[dimension]; ++a) { + size_t input1_offset_c = *input1_offset_p; + size_t input2_offset_c = *input2_offset_p; + BroadcastMulRecursiveDimensions( + params, dimension + 1, input1_data, input2_data, output_data, + &input1_offset_c, &input2_offset_c, output_offset, desc1, desc2, + extended_output_shape_dims, binary_func); + *input1_offset_p += desc1.strides[dimension]; + *input2_offset_p += desc2.strides[dimension]; + } + } +} + +inline void BroadcastMul6DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const uint8_t* input1_data, + const RuntimeShape& input2_shape, + const uint8_t* input2_data, + const RuntimeShape& output_shape, + uint8_t* output_data) { + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape); + // Cache output shape dimensions. + int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; + std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), + sizeof(extended_output_shape_dims)); + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastMulRecursiveDimensions( + params, 0, input1_data, input2_data, output_data, &input1_offset, + &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, + [](const ArithmeticParams& params, const uint8_t input1_val, + const uint8_t input2_val) { + const int32_t offsetted_input1_val = params.input1_offset + input1_val; + const int32_t offsetted_input2_val = params.input2_offset + input2_val; + const int32_t unclamped_result = + params.output_offset + + MultiplyByQuantizedMultiplier( + offsetted_input1_val * offsetted_input2_val, + params.output_multiplier, params.output_shift); + const int32_t clamped_output = std::min( + params.quantized_activation_max, + std::max(params.quantized_activation_min, unclamped_result)); + return static_cast(clamped_output); + }); +} + +template +inline typename std::enable_if< + !is_small_integer::value || enable_for_short_integers, void>::type +BroadcastMul6DSlow(const ArithmeticParams& params, + const RuntimeShape& unextended_input1_shape, + const T* input1_data, + const RuntimeShape& unextended_input2_shape, + const T* input2_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6); + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, + unextended_input2_shape, &desc1, &desc2); + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape); + // Cache output shape dimensions. + int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; + std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), + sizeof(extended_output_shape_dims)); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest + // stride, typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for + // the best cache behavior. + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastMulRecursiveDimensions( + params, 0, input1_data, input2_data, output_data, &input1_offset, + &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, + [](const ArithmeticParams& params, const T input1_val, + const T input2_val) { + T output_activation_min; + T output_activation_max; + GetActivationParams(params, &output_activation_min, + &output_activation_max); + return ActivationFunctionWithMinMax(input1_val * input2_val, + output_activation_min, + output_activation_max); + }); +} + +inline void BroadcastMul6DSlow(const ArithmeticParams& params, + const RuntimeShape& unextended_input1_shape, + const std::complex* input1_data, + const RuntimeShape& unextended_input2_shape, + const std::complex* input2_data, + const RuntimeShape& unextended_output_shape, + std::complex* output_data) { + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6); + TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6); + + NdArrayDesc desc1; + NdArrayDesc desc2; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, + unextended_input2_shape, &desc1, &desc2); + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape); + // Cache output shape dimensions. + int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; + std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), + sizeof(extended_output_shape_dims)); + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastMulRecursiveDimensions( + params, 0, input1_data, input2_data, output_data, &input1_offset, + &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, + [](const ArithmeticParams& params, const std::complex input1_val, + const std::complex input2_val) { + return input1_val * input2_val; + }); +} + +template +inline void BroadcastMul4DSlow( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data) { + return BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h new file mode 100644 index 00000000..18c291c4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h @@ -0,0 +1,37 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void Negate(const RuntimeShape& input_shape, const T* input_data, + const RuntimeShape& output_shape, T* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; ++i) { + output_data[i] = -input_data[i]; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h new file mode 100644 index 00000000..feb3f3a7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h @@ -0,0 +1,169 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +// TFLite Pad supports activation tensors with up to 5 dimensions. +constexpr int PadKernelMaxDimensionCount() { return 5; } + +// There are two versions of pad: Pad and PadV2. In PadV2 there is a second +// scalar input that provides the padding value. Therefore pad_value_ptr can be +// equivalent to a simple input1_data. For Pad, it should point to a zero +// value. +// +// Note that two typenames are required, so that T=P=int32_t is considered a +// specialization distinct from P=int32_t. +template +inline void PadImpl(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const P* pad_value_ptr, const RuntimeShape& output_shape, + T* output_data) { + const RuntimeShape ext_input_shape = + RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), input_shape); + const RuntimeShape ext_output_shape = + RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), output_shape); + TFLITE_DCHECK_LE(op_params.left_padding_count, PadKernelMaxDimensionCount()); + TFLITE_DCHECK_LE(op_params.right_padding_count, PadKernelMaxDimensionCount()); + + // Runtime calls are currently fixed at 5 dimensions. Copy inputs so we can + // pad them to 5 dims (yes, we are "padding the padding"). + int left_padding_copy[PadKernelMaxDimensionCount()]; + for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { + left_padding_copy[i] = 0; + } + for (int i = 0; i < op_params.left_padding_count; ++i) { + left_padding_copy[i + PadKernelMaxDimensionCount() - + op_params.left_padding_count] = op_params.left_padding[i]; + } + int right_padding_copy[PadKernelMaxDimensionCount()]; + for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { + right_padding_copy[i] = 0; + } + for (int i = 0; i < op_params.right_padding_count; ++i) { + right_padding_copy[i + PadKernelMaxDimensionCount() - + op_params.right_padding_count] = + op_params.right_padding[i]; + } + + const int output_batch = ext_output_shape.Dims(0); + const int output_plane = ext_output_shape.Dims(1); + const int output_height = ext_output_shape.Dims(2); + const int output_width = ext_output_shape.Dims(3); + const int output_depth = ext_output_shape.Dims(4); + + const int left_b_padding = left_padding_copy[0]; + const int left_p_padding = left_padding_copy[1]; + const int left_h_padding = left_padding_copy[2]; + const int left_w_padding = left_padding_copy[3]; + const int left_d_padding = left_padding_copy[4]; + + const int right_b_padding = right_padding_copy[0]; + const int right_p_padding = right_padding_copy[1]; + const int right_h_padding = right_padding_copy[2]; + const int right_w_padding = right_padding_copy[3]; + const int right_d_padding = right_padding_copy[4]; + + const T pad_value = *pad_value_ptr; + + const T* in_ptr = input_data; + T* out_ptr = output_data; + for (int out_b = 0; out_b < output_batch; ++out_b) { + for (int out_p = 0; out_p < output_plane; ++out_p) { + for (int out_h = 0; out_h < output_height; ++out_h) { + for (int out_w = 0; out_w < output_width; ++out_w) { + for (int out_d = 0; out_d < output_depth; ++out_d) { + if (out_b < left_b_padding || + out_b >= output_batch - right_b_padding || + out_p < left_p_padding || + out_p >= output_plane - right_p_padding || + out_h < left_h_padding || + out_h >= output_height - right_h_padding || + out_w < left_w_padding || + out_w >= output_width - right_w_padding || + out_d < left_d_padding || + out_d >= output_depth - right_d_padding) { + *out_ptr++ = pad_value; + } else { + *out_ptr++ = *in_ptr++; + } + } + } + } + } + } +} + +template +inline void Pad(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const P* pad_value_ptr, const RuntimeShape& output_shape, + T* output_data) { + PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, + output_data); +} + +// The second (pad-value) input can be int32_t when, say, the first is uint8_t. +template +inline void Pad(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const int32_t* pad_value_ptr, const RuntimeShape& output_shape, + T* output_data) { + const T converted_pad_value = static_cast(*pad_value_ptr); + PadImpl(op_params, input_shape, input_data, &converted_pad_value, + output_shape, output_data); +} + +// This version avoids conflicting template matching. +template <> +inline void Pad(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, const int32_t* input_data, + const int32_t* pad_value_ptr, const RuntimeShape& output_shape, + int32_t* output_data) { + PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, + output_data); +} + +template +inline void PadImageStyle(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const P* pad_value_ptr, + const RuntimeShape& output_shape, T* output_data) { + Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, + output_data); +} + +template +inline void PadImageStyle(const tflite_micro::PadParams& op_params, + const RuntimeShape& input_shape, + const float* input_data, const P* pad_value_ptr, + const RuntimeShape& output_shape, + float* output_data) { + Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, + output_data); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h new file mode 100644 index 00000000..c4294803 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h @@ -0,0 +1,303 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/quantization_util.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline bool AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + float total = 0.f; + float filter_count = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + total += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + filter_count++; + } + } + if (filter_count == 0) return false; + const float average = total / filter_count; + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + ActivationFunctionWithMinMax(average, params.float_activation_min, + params.float_activation_max); + } + } + } + } + return true; +} + +inline bool AveragePool(const PoolParams& params, + const RuntimeShape& input_shape, + const uint8_t* input_data, + const RuntimeShape& output_shape, + uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + int32_t acc = 0; + int filter_count = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + acc += + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + filter_count++; + } + } + if (filter_count == 0) return false; + acc = (acc + filter_count / 2) / filter_count; + acc = std::max(acc, params.quantized_activation_min); + acc = std::min(acc, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(acc); + } + } + } + } + return true; +} + +inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + float sum_squares = 0.f; + int filter_count = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + const float val = + input_data[Offset(input_shape, batch, in_y, in_x, channel)]; + sum_squares += val * val; + filter_count++; + } + } + const float l2pool_result = std::sqrt(sum_squares / filter_count); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + ActivationFunctionWithMinMax(l2pool_result, + params.float_activation_min, + params.float_activation_max); + } + } + } + } +} + +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + float max = std::numeric_limits::lowest(); + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + max = std::max( + max, + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + } + } + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + ActivationFunctionWithMinMax(max, params.float_activation_min, + params.float_activation_max); + } + } + } + } +} + +inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& output_shape, + uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + TFLITE_DCHECK_GE(params.quantized_activation_min, 0); + TFLITE_DCHECK_LE(params.quantized_activation_max, 255); + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int depth = MatchingDim(input_shape, 3, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int stride_height = params.stride_height; + const int stride_width = params.stride_width; + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int channel = 0; channel < depth; ++channel) { + const int in_x_origin = + (out_x * stride_width) - params.padding_values.width; + const int in_y_origin = + (out_y * stride_height) - params.padding_values.height; + // Compute the boundaries of the filter region clamped so as to + // ensure that the filter window fits in the input array. + const int filter_x_start = std::max(0, -in_x_origin); + const int filter_x_end = + std::min(params.filter_width, input_width - in_x_origin); + const int filter_y_start = std::max(0, -in_y_origin); + const int filter_y_end = + std::min(params.filter_height, input_height - in_y_origin); + uint8_t max = 0; + for (int filter_y = filter_y_start; filter_y < filter_y_end; + ++filter_y) { + for (int filter_x = filter_x_start; filter_x < filter_x_end; + ++filter_x) { + const int in_x = in_x_origin + filter_x; + const int in_y = in_y_origin + filter_y; + max = std::max( + max, + input_data[Offset(input_shape, batch, in_y, in_x, channel)]); + } + } + max = std::max(max, params.quantized_activation_min); + max = std::min(max, params.quantized_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, channel)] = + static_cast(max); + } + } + } + } +} +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h new file mode 100644 index 00000000..edcaf642 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h @@ -0,0 +1,333 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ + +#include "tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h" + +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + +namespace tflite_micro { +namespace tensor_utils { + +// Check if all entries of a vector are zero for float. +bool IsZeroVector(const float* vector, int v_size) { + return PortableIsZeroVector(vector, v_size); +} + +// Check if all entries of a vector are zero for int8_t. +bool IsZeroVector(const int8_t* vector, int v_size) { + return PortableIsZeroVector(vector, v_size); +} + +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min, float* max, + float* scaling_factor) { + PortableSymmetricQuantizeFloats(values, size, quantized_values, min, max, + scaling_factor); +} + +void SymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float min_value, + float max_value, float* scaling_factor) { + PortableSymmetricQuantizeFloats(values, size, quantized_values, min_value, + max_value, scaling_factor); +} + +void AsymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* scaling_factor, + int32_t* offset) { + PortableAsymmetricQuantizeFloats(values, size, quantized_values, + scaling_factor, offset); +} + +void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, + int m_cols, const float* vector, + int n_batch, float* result) { + PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, + n_batch, result); +} + +void MatrixBatchVectorMultiplyAccumulate(const int8_t* __restrict__ matrix, + const int m_rows, const int m_cols, + const int8_t* __restrict__ vector, + const float* scaling_factors, + int n_batch, + float* __restrict__ result) { + PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, + scaling_factors, n_batch, result); +} + +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, const float* per_channel_scale, + const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, + bool* compute_row_sums, CpuBackendContext* context) { + PortableMatrixBatchVectorMultiplyAccumulate( + matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result, + per_channel_scale, input_offset, scratch, row_sums, compute_row_sums, + context); +} + +void MatrixBatchVectorMultiplyAccumulate(const int8_t* __restrict__ matrix, + const int m_rows, const int m_cols, + const int8_t* __restrict__ vector, + const float* scaling_factors, + int n_batch, int32_t* scratch, + float* __restrict__ result, + CpuBackendContext* context) { + PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, + scaling_factors, n_batch, result); +} + +void SparseMatrixBatchVectorMultiplyAccumulate1x4( + const float* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const float* __restrict__ vector, int n_batch, float* __restrict__ result) { + PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( + matrix, segments, indices, m_rows, m_cols, vector, n_batch, result); +} + +void SparseMatrixBatchVectorMultiplyAccumulate( + const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, + int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, + float* __restrict__ result) { + PortableSparseMatrixBatchVectorMultiplyAccumulate( + matrix, ledger, m_rows, m_cols, vector, n_batch, result); +} + +void SparseMatrixBatchVectorMultiplyAccumulate1x16( + const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, + int n_batch, const int32_t input_offset, const int32_t output_multiplier, + const int32_t output_shift, const int32_t output_offset, + const int32_t output_activation_min, const int32_t output_activation_max, + + int8_t* __restrict__ result) { + PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( + matrix, segments, indices, m_rows, m_cols, vector, bias_vector, n_batch, + input_offset, output_multiplier, output_shift, output_offset, + output_activation_min, output_activation_max, result); +} + +void SparseMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, + const int m_cols, const int8_t* __restrict__ vectors, + const float* scaling_factors, int n_batch, float* __restrict__ result) { + PortableSparseMatrixBatchVectorMultiplyAccumulate( + matrix, ledger, m_rows, m_cols, vectors, scaling_factors, n_batch, + result); +} + +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int16_t* output, CpuBackendContext* context) { + PortableMatrixBatchVectorMultiplyAccumulate( + input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, + n_output, output_zp, scratch, output, context); +} + +void MatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int8_t* output, CpuBackendContext* context) { + PortableMatrixBatchVectorMultiplyAccumulate( + input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, + n_output, output_zp, scratch, output, context); +} + +void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, + int32_t n_row, int32_t n_col, + int32_t* output) { + PortableMatrixScalarMultiplyAccumulate(matrix, scalar, n_row, n_col, output); +} + +void MatrixBatchVectorMultiply(const int8_t* input, int32_t input_zeropoint, + const int8_t* input_to_gate_weights, + int32_t input_to_gate_effective_scale_a, + int32_t input_to_gate_effective_scale_b, + int32_t n_batch, int32_t n_input, int32_t n_cell, + int8_t* gate_output, int8_t gate_output_zp) { + PortableMatrixBatchVectorMultiply( + input, input_zeropoint, input_to_gate_weights, + input_to_gate_effective_scale_a, input_to_gate_effective_scale_b, n_batch, + n_input, n_cell, gate_output, gate_output_zp); +} + +void MatrixBatchVectorMultiply(const int16_t* hidden, + const int8_t* hidden_to_output_weights, + int32_t proj_effective_scale_a, + int32_t proj_effective_scale_b, + const int32_t* gate_bias, int32_t n_batch, + int32_t n_hidden, int32_t n_output, + int32_t output_zp, int8_t* proj_output) { + PortableMatrixBatchVectorMultiply(hidden, hidden_to_output_weights, + proj_effective_scale_a, + proj_effective_scale_b, gate_bias, n_batch, + n_hidden, n_output, output_zp, proj_output); +} + +void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, + const int32_t* bias, int32_t layer_norm_scale_a, + int32_t layer_norm_scale_b, int32_t variance_limit, + int n_batch, int n_input, int16_t* output) { + PortableApplyLayerNorm(input, layer_norm_weights, bias, layer_norm_scale_a, + layer_norm_scale_b, variance_limit, n_batch, n_input, + output); +} + +void ApplyLayerNormFloat(const int16_t* input, + const int16_t* layer_norm_weights, + int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, + const int32_t* bias, int n_batch, int n_input, + int16_t* output) { + PortableApplyLayerNormFloat(input, layer_norm_weights, layer_norm_scale_a, + layer_norm_scale_b, bias, n_batch, n_input, + output); +} + +void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, + int16_t* output) { + PortableApplySigmoid(input, n_batch, n_input, output); +} + +void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input, + int16_t* output) { + PortableApplySigmoidFloat(input, n_batch, n_input, output); +} + +void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch, + int32_t n_input, int16_t* output) { + PortableApplyTanh(integer_bits, input, n_batch, n_input, output); +} + +void ApplyTanhFloat(const int16_t* input, int32_t n_batch, int32_t n_input, + int32_t integer_bits, int16_t* output) { + PortableApplyTanhFloat(input, n_batch, n_input, integer_bits, output); +} + +void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int shift, int16_t* output) { + PortableCwiseMul(input_1, input_2, n_batch, n_input, shift, output); +} + +void CwiseMul(const int16_t* input_1, const int16_t* input_2, + int32_t multiplier, int32_t shift, int32_t n_batch, + int32_t n_input, int32_t output_zp, int8_t* output) { + PortableCwiseMul(input_1, input_2, multiplier, shift, n_batch, n_input, + output_zp, output); +} + +void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int16_t* output) { + PortableCwiseAdd(input_1, input_2, n_batch, n_input, output); +} + +void CwiseClipping(float* vector, const int v_size, + const float clipping_value) { + PortableCwiseClipping(vector, v_size, clipping_value); +} + +void CwiseClipping(int16_t* vector, const int v_size, + const int16_t clipping_value) { + PortableCwiseClipping(vector, v_size, clipping_value); +} + +void CwiseClipping(int8_t* vector, const int v_size, + const int8_t clipping_value) { + PortableCwiseClipping(vector, v_size, clipping_value); +} + +void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size, + const int16_t* batch_vector, + int n_batch, int32_t multiplier, + int shift, int16_t* result) { + PortableVectorBatchVectorCwiseProductAccumulate( + vector, v_size, batch_vector, n_batch, multiplier, shift, result); +} + +float VectorVectorDotProduct(const float* vector1, const float* vector2, + int v_size) { + return PortableVectorVectorDotProduct(vector1, vector2, v_size); +} + +void BatchVectorBatchVectorDotProduct(const int16_t* vector1, + const int16_t* vector2, int v_size, + int n_batch, int32_t* result) { + PortableBatchVectorBatchVectorDotProduct(vector1, vector2, v_size, n_batch, + result); +} + +void Sub1Vector(const float* vector, int v_size, float* result) { + PortableSub1Vector(vector, v_size, result); +} + +void Sub1Vector(const int16_t* vector, int v_size, int16_t* result) { + PortableSub1Vector(vector, v_size, result); +} + +// Multiply all elements of vector with a scalar. +void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result) { + PortableVectorScalarMultiply(vector, v_size, scale, result); +} + +void ReductionSumVector(const float* input_vector, float* output_vector, + int output_size, int reduction_size) { + PortableReductionSumVector(input_vector, output_vector, output_size, + reduction_size); +} + +void ReductionSumVector(const int32_t* input_vector, int32_t* output_vector, + int output_size, int reduction_size) { + PortableReductionSumVector(input_vector, output_vector, output_size, + reduction_size); +} + +void ReductionSumVector(const int8_t* input_vector, int32_t* output_vector, + int output_size, int reduction_size) { + PortableReductionSumVector(input_vector, output_vector, output_size, + reduction_size); +} + +void MeanStddevNormalization(const float* input_vector, float* output_vector, + int v_size, int n_batch) { + PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch); +} + +void TwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, + const int8_t* recurrent, int8_t recurrent_zp, + int32_t input_effective_scale_a, + int32_t input_effective_scale_b, + int32_t recurrent_effective_scale_a, + int32_t recurrent_effective_scale_b, int32_t n_batch, + int32_t n_cell, int16_t* output) { + PortableTwoGateSaturatingAdd( + input, input_zp, recurrent, recurrent_zp, input_effective_scale_a, + input_effective_scale_b, recurrent_effective_scale_a, + recurrent_effective_scale_b, n_batch, n_cell, output); +} + +} // namespace tensor_utils +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h new file mode 100644 index 00000000..88d9c640 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h @@ -0,0 +1,244 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ + +#include +#include + +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + +namespace tflite_micro { + +// Not all backends support CpuBackendContext usage, so forward declare to avoid +// pulling in its implementation. +class CpuBackendContext; + +namespace tensor_utils { + +template +bool PortableIsZeroVector(const T* vector, int v_size) { + for (int i = 0; i < v_size; ++i) { + if (vector[i] != 0) { + return false; + } + } + return true; +} + +void PortableSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); + +void PortableSymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, float min_value, + float max_value, float* scaling_factor); + +void PortableAsymmetricQuantizeFloats(const float* values, const int size, + int8_t* quantized_values, + float* scaling_factor, int32_t* offset); + +// Multiply a matrix by a batch vector, and store results in a batch-size +// vector. +void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, + int m_rows, int m_cols, + const float* vector, + int n_batch, float* result); + +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result); + +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, const float* per_channel_scale, + const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, + bool* compute_row_sums, CpuBackendContext* context); + +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, + const int8_t* __restrict__ vector, const float* scaling_factors, + int n_batch, int32_t* scratch, float* __restrict__ result, + CpuBackendContext* context); + +void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( + const float* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const float* __restrict__ vector, int n_batch, float* __restrict__ result); + +void PortableSparseMatrixBatchVectorMultiplyAccumulate( + const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, + int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, + float* __restrict__ result); + +void PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( + const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, + const int32_t* __restrict__ indices, int m_rows, int m_cols, + const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, + int n_batch, const int32_t input_offset, const int32_t output_multiplier, + const int32_t output_shift, const int32_t output_offset, + const int32_t output_activation_min, const int32_t output_activation_max, + int8_t* __restrict__ result); + +void PortableSparseMatrixBatchVectorMultiplyAccumulate( + const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, + const int m_cols, const int8_t* __restrict__ vectors, + const float* scaling_factors, int n_batch, float* __restrict__ result); + +// Dot product of two vectors. +float PortableVectorVectorDotProduct(const float* vector1, const float* vector2, + int v_size); + +void PortableBatchVectorBatchVectorDotProduct(const int16_t* vector1, + const int16_t* vector2, + int v_size, int n_batch, + int32_t* result); + +void PortableVectorBatchVectorCwiseProductAccumulate( + const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch, + int32_t multiplier, int shift, int16_t* result); + +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int16_t* output, CpuBackendContext* context); + +void PortableMatrixBatchVectorMultiplyAccumulate( + const int8_t* input, const int32_t* bias, + const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, + int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, + int32_t* scratch, int8_t* output, CpuBackendContext* context); + +void PortableMatrixBatchVectorMultiply(const int8_t* input, + int32_t input_zeropoint, + const int8_t* input_to_gate_weights, + int32_t input_to_gate_effective_scale_a, + int32_t input_to_gate_effective_scale_b, + int32_t n_batch, int32_t n_input, + int32_t n_cell, int8_t* gate_output, + int8_t gate_output_zp); + +void PortableMatrixBatchVectorMultiply( + const int16_t* hidden, const int8_t* hidden_to_output_weights, + int32_t proj_effective_scale_a, int32_t proj_effective_scale_b, + const int32_t* gate_bias, int32_t n_batch, int32_t n_hidden, + int32_t n_output, int32_t output_zp, int8_t* proj_output); + +void PortableMatrixScalarMultiplyAccumulate(const int8_t* matrix, + int32_t scalar, int32_t n_row, + int32_t n_col, int32_t* output); + +void PortableApplyLayerNorm(const int16_t* input, + const int16_t* layer_norm_weights, + const int32_t* bias, int32_t layer_norm_scale_a, + int32_t layer_norm_scale_b, int32_t variance_limit, + int n_batch, int n_input, int16_t* output); + +void PortableApplyLayerNormFloat(const int16_t* input, + const int16_t* layer_norm_weights, + int32_t layer_norm_scale_a, + int32_t layer_norm_scale_b, + const int32_t* bias, int n_batch, int n_input, + int16_t* output); + +void PortableApplySigmoid(const int16_t* input, int32_t n_batch, + int32_t n_input, int16_t* output); + +void PortableApplySigmoidFloat(const int16_t* input, int32_t n_batch, + int32_t n_input, int16_t* output); + +void PortableApplyTanh(int32_t integer_bits, const int16_t* input, + int32_t n_batch, int32_t n_input, int16_t* output); + +void PortableApplyTanhFloat(const int16_t* input, int32_t n_batch, + int32_t n_input, int32_t integer_bits, + int16_t* output); + +void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, + int n_batch, int n_input, int shift, int16_t* output); + +void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, + int32_t multiplier, int32_t shift, int32_t n_batch, + int32_t n_input, int32_t output_zp, int8_t* output); + +void PortableCwiseAdd(const int16_t* input_1, const int16_t* input_2, + int n_batch, int n_input, int16_t* output); + +template +void PortableCwiseClipping(T* vector, const int v_size, + const T& clipping_value) { + for (int i = 0; i < v_size; i++) { + vector[i] = std::max(std::min(clipping_value, vector[i]), + static_cast(-clipping_value)); + } +} + +// Batch vector initialization with another vector. +void PortableVectorBatchVectorAssign(const float* vector, int v_size, + int n_batch, float* batch_vector); + +// Compute "1.0f - elements of vector" (used in CIFG). +void PortableSub1Vector(const float* vector, int v_size, float* result); + +void PortableSub1Vector(const int16_t* vector, int v_size, int16_t* result); + +// Multiply all elements of vector with a scalar. +void PortableVectorScalarMultiply(const int8_t* vector, int v_size, float scale, + float* result); + +// Reduce-sum on a vector: +// input_vector: pointer to input vector. +// output_vector: pointer to vector. +// output_size: output vector size. +// reduction_size: number of consecutive elements from input vector which are +// added to get one element of output. +template +void PortableReductionSumVector(const INPUT* input_vector, + OUTPUT* output_vector, int output_size, + int reduction_size) { + for (int o = 0; o < output_size; o++) { + OUTPUT result = 0; + for (int r = 0; r < reduction_size; r++) { + result += input_vector[r]; + } + output_vector[o] = result; + input_vector += reduction_size; + } +} + +// Layer norm for each batch. +void PortableMeanStddevNormalization(const float* __restrict__ input_vector, + float* __restrict__ output_vector, + int v_size, int n_batch); + +// Saturate Add. +void PortableTwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, + const int8_t* recurrent, int8_t recurrent_zp, + int32_t input_effective_scale_a, + int32_t input_effective_scale_b, + int32_t recurrent_effective_scale_a, + int32_t recurrent_effective_scale_b, + int32_t n_batch, int32_t n_cell, + int16_t* output); + +} // namespace tensor_utils +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h new file mode 100644 index 00000000..89d4be10 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h @@ -0,0 +1,111 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +// Broadcast prelu to output_shape for quantized uint8_t/int8_t data. +template +inline void BroadcastPrelu4DSlow( + const PreluParams& params, const RuntimeShape& input_shape, + const T* input_data, const RuntimeShape& alpha_shape, const T* alpha_data, + const RuntimeShape& output_shape, T* output_data) { + TFLITE_DCHECK_LE(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(alpha_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 4); + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input_shape, alpha_shape, &desc1, &desc2); + + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + int output_index = Offset(extended_output_shape, b, y, x, c); + int input_index = SubscriptToIndex(desc1, b, y, x, c); + const int32_t input_value = + params.input_offset + input_data[input_index]; + int32_t output_value; + if (input_value >= 0) { + output_value = MultiplyByQuantizedMultiplier( + input_value, params.output_multiplier_1, params.output_shift_1); + } else { + auto alpha_index = SubscriptToIndex(desc2, b, y, x, c); + const int32_t alpha_value = + params.alpha_offset + alpha_data[alpha_index]; + + output_value = MultiplyByQuantizedMultiplier( + input_value * alpha_value, params.output_multiplier_2, + params.output_shift_2); + } + output_value += params.output_offset; + + const int32_t quantized_min = std::numeric_limits::min(); + const int32_t quantized_max = std::numeric_limits::max(); + const int32_t clamped_output = + std::min(quantized_max, std::max(quantized_min, output_value)); + output_data[output_index] = static_cast(clamped_output); + } + } + } + } +} + +template +inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape, + const T* input_data, const RuntimeShape& alpha_shape, + const T* alpha_data, const RuntimeShape& output_shape, + T* output_data) { + const int32_t quantized_min = std::numeric_limits::min(); + const int32_t quantized_max = std::numeric_limits::max(); + + const int flat_size = + MatchingElementsSize(input_shape, alpha_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + const int32_t input_value = params.input_offset + input_data[i]; + int32_t output_value; + if (input_value >= 0) { + output_value = MultiplyByQuantizedMultiplier( + input_value, params.output_multiplier_1, params.output_shift_1); + } else { + const int32_t alpha_value = params.alpha_offset + alpha_data[i]; + + output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value, + params.output_multiplier_2, + params.output_shift_2); + } + output_value += params.output_offset; + + const int32_t clamped_output = + std::min(quantized_max, std::max(quantized_min, output_value)); + output_data[i] = static_cast(clamped_output); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h new file mode 100644 index 00000000..0cae0eb3 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h @@ -0,0 +1,140 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +// Consolidates dimensions in broadcast inputs, checks for five-fold pattern. +// +// For example, if sequence of dimensions of one input is +// ..., 1, 3, 1, 7, 9, 5,... and the other is ..., 2, 3, 1, 7, 1, 1, ... +// we can consolidate these as +// ..., 1, 3*7, 9*5, ... and 2, 3*7, 1. +// +// The category is updated in the less-frequent case of shapes that are +// not suited to a fivefold-loop broadcast. +// +// Falls back to generic pattern when it does not know how to process properly. +// +// Returns true iff there is some sort of broadcast, which includes five-fold +// patterns and falling back to generic broadcast. +inline bool ProcessBroadcastShapes(const RuntimeShape& shape0, + const RuntimeShape& shape1, + tflite_micro::ArithmeticParams* params) { + const int dims_count = + std::max(shape0.DimensionsCount(), shape1.DimensionsCount()); + + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + RuntimeShape scalar_shape(dims_count, 1); + + auto extended_shape0 = RuntimeShape::ExtendedShape(dims_count, shape0); + auto extended_shape1 = RuntimeShape::ExtendedShape(dims_count, shape1); + + // Check for "exact" match, implicitly accepting any scalar shapes. + if (extended_shape0 == extended_shape1) { + params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; + return false; + } + + for (int i = dims_count - 1; i >= 0; --i) { + if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) { + continue; + } else if (extended_shape0.Dims(i) == 1) { + params->broadcast_category = + BroadcastableOpCategory::kFirstInputBroadcastsFast; + break; + } else if (extended_shape1.Dims(i) == 1) { + params->broadcast_category = + BroadcastableOpCategory::kSecondInputBroadcastsFast; + break; + } else { + // This case is erroneous: there is a dimension that does not match and + // is not a broadcast from one shape to the other. + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + return true; + } + } + + if (params->broadcast_category != + BroadcastableOpCategory::kFirstInputBroadcastsFast && + params->broadcast_category != + BroadcastableOpCategory::kSecondInputBroadcastsFast) { + // This is unreachable because at least one else clause in the above loop + // must be reached. + TFLITE_DCHECK(false); + params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; + return false; + } + + // From this point it is assumed contractually that corresponding dimensions + // in shape0 and shape1 are either (a) equal or (b) one or other equals 1. + const bool swap_inputs = params->broadcast_category == + BroadcastableOpCategory::kSecondInputBroadcastsFast; + const RuntimeShape* shape_a = + swap_inputs ? &extended_shape1 : &extended_shape0; + const RuntimeShape* shape_b = + swap_inputs ? &extended_shape0 : &extended_shape1; + + int i = dims_count - 1; + params->broadcast_shape[0] = 1; + params->broadcast_shape[1] = 1; + params->broadcast_shape[2] = 1; + params->broadcast_shape[3] = 1; + params->broadcast_shape[4] = 1; + // y_0 is greedy: include dims if both or neither equal 1: in other words, + // test for equality rather than (shape_a->Dims(i) != 1). + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { + params->broadcast_shape[4] *= shape_b->Dims(i); + --i; + } + // Here either input_a or input_b has dim of 1 (if i >= 0). If it is input_b + // that has the unit dimension, the next two loops are not entered. + while (i >= 0 && shape_a->Dims(i) == 1) { + params->broadcast_shape[3] *= shape_b->Dims(i); + --i; + } + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { + params->broadcast_shape[2] *= shape_a->Dims(i); + --i; + } + // Here either input_a or input_b has dim of 1 (if i >= 0). + while (i >= 0 && shape_b->Dims(i) == 1) { + params->broadcast_shape[1] *= shape_a->Dims(i); + --i; + } + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { + params->broadcast_shape[0] *= shape_b->Dims(i); + --i; + } + + // Rarer case is when the broadcast dimensions cannot be handled by a fivefold + // loop. + if (i >= 0) { + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + } + return true; +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h new file mode 100644 index 00000000..05af1959 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h @@ -0,0 +1,89 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void AffineQuantize(const tflite_micro::QuantizationParams& op_params, + const RuntimeShape& input_shape, + const InputT* input_data, + const RuntimeShape& output_shape, + OutputT* output_data) { + const int32_t zero_point = op_params.zero_point; + const double scale = op_params.scale; + const int flat_size = MatchingFlatSize(input_shape, output_shape); + static constexpr int32_t min_val = std::numeric_limits::min(); + static constexpr int32_t max_val = std::numeric_limits::max(); + + for (int i = 0; i < flat_size; i++) { + const InputT val = input_data[i]; + int32_t unclamped = + static_cast(TfLiteRound(val / static_cast(scale))) + + zero_point; + int32_t clamped = std::min(std::max(unclamped, min_val), max_val); + output_data[i] = clamped; + } +} + +// Quantizes per-channel. +template +inline void PerChannelQuantize( + const tflite_micro::PerChannelQuantizationParams& op_params, + const RuntimeShape& input_shape, const InputT* input_data, + const RuntimeShape& output_shape, OutputT* output_data) { + // Ensure flat size is same. + MatchingFlatSize(input_shape, output_shape); + + const int32_t* zero_point = op_params.zero_point; + const float* scale = op_params.scale; + const int32_t quantized_dimension = op_params.quantized_dimension; + const int32_t num_dims = input_shape.DimensionsCount(); + const int32_t* dims_data = input_shape.DimsData(); + std::vector current_dim(num_dims, 0); + static constexpr int32_t min_val = std::numeric_limits::min(); + static constexpr int32_t max_val = std::numeric_limits::max(); + + do { + size_t offset = + ReducedOutputOffset(num_dims, reinterpret_cast(dims_data), + current_dim.data(), 0, nullptr); + const InputT val = input_data[offset]; + const int channel = current_dim[quantized_dimension]; + int32_t unclamped = static_cast(TfLiteRound( + val / static_cast(scale[channel]))) + + zero_point[channel]; + int32_t clamped = std::min(std::max(unclamped, min_val), max_val); + output_data[offset] = static_cast(clamped); + } while (NextIndex(num_dims, reinterpret_cast(dims_data), + current_dim.data())); +} + +} // namespace reference_ops + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h new file mode 100644 index 00000000..6016a8df --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h @@ -0,0 +1,491 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/max.h" +#include "tensorflow/lite/kernels/internal/min.h" +#include "tensorflow/lite/kernels/internal/quantization_util.h" +#include "tensorflow/lite/kernels/internal/types.h" + +// Check if the reduction at index is the first one along the dimensions given +// in axis. +inline bool IsFirstReduction(const int* index, const int num_axis, + const int* axis) { + if (num_axis == 0) { + return true; + } + + TFLITE_DCHECK(index != nullptr); + TFLITE_DCHECK(axis != nullptr); + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { + if (index[axis[axis_idx]] != 0) { + return false; + } + } + + return true; +} + +namespace tflite_micro { + +namespace reference_ops { + +// A generic reduce method that can be used for reduce_sum, reduce_mean, etc. +// This method iterates through input data and reduce elements along the +// dimensions given in axis. +template +inline bool Reduce(const In* input_data, const int* input_dims, + const int* output_dims, const int input_num_dims, + const int output_num_dims, const int* axis, + const int num_axis, int* input_iter, + Out reducer(Out current, const In in), Out* output_data) { + // Reset input iterator. + for (int idx = 0; idx < input_num_dims; ++idx) { + input_iter[idx] = 0; + } + // Iterate through input_data. + do { + size_t input_offset = + ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); + size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, + input_iter, num_axis, axis); + output_data[output_offset] = + reducer(output_data[output_offset], input_data[input_offset]); + } while (NextIndex(input_num_dims, input_dims, input_iter)); + return true; +} + +// Similar to above Reduce function but takes two reducer functions. +// The 'reducer_first' is called with the first value of the reduction, +// 'reducer_next' is then called for all the others. +template +inline bool Reduce(const In* input_data, const int* input_dims, + const int* output_dims, const int input_num_dims, + const int output_num_dims, const int* axis, + const int num_axis, int* input_iter, + const std::function& reducer_first, + const std::function& reducer_next, + Out* output_data) { + // Reset input iterator. + for (int idx = 0; idx < input_num_dims; ++idx) { + input_iter[idx] = 0; + } + // Iterate through input_data. + do { + size_t input_offset = + ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); + size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, + input_iter, num_axis, axis); + if (IsFirstReduction(input_iter, num_axis, axis)) { + output_data[output_offset] = reducer_first(input_data[input_offset]); + } else { + output_data[output_offset] = + reducer_next(output_data[output_offset], input_data[input_offset]); + } + } while (NextIndex(input_num_dims, input_dims, input_iter)); + return true; +} + +// This method parses the input 'axis' to remove duplicates and handle negative +// values, and returns a valid 'out_axis' +inline bool ResolveAxis(const int num_dims, const int* axis, + const int64_t num_axis, int* out_axis, + int* out_num_axis) { + *out_num_axis = 0; // Just in case. + // Short-circuit axis resolution for scalars; the axis will go unused. + if (num_dims == 0) { + return true; + } + // o(n^2) is fine since out_num_axis should be really small, mostly <= 4 + for (int64_t idx = 0; idx < num_axis; ++idx) { + // Handle negative index. A positive index 'p_idx' can be represented as a + // negative index 'n_idx' as: n_idx = p_idx-num_dims + // eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1] */ + int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx]; + TFLITE_DCHECK(current >= 0 && current < num_dims); + if (current < 0 || current >= num_dims) { + return false; + } + bool is_dup = false; + for (int j = 0; j < *out_num_axis; ++j) { + if (out_axis[j] == current) { + is_dup = true; + break; + } + } + if (!is_dup) { + out_axis[*out_num_axis] = current; + *out_num_axis += 1; + } + } + return true; +} + +// This method expects that output_data has been initialized. +template +inline bool ReduceSumImpl(const In* input_data, const int* input_dims, + const int* output_dims, const int input_num_dims, + const int output_num_dims, const int* axis, + const int num_axis, int* input_iter, + Out* output_data) { + auto reducer = [](const Out current, const In in) -> Out { + const Out actual_in = static_cast(in); + return current + actual_in; + }; + return Reduce(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, axis, num_axis, input_iter, reducer, + output_data); +} + +template +inline bool InitTensorDataForReduce(const int* dims, const int num_dims, + const T init_value, T* data) { + size_t num_elements = 1; + for (int idx = 0; idx < num_dims; ++idx) { + size_t current = static_cast(dims[idx]); + // Overflow prevention. + if (current > 0 && + num_elements > std::numeric_limits::max() / current) { + return false; + } + num_elements *= current; + } + for (size_t idx = 0; idx < num_elements; ++idx) { + data[idx] = init_value; + } + return true; +} + +// Computes the generic value (i.e., sum/max/min/prod) of elements across +// dimensions given in axis. It needs to pass in init_value and reducer. +template +inline bool ReduceGeneric(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int64_t num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis, + T init_value, + T reducer(const T current, const T in)) { + // Reset output data. + if (!InitTensorDataForReduce(output_dims, output_num_dims, init_value, + output_data)) { + return false; + } + + // Return early when input shape has zero dim. This is done after initializing + // data for output tensor because there are cases that the input tensor is + // empty but output tensor is not. In that case, output tensor should be + // filled with init_value. + for (int i = 0; i < input_num_dims; ++i) { + if (input_dims[i] == 0) return true; + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + return Reduce(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, resolved_axis, num_resolved_axis, + temp_index, reducer, output_data); +} + +// Computes the mean of elements across dimensions given in axis. +// It does so in two stages, first calculates the sum of elements along the axis +// then divides it by the number of element in axis. +template +inline bool Mean(const T* input_data, const int* input_dims, + const int input_num_dims, T* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int num_axis_dimensions, bool keep_dims, + int* temp_index, int* resolved_axis, U* temp_sum) { + ruy::profiler::ScopeLabel label("Mean"); + // Reset output data. + size_t num_outputs = 1; + for (int idx = 0; idx < output_num_dims; ++idx) { + size_t current = static_cast(output_dims[idx]); + // Overflow prevention. + if (num_outputs > std::numeric_limits::max() / current) { + return false; + } + num_outputs *= current; + } + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = T(); + temp_sum[idx] = U(); + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, resolved_axis, num_resolved_axis, + temp_index, temp_sum)) { + return false; + } + + // Calculate mean by dividing output_data by num of aggregated element. + size_t num_elements_in_axis = 1; + for (int idx = 0; idx < num_resolved_axis; ++idx) { + size_t current = static_cast(input_dims[resolved_axis[idx]]); + // Overflow prevention. + if (current > (std::numeric_limits::max() / num_elements_in_axis)) { + return false; + } + num_elements_in_axis *= current; + } + + if (num_elements_in_axis > 0) { + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = + static_cast(temp_sum[idx] / static_cast(num_elements_in_axis)); + } + } + return true; +} + +inline void Mean(const tflite_micro::MeanParams& op_params, + const RuntimeShape& unextended_input_shape, + const float* input_data, + const RuntimeShape& unextended_output_shape, + float* output_data) { + ruy::profiler::ScopeLabel label("Mean4D"); + + // Current implementation only supports dimension equals 4 and simultaneous + // reduction over width and height. + TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4); + TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + const int output_batch = output_shape.Dims(0); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int output_depth = output_shape.Dims(3); + + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + + TFLITE_CHECK_EQ(op_params.axis_count, 2); + TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) || + (op_params.axis[0] == 2 && op_params.axis[1] == 1)); + TFLITE_CHECK_EQ(output_height, 1); + TFLITE_CHECK_EQ(output_width, 1); + + for (int out_b = 0; out_b < output_batch; ++out_b) { + for (int out_d = 0; out_d < output_depth; ++out_d) { + float value = 0; + for (int in_h = 0; in_h < input_height; ++in_h) { + for (int in_w = 0; in_w < input_width; ++in_w) { + value += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)]; + } + } + output_data[Offset(output_shape, out_b, 0, 0, out_d)] = + value / (input_width * input_height); + } + } +} + +// Computes the mean of elements across dimensions given in axis. +// It does so in two stages, first calculates the sum of elements along the axis +// then divides it by the number of element in axis for quantized values. +template +inline bool QuantizedMeanOrSum(const T* input_data, int32_t input_zero_point, + const int* input_dims, const int input_num_dims, + T* output_data, int32_t output_multiplier, + int output_shift, int32_t output_zero_point, + const int* output_dims, + const int output_num_dims, const int* axis, + const int num_axis_dimensions, bool keep_dims, + int* temp_index, int* resolved_axis, U* temp_sum, + bool compute_sum) { + const int32_t kMinValue = std::numeric_limits::min(); + const int32_t kMaxValue = std::numeric_limits::max(); + const bool uint8_case = std::is_same::value; + const bool int16_case = std::is_same::value; + if (uint8_case) { + ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Uint8" : "Mean/Uint8"); + } else if (int16_case) { + ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int16" : "Mean/Int16"); + } else { + ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int8" : "Mean/Int8"); + } + // Reset output data. + size_t num_outputs = 1; + for (int idx = 0; idx < output_num_dims; ++idx) { + size_t current = static_cast(output_dims[idx]); + // Overflow prevention. + if (num_outputs > std::numeric_limits::max() / current) { + return false; + } + num_outputs *= current; + } + for (size_t idx = 0; idx < num_outputs; ++idx) { + output_data[idx] = T(); + temp_sum[idx] = U(); + } + + // Return early when input shape has zero dim. This is done after initializing + // data for output tensor because there are cases that the input tensor is + // empty but output tensor is not. In that case, output tensor should be + // filled with init_value. + for (int i = 0; i < input_num_dims; ++i) { + if (input_dims[i] == 0) return true; + } + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, + &num_resolved_axis)) { + return false; + } + + if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, + output_num_dims, resolved_axis, num_resolved_axis, + temp_index, temp_sum)) { + return false; + } + + // Calculate mean by dividing output_data by num of aggregated element. + int64_t num_elements_in_axis = 1; + for (int idx = 0; idx < num_resolved_axis; ++idx) { + size_t current = static_cast(input_dims[resolved_axis[idx]]); + // Overflow prevention. + if (current > static_cast(std::numeric_limits::max() / + num_elements_in_axis)) { + return false; + } + num_elements_in_axis *= current; + } + + if (num_elements_in_axis == 0) { + return true; + } + + // Readapt output rescaling when calculating the mean to integrate a + // 1/num_elements_in_axis multiplier. + if (!compute_sum) { + TFLITE_DCHECK_GE(num_elements_in_axis, 0); + int shift = + 63 - CountLeadingZeros(static_cast(num_elements_in_axis)); + // To avoid any overflow risk 'shift' should be <= 32 and to satisfy + // 'MultiplyByQuantizedMultiplier' pre-conditions 'output_shift - shift' + // should be >= -31. Clamp the value at the price of some precision loss. + shift = std::min(shift, 32); + shift = std::min(shift, 31 + output_shift); + output_multiplier = static_cast( + (static_cast(output_multiplier) << shift) / + num_elements_in_axis); + output_shift = output_shift - shift; + } + + for (size_t idx = 0; idx < num_outputs; ++idx) { + const U shifted_sum = + static_cast(temp_sum[idx] - input_zero_point * num_elements_in_axis); + int32_t output = MultiplyByQuantizedMultiplier( + shifted_sum, output_multiplier, output_shift) + + output_zero_point; + output = std::min(std::max(output, kMinValue), kMaxValue); + output_data[idx] = static_cast(output); + } + return true; +} + +template +inline bool QuantizedMeanOrSumExtraArgs( + const T* input_data, int32_t input_zero_point, float input_scale, + const int* input_dims, const int input_num_dims, T* output_data, + float output_scale, int32_t output_multiplier, int output_shift, + int32_t output_zero_point, const int* output_dims, + const int output_num_dims, const int* axis, const int num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis, U* temp_sum, + bool compute_sum) { + return QuantizedMeanOrSum( + input_data, input_zero_point, input_dims, input_num_dims, output_data, + output_multiplier, output_shift, output_zero_point, output_dims, + output_num_dims, axis, num_axis_dimensions, keep_dims, temp_index, + resolved_axis, temp_sum, compute_sum); +} + +template +inline bool QuantizedReduceProd(const T* input_data, int32_t input_zero_point, + const RuntimeShape& input_shape, T* output_data, + int32_t output_zero_point, + const RuntimeShape& output_shape, + const int* axis, + const int64_t num_axis_dimensions, + bool keep_dims, int* temp_index, + int* resolved_axis, int32_t* temp_prod, + int32_t scaling_multiplier, int scaling_shift) { + const int32_t kMinValue = std::numeric_limits::min(); + const int32_t kMaxValue = std::numeric_limits::max(); + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_shape.DimensionsCount(), axis, num_axis_dimensions, + resolved_axis, &num_resolved_axis)) { + return false; + } + + // Calculate the reduced product by rescaling each multiplication step to + // avoid an overflow. + auto reducer_first = [&](T in) -> int32_t { return in - input_zero_point; }; + + auto reducer_next = [&](int32_t current, T in) -> int32_t { + const int64_t result = + static_cast(current) * (in - input_zero_point); + return MultiplyByQuantizedMultiplier(result, scaling_multiplier, + scaling_shift); + }; + + if (!Reduce( + input_data, input_shape.DimsData(), output_shape.DimsData(), + input_shape.DimensionsCount(), output_shape.DimensionsCount(), + resolved_axis, num_resolved_axis, temp_index, reducer_first, + reducer_next, temp_prod)) { + return false; + } + + for (int i = 0; i < output_shape.FlatSize(); i++) { + int32_t result = + MultiplyByQuantizedMultiplier(static_cast(temp_prod[i]), + scaling_multiplier, scaling_shift) + + output_zero_point; + result = std::min(std::max(result, kMinValue), kMaxValue); + output_data[i] = static_cast(result); + } + + return true; +} + +} // namespace reference_ops + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h new file mode 100644 index 00000000..db89dc14 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h @@ -0,0 +1,70 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void Requantize(const input_type* input_data, int32_t size, + int32_t effective_scale_multiplier, + int32_t effective_scale_shift, int32_t input_zeropoint, + int32_t output_zeropoint, output_type* output_data) { + ruy::profiler::ScopeLabel label("Requantize"); + const bool same_scale = + (effective_scale_multiplier == 1 << 30 && effective_scale_shift == 1); + if (same_scale) { + const bool mixed_type_int8_uint8 = + std::is_same::value && + std::is_same::value; + const bool mixed_type_uint8_int8 = + std::is_same::value && + std::is_same::value; + const int32_t zero_point_diff = input_zeropoint - output_zeropoint; + // Fast path to do requantization for the case when just a shift of 128 is + // needed. + if ((mixed_type_int8_uint8 && zero_point_diff == -128) || + (mixed_type_uint8_int8 && zero_point_diff == 128)) { + for (int i = 0; i < size; ++i) { + output_data[i] = input_data[i] ^ 0x80; + } + return; + } + } + static constexpr int32_t kMinOutput = std::numeric_limits::min(); + static constexpr int32_t kMaxOutput = std::numeric_limits::max(); + for (int i = 0; i < size; ++i) { + const int32_t input = input_data[i] - input_zeropoint; + const int32_t output = + MultiplyByQuantizedMultiplier(input, effective_scale_multiplier, + effective_scale_shift) + + output_zeropoint; + const int32_t clamped_output = + std::max(std::min(output, kMaxOutput), kMinOutput); + output_data[i] = static_cast(clamped_output); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h new file mode 100644 index 00000000..73108fe7 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h @@ -0,0 +1,233 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ + +#include +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void ComputeInterpolationValues(const float value, const float scale, + const bool half_pixel_centers, + int32_t input_size, float* scaled_value, + int32_t* lower_bound, + int32_t* upper_bound) { + if (half_pixel_centers) { + *scaled_value = (value + 0.5f) * scale - 0.5f; + } else { + *scaled_value = value * scale; + } + float scaled_value_floor = std::floor(*scaled_value); + *lower_bound = std::max(static_cast(scaled_value_floor), + static_cast(0)); + *upper_bound = + std::min(static_cast(std::ceil(*scaled_value)), input_size - 1); +} + +template +inline void ResizeBilinear(const tflite_micro::ResizeBilinearParams& op_params, + const RuntimeShape& unextended_input_shape, + const T* input_data, + const RuntimeShape& unextended_output_size_shape, + const int32_t* output_size_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + // If half_pixel_centers is True, align_corners must be False. + TFLITE_DCHECK(!op_params.half_pixel_centers || !op_params.align_corners); + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_size_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_size_shape = + RuntimeShape::ExtendedShape(4, unextended_output_size_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); + int32_t input_height = input_shape.Dims(1); + int32_t input_width = input_shape.Dims(2); + int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); + + TFLITE_DCHECK_EQ(output_size_shape.Dims(0), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(1), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(2), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(3), 2); + int32_t output_height = + output_size_data[Offset(output_size_shape, 0, 0, 0, 0)]; + int32_t output_width = + output_size_data[Offset(output_size_shape, 0, 0, 0, 1)]; + + float height_scale = static_cast(input_height) / output_height; + float width_scale = static_cast(input_width) / output_width; + if (op_params.align_corners && output_height > 1) { + height_scale = static_cast(input_height - 1) / (output_height - 1); + } + if (op_params.align_corners && output_width > 1) { + width_scale = static_cast(input_width - 1) / (output_width - 1); + } + const float rounding_offset = std::numeric_limits::is_integer ? .5f : .0f; + + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < output_height; ++y) { + float input_y; + int32_t y0, y1; + ComputeInterpolationValues(y, height_scale, op_params.half_pixel_centers, + input_height, &input_y, &y0, &y1); + for (int x = 0; x < output_width; ++x) { + float input_x; + int32_t x0, x1; + ComputeInterpolationValues(x, width_scale, op_params.half_pixel_centers, + input_width, &input_x, &x0, &x1); + for (int c = 0; c < depth; ++c) { + T interpolation = + static_cast(input_data[Offset(input_shape, b, y0, x0, c)] * + (1 - (input_y - y0)) * (1 - (input_x - x0)) + + input_data[Offset(input_shape, b, y1, x0, c)] * + (input_y - y0) * (1 - (input_x - x0)) + + input_data[Offset(input_shape, b, y0, x1, c)] * + (1 - (input_y - y0)) * (input_x - x0) + + input_data[Offset(input_shape, b, y1, x1, c)] * + (input_y - y0) * (input_x - x0) + + rounding_offset); + output_data[Offset(output_shape, b, y, x, c)] = interpolation; + } + } + } + } +} + +inline void ComputeInterpolationValuesInteger( + const int32_t value, const int32_t scale_10, const bool half_pixel_centers, + int32_t input_size, int32_t* scaled_value, int32_t* lower_bound, + int32_t* upper_bound) { + if (half_pixel_centers) { + *scaled_value = value * scale_10 + scale_10 / 2 - (1 << 9); + } else { + *scaled_value = value * scale_10; + } + constexpr int32_t zero = 0; + *lower_bound = std::max(*scaled_value / (1 << 10), zero); + *upper_bound = + std::min((*scaled_value + (1 << 10) - 1) / (1 << 10), input_size - 1); +} + +// Same as above but doesn't use any floating-point for the resize +template +inline void ResizeBilinearInteger( + const tflite_micro::ResizeBilinearParams& op_params, + const RuntimeShape& unextended_input_shape, const T* input_data, + const RuntimeShape& unextended_output_size_shape, + const int32_t* output_size_data, + const RuntimeShape& unextended_output_shape, T* output_data) { + // If half_pixel_centers is True, align_corners must be False. + TFLITE_DCHECK(!op_params.half_pixel_centers || !op_params.align_corners); + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_size_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_size_shape = + RuntimeShape::ExtendedShape(4, unextended_output_size_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); + const int32_t input_height = input_shape.Dims(1); + const int32_t input_width = input_shape.Dims(2); + const int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); + + TFLITE_DCHECK_EQ(output_size_shape.Dims(0), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(1), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(2), 1); + TFLITE_DCHECK_EQ(output_size_shape.Dims(3), 2); + const int32_t output_height = + output_size_data[Offset(output_size_shape, 0, 0, 0, 0)]; + const int32_t output_width = + output_size_data[Offset(output_size_shape, 0, 0, 0, 1)]; + + int32_t height_scale_10 = + ((1 << 10) * input_height + output_height / 2) / output_height; + int32_t width_scale_10 = + ((1 << 10) * input_width + output_width / 2) / output_width; + if (op_params.align_corners && output_height > 1) { + height_scale_10 = + ((1 << 10) * (input_height - 1) + (output_height - 1) / 2) / + (output_height - 1); + } + if (op_params.align_corners && output_width > 1) { + width_scale_10 = ((1 << 10) * (input_width - 1) + (output_width - 1) / 2) / + (output_width - 1); + } + + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < output_height; ++y) { + int32_t input_y, y0, y1; + ComputeInterpolationValuesInteger(y, height_scale_10, + op_params.half_pixel_centers, + input_height, &input_y, &y0, &y1); + for (int x = 0; x < output_width; ++x) { + int32_t input_x, x0, x1; + ComputeInterpolationValuesInteger(x, width_scale_10, + op_params.half_pixel_centers, + input_width, &input_x, &x0, &x1); + for (int c = 0; c < depth; ++c) { + const int64_t output_20_ll = + static_cast( + input_data[Offset(input_shape, b, y0, x0, c)]) * + ((1 << 10) - (input_y - (1 << 10) * y0)) * + ((1 << 10) - (input_x - (1 << 10) * x0)); + const int64_t output_20_lu = + static_cast( + input_data[Offset(input_shape, b, y1, x0, c)]) * + (input_y - (1 << 10) * y0) * + ((1 << 10) - (input_x - (1 << 10) * x0)); + const int64_t output_20_rl = + static_cast( + input_data[Offset(input_shape, b, y0, x1, c)]) * + ((1 << 10) - (input_y - (1 << 10) * y0)) * + (input_x - (1 << 10) * x0); + const int64_t output_20_ru = + static_cast( + input_data[Offset(input_shape, b, y1, x1, c)]) * + (input_y - (1 << 10) * y0) * (input_x - (1 << 10) * x0); + const int64_t output_20 = + output_20_ll + output_20_lu + output_20_rl + output_20_ru; +#if TFLITE_SINGLE_ROUNDING + const int64_t round = 1 << 19; + const T interpolation = static_cast((output_20 + round) >> 20); +#else + const int64_t round = (output_20 > 0) ? (1 << 19) : -(1 << 19); + const T interpolation = + static_cast((output_20 + round) / (1 << 20)); +#endif // TFLITE_SINGLE_ROUNDING + output_data[Offset(output_shape, b, y, x, c)] = interpolation; + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h new file mode 100644 index 00000000..2860df3e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h @@ -0,0 +1,102 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline int32_t GetNearestNeighbor(const int input_value, + const int32_t input_size, + const int32_t output_size, + const bool align_corners, + const bool half_pixel_centers) { + const float scale = + (align_corners && output_size > 1) + ? (input_size - 1) / static_cast(output_size - 1) + : input_size / static_cast(output_size); + const float offset = half_pixel_centers ? 0.5f : 0.0f; + int32_t output_value = std::min( + align_corners + ? static_cast(TfLiteRound((input_value + offset) * scale)) + : static_cast(std::floor((input_value + offset) * scale)), + input_size - 1); + if (half_pixel_centers) { + output_value = std::max(static_cast(0), output_value); + } + return output_value; +} + +template +inline void ResizeNearestNeighbor( + const tflite_micro::ResizeNearestNeighborParams& op_params, + const RuntimeShape& unextended_input_shape, const T* input_data, + const RuntimeShape& output_size_shape, const int32_t* output_size_data, + const RuntimeShape& unextended_output_shape, T* output_data) { + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); + int32_t input_height = input_shape.Dims(1); + int32_t input_width = input_shape.Dims(2); + int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); + + // The Tensorflow version of this op allows resize on the width and height + // axis only. + TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2); + int32_t output_height = output_size_data[0]; + int32_t output_width = output_size_data[1]; + + const int col_offset = input_shape.Dims(3); + const int row_offset = input_shape.Dims(2) * col_offset; + const int batch_offset = input_shape.Dims(1) * row_offset; + + const T* input_ptr = input_data; + T* output_ptr = output_data; + for (int b = 0; b < batches; ++b) { + for (int y = 0; y < output_height; ++y) { + int32_t in_y = GetNearestNeighbor(y, input_height, output_height, + op_params.align_corners, + op_params.half_pixel_centers); + const T* y_input_ptr = input_ptr + in_y * row_offset; + for (int x = 0; x < output_width; ++x) { + int32_t in_x = GetNearestNeighbor(x, input_width, output_width, + op_params.align_corners, + op_params.half_pixel_centers); + const T* x_input_ptr = y_input_ptr + in_x * col_offset; + memcpy(output_ptr, x_input_ptr, depth * sizeof(T)); + output_ptr += depth; + } + } + input_ptr += batch_offset; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h new file mode 100644 index 00000000..c8b4552c --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h @@ -0,0 +1,51 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline float RoundToNearest(float value) { + auto floor_val = std::floor(value); + auto diff = value - floor_val; + if ((diff < 0.5f) || + ((diff == 0.5f) && (static_cast(floor_val) % 2 == 0))) { + return floor_val; + } else { + return floor_val = floor_val + 1.0f; + } +} + +inline void Round(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + // Note that this implementation matches that of tensorFlow tf.round + // and corresponds to the bankers rounding method. + // cfenv (for fesetround) is not yet supported universally on Android, so + // using a work around. + output_data[i] = RoundToNearest(input_data[i]); + } +} + +} // namespace reference_ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h new file mode 100644 index 00000000..e4347246 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h @@ -0,0 +1,151 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +void Select(const RuntimeShape& input_condition_shape, + const D* input_condition_data, const RuntimeShape& input_x_shape, + const T* input_x_data, const RuntimeShape& input_y_shape, + const T* input_y_data, const RuntimeShape& output_shape, + T* output_data) { + ruy::profiler::ScopeLabel label("Select"); + int64_t flatsize; + // Allow select operator executions on mixed scalar tensors and one element + // tensors. + if (input_condition_shape.FlatSize() == 1 && input_x_shape.FlatSize() == 1 && + input_y_shape.FlatSize() == 1 && output_shape.FlatSize() == 1) { + flatsize = 1; + } else { + flatsize = MatchingFlatSize(input_condition_shape, input_x_shape, + input_y_shape, output_shape); + } + for (int64_t i = 0; i < flatsize; ++i) { + output_data[i] = + input_condition_data[i] ? input_x_data[i] : input_y_data[i]; + } +} + +template +void RankOneSelect(const RuntimeShape& input_condition_shape, + const D* input_condition_data, + const RuntimeShape& input_x_shape, const T* input_x_data, + const RuntimeShape& input_y_shape, const T* input_y_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("Select/RankOneSelect"); + const int64_t outer_size = input_condition_shape.FlatSize(); + int64_t inner_size; + if (input_condition_shape.DimensionsCount() == 0) { + inner_size = MatchingFlatSize(input_x_shape, input_y_shape, output_shape); + } else { + TFLITE_DCHECK_EQ( + MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0), + outer_size); + inner_size = + MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape); + } + + int64_t offset = 0; + for (int64_t i = 0; i < outer_size; i++) { + const T* input_data = input_condition_data[i] ? input_x_data : input_y_data; + memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T)); + offset += inner_size; + } +} + +template +void BroadcastSelect5DSlow(const RuntimeShape& input_condition_shape, + const D* input_condition_data, + const RuntimeShape& input_x_shape, + const T* input_x_data, + const RuntimeShape& input_y_shape, + const T* input_y_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("Select/BroadcastSelectSlow"); + TFLITE_DCHECK_LE(input_condition_shape.DimensionsCount(), 5); + TFLITE_DCHECK_LE(input_x_shape.DimensionsCount(), 5); + TFLITE_DCHECK_LE(input_y_shape.DimensionsCount(), 5); + TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 5); + + NdArrayDesc<5> desc_condition; + NdArrayDesc<5> desc_x; + NdArrayDesc<5> desc_y; + NdArrayDesc<5> desc_output; + const RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(5, output_shape); + CopyDimsToDesc(extended_output_shape, &desc_output); + NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape, + input_y_shape, &desc_condition, &desc_x, + &desc_y); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest + // stride, typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for + // the best cache behavior. + for (int n = 0; n < desc_output.extents[0]; ++n) { + int out_idx_n = desc_output.extents[1] * n; + int cond_idx_n = desc_condition.strides[0] * n; + int in_idx1_n = desc_x.strides[0] * n; + int in_idx2_n = desc_y.strides[0] * n; + for (int b = 0; b < desc_output.extents[1]; ++b) { + int out_idx_b = (out_idx_n + b) * desc_output.extents[2]; + int cond_idx_b = cond_idx_n + desc_condition.strides[1] * b; + int in_idx1_b = in_idx1_n + desc_x.strides[1] * b; + int in_idx2_b = in_idx2_n + desc_y.strides[1] * b; + for (int y = 0; y < desc_output.extents[2]; ++y) { + int out_idx_y = (out_idx_b + y) * desc_output.extents[3]; + int cond_idx_y = cond_idx_b + desc_condition.strides[2] * y; + int in_idx1_y = in_idx1_b + desc_x.strides[2] * y; + int in_idx2_y = in_idx2_b + desc_y.strides[2] * y; + for (int x = 0; x < desc_output.extents[3]; ++x) { + int out_idx = (out_idx_y + x) * desc_output.extents[4]; + int cond_idx = cond_idx_y + desc_condition.strides[3] * x; + int in_idx1 = in_idx1_y + desc_x.strides[3] * x; + int in_idx2 = in_idx2_y + desc_y.strides[3] * x; + for (int c = 0; c < desc_output.extents[4]; ++c) { + output_data[out_idx] = input_condition_data[cond_idx] + ? input_x_data[in_idx1] + : input_y_data[in_idx2]; + out_idx++; + cond_idx += desc_condition.strides[4]; + in_idx1 += desc_x.strides[4]; + in_idx2 += desc_y.strides[4]; + } + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h new file mode 100644 index 00000000..277d25ff --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h @@ -0,0 +1,80 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ + +#include "tensorflow/lite/kernels/internal/portable_tensor.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void Slice(const tflite_micro::SliceParams& op_params, + const RuntimeShape& input_shape, + const RuntimeShape& output_shape, + SequentialTensorWriter* writer) { + const RuntimeShape ext_shape = RuntimeShape::ExtendedShape(5, input_shape); + TFLITE_DCHECK_LE(op_params.begin_count, 5); + TFLITE_DCHECK_LE(op_params.size_count, 5); + const int begin_count = op_params.begin_count; + const int size_count = op_params.size_count; + // We front-pad the begin and size vectors. + int start[5]; + int stop[5]; + for (int i = 0; i < 5; ++i) { + int padded_i = 5 - i; + start[i] = + begin_count < padded_i ? 0 : op_params.begin[begin_count - padded_i]; + stop[i] = + (size_count < padded_i || op_params.size[size_count - padded_i] == -1) + ? ext_shape.Dims(i) + : start[i] + op_params.size[size_count - padded_i]; + } + + for (int i0 = start[0]; i0 < stop[0]; ++i0) { + for (int i1 = start[1]; i1 < stop[1]; ++i1) { + for (int i2 = start[2]; i2 < stop[2]; ++i2) { + for (int i3 = start[3]; i3 < stop[3]; ++i3) { + for (int i4 = start[4]; i4 < stop[4]; ++i4) { + writer->Write(Offset(ext_shape, i0, i1, i2, i3, i4)); + } + } + } + } + } +} + +template +inline void Slice(const tflite_micro::SliceParams& op_params, + const RuntimeShape& input_shape, const T* input_data, + const RuntimeShape& output_shape, T* output_data) { + SequentialTensorWriter writer(input_data, output_data); + return Slice(op_params, input_shape, output_shape, &writer); +} + +template +inline void Slice(const tflite_micro::SliceParams& op_params, + const RuntimeShape& input_shape, const TfLiteTensor* input, + const RuntimeShape& output_shape, TfLiteTensor* output) { + SequentialTensorWriter writer(input, output); + return Slice(op_params, input_shape, output_shape, &writer); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h new file mode 100644 index 00000000..81274043 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h @@ -0,0 +1,233 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ + +#include +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/quantization_util.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/kernels/op_macros.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void Softmax(const SoftmaxParams& params, + const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + + for (int i = 0; i < outer_size; ++i) { + // Find max element value which we'll use to ensure numerical stability + // taking advantage of the following equality: + // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C)) + float max = std::numeric_limits::lowest(); + for (int c = 0; c < depth; ++c) { + max = std::max(max, input_data[i * depth + c]); + } + + // Compute sum. + float sum = 0.f; + for (int c = 0; c < depth; ++c) { + const float exp_c = std::exp((input_data[i * depth + c] - max) * + static_cast(params.beta)); + output_data[i * depth + c] = exp_c; + sum += exp_c; + } + + // Compute result. + for (int c = 0; c < depth; ++c) { + output_data[i * depth + c] = output_data[i * depth + c] / sum; + } + } +} + +// Quantized softmax with int8_t/uint8_t input and int8_t/uint8_t/int16_t +// output. +template +inline void Softmax(const SoftmaxParams& params, + const RuntimeShape& input_shape, const InputT* input_data, + const RuntimeShape& output_shape, OutputT* output_data) { + const int32_t input_beta_multiplier = params.input_multiplier; + const int32_t input_beta_left_shift = params.input_left_shift; + const int diff_min = params.diff_min; + // The representation chosen for the input to the exp() function is Q5.26. + // We need to leave extra space since values that we skip might be as large as + // -32 before multiplying by input_beta_multiplier, and therefore as large as + // -16 afterwards. Note that exp(-8) is definitely not insignificant to + // accumulation, but exp(-16) definitely is. + static const int kScaledDiffIntegerBits = 5; + static const int kAccumulationIntegerBits = 12; + using FixedPointScaledDiff = + gemmlowp::FixedPoint; + using FixedPointAccum = + gemmlowp::FixedPoint; + using FixedPoint0 = gemmlowp::FixedPoint; + + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + + for (int i = 0; i < outer_size; ++i) { + InputT max_in_row = std::numeric_limits::min(); + for (int c = 0; c < depth; ++c) { + max_in_row = std::max(max_in_row, input_data[i * depth + c]); + } + + FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); + for (int c = 0; c < depth; ++c) { + int32_t input_diff = + static_cast(input_data[i * depth + c]) - max_in_row; + if (input_diff >= diff_min) { + const int32_t input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_beta_multiplier, input_beta_left_shift); + const FixedPointScaledDiff scaled_diff_f8 = + FixedPointScaledDiff::FromRaw(input_diff_rescaled); + sum_of_exps = sum_of_exps + gemmlowp::Rescale( + exp_on_negative_values(scaled_diff_f8)); + } + } + + int num_bits_over_unit; + FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal( + sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit)); + + for (int c = 0; c < depth; ++c) { + int32_t input_diff = + static_cast(input_data[i * depth + c]) - max_in_row; + if (input_diff >= diff_min) { + const int32_t input_diff_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_diff, input_beta_multiplier, input_beta_left_shift); + const FixedPointScaledDiff scaled_diff_f8 = + FixedPointScaledDiff::FromRaw(input_diff_rescaled); + + FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); + int32_t unsat_output = gemmlowp::RoundingDivideByPOT( + (shifted_scale * exp_in_0).raw(), + num_bits_over_unit + 31 - (sizeof(OutputT) * 8)); + + const int32_t shifted_output = + unsat_output + + static_cast(std::numeric_limits::min()); + + output_data[i * depth + c] = static_cast(std::max( + std::min(shifted_output, + static_cast(std::numeric_limits::max())), + static_cast(std::numeric_limits::min()))); + } else { + output_data[i * depth + c] = std::numeric_limits::min(); + } + } + } +} + +// Computes exp(input - max_input) +inline int16_t SoftMaxCalculateExp(const SoftmaxParams& params, + const int16_t* input_data, const int depth, + int16_t max_in_row, int i, int c) { + int32_t input_diff = input_data[i * depth + c] - max_in_row; + // scale the input_diff such that [-65535, 0] correspond to [-10.0, 0.0] + // exp lut generated with range [-10, 0], as exp(-10) is negligible. + int32_t scaled_diff = MultiplyByQuantizedMultiplier( + input_diff, params.input_multiplier, params.input_left_shift); + // recenter to [-32768, 32767] + int32_t sym_scaled_diff = scaled_diff + 32767; + int16_t sat_sym_scaled_diff = + std::min(std::max(sym_scaled_diff, static_cast(-32768)), + static_cast(32767)); + // apply the exp() LUT activation function + return LUTLookup(sat_sym_scaled_diff, params.exp_lut); +} +// Quantized softmax with int16_t input and int16_t output. +inline void SoftmaxInt16(const SoftmaxParams& params, + const RuntimeShape& input_shape, + const int16_t* input_data, + const RuntimeShape& output_shape, + int16_t* output_data) { + const int trailing_dim = input_shape.DimensionsCount() - 1; + const int outer_size = + MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); + const int depth = + MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); + + for (int i = 0; i < outer_size; ++i) { + // Find the largest element + int16_t max_in_row = std::numeric_limits::min(); + for (int c = 0; c < depth; ++c) { + max_in_row = std::max(max_in_row, input_data[i * depth + c]); + } + + // This loops computes the exp values and their sum. We will need the exp + // values later on in the function so we cache them in the output_data + // buffer. This is an optimization done to avoid calculating the exp values + // twice making use of the output_data buffer as scratch memory. + int32_t sum_of_exps = 0; // Q16.15 fixed point format. + int16_t* exp_results_Q015 = output_data + i * depth; + for (int c = 0; c < depth; ++c) { + exp_results_Q015[c] = + SoftMaxCalculateExp(params, input_data, depth, max_in_row, i, c); + sum_of_exps += exp_results_Q015[c]; + } + + // Compute the reciprocal 1/sum_of_exps + uint8_t headroom_plus_one = + CountLeadingZeros(static_cast(sum_of_exps)); + int32_t shifted_sum = + ((static_cast(sum_of_exps) << (headroom_plus_one - 1)) + + (1 << 13)) >> + 14; + // since the LUT computes 1/(1 + x) we need to first compute x = (sum - 1). + // also, the LUT expects a symmetrical input, so we must also recenter x + // from [0, 65535] to [-32768, 32767]. + int32_t sym_shifted_sum = shifted_sum + (-((1 << 15) + (1 << 16))); + int16_t sat_sym_shifted_sum = static_cast( + std::min(std::max(sym_shifted_sum, static_cast(-32768)), + static_cast(32767))); + // apply 1/(1 + x) LUT activation function + int16_t reciprocal_scale_Q015 = + LUTLookup(sat_sym_shifted_sum, params.one_over_one_plus_x_lut); + + // Rescale the exp_result with reciprocal + // range of output is [0, 32767] correspond to [0.0, 1.0] + for (int c = 0; c < depth; ++c) { + uint8_t right_shift = 31 - headroom_plus_one; + int64_t round = 1 << (right_shift - 1); + int32_t result = (static_cast(exp_results_Q015[c]) * + static_cast(reciprocal_scale_Q015) + + round) >> + right_shift; + output_data[i * depth + c] = static_cast( + std::min(std::max(result, static_cast(0)), + static_cast(32767))); + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h new file mode 100644 index 00000000..77d5bd5b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h @@ -0,0 +1,109 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ + +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +// TODO(b/135760455): Move this method anonymous namespace in a cc file. +inline RuntimeShape ExtendShapeSpaceToBatch(const RuntimeShape& shape) { + if (shape.DimensionsCount() == 4) { + return shape; + } + RuntimeShape new_shape(4, 1); + new_shape.SetDim(0, shape.Dims(0)); + new_shape.SetDim(1, shape.Dims(1)); + new_shape.SetDim(3, shape.Dims(2)); + return new_shape; +} + +template +inline void SpaceToBatchND(const SpaceToBatchParams& params, + const RuntimeShape& unextended_input1_shape, + const T* input1_data, + const RuntimeShape& unextended_input2_shape, + const int32_t* block_shape_data, + const RuntimeShape& unextended_input3_shape, + const int32_t* paddings_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + ruy::profiler::ScopeLabel label("SpaceToBatchND"); + TFLITE_DCHECK_GE(unextended_input1_shape.DimensionsCount(), 3); + TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(unextended_input1_shape.DimensionsCount(), + unextended_output_shape.DimensionsCount()); + + // Extends the input/output shape from 3D to 4D if needed, NHC -> NH1C. + const RuntimeShape input1_shape = + ExtendShapeSpaceToBatch(unextended_input1_shape); + const RuntimeShape output_shape = + ExtendShapeSpaceToBatch(unextended_output_shape); + + const int depth = input1_shape.Dims(3); + const int input_width = input1_shape.Dims(2); + const int input_height = input1_shape.Dims(1); + const int input_batch_size = input1_shape.Dims(0); + + const int output_width = output_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_batch_size = output_shape.Dims(0); + + const int block_shape_height = block_shape_data[0]; + const int block_shape_width = + unextended_input1_shape.DimensionsCount() == 4 ? block_shape_data[1] : 1; + const int padding_top = paddings_data[0]; + const int padding_left = + unextended_input1_shape.DimensionsCount() == 4 ? paddings_data[2] : 0; + + // For uint8 quantized, the correct padding "zero value" is the output offset. + const int32_t pad_value = params.output_offset; + for (int out_b = 0; out_b < output_batch_size; ++out_b) { + int input_batch = out_b % input_batch_size; + int shift_w = (out_b / input_batch_size) % block_shape_width; + int shift_h = (out_b / input_batch_size) / block_shape_width; + for (int out_h = 0; out_h < output_height; ++out_h) { + for (int out_w = 0; out_w < output_width; ++out_w) { + T* out = output_data + Offset(output_shape, out_b, out_h, out_w, 0); + if (out_h * block_shape_height + shift_h < padding_top || + out_h * block_shape_height + shift_h >= + padding_top + input_height || + out_w * block_shape_width + shift_w < padding_left || + out_w * block_shape_width + shift_w >= padding_left + input_width) { + // This may not execute correctly when pad_value != 0 and T != uint8. + memset(out, pad_value, depth * sizeof(T)); + } else { + const T* in = + input1_data + + Offset(input1_shape, input_batch, + (out_h * block_shape_height + shift_h) - padding_top, + (out_w * block_shape_width + shift_w) - padding_left, 0); + memcpy(out, in, depth * sizeof(T)); + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h new file mode 100644 index 00000000..c576e3db --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h @@ -0,0 +1,80 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace reference_ops { + +template +inline void SpaceToDepth(const tflite_micro::SpaceToDepthParams& op_params, + const RuntimeShape& unextended_input_shape, + const T* input_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(4, unextended_input_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(4, unextended_output_shape); + + const int input_depth = input_shape.Dims(3); + const int input_width = input_shape.Dims(2); + const int input_height = input_shape.Dims(1); + const int input_batch = input_shape.Dims(0); + + const int output_depth = output_shape.Dims(3); + const int output_width = output_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_batch = output_shape.Dims(0); + + const int32_t block_size = op_params.block_size; + + TFLITE_DCHECK_EQ(input_width, output_width * block_size); + TFLITE_DCHECK_EQ(input_height, output_height * block_size); + TFLITE_DCHECK_EQ(input_depth * block_size * block_size, output_depth); + TFLITE_DCHECK_EQ(input_batch, output_batch); + + for (int in_b = 0; in_b < input_batch; ++in_b) { + for (int in_h = 0; in_h < input_height; ++in_h) { + for (int in_w = 0; in_w < input_width; ++in_w) { + for (int in_d = 0; in_d < input_depth; ++in_d) { + const int out_d = + in_d + ((in_h % block_size) * block_size + in_w % block_size) * + input_depth; + const int out_w = in_w / block_size; + const int out_h = in_h / block_size; + const int out_b = in_b; + + const int input_index = Offset(input_shape, in_b, in_h, in_w, in_d); + const int output_index = + Offset(output_shape, out_b, out_h, out_w, out_d); + + output_data[output_index] = input_data[input_index]; + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h new file mode 100644 index 00000000..4eefdd19 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h @@ -0,0 +1,147 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/portable_tensor.h" +#include "tensorflow/lite/kernels/internal/strided_slice_logic.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, + const RuntimeShape& unextended_input_shape, + const RuntimeShape& unextended_output_shape, + SequentialTensorWriter* writer) { + ruy::profiler::ScopeLabel label("StridedSlice"); + + // Note that the output_shape is not used herein. + tflite_micro::StridedSliceParams params_copy = op_params; + + TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 5); + TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 5); + const RuntimeShape input_shape = + RuntimeShape::ExtendedShape(5, unextended_input_shape); + const RuntimeShape output_shape = + RuntimeShape::ExtendedShape(5, unextended_output_shape); + + // Reverse and pad to 5 dimensions because that is what the runtime code + // requires (ie. all shapes must be 5D and are given backwards). + strided_slice::StridedSlicePadIndices(¶ms_copy, 5); + + const int start_0 = + strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 0); + const int stop_0 = strided_slice::StridedSliceEndForAxis( + params_copy, input_shape, 0, start_0); + const int start_1 = + strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 1); + const int stop_1 = strided_slice::StridedSliceEndForAxis( + params_copy, input_shape, 1, start_1); + const int start_2 = + strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 2); + const int stop_2 = strided_slice::StridedSliceEndForAxis( + params_copy, input_shape, 2, start_2); + const int start_3 = + strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 3); + const int stop_3 = strided_slice::StridedSliceEndForAxis( + params_copy, input_shape, 3, start_3); + const int start_4 = + strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 4); + const int stop_4 = strided_slice::StridedSliceEndForAxis( + params_copy, input_shape, 4, start_4); + + auto lc = [&](int end, int stride, int index) { + if (stride < 0) { + return index > end; + } else { + return index < end; + } + }; + // With a static_cast it is not possible to initialize + // a variable of type 'const int *' + // with an rvalue of type 'const int32_t *' (aka 'const long *'). + // reinterpret_cast is required to handle this casting. + const int* shape = reinterpret_cast(input_shape.DimsData()); + const int* stride = reinterpret_cast(params_copy.strides); + const bool inner_stride_is_1 = params_copy.strides[4] == 1; + + for (int offset_0 = start_0; lc(stop_0, stride[0], offset_0); + offset_0 += stride[0]) { + for (int offset_1 = start_1; lc(stop_1, stride[1], offset_1); + offset_1 += stride[1]) { + for (int offset_2 = start_2; lc(stop_2, stride[2], offset_2); + offset_2 += stride[2]) { + for (int offset_3 = start_3; lc(stop_3, stride[3], offset_3); + offset_3 += stride[3]) { + // When the stride is 1, the inner loop is equivalent to the + // optimized slice inner loop. Otherwise, it is identical to the + // strided_slice reference implementation inner loop. + if (inner_stride_is_1) { + const int len = stop_4 - start_4; + int index = start_4 + offset_3 * shape[4] + + offset_2 * shape[3] * shape[4] + + offset_1 * shape[2] * shape[3] * shape[4] + + offset_0 * shape[1] * shape[2] * shape[3] * shape[4]; + if (len > 0) { + writer->WriteN(index, len); + } + } else { + for (int offset_4 = start_4; lc(stop_4, stride[4], offset_4); + offset_4 += stride[4]) { + int index = offset_4 + offset_3 * shape[4] + + offset_2 * shape[3] * shape[4] + + offset_1 * shape[2] * shape[3] * shape[4] + + offset_0 * shape[1] * shape[2] * shape[3] * shape[4]; + writer->Write(index); + } + } + } + } + } + } +} + +template +inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, + const RuntimeShape& unextended_input_shape, + const T* input_data, + const RuntimeShape& unextended_output_shape, + T* output_data) { + SequentialTensorWriter writer(input_data, output_data); + StridedSlice(op_params, unextended_input_shape, unextended_output_shape, + &writer); +} + +template +inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, + const RuntimeShape& unextended_input_shape, + const TfLiteTensor* input, + const RuntimeShape& unextended_output_shape, + TfLiteTensor* output) { + SequentialTensorWriter writer(input, output); + StridedSlice(op_params, unextended_input_shape, unextended_output_shape, + &writer); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h new file mode 100644 index 00000000..e80b3c27 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h @@ -0,0 +1,465 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ + +#include + +#include +#include +#include + +#include "ruy/profiler/instrumentation.h" // from @ruy +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +template +struct SubImpl { + template + static void BroadcastInput1(const ArithmeticParams& params, + const T* input1_data, const T* input2_data, + T* output_data, size_t size, F binary_func) { + for (size_t c = 0; c < size; ++c) { + output_data[c] = binary_func(input1_data[0], input2_data[c], params); + } + } + + template + static void BroadcastInput2(const ArithmeticParams& params, + const T* input1_data, const T* input2_data, + T* output_data, size_t size, F binary_func) { + for (size_t c = 0; c < size; ++c) { + output_data[c] = binary_func(input1_data[c], input2_data[0], params); + } + } + + template + static void ElementWise(const ArithmeticParams& params, const T* input1_data, + const T* input2_data, T* output_data, size_t size, + F binary_func) { + for (size_t c = 0; c < size; ++c) { + output_data[c] = binary_func(input1_data[c], input2_data[c], params); + } + } +}; + +template <> +struct SubImpl { + template + static void BroadcastInput1(const ArithmeticParams& params, + const int32_t* input1_data, + const int32_t* input2_data, int32_t* output_data, + size_t size, F binary_func) { + size_t c = 0; + int32_t activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); +#ifdef USE_NEON + const int32x4_t vmax = vdupq_n_s32(activation_max); + const int32x4_t vmin = vdupq_n_s32(activation_min); + const int32x4_t va = vdupq_n_s32(input1_data[0]); + for (; c + 4 <= size; c += 4) { + const int32x4_t vb = vld1q_s32(&input2_data[c]); + int32x4_t vres = vsubq_s32(va, vb); + vres = vmaxq_s32(vmin, vres); + vres = vminq_s32(vmax, vres); + vst1q_s32(&output_data[c], vres); + } +#endif + for (; c < size; ++c) { + output_data[c] = binary_func(input1_data[0], input2_data[c], params); + } + } + + template + static void BroadcastInput2(const ArithmeticParams& params, + const int32_t* input1_data, + const int32_t* input2_data, int32_t* output_data, + size_t size, F binary_func) { + size_t c = 0; + int32_t activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); +#ifdef USE_NEON + const int32x4_t vmax = vdupq_n_s32(activation_max); + const int32x4_t vmin = vdupq_n_s32(activation_min); + const int32x4_t vb = vdupq_n_s32(input2_data[0]); + for (; c + 4 <= size; c += 4) { + const int32x4_t va = vld1q_s32(&input1_data[c]); + int32x4_t vres = vsubq_s32(va, vb); + vres = vmaxq_s32(vmin, vres); + vres = vminq_s32(vmax, vres); + vst1q_s32(&output_data[c], vres); + } +#endif + for (; c < size; ++c) { + output_data[c] = binary_func(input1_data[c], input2_data[0], params); + } + } + + template + static void ElementWise(const ArithmeticParams& params, + const int32_t* input1_data, + const int32_t* input2_data, int32_t* output_data, + size_t size, F binary_func) { + size_t c = 0; + int32_t activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); +#ifdef USE_NEON + int32x4_t vmax = vdupq_n_s32(activation_max); + int32x4_t vmin = vdupq_n_s32(activation_min); + for (; c + 4 <= size; c += 4) { + const int32x4_t va = vld1q_s32(&input1_data[c]); + const int32x4_t vb = vld1q_s32(&input2_data[c]); + int32x4_t vres = vsubq_s32(va, vb); + vres = vmaxq_s32(vmin, vres); + vres = vminq_s32(vmax, vres); + vst1q_s32(&output_data[c], vres); + } +#endif + for (; c < size; ++c) { + output_data[c] = binary_func(input1_data[c], input2_data[c], params); + } + } +}; + +template +inline void BroadcastSubRecursiveDimensions( + int dimension, const ArithmeticParams& params, const T* input1_data, + const T* input2_data, T* output_data, size_t* input1_offset_p, + size_t* input2_offset_p, size_t* output_offset, + size_t* compressed_input1_stride, size_t* compressed_input2_stride, + size_t* compressed_output_shape, F binary_func) { + if (dimension > 0) { + for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { + size_t input1_offset_c = *input1_offset_p; + size_t input2_offset_c = *input2_offset_p; + BroadcastSubRecursiveDimensions( + dimension - 1, params, input1_data, input2_data, output_data, + &input1_offset_c, &input2_offset_c, output_offset, + compressed_input1_stride, compressed_input2_stride, + compressed_output_shape, binary_func); + *input1_offset_p += compressed_input1_stride[dimension]; + *input2_offset_p += compressed_input2_stride[dimension]; + } + } else { + TFLITE_DCHECK(dimension == 0); + bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; + bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; + TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); + const T* input1_data_ptr = input1_data + *input1_offset_p; + const T* input2_data_ptr = input2_data + *input2_offset_p; + T* output_data_ptr = output_data + *output_offset; + if (input1_is_broadcast) { + // input1 is broadcast. + SubImpl::BroadcastInput1( + params, input1_data_ptr, input2_data_ptr, output_data_ptr, + compressed_output_shape[dimension], binary_func); + *input2_offset_p += compressed_output_shape[dimension]; + } else if (input2_is_broadcast) { + // input2 is broadcast. + SubImpl::BroadcastInput2( + params, input1_data_ptr, input2_data_ptr, output_data_ptr, + compressed_output_shape[dimension], binary_func); + *input1_offset_p += compressed_output_shape[dimension]; + } else { + // Add element-wise. + SubImpl::ElementWise(params, input1_data_ptr, input2_data_ptr, + output_data_ptr, + compressed_output_shape[dimension], binary_func); + *input1_offset_p += compressed_output_shape[dimension]; + *input2_offset_p += compressed_output_shape[dimension]; + } + *output_offset += compressed_output_shape[dimension]; + } +} + +// TODO: b/296510380 - we may be able to factor out this to common.h for all +// binary arithmetic ops (add, sub, mul). +template +inline void BroadcastSubCommon(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const T* input1_data, + const RuntimeShape& input2_shape, + const T* input2_data, + const RuntimeShape& output_shape, T* output_data, + F binary_func) { + constexpr int kMaxBroadcastDim = 6; + TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), kMaxBroadcastDim); + TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), kMaxBroadcastDim); + TFLITE_DCHECK_LE(output_shape.DimensionsCount(), kMaxBroadcastDim); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + + size_t compressed_input1_stride[kMaxBroadcastDim]; + size_t compressed_input2_stride[kMaxBroadcastDim]; + size_t compressed_output_shape[kMaxBroadcastDim]; + bool broadcastable_shape = ReduceDimensionsForBroadcast( + input1_shape, input2_shape, compressed_input1_stride, + compressed_input2_stride, compressed_output_shape); + // Skip broadcasting for degenerate shapes. + if (!broadcastable_shape) { + return; + } + + size_t input1_offset = 0; + size_t input2_offset = 0; + size_t output_offset = 0; + BroadcastSubRecursiveDimensions( + kMaxBroadcastDim - 1, params, input1_data, input2_data, output_data, + &input1_offset, &input2_offset, &output_offset, compressed_input1_stride, + compressed_input2_stride, compressed_output_shape, binary_func); +} + +// TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary +// dimensionality if the runtime code does a single loop over one dimension +// that handles broadcasting as the base case. The code generator would then +// generate max(D1, D2) nested for loops. +template +void BroadcastSubSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("BroadcastSubSlow/T"); + BroadcastSubCommon( + params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data, + [](T input1_val, T input2_val, const ArithmeticParams& params) { + T activation_min, activation_max; + GetActivationParams(params, &activation_min, &activation_max); + return ActivationFunctionWithMinMax(input1_val - input2_val, + activation_min, activation_max); + }); +} + +inline void BroadcastSub16POTSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int16_t* input1_data, + const RuntimeShape& input2_shape, + const int16_t* input2_data, + const RuntimeShape& output_shape, + int16_t* output_data) { + ruy::profiler::ScopeLabel label("BroadcastSub16POTSlow/int16_t"); + BroadcastSubCommon( + params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data, + [](int16_t input1_val, int16_t input2_val, + const ArithmeticParams& params) { + const int32_t scaled_input1_val = + gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift); + const int32_t scaled_input2_val = + gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift); + const int32_t raw_output = scaled_input1_val - scaled_input2_val; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + return static_cast(clamped_output); + }); +} + +template +void BroadcastQuantSubSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const T* input1_data, + const RuntimeShape& input2_shape, + const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("BroadcastQuantSubSlow/T"); + BroadcastSubCommon( + params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data, + [](T input1_val, T input2_val, const ArithmeticParams& params) { + const int32_t shifted_input1_val = + (params.input1_offset + input1_val) * (1 << params.left_shift); + const int32_t shifted_input2_val = + (params.input2_offset + input2_val) * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, + params.input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, + params.input2_shift); + const int32_t raw_sub = scaled_input1_val - scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sub, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + return static_cast(clamped_output); + }); +} + +// Element-wise add that can often be used for inner loop of broadcast add as +// well as the non-broadcast add. +template +inline void SubElementwise(int size, const ArithmeticParams& params, + const T* input1_data, const T* input2_data, + T* output_data) { + for (int i = 0; i < size; ++i) { + const int32_t input1_val = params.input1_offset + input1_data[i]; + const int32_t input2_val = params.input2_offset + input2_data[i]; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32_t scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32_t raw_sub = scaled_input1_val - scaled_input2_val; + const int32_t raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sub, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); + } +} + +inline void Sub(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8_t* input1_data, + const RuntimeShape& input2_shape, const uint8_t* input2_data, + const RuntimeShape& output_shape, uint8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + SubElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Sub(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int8_t* input1_data, + const RuntimeShape& input2_shape, const int8_t* input2_data, + const RuntimeShape& output_shape, int8_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GE(params.input1_offset, -128); + TFLITE_DCHECK_GE(params.input2_offset, -128); + // offset = -quantization_params.zero_point in PrepareGeneralSubOp(). + // So it's maximum can be 128 not 127. + TFLITE_DCHECK_LE(params.input1_offset, 128); + TFLITE_DCHECK_LE(params.input2_offset, 128); + SubElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Sub(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16_t* input1_data, + const RuntimeShape& input2_shape, const int16_t* input2_data, + const RuntimeShape& output_shape, int16_t* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_EQ(params.input1_offset, 0); + TFLITE_DCHECK_EQ(params.input2_offset, 0); + SubElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +template +void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, + T* output_data) { + BroadcastSubCommon( + params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data, + [](T input1_val, T input2_val, const ArithmeticParams& params) { + return input1_val - input2_val; + }); +} + +inline void SetActivationMinMax(const ArithmeticParams& params, + int32_t* activation_min, + int32_t* activation_max) { + *activation_min = params.quantized_activation_min; + *activation_max = params.quantized_activation_max; +} + +inline void SetActivationMinMax(const ArithmeticParams& params, + float* activation_min, float* activation_max) { + *activation_min = params.float_activation_min; + *activation_max = params.float_activation_max; +} + +inline void SetActivationMinMax(const ArithmeticParams& params, + int64_t* activation_min, + int64_t* activation_max) { + *activation_min = params.int64_activation_min; + *activation_max = params.int64_activation_max; +} + +template +inline void SubWithActivation( + const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, T* output_data) { + ruy::profiler::ScopeLabel label("SubWithActivation"); + const int flat_size = + MatchingElementsSize(input1_shape, input2_shape, output_shape); + T activation_min, activation_max; + SetActivationMinMax(params, &activation_min, &activation_max); + + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], activation_min, activation_max); + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h new file mode 100644 index 00000000..578a1a90 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h @@ -0,0 +1,129 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ + +#include + +#include "fixedpoint/fixedpoint.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/kernels/op_macros.h" + +namespace tflite_micro { +namespace reference_ops { + +inline void Tanh(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data) { + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + float val = input_data[i]; + float result = std::tanh(val); + output_data[i] = result; + } +} + +// Convenience version that allows, for example, generated-code calls to be +// uniform between data types. +inline void Tanh(const TanhParams&, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& output_shape, + float* output_data) { + // Drop params: not needed. + Tanh(input_shape, input_data, output_shape, output_data); +} + +inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& output_shape, + int16_t* output_data) { + const int input_left_shift = params.input_left_shift; + // Support for shifts is limited until we have a parameterized version of + // SaturatingRoundingMultiplyByPOT(). + TFLITE_DCHECK_GE(input_left_shift, 0); + TFLITE_DCHECK_LE(input_left_shift, 1); + + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + // F0 uses 0 integer bits, range [-1, 1]. + // This is the return type of math functions such as tanh, logistic, + // whose range is in [-1, 1]. + using F0 = gemmlowp::FixedPoint; + // F3 uses 3 integer bits, range [-8, 8], the input range expected here. + using F3 = gemmlowp::FixedPoint; + + if (input_left_shift == 0) { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw(input_data[i]); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } else { + for (int i = 0; i < flat_size; i++) { + F3 input = F3::FromRaw( + gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i])); + F0 output = gemmlowp::tanh(input); + output_data[i] = output.raw(); + } + } +} + +inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& output_shape, + uint8_t* output_data) { + const int32_t input_zero_point = params.input_zero_point; + const int32_t input_range_radius = params.input_range_radius; + const int32_t input_multiplier = params.input_multiplier; + const int input_left_shift = params.input_left_shift; + const int32_t output_zero_point = 128; + const int flat_size = MatchingFlatSize(input_shape, output_shape); + + for (int i = 0; i < flat_size; i++) { + const uint8_t input_val_u8 = input_data[i]; + const int32_t input_val_centered = + static_cast(input_val_u8) - input_zero_point; + uint8_t output_val; + if (input_val_centered <= -input_range_radius) { + output_val = 0; + } else if (input_val_centered >= input_range_radius) { + output_val = 255; + } else { + const int32_t input_val_rescaled = + MultiplyByQuantizedMultiplierGreaterThanOne( + input_val_centered, input_multiplier, input_left_shift); + using FixedPoint4 = gemmlowp::FixedPoint; + using FixedPoint0 = gemmlowp::FixedPoint; + const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); + const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); + // Convert from Q0.31 to Q24.7. + using gemmlowp::RoundingDivideByPOT; + int32_t output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); + output_val_s32 += output_zero_point; + if (output_val_s32 == 256) { + output_val_s32 = 255; + } + // Reinterpret as Q0.7, encoded in uint8_t. + TFLITE_DCHECK_GE(output_val_s32, 0); + TFLITE_DCHECK_LE(output_val_s32, 255); + output_val = static_cast(output_val_s32); + } + output_data[i] = output_val; + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h new file mode 100644 index 00000000..195202e6 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h @@ -0,0 +1,203 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ + +#include + +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +namespace transpose_internal { + +// Recursively explores all the dimensions of the output tensor and writes the +// corresponding input tensor data. +// +// - depth: the current depth of the recursion. +// - dims: tensor dimension count, also `perm` size. +// - perm: permutation array. +// - input_data: Running input data pointer. If depth == num_dims-1, this points +// to the first element of the last dimension to traverse. +// - input_stride: Reverse partial product of input shapes. +// - output_data: Running output data pointer. If depth == num_dims-1, this +// points to the first element of the last dimension to traverse. +// - output_stride: Reverse partial product of output shapes. +// - output_shape: Shape of the output tensor. +// +// ## Algorithm explanation +// +// Assume a 3D tensor T with a shape of [I, J, K] stored in row major order. +// T[i, j, k] is at position `i*J*K + j*K + k` in the tensor buffer. +// +// If we want to go through the whole tensor iteratively, we can use loops. +// +// ``` +// for(i = 0; i < I; ++i) { +// for(j = 0; j < J; ++j) { +// for(k = 0; k < K; ++k) { +// T.data[i*J*K + j*K + k] = ... +// } +// } +// } +// ``` +// +// We can also compute the offset as we go through the loops. +// +// ``` +// stride_i = K * J; +// stride_j = K; +// stride_k = 1; +// for(i = 0; i < I; ++i) { +// offset_i = i * stride_i; +// offset_j = 0; +// for(j = 0; j < J; ++j) { +// offset_j += stride_j; +// offset_k = 0; +// for(k = 0; k < K; ++k) { +// offset_k += stride_k; +// T.data[offset_i + offset_j + offset_k] = ... +// } +// } +// } +// ``` +// +// This nicely extends to a recursive version which is the base of this +// algorithm and supports any number of dimensions. +// +// ``` +// shape = [I, J, K] +// strides = [K*J, K, 1] +// void recurse(T* data, shape, strides, depth = 0) { +// if(depth == shape.size) { +// *data = ... +// } else { +// for(a = 0; a < shape[depth]; ++a) { +// recurse(data, shape, strides, depth+1); +// data += strides[depth]; +// } +// } +// } +// ``` +template +void TransposeImpl(const int depth, const int dims, const int32_t* perm, + const T* input_data, const int* input_stride, T* output_data, + const int* output_stride, const int32_t* output_shape) { + const int dimension_size = output_shape[depth]; + if (depth == dims - 1) { + const int loop_stride = input_stride[perm[depth]]; + for (int i = 0; i < dimension_size; ++i) { + output_data[i] = *input_data; + input_data += loop_stride; + } + } else { + for (int i = 0; i < dimension_size; ++i) { + TransposeImpl(depth + 1, dims, perm, input_data, input_stride, + output_data, output_stride, output_shape); + + input_data += input_stride[perm[depth]]; + output_data += output_stride[depth]; + } + } +} + +// Compile-time switch to get the storage type of the transposition. +template +struct TransposeStorageType; + +template <> +struct TransposeStorageType<1> { + using type = int8_t; +}; + +template <> +struct TransposeStorageType<2> { + using type = int16_t; +}; + +template <> +struct TransposeStorageType<4> { + using type = int32_t; +}; + +template <> +struct TransposeStorageType<8> { + using type = int64_t; +}; + +// Sets up the stride arrays for the recursive transpose algorithm. +// +// Implementation notes: +// +// This is a reverse partial product. We could use standard algorithms to +// implement this but the result is not a readable and is tricky to get right +// because the first element must be set to 1, which leads to offset +// shenanigans: +// +// ``` +// stride[dims - 1] = 1; +// std::partial_sum(std::make_reverse_iterator(shape + dims), +// std::make_reverse_iterator(shape + 1), +// stride.rend() - input_rank + 1, std::multiplies()); +// ``` +// +// Note that Abseil isn't used in kernels implementation. That would make the +// above solution more readable. +inline void SetupTransposeStrides( + std::array& stride, const int32_t* shape, + const int dims) { + stride[dims - 1] = 1; + for (int i = dims - 2; i >= 0; --i) { + stride[i] = stride[i + 1] * shape[i + 1]; + } +} + +} // namespace transpose_internal + +// Copies a tensor to an other buffer and permutes its dimensions. +// +// Note: template parameter N is not used anymore. It is kept for API +// compatibility with TFLite micro. +template +void Transpose(const TransposeParams& params, const RuntimeShape& input_shape, + const T* input_data, const RuntimeShape& output_shape, + T* output_data) { + using transpose_internal::SetupTransposeStrides; + using transpose_internal::TransposeImpl; + using transpose_internal::TransposeStorageType; + // Transpose kernel only does rearranging values not numeric evaluations on + // each cell. It's safe to implement per size of scalar type and this trick + // keeps the total code size in a reasonable range. + using StorageType = typename TransposeStorageType::type; + const StorageType* const input_data_storage = + reinterpret_cast(input_data); + StorageType* const output_data_storage = + reinterpret_cast(output_data); + + const int dims = input_shape.DimensionsCount(); + std::array input_stride, output_stride; + SetupTransposeStrides(input_stride, input_shape.DimsData(), dims); + SetupTransposeStrides(output_stride, output_shape.DimsData(), dims); + TransposeImpl(0, dims, ¶ms.perm[0], input_data_storage, + input_stride.data(), output_data_storage, output_stride.data(), + output_shape.DimsData()); +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h new file mode 100644 index 00000000..4a588a34 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h @@ -0,0 +1,225 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ + +#include + +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +namespace reference_ops { + +inline void TransposeConv( + const ConvParams& params, const RuntimeShape& input_shape, + const float* input_data, const RuntimeShape& filter_shape, + const float* filter_data, const RuntimeShape& bias_shape, + const float* bias_data, const RuntimeShape& output_shape, + float* output_data, const RuntimeShape& im2col_shape, float* im2col_data) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const float output_activation_min = params.float_activation_min; + const float output_activation_max = params.float_activation_max; + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + + // Although transpose convolution simplifies to convolution with transposed + // weights for strides of 1, non-unitary striding complicates matters. To + // keep this reference implementation as clear as possible, we use a + // "scatter" access pattern, where we loop through all the input elements, + // computing their influence on the output, rather than looping through the + // output elements in the typical "gather" access pattern of a conv. We + // therefore must initialize the output array to zero. + const int num_elements = output_shape.FlatSize(); + for (int i = 0; i < num_elements; i++) { + output_data[i] = 0.0f; + } + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + float input_value = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + float filter_value = + filter_data[Offset(filter_shape, out_channel, filter_y, + filter_x, in_channel)]; + output_data[Offset(output_shape, batch, out_y, out_x, + out_channel)] += + input_value * filter_value; + } + } + } + } + } + } + } + } + + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + float acc = output_data[Offset(output_shape, batch, out_y, out_x, + out_channel)]; + if (bias_data) acc += bias_data[out_channel]; + + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + ActivationFunctionWithMinMax(acc, output_activation_min, + output_activation_max); + } + } + } + } +} + +inline void TransposeConv( + const ConvParams& params, const RuntimeShape& input_shape, + const uint8_t* input_data, const RuntimeShape& filter_shape, + const uint8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + uint8_t* output_data, const RuntimeShape& im2col_shape, + uint8_t* im2col_data, int32_t* scratch_buffer) { + const int stride_width = params.stride_width; + const int stride_height = params.stride_height; + const int pad_width = params.padding_values.width; + const int pad_height = params.padding_values.height; + TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); + TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); + (void)im2col_data; // only used in optimized code. + (void)im2col_shape; // only used in optimized code. + + const int batches = MatchingDim(input_shape, 0, output_shape, 0); + const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); + const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); + const int input_height = input_shape.Dims(1); + const int input_width = input_shape.Dims(2); + const int filter_height = filter_shape.Dims(1); + const int filter_width = filter_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_width = output_shape.Dims(2); + const int32_t input_offset = params.input_offset; + const int32_t filter_offset = params.weights_offset; + const int32_t output_offset = params.output_offset; + const int32_t output_multiplier = params.output_multiplier; + const int output_shift = params.output_shift; + const int32_t output_activation_min = params.quantized_activation_min; + const int32_t output_activation_max = params.quantized_activation_max; + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (bias_data) { + TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); + } + + const int num_elements = output_shape.FlatSize(); + // We need to initialize scratch_buffer to all 0s, as we apply the same + // 'scatter' based trick as in float version. + memset(scratch_buffer, 0, num_elements * sizeof(int32_t)); + + // Loop through input elements one at a time. + for (int batch = 0; batch < batches; ++batch) { + for (int in_y = 0; in_y < input_height; ++in_y) { + for (int in_x = 0; in_x < input_width; ++in_x) { + for (int in_channel = 0; in_channel < input_depth; ++in_channel) { + // Loop through the output elements it will influence. + const int out_x_origin = (in_x * stride_width) - pad_width; + const int out_y_origin = (in_y * stride_height) - pad_height; + for (int filter_y = 0; filter_y < filter_height; ++filter_y) { + for (int filter_x = 0; filter_x < filter_width; ++filter_x) { + for (int out_channel = 0; out_channel < output_depth; + ++out_channel) { + // Compute output element location. + const int out_x = out_x_origin + filter_x; + const int out_y = out_y_origin + filter_y; + // We cannot accumulate out of bounds. + if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && + (out_y < output_height)) { + uint8_t input_value = input_data[Offset( + input_shape, batch, in_y, in_x, in_channel)]; + uint8_t filter_value = + filter_data[Offset(filter_shape, out_channel, filter_y, + filter_x, in_channel)]; + scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)] += + (input_value + input_offset) * + (filter_value + filter_offset); + } + } + } + } + } + } + } + } + for (int batch = 0; batch < batches; ++batch) { + for (int out_y = 0; out_y < output_height; ++out_y) { + for (int out_x = 0; out_x < output_width; ++out_x) { + for (int out_channel = 0; out_channel < output_depth; ++out_channel) { + int32_t acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, + out_channel)]; + if (bias_data) { + acc += bias_data[out_channel]; + } + int32_t scaled_acc = MultiplyByQuantizedMultiplier( + acc, output_multiplier, output_shift); + scaled_acc += output_offset; + scaled_acc = std::max(scaled_acc, output_activation_min); + scaled_acc = std::min(scaled_acc, output_activation_max); + output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = + static_cast(scaled_acc); + } + } + } + } +} + +} // namespace reference_ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h new file mode 100644 index 00000000..28fbfe27 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h @@ -0,0 +1,168 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ + +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" + +namespace tflite_micro { + +template +struct Dims { + int sizes[N]; + int strides[N]; +}; + +class RuntimeShape { + public: + RuntimeShape& operator=(RuntimeShape const&) = delete; + + // RuntimeShape in TFLM supports up to 6 dimensions. + // The name kMaxSmallSize comes from the same file of the upstream + // tensorflow lite repo and need to be kept the same for max reuse. + static constexpr int kMaxSmallSize = 6; + + RuntimeShape() : size_(0) {} + + explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { + TFLITE_DCHECK_LE(dimensions_count, kMaxSmallSize); + } + + RuntimeShape(int shape_size, int32_t value) : size_(shape_size) { + TFLITE_DCHECK_LE(shape_size, kMaxSmallSize); + for (int i = 0; i < shape_size; ++i) { + SetDim(i, value); + } + } + + RuntimeShape(int dimensions_count, const int32_t* dims_data) + : size_(dimensions_count) { + // check of dimensions_count handled by ReplaceWith() + ReplaceWith(dimensions_count, dims_data); + } + + bool operator==(const RuntimeShape& comp) const { + return this->size_ == comp.size_ && + std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32_t)) == + 0; + } + + ~RuntimeShape() {} + + int32_t DimensionsCount() const { return size_; } + int32_t Dims(int i) const { + TFLITE_DCHECK_GE(i, 0); + TFLITE_DCHECK_LT(i, size_); + return dims_[i]; + } + void SetDim(int i, int32_t val) { + TFLITE_DCHECK_GE(i, 0); + TFLITE_DCHECK_LT(i, size_); + dims_[i] = val; + } + + static RuntimeShape ExtendedShape(int new_shape_size, + const RuntimeShape& shape) { + TFLITE_DCHECK_LE(new_shape_size, kMaxSmallSize); + return RuntimeShape(new_shape_size, shape, 1); + } + int32_t* DimsData() { return dims_; } + const int32_t* DimsData() const { return dims_; } + const int32_t* DimsDataUpTo5D() const { return dims_; } + + void ReplaceWith(int dimensions_count, const int32_t* dims_data) { + TFLITE_DCHECK_LE(dimensions_count, kMaxSmallSize); + size_ = dimensions_count; + int32_t* dst_dims = DimsData(); + std::memcpy(dst_dims, dims_data, dimensions_count * sizeof(int32_t)); + } + + // Returns the total count of elements, that is the size when flattened into a + // vector. + int FlatSize() const { + int buffer_size = 1; + const int* dims_data = reinterpret_cast(DimsData()); + for (int i = 0; i < size_; i++) { + buffer_size *= dims_data[i]; + } + return buffer_size; + } + + private: + // For use only by ExtendedShape(), written to guarantee (return-value) copy + // elision in C++17. + // This creates a shape padded to the desired size with the specified value. + RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) + : size_(new_shape_size) { + // If the following check fails, it is likely because a 4D-only kernel is + // being used with an array of larger dimension count. + TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); + const int size_increase = new_shape_size - shape.DimensionsCount(); + for (int i = 0; i < size_increase; ++i) { + SetDim(i, pad_value); + } + std::memcpy(DimsData() + size_increase, shape.DimsData(), + sizeof(int32_t) * shape.DimensionsCount()); + } + + int32_t size_; + union { + int32_t dims_[kMaxSmallSize]; + }; +}; + +// Since tensors with '0' in their shape are valid in TF, these offset functions +// allow that as long as the corresponding index is also 0. It is upto the +// calling ops to ensure that they perform verification checks on tensor shapes +// if they don't support a particular behavior. + +inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), 4); + const int* dims_data = reinterpret_cast(shape.DimsData()); + TFLITE_DCHECK((dims_data[0] == 0 && i0 == 0) || + (i0 >= 0 && i0 < dims_data[0])); + TFLITE_DCHECK((dims_data[1] == 0 && i1 == 0) || + (i1 >= 0 && i1 < dims_data[1])); + TFLITE_DCHECK((dims_data[2] == 0 && i2 == 0) || + (i2 >= 0 && i2 < dims_data[2])); + TFLITE_DCHECK((dims_data[3] == 0 && i3 == 0) || + (i3 >= 0 && i3 < dims_data[3])); + return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; +} + +inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3, + int i4) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), 5); + const int* dims_data = reinterpret_cast(shape.DimsData()); + TFLITE_DCHECK((dims_data[0] == 0 && i0 == 0) || + (i0 >= 0 && i0 < dims_data[0])); + TFLITE_DCHECK((dims_data[1] == 0 && i1 == 0) || + (i1 >= 0 && i1 < dims_data[1])); + TFLITE_DCHECK((dims_data[2] == 0 && i2 == 0) || + (i2 >= 0 && i2 < dims_data[2])); + TFLITE_DCHECK((dims_data[3] == 0 && i3 == 0) || + (i3 >= 0 && i3 < dims_data[3])); + TFLITE_DCHECK((dims_data[4] == 0 && i4 == 0) || + (i4 >= 0 && i4 < dims_data[4])); + return (((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3) * + dims_data[4] + + i4; +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h new file mode 100644 index 00000000..85849365 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h @@ -0,0 +1,278 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ + +#include +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { +namespace strided_slice { + +// Use until std::clamp() is available from C++17. +inline int Clamp(const int v, const int lo, const int hi) { + TFLITE_DCHECK(!(hi < lo)); + if (hi < v) return hi; + if (v < lo) return lo; + return v; +} + +inline void StridedSlicePadIndices(tflite_micro::StridedSliceParams* p, + int dim_count) { + // Add indices and mask bits to fully include extra dimensions + TFLITE_CHECK_LE(dim_count, 5); + TFLITE_CHECK_GE(dim_count, p->start_indices_count); + TFLITE_CHECK_EQ(p->start_indices_count, p->stop_indices_count); + TFLITE_CHECK_EQ(p->stop_indices_count, p->strides_count); + + const int pad_count = dim_count - p->start_indices_count; + + // Pad indices at start, so move arrays by pad_count. + for (int i = p->start_indices_count - 1; i >= 0; --i) { + p->strides[i + pad_count] = p->strides[i]; + p->start_indices[i + pad_count] = p->start_indices[i]; + p->stop_indices[i + pad_count] = p->stop_indices[i]; + } + for (int i = 0; i < pad_count; ++i) { + p->start_indices[i] = 0; + p->stop_indices[i] = 1; + p->strides[i] = 1; + } + + // Pad masks with 0s or 1s as required. + p->shrink_axis_mask <<= pad_count; + p->ellipsis_mask <<= pad_count; + p->new_axis_mask <<= pad_count; + p->begin_mask <<= pad_count; + p->end_mask <<= pad_count; + p->begin_mask |= (1 << pad_count) - 1; + p->end_mask |= (1 << pad_count) - 1; + + p->start_indices_count = dim_count; + p->stop_indices_count = dim_count; + p->strides_count = dim_count; +} + +// Return the index for the first element along that axis. This index will be a +// positive integer between [0, axis_size] (or [-1, axis_size -1] if stride < 0) +// that can be used to index directly into the data. +inline int StridedSliceStartForAxis(const tflite_micro::StridedSliceParams& params, + const RuntimeShape& input_shape, + int32_t axis) { + const int32_t axis_size = input_shape.Dims(axis); + int32_t start = params.start_indices[axis]; + const int32_t stride = params.strides[axis]; + const int32_t begin_mask = (params.begin_mask & 1 << axis); + if (start < 0) { + start += axis_size; + } + if (stride > 0) { + start = Clamp(start, 0, axis_size); + } else { + start = Clamp(start, -1, axis_size - 1); + } + if (begin_mask) { + if (stride > 0) { + start = 0; + } else { + start = axis_size - 1; + } + } + return start; +} + +inline int StridedSliceEndForAxis(const tflite_micro::StridedSliceParams& params, + const RuntimeShape& input_shape, int axis, + int start) { + const auto shrink_axis_mask = params.shrink_axis_mask; + const bool shrink_axis = shrink_axis_mask & (1 << axis); + const int axis_size = input_shape.Dims(axis); + const bool offset = params.offset; + if (shrink_axis) { + if (start >= axis_size) { + return start; + } else { + return start + 1; + } + } + const auto* indices = params.stop_indices; + int end = indices[axis]; + if (offset) { + end += start; + } + const int32_t stride = params.strides[axis]; + const int32_t end_mask = (params.end_mask & 1 << axis); + if (end < 0) { + end += axis_size; + } + if (stride > 0) { + end = Clamp(end, 0, axis_size); + } else { + end = Clamp(end, -1, axis_size - 1); + } + if (end_mask) { + if (stride > 0) { + end = axis_size; + } else { + end = -1; + } + } + return end; +} + +// Return the index for the first element along that axis. This index will be a +// positive integer between [0, axis_size] (or [-1, axis_size -1] if stride < 0) +// that can be used to index directly into the data. +inline int StartForAxis(const tflite_micro::StridedSliceParams& params, + const RuntimeShape& input_shape, int axis) { + const auto begin_mask = params.begin_mask; + const auto* start_indices = params.start_indices; + const auto* strides = params.strides; + const int axis_size = input_shape.Dims(axis); + if (axis_size == 0) { + return 0; + } + // Begin with the specified index. + int start = start_indices[axis]; + + // begin_mask override + if (begin_mask & 1 << axis) { + if (strides[axis] > 0) { + // Forward iteration - use the first element. These values will get + // clamped below (Note: We could have set them to 0 and axis_size-1, but + // use lowest() and max() to maintain symmetry with StopForAxis()) + start = std::numeric_limits::lowest(); + } else { + // Backward iteration - use the last element. + start = std::numeric_limits::max(); + } + } + + // Handle negative indices + if (start < 0) { + start += axis_size; + } + + // Clamping + if (strides[axis] > 0) { + // Forward iteration + start = Clamp(start, 0, axis_size); + } else { + // Backward iteration + start = Clamp(start, -1, axis_size - 1); + } + + return start; +} + +// Return the "real" index for the end of iteration along that axis. This is an +// "end" in the traditional C sense, in that it points to one past the last +// element. ie. So if you were iterating through all elements of a 1D array of +// size 4, this function would return 4 as the stop, because it is one past the +// "real" indices of 0, 1, 2 & 3. +inline int StopForAxis(const tflite_micro::StridedSliceParams& params, + const RuntimeShape& input_shape, int axis, + int start_for_axis) { + const auto end_mask = params.end_mask; + const auto shrink_axis_mask = params.shrink_axis_mask; + const auto* stop_indices = params.stop_indices; + const auto* strides = params.strides; + const int axis_size = input_shape.Dims(axis); + if (axis_size == 0) { + return 0; + } + + // Begin with the specified index + const bool shrink_axis = shrink_axis_mask & (1 << axis); + int stop = stop_indices[axis]; + + // When shrinking an axis, the end position does not matter (and can be + // incorrect when negative indexing is used, see Issue #19260). Always use + // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has + // already been adjusted for negative indices. + if (shrink_axis) { + return start_for_axis + 1; + } + + // end_mask override + if (end_mask & (1 << axis)) { + if (strides[axis] > 0) { + // Forward iteration - use the last element. These values will get + // clamped below + stop = std::numeric_limits::max(); + } else { + // Backward iteration - use the first element. + stop = std::numeric_limits::lowest(); + } + } + + // Handle negative indices + if (stop < 0) { + stop += axis_size; + } + + // Clamping + // Because the end index points one past the last element, we need slightly + // different clamping ranges depending on the direction. + if (strides[axis] > 0) { + // Forward iteration + stop = Clamp(stop, 0, axis_size); + } else { + // Backward iteration + stop = Clamp(stop, -1, axis_size - 1); + } + + return stop; +} + +inline bool LoopCondition(int index, int stop, int stride) { + // True when we have reached the end of an axis and should loop. + return stride > 0 ? index >= stop : index <= stop; +} + +inline tflite_micro::StridedSliceParams BuildStridedSliceParams( + int begin_mask, int end_mask, int shrink_axis_mask, + const std::vector& start_indices, const std::vector& stop_indices, + const std::vector& strides) { + tflite_micro::StridedSliceParams op_params{}; + const int dims_count = start_indices.size(); + + op_params.start_indices_count = dims_count; + op_params.stop_indices_count = dims_count; + op_params.strides_count = dims_count; + for (int i = 0; i < dims_count; ++i) { + op_params.start_indices[i] = start_indices[i]; + op_params.stop_indices[i] = stop_indices[i]; + op_params.strides[i] = strides[i]; + } + + op_params.begin_mask = begin_mask; + op_params.ellipsis_mask = 0; + op_params.end_mask = end_mask; + op_params.new_axis_mask = 0; + op_params.shrink_axis_mask = shrink_axis_mask; + + return op_params; +} + +} // namespace strided_slice + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h new file mode 100644 index 00000000..7cc05ff4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h @@ -0,0 +1,42 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ + +#include + +#include "tensorflow/lite/core/c/common.h" +#include "tensorflow/lite/core/macros.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +template +inline T* GetTensorData(TfLiteTensor* tensor) { + return tensor != nullptr ? reinterpret_cast(tensor->data.raw) : nullptr; +} + +template +inline const T* GetTensorData(const TfLiteTensor* tensor) { + return tensor != nullptr ? reinterpret_cast(tensor->data.raw) + : nullptr; +} + +TFLITE_NOINLINE RuntimeShape GetTensorShape(const TfLiteTensor* tensor); +RuntimeShape GetTensorShape(std::vector data); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h new file mode 100644 index 00000000..bddd87b5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h @@ -0,0 +1,1096 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ +#define TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ + +#include +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/runtime_shape.h" + +namespace tflite_micro { + +enum class FusedActivationFunctionType : uint8_t { + kNone, + kRelu6, + kRelu1, + kRelu +}; +enum class PaddingType : uint8_t { kNone, kSame, kValid }; + +struct PaddingValues { + int16_t width; + int16_t height; + // offset is used for calculating "remaining" padding, for example, `width` + // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is + // 1 + 1 = 2. + int16_t width_offset; + // Same as width_offset except it's over the height dimension. + int16_t height_offset; +}; + +struct Padding3DValues { + int16_t width; + int16_t height; + int16_t depth; + // offset is used for calculating "remaining" padding, for example, `width` + // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is + // 1 + 1 = 2. + int16_t width_offset; + // Same as width_offset except it's over the height dimension. + int16_t height_offset; + // Same as width_offset except it's over the depth dimension. + int16_t depth_offset; +}; + +// This enumeration allows for non-default formats for the weights array +// of a fully-connected operator, allowing the use of special optimized +// runtime paths. +enum class FullyConnectedWeightsFormat : uint8_t { + // Default format (flat 2D layout, the inner contiguous dimension + // is input_depth, the outer non-contiguous dimension is output_depth) + kDefault, + // Summary: optimized layout for fast CPU runtime implementation, + // aimed specifically at ARM CPUs at the moment, and specialized for + // 8-bit quantized layers. + // + // The use case we're concerned with here is: 8-bit quantization, + // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in + // a key application that drove this), very small batch size (e.g. 1 -- 4). + // + // Even with 8-bit quantization of weights, the performance of memory + // accesses to the weights can become the dominant issue when + // the batch size is small, so each weight value is used in only a few + // arithmetic ops, i.e. the fully-connected node has a low arithmetic + // intensity. The specific issues that arise are of three kinds: + // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory + // bound. That's the "good" issue to run into. + // (2) One may run into sub-optimal pre-fetching: the data hasn't been + // prefetched into the cache by the time we need it. + // (3) One may run into cache aliasing: multiple values that are + // pre-fetched, alias each other in the L1 cache (which typically + // has only 4-way set associativity in ARM CPUs) and thus evict + // each other before we get to using them. + // + // The point of this shuffling is to avoid issues (2) and (3) so that + // we get as fast as possible given only the hard constraint (1). + // This is achieved by turning the difficulty into a solution: the + // difficulty, that each value loaded from memory is used only in + // one kernel iteration, making this operation memory-intensive, hints at + // the solution, of shuffling the weights so that they are stored in the + // exact order as the kernel needs to load them, so that the memory + // accesses made by the kernel are trivial. This solves (2) because the + // trivial memory access pattern allows the CPU's automatic prefetching + // to perform very well (no need even for preload instructions), and this + // solves (3) because the values being loaded concurrently are now + // contiguous in the address space, thus don't alias each other in the cache. + // + // On ARM, we typically want our kernel to process a 4x16 block of weights + // at a time, because: + // - 16 is the number of bytes in a NEON register. + // - 4 is how many rows we need to handle concurrently in the kernel in + // order to have sufficient mutual independence of instructions to + // maximize arithmetic throughput. + // + // Finally, the 'Int8' part in the name refers to the fact that this + // weights format has each weights value encoded as a signed int8_t value, + // even if the data type of the weights buffer is uint8_t. This is intended + // to save runtime kernels the effort to have to XOR the top bit of these + // bytes before using them in signed arithmetic, see this file for more + // explanations on the 'signed int8_t trick' in matrix multiplication kernels: + // + // tensorflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc + // + kShuffled4x16Int8, +}; + +// Quantization parameters, determining the mapping of quantized values +// to real values (i.e. determining how quantized values are mathematically +// interpreted). +// +// The correspondence is as follows: +// +// real_value = scale * (quantized_value - zero_point); +// +// In other words, zero_point designates which quantized value corresponds to +// the real 0 value, and scale designates the difference between the real values +// corresponding to consecutive quantized values differing by 1. +struct QuantizationParams { + int32_t zero_point = 0; + double scale = 0.0; +}; + +inline bool operator==(const QuantizationParams& qp1, + const QuantizationParams& qp2) { + return qp1.zero_point == qp2.zero_point && qp1.scale == qp2.scale; +} + +// Quantization parameters for each channel, determining the mapping of +// quantized values to real values. See QuantizationParams for a single set of +// parameters per tensor. This has one parameters set per each channel. +// +// The correspondence is as follows: +// +// real_value = scale[channel] * (quantized_value - zero_point[channel]); +// +struct PerChannelQuantizationParams { + // The following members typically point to the corresponding members of a + // TfLiteAffineQuantization struct. + const float* scale; + const int32_t* zero_point; + int32_t quantized_dimension; +}; + +// Gets next index to iterate through a multidimensional array. +template +inline bool NextIndex(const int num_dims, const int* dims, IndexType* current) { + if (num_dims == 0) { + return false; + } + TFLITE_DCHECK(dims != nullptr); + TFLITE_DCHECK(current != nullptr); + int carry = 1; + for (int idx = num_dims - 1; idx >= 0; --idx) { + IndexType current_val = current[idx] + carry; + TFLITE_DCHECK_GE(dims[idx], current_val); + if (dims[idx] == current_val) { + current[idx] = 0; + } else { + current[idx] = current_val; + carry = 0; + break; + } + } + return (carry == 0); +} + +// Gets offset of index if reducing on axis. When reducing, the flattened offset +// will not change, if the input index changes on the given axis. For example, +// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened +// offset. +// TODO(kanlig): uses Dims to represent dimensions. +inline size_t ReducedOutputOffset(const int num_dims, const int* dims, + const int* index, const int num_axis, + const int* axis) { + if (num_dims == 0) { + return 0; + } + TFLITE_DCHECK(dims != nullptr); + TFLITE_DCHECK(index != nullptr); + size_t offset = 0; + for (int idx = 0; idx < num_dims; ++idx) { + // if we need to skip this axis + bool is_axis = false; + if (axis != nullptr) { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { + if (idx == axis[axis_idx]) { + is_axis = true; + break; + } + } + } + if (!is_axis) { + offset = offset * static_cast(dims[idx]) + + static_cast(index[idx]); + } + } + return offset; +} + +// Since tensors with '0' in their shape are valid in TF, these offset functions +// allow that as long as the corresponding index is also 0. It is upto the +// calling ops to ensure that they perform verification checks on tensor shapes +// if they don't support a particular behavior. + +inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK((i0 == 0 && dims.sizes[0] == 0) || + (i0 >= 0 && i0 < dims.sizes[0])); + TFLITE_DCHECK((i1 == 0 && dims.sizes[1] == 0) || + (i1 >= 0 && i1 < dims.sizes[1])); + TFLITE_DCHECK((i2 == 0 && dims.sizes[2] == 0) || + (i2 >= 0 && i2 < dims.sizes[2])); + TFLITE_DCHECK((i3 == 0 && dims.sizes[3] == 0) || + (i3 >= 0 && i3 < dims.sizes[3])); + return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + + i3 * dims.strides[3]; +} + +inline int Offset(const Dims<4>& dims, int* index) { + return Offset(dims, index[0], index[1], index[2], index[3]); +} + +// Get array size, DCHECKing that the dim index is in range. +// +// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() +// already performs this check. +template +int ArraySize(const Dims& array, int index) { + TFLITE_DCHECK(index >= 0 && index < N); + return array.sizes[index]; +} + +// Get common array size, DCHECKing that they all agree. +template +int MatchingArraySize(const ArrayType1& array1, int index1, + const ArrayType2& array2, int index2) { + TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); + return ArraySize(array1, index1); +} + +template +int MatchingArraySize(const ArrayType1& array1, int index1, + const ArrayType2& array2, int index2, Args... args) { + TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); + return MatchingArraySize(array1, index1, args...); +} + +// Get common shape dim, DCHECKing that they all agree. +inline int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return std::min(shape1.Dims(index1), shape2.Dims(index2)); +} + +template +int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2, Args... args) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return MatchingDim(shape1, index1, args...); +} + +// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). +template +inline int FlatSize(const Dims& dims) { + int flat_size = 1; + for (int i = 0; i < N; ++i) { + flat_size *= dims.sizes[i]; + } + return flat_size; +} + +TFLITE_DEPRECATED("Prefer FlatSize.") +inline int RequiredBufferSizeForDims(const Dims<4>& dims) { + return FlatSize(dims); +} + +inline int MatchingElementsSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int size_1 = shape.FlatSize(); + const int size_2 = check_shape_0.FlatSize(); + TFLITE_CHECK_EQ(size_1, size_2); + return size_1; +} + +inline int MatchingElementsSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int size_1 = shape.FlatSize(); + const int size_2 = check_shape_0.FlatSize(); + const int size_3 = check_shape_1.FlatSize(); + TFLITE_CHECK_EQ(size_1, size_2); + TFLITE_CHECK_EQ(size_2, size_3); + return size_1; +} + +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return shape.FlatSize(); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); +} + +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return FlatSize(dims); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1, check_dims_2); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2, + const Dims& check_dims_3) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); +} + +// Flat size calculation, checking if their extended shapes match. +inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int shape_dims = shape.DimensionsCount(); + const int check_shape_0_dims = check_shape_0.DimensionsCount(); + const int min_dims = std::min(shape_dims, check_shape_0_dims); + + for (int i = 0; i < min_dims; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(shape_dims - 1 - i), + check_shape_0.Dims(check_shape_0_dims - 1 - i)); + } + for (int i = min_dims; i < shape_dims; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(shape_dims - 1 - i), 1); + } + for (int i = min_dims; i < check_shape_0_dims; ++i) { + TFLITE_DCHECK_EQ(check_shape_0.Dims(check_shape_0_dims - 1 - i), 1); + } + return shape.FlatSize(); +} + +inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); + TFLITE_DCHECK_EQ(MatchingExtendedShapeFlatSize(shape, check_shape_1), + flat_size); + return flat_size; +} + +inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); + TFLITE_DCHECK_EQ( + MatchingExtendedShapeFlatSize(shape, check_shape_1, check_shape_2), + flat_size); + return flat_size; +} + +inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); + TFLITE_DCHECK_EQ(MatchingExtendedShapeFlatSize(shape, check_shape_1, + check_shape_2, check_shape_3), + flat_size); + return flat_size; +} + +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +template +inline int FlatSizeSkipDim(const Dims& dims, int skip_dim) { + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < N); + int flat_size = 1; + for (int i = 0; i < N; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims.sizes[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return FlatSizeSkipDim(dims, skip_dim); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2, + const Dims& check_dims_3) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2, + check_dims_3); +} + +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { + const int dims_count = shape.DimensionsCount(); + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); + const auto* dims_data = shape.DimsData(); + int flat_size = 1; + for (int i = 0; i < dims_count; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims_data[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return FlatSizeSkipDim(shape, skip_dim); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, + check_shape_3); +} + +template +bool IsPackedWithoutStrides(const Dims& dims) { + int expected_stride = 1; + for (int d = 0; d < N; d++) { + if (dims.strides[d] != expected_stride) return false; + expected_stride *= dims.sizes[d]; + } + return true; +} + +template +void ComputeStrides(Dims* dims) { + dims->strides[0] = 1; + for (int d = 1; d < N; d++) { + dims->strides[d] = dims->strides[d - 1] * dims->sizes[d - 1]; + } +} + +enum class BroadcastableOpCategory : uint8_t { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + +struct MinMax { + float min; + float max; +}; +static_assert(sizeof(MinMax) == 8, ""); + +struct ActivationParams { + FusedActivationFunctionType activation_type; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; +}; + +struct ReluParams : public ActivationParams { + int32_t input_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; +}; + +// Styles of resizing op usages. For example, kImageStyle can be used with a Pad +// op for pattern-specific optimization. +enum class ResizingCategory : uint8_t { + kNone, + kImageStyle, // 4D, operating on inner dimensions, say {0, a, b, 0}. + kGenericResize, +}; + +// For Add, Sub, Mul ops. +struct ArithmeticParams { + // Shape dependent / common to data / op types. + BroadcastableOpCategory broadcast_category; + // uint8_t inference params. + int32_t input1_offset; + int32_t input2_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // Add / Sub, not Mul, uint8_t inference params. + int left_shift; + int32_t input1_multiplier; + int input1_shift; + int32_t input2_multiplier; + int input2_shift; + + // TODO(b/158622529): Union the following activation params. + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + // int64_t activation params. + int64_t int64_activation_min; + int64_t int64_activation_max; + // int16_t activation params. + int16_t int16_activation_min; + int16_t int16_activation_max; + + // Processed output dimensions. + // Let input "a" be the one that broadcasts in the faster-changing dimension. + // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and + // {b0, b1, b2, b3, b4}, + // broadcast_shape[4] = b0 = a0. + // broadcast_shape[3] = b1; a1 = 1. + // broadcast_shape[2] = b2 = a2. + // broadcast_shape[1] = a3; b3 = 1. + // broadcast_shape[0] = b4 = a4. + int broadcast_shape[5]; +}; + +struct ConcatenationParams { + int8_t axis; + const int32_t* input_zeropoint; + const float* input_scale; + uint16_t inputs_count; + int32_t output_zeropoint; + float output_scale; +}; + +struct ComparisonParams { + // uint8_t inference params. + int left_shift; + int32_t input1_offset; + int32_t input1_multiplier; + int input1_shift; + int32_t input2_offset; + int32_t input2_multiplier; + int input2_shift; + // Shape dependent / common to inference types. + bool is_broadcast; +}; + +struct ConvParams { + PaddingType padding_type; + PaddingValues padding_values; + // TODO(starka): This was just "stride", so check that width+height is OK. + int16_t stride_width; + int16_t stride_height; + int16_t dilation_width_factor; + int16_t dilation_height_factor; + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +struct Conv3DParams { + Padding3DValues padding_values; + int stride_width; + int stride_height; + int stride_depth; + int dilation_width; + int dilation_height; + int dilation_depth; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +typedef Conv3DParams Conv3DTransposeParams; + +struct DepthToSpaceParams { + int32_t block_size; +}; + +struct DepthwiseParams { + PaddingType padding_type; + PaddingValues padding_values; + int16_t stride_width; + int16_t stride_height; + int16_t dilation_width_factor; + int16_t dilation_height_factor; + int16_t depth_multiplier; + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + const int32_t* output_multiplier_per_channel; + const int32_t* output_shift_per_channel; +}; + +struct DequantizationParams { + double scale; + int32_t zero_point; +}; + +struct PerChannelDequantizationParams { + const float* scale; + const int32_t* zero_point; + int32_t quantized_dimension; +}; + +struct FakeQuantParams { + MinMax minmax; + int32_t num_bits; +}; + +struct FullyConnectedParams { + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + // Mark the operands as cacheable if they are unchanging, e.g. weights. + bool lhs_cacheable; + bool rhs_cacheable; + FullyConnectedWeightsFormat weights_format; +}; + +struct GatherParams { + int16_t axis; + int16_t batch_dims; +}; + +struct L2NormalizationParams { + // uint8_t inference params. + int32_t input_zero_point; +}; + +struct LocalResponseNormalizationParams { + int32_t range; + double bias; + double alpha; + double beta; +}; + +struct HardSwishParams { + // zero_point of the input activations. + int16_t input_zero_point; + // zero_point of the output activations. + int16_t output_zero_point; + // 16bit fixed-point component of the multiplier to apply to go from the + // "high-res input scale", which is the input scale multiplied by 2^7, to the + // "relu-ish scale", which 3.0/32768. + // See the implementation of HardSwishPrepare. + int16_t reluish_multiplier_fixedpoint_int16; + // exponent/bit-shift component of the aforementioned multiplier. + int reluish_multiplier_exponent; + // 16bit fixed-point component of the multiplier to apply to go from the + // "high-res input scale", which is the input scale multiplied by 2^7, to the + // output scale. + // See the implementation of HardSwishPrepare. + int16_t output_multiplier_fixedpoint_int16; + // exponent/bit-shift component of the aforementioned multiplier. + int output_multiplier_exponent; +}; + +struct LogisticParams { + // uint8_t inference params. + int32_t input_zero_point; + int32_t input_range_radius; + int32_t input_multiplier; + int input_left_shift; +}; + +struct LstmCellParams { + int32_t weights_zero_point; + int32_t accum_multiplier; + int accum_shift; + int state_integer_bits; +}; + +struct MeanParams { + int8_t axis_count; + int16_t axis[4]; +}; + +struct PackParams { + int8_t axis; + const int32_t* input_zeropoint; + const float* input_scale; + uint16_t inputs_count; + int32_t output_zeropoint; + float output_scale; +}; + +struct PadParams { + int8_t left_padding_count; + int32_t left_padding[5]; + int8_t right_padding_count; + int32_t right_padding[5]; + ResizingCategory resizing_category; +}; + +struct PreluParams { + int32_t input_offset; + int32_t alpha_offset; + int32_t output_offset; + int32_t output_multiplier_1; + int output_shift_1; + int32_t output_multiplier_2; + int output_shift_2; +}; + +struct PoolParams { + FusedActivationFunctionType activation; + PaddingType padding_type; + PaddingValues padding_values; + int stride_height; + int stride_width; + int filter_height; + int filter_width; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +struct ReshapeParams { + int8_t shape_count; + int32_t shape[4]; +}; + +struct ResizeBilinearParams { + bool align_corners; + // half_pixel_centers assumes pixels are of half the actual dimensions, and + // yields more accurate resizes. Corresponds to the same argument for the + // original TensorFlow op in TF2.0. + bool half_pixel_centers; +}; + +struct ResizeNearestNeighborParams { + bool align_corners; + bool half_pixel_centers; +}; + +struct SliceParams { + int8_t begin_count; + int32_t begin[5]; + int8_t size_count; + int32_t size[5]; +}; + +struct SoftmaxParams { + // beta is not really used (not a Tensorflow parameter) and not implemented + // for LogSoftmax. + double beta; + // uint8_t inference params. Used even when beta defaults to 1.0. + int32_t input_multiplier; + int32_t input_left_shift; + // Reverse scaling is only used by LogSoftmax. + int32_t reverse_scaling_divisor; + int32_t reverse_scaling_right_shift; + int diff_min; + int32_t zero_point; + float scale; + float* table; + // int16 LUT for exp(x), where x uniform distributed between [-10.0 , 0.0] + int16_t* exp_lut; + // int16 LUT for 1 / (1 + x), where x uniform distributed between [0.0 , 1.0] + int16_t* one_over_one_plus_x_lut; + uint8_t* uint8_table1; + uint8_t* uint8_table2; +}; + +struct SpaceToBatchParams { + // "Zero" padding for uint8_t means padding with the output offset. + int32_t output_offset; +}; + +struct SpaceToDepthParams { + int32_t block_size; +}; + +struct SplitParams { + // Graphs that split into, say, 2000 nodes are encountered. The indices in + // OperatorEdges are of type uint16_t. + uint16_t num_split; + int16_t axis; +}; + +struct SqueezeParams { + int8_t squeeze_dims_count; + int32_t squeeze_dims[4]; +}; + +struct StridedSliceParams { + int8_t start_indices_count; + int32_t start_indices[5]; + int8_t stop_indices_count; + int32_t stop_indices[5]; + int8_t strides_count; + int32_t strides[5]; + + uint16_t begin_mask; + uint16_t ellipsis_mask; + uint16_t end_mask; + uint16_t new_axis_mask; + uint16_t shrink_axis_mask; + bool offset; +}; + +struct TanhParams { + int32_t input_zero_point; + int32_t input_range_radius; + int32_t input_multiplier; + int input_left_shift; +}; + +constexpr int kTransposeMaxDimensions = 6; + +struct TransposeParams { + int8_t perm_count; + int32_t perm[kTransposeMaxDimensions]; +}; + +struct UnpackParams { + uint16_t num_split; + int16_t axis; +}; + +struct LeakyReluParams { + float alpha; + int32_t input_offset; + int32_t output_offset; + int32_t output_multiplier_alpha; + int32_t output_shift_alpha; + int32_t output_multiplier_identity; + int32_t output_shift_identity; +}; + +template +inline void SetActivationParams(float min, float max, P* params) { + params->float_activation_min = min; + params->float_activation_max = max; +} + +template +inline void SetActivationParams(int32_t min, int32_t max, P* params) { + params->quantized_activation_min = min; + params->quantized_activation_max = max; +} + +template +inline void SetActivationParams(uint32_t min, uint32_t max, P* params) { + params->quantized_activation_min = min; + params->quantized_activation_max = max; +} + +template +inline void SetActivationParams(int16_t min, int16_t max, P* params) { + params->int16_activation_min = min; + params->int16_activation_max = max; +} + +template +inline void SetActivationParams(int64_t min, int64_t max, P* params) { + params->int64_activation_min = min; + params->int64_activation_max = max; +} + +template +inline void GetActivationParams(const P& params, int32_t* min, int32_t* max) { + *min = params.quantized_activation_min; + *max = params.quantized_activation_max; +} + +template +inline void GetActivationParams(const P& params, uint32_t* min, uint32_t* max) { + *min = params.quantized_activation_min; + *max = params.quantized_activation_max; +} + +template +inline void GetActivationParams(const P& params, int16_t* min, int16_t* max) { + *min = params.int16_activation_min; + *max = params.int16_activation_max; +} + +template +inline void GetActivationParams(const P& params, float* min, float* max) { + *min = params.float_activation_min; + *max = params.float_activation_max; +} + +template +inline void GetActivationParams(const P& params, int64_t* min, int64_t* max) { + *min = params.int64_activation_min; + *max = params.int64_activation_max; +} + +// Type trait to check of given type has size smaller than 4 bytes. +template +struct is_small_integer + : public std::integral_constant::value || + std::is_same::value || + std::is_same::value || + std::is_same::value> {}; + +// Type trait to check of given type is int32 or int64. +template +struct is_int32_or_int64 + : public std::integral_constant::value || + std::is_same::value> { +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h b/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h new file mode 100644 index 00000000..4ca74708 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h @@ -0,0 +1,350 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ +#define TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ + +#include +#include + +#include +#ifndef TF_LITE_STATIC_MEMORY +#include +#endif // TF_LITE_STATIC_MEMORY + +#include "tensorflow/lite/core/c/builtin_op_data.h" +#include "tensorflow/lite/core/c/common.h" +#ifndef NDEBUG +#include "tensorflow/lite/kernels/op_macros.h" +#endif + +namespace tflite_micro { + +// A fair number of functions in this header have historically been inline. +// It is ok to change functions to not be inline if the latency with +// benchmark_model for MobileNet + MobileBERT is unaffected. If such a change is +// made, move the newly non-inlined function declarations to the top of this +// header file. + +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetInput(context, node, kMyTensorIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +const TfLiteTensor* GetInput(const TfLiteContext* context, + const TfLiteNode* node, int index); + +// Same as `GetInput` but returns boolean and uses output argument for tensor. +// +// TfLiteTensor* my_tensor; +// TF_LITE_ENSURE_OK(context, +// GetInputSafe(context, node, kMyTensorIdx, &my_tensor)); +// // can use my_tensor directly from here onwards, it is not nullptr +// +// Should be used in cases where the binary size is too large. +TfLiteStatus GetInputSafe(const TfLiteContext* context, const TfLiteNode* node, + int index, const TfLiteTensor** tensor); + +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetVariableInput(context, node, kMyTensorIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +TfLiteTensor* GetVariableInput(TfLiteContext* context, const TfLiteNode* node, + int index); + +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetOutput(context, node, kMyTensorIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node, + int index); + +// Same as `GetOutput` but returns boolean and uses output argument for tensor. +// +// TfLiteTensor* my_tensor; +// TF_LITE_ENSURE_OK(context, +// GetOutputSafe(context, node, kMyTensorIdx, &my_tensor)); +// // can use my_tensor directly from here onwards, it is not nullptr +// +// Should be used in cases where the binary size is too large. +TfLiteStatus GetOutputSafe(const TfLiteContext* context, const TfLiteNode* node, + int index, TfLiteTensor** tensor); + +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetOptionalInputTensor(context, node, kIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +// +// Deprecated. GetInput has the same functionality. +const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context, + const TfLiteNode* node, int index); + +#ifndef TF_LITE_STATIC_MEMORY +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetTemporary(context, node, kMyTensorIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +TfLiteTensor* GetTemporary(TfLiteContext* context, const TfLiteNode* node, + int index); + +// Same as `GetTemporary` but returns boolean and uses output argument for +// tensor. +// +// TfLiteTensor* my_tensor; +// TF_LITE_ENSURE_OK(context, +// GetTemporarySafe(context, node, kMyTensorIdx, +// &my_tensor)); +// // can use my_tensor directly from here onwards, it is not nullptr +// +// Should be used in cases where the binary size is too large. +TfLiteStatus GetTemporarySafe(const TfLiteContext* context, + const TfLiteNode* node, int index, + TfLiteTensor** tensor); + +// Note: You must check if result is not null: +// +// TfLiteTensor* my_tensor = GetIntermediates(context, node, kMyTensorIdx); +// TF_LITE_ENSURE(context, my_tensor != nullptr); +// +// This is because the index might point to the optional tensor constant +// (kTfLiteOptionalTensor) in which case there is no tensor to return. +const TfLiteTensor* GetIntermediates(TfLiteContext* context, + const TfLiteNode* node, int index); + +// Same as `GetIntermediates` but returns boolean and uses output argument for +// tensor. +// +// TfLiteTensor* my_tensor; +// TF_LITE_ENSURE_OK(context, +// GetIntermediatesSafe(context, node, kMyTensorIdx, +// &my_tensor)); +// // can use my_tensor directly from here onwards, it is not nullptr +// +// Should be used in cases where the binary size is too large. +TfLiteStatus GetIntermediatesSafe(const TfLiteContext* context, + const TfLiteNode* node, int index, + TfLiteTensor** tensor); +#endif // TF_LITE_STATIC_MEMORY + +inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; } +inline int SizeOfDimension(const TfLiteTensor* t, int dim) { + return t->dims->data[dim]; +} + +inline int NumInputs(const TfLiteNode* node) { + return node->inputs == nullptr ? 0 : node->inputs->size; +} +inline int NumOutputs(const TfLiteNode* node) { + return node->outputs == nullptr ? 0 : node->outputs->size; +} + +#ifndef TF_LITE_STATIC_MEMORY +inline int NumIntermediates(const TfLiteNode* node) { + return node->intermediates->size; +} +#endif // TF_LITE_STATIC_MEMORY + +inline int64_t NumElements(const int* dims, int num_dims) { + int64_t count = 1; + for (int i = 0; i < num_dims; ++i) { +#ifndef NDEBUG + if (count <= 0) { + break; + } + // Check that number of elements can fit in 32 bit int. Most of tflite + // assumes the result of `NumElements` is < MAX_INT and static or implicit + // casts to `int32_t` without any checks. It is more meaningful to check + // that the result fits into 32 bits than for standard overflow on 64 bit + // type. + TF_LITE_ASSERT(dims[i] < std::numeric_limits::max() / count); +#endif + count *= dims[i]; + } + return count; +} + +inline int64_t NumElements(const TfLiteIntArray* dims) { + return NumElements(dims->data, dims->size); +} + +inline int64_t NumElements(const TfLiteTensor* t) { + return NumElements(t->dims); +} + +// Determines whether tensor is constant. +// TODO(b/138199592): Introduce new query which checks for constant OR +// persistent-read-only, which would be useful for most tensor kernels that +// are potentially dynamic based on the input tensor value availability at the +// time of prepare. +inline bool IsConstantTensor(TfLiteContext *context, TfLiteTensor* tensor) { + #ifndef NO_INTERPRETER + return tensor->allocation_type == kTfLiteMmapRo; + #else + return context->IsConstantTensor(context, tensor); + #endif +} + +inline bool IsConstantOrPersistentTensor(const TfLiteTensor* tensor) { + assert(false && "Disabled function!"); + return false; + // return IsConstantTensor(tensor) || + // (tensor->allocation_type == kTfLitePersistentRo); +} + +// Determines whether tensor is dynamic. Note that a tensor can be non-const and +// not dynamic. This function specifically checks for a dynamic tensor. +inline bool IsDynamicTensor(const TfLiteTensor* tensor) { + assert(false && "Disabled function!"); + return false; + // return tensor->allocation_type == kTfLiteDynamic; +} +#ifndef TF_LITE_STATIC_MEMORY +// Sets tensor to dynamic. +inline void SetTensorToDynamic(TfLiteTensor* tensor) { + if (tensor->allocation_type != kTfLiteDynamic) { + TfLiteTensorDataFree(tensor); + tensor->allocation_type = kTfLiteDynamic; + } +} + +// Sets tensor to persistent and read-only. +inline void SetTensorToPersistentRo(TfLiteTensor* tensor) { + if (tensor->allocation_type != kTfLitePersistentRo) { + TfLiteTensorDataFree(tensor); + tensor->allocation_type = kTfLitePersistentRo; + } +} +#endif // TF_LITE_STATIC_MEMORY + +// Determines whether it is a hybrid op - one that has float inputs and +// quantized weights. +inline bool IsHybridOp(const TfLiteTensor* input, const TfLiteTensor* weight) { + return ((weight->type == kTfLiteUInt8 || weight->type == kTfLiteInt8) && + input->type == kTfLiteFloat32); +} + +// Check dimensionality match and populate OpData for Conv and DepthwiseConv. +TfLiteStatus PopulateConvolutionQuantizationParams( + TfLiteContext* context, const TfLiteTensor* input, + const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, + const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, + int32_t* output_activation_min, int32_t* output_activation_max, + int32_t* per_channel_multiplier, int32_t* per_channel_shift); + +TfLiteStatus PopulateConvolutionQuantizationParams( + TfLiteContext* context, const TfLiteTensor* input, + const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, + const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, + int32_t* output_activation_min, int32_t* output_activation_max, + int32_t* per_channel_multiplier, int32_t* per_channel_shift, + int num_channels); + +// Calculates the multiplication factor for a quantized convolution (or +// quantized depthwise convolution) involving the given tensors. Returns an +// error if the scales of the tensors are not compatible. +TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, + const TfLiteTensor* input, + const TfLiteTensor* filter, + const TfLiteTensor* bias, + TfLiteTensor* output, + double* multiplier); + +TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, + const TfLiteTensor* input, + const TfLiteTensor* filter, + TfLiteTensor* output, + double* multiplier); + +// Calculates the useful quantized range of an activation layer given its +// activation tensor. +TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, + TfLiteFusedActivation activation, + TfLiteTensor* output, + int32_t* act_min, + int32_t* act_max); + +// Calculates the useful range of an activation layer given its activation +// tensor.a +template +void CalculateActivationRange(TfLiteFusedActivation activation, + T* activation_min, T* activation_max) { + if (activation == kTfLiteActRelu) { + *activation_min = 0; + *activation_max = std::numeric_limits::max(); + } else if (activation == kTfLiteActRelu6) { + *activation_min = 0; + *activation_max = 6; + } else if (activation == kTfLiteActReluN1To1) { + *activation_min = -1; + *activation_max = 1; + } else { + *activation_min = std::numeric_limits::lowest(); + *activation_max = std::numeric_limits::max(); + } +} + +// Return true if the given tensors have the same shape. +bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2); + +#if !defined(TF_LITE_STATIC_MEMORY) +// Gets the output shape from the input tensor. +TfLiteStatus GetOutputShapeFromInput(TfLiteContext* context, + const TfLiteTensor* input, + TfLiteIntArray** output_shape); + +std::string GetShapeDebugString(const TfLiteIntArray* shape); + +#endif // !defined(TF_LITE_STATIC_MEMORY) + +// Calculates the output_shape that is necessary for element-wise operations +// with broadcasting involving the two input tensors. +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteIntArray** output_shape); + +// Calculates the output_shape that is necessary for element-wise operations +// with broadcasting involving the three input tensors. +TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + const TfLiteTensor* input3, + TfLiteIntArray** output_shape); + +// Return the size of given type in bytes. Return 0 in case of string. +int TfLiteTypeGetSize(TfLiteType type); + +// Whether the current platform is mobile (Android or iOS). +bool IsMobilePlatform(); + +// Returns whether there is unspecified dimension in the tensor's dim signature. +bool HasUnspecifiedDimension(const TfLiteTensor* tensor); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h b/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h new file mode 100644 index 00000000..9c4e2fd0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h @@ -0,0 +1,49 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ +#define TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ + +#include "tensorflow/lite/micro/micro_log.h" + +#if !defined(TF_LITE_MCU_DEBUG_LOG) +#include +#define TFLITE_ABORT abort() +#else +inline void AbortImpl() { + MicroPrintf("HALTED"); + while (1) { + } +} +#define TFLITE_ABORT AbortImpl(); +#endif + +#if defined(NDEBUG) +#define TFLITE_ASSERT_FALSE (static_cast(0)) +#else +#define TFLITE_ASSERT_FALSE TFLITE_ABORT +#endif + +#define TF_LITE_FATAL(msg) \ + do { \ + MicroPrintf("%s", (msg)); \ + TFLITE_ABORT; \ + } while (0) + +#define TF_LITE_ASSERT(x) \ + do { \ + if (!(x)) TF_LITE_FATAL(#x); \ + } while (0) + +#endif // TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/padding.h b/new_ai_tools/include/tensorflow/lite/kernels/padding.h new file mode 100644 index 00000000..02437cb4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/kernels/padding.h @@ -0,0 +1,115 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_ +#define TENSORFLOW_LITE_KERNELS_PADDING_H_ + +#include "tensorflow/lite/core/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +inline int ComputePadding(int stride, int dilation_rate, int in_size, + int filter_size, int out_size) { + int effective_filter_size = (filter_size - 1) * dilation_rate + 1; + int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2; + return padding > 0 ? padding : 0; +} + +// It's not guaranteed that padding is symmetric. It's important to keep +// offset for algorithms need all paddings. +inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size, + int filter_size, int out_size, + int* offset) { + int effective_filter_size = (filter_size - 1) * dilation_rate + 1; + int total_padding = + ((out_size - 1) * stride + effective_filter_size - in_size); + total_padding = total_padding > 0 ? total_padding : 0; + *offset = total_padding % 2; + return total_padding / 2; +} + +// Matching GetWindowedOutputSize in TensorFlow. +inline int ComputeOutSize(TfLitePadding padding, int image_size, + int filter_size, int stride, int dilation_rate = 1) { + int effective_filter_size = (filter_size - 1) * dilation_rate + 1; + + // TODO(b/186448822): This uses 0 since the function has no other way to + // report error case + if (stride == 0) return 0; + + switch (padding) { + case kTfLitePaddingSame: + return (image_size + stride - 1) / stride; + case kTfLitePaddingValid: + return (image_size + stride - effective_filter_size) / stride; + default: + return 0; + } +} + +inline TfLitePaddingValues ComputePaddingHeightWidth( + int stride_height, int stride_width, int dilation_rate_height, + int dilation_rate_width, int in_height, int in_width, int filter_height, + int filter_width, TfLitePadding padding, int* out_height, int* out_width) { + *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width, + dilation_rate_width); + *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height, + dilation_rate_height); + + TfLitePaddingValues padding_values; + int offset = 0; + padding_values.height = + ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height, + filter_height, *out_height, &offset); + padding_values.height_offset = offset; + padding_values.width = + ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width, + filter_width, *out_width, &offset); + padding_values.width_offset = offset; + return padding_values; +} + +inline Padding3DValues ComputePadding3DValues( + int stride_height, int stride_width, int stride_depth, + int dilation_rate_height, int dilation_rate_width, int dilation_rate_depth, + int in_height, int in_width, int in_depth, int filter_height, + int filter_width, int filter_depth, TfLitePadding padding, int* out_height, + int* out_width, int* out_depth) { + *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width, + dilation_rate_width); + *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height, + dilation_rate_height); + *out_depth = ComputeOutSize(padding, in_depth, filter_depth, stride_depth, + dilation_rate_depth); + + Padding3DValues padding_values; + int offset = 0; + padding_values.depth = + ComputePaddingWithOffset(stride_depth, dilation_rate_depth, in_depth, + filter_depth, *out_depth, &offset); + padding_values.depth_offset = offset; + padding_values.height = + ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height, + filter_height, *out_height, &offset); + padding_values.height_offset = offset; + padding_values.width = + ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width, + filter_width, *out_width, &offset); + padding_values.width_offset = offset; + return padding_values; +} +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_KERNELS_PADDING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h new file mode 100644 index 00000000..41e32c9a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h @@ -0,0 +1,100 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ + +#include +#include + +#include "tensorflow/lite/c/c_api_types.h" + +namespace tflite_micro { +// Interface classes that the TFLM framework relies on to get buffers it needs. +// There are two types of buffers that the TFLM framework requires: persistent +// and non-persistent. Persistent buffers, once allocated, are never freed by +// the TFLM framework. Non-persist buffers can be allocated and deallocated by +// the TFLM framework. This file defines two interfaces classes that TFLM +// framework will rely on to manage these buffers. + +// Interface class for managing persistent buffers. +class IPersistentBufferAllocator { + public: + IPersistentBufferAllocator() {} + virtual ~IPersistentBufferAllocator() {} + + // Allocates persistent memory. The persistent buffer is never freed. + virtual uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) = 0; + + // Returns the size of all persistent allocations in bytes. + virtual size_t GetPersistentUsedBytes() const = 0; +}; + +// Interface class for managing non-persistent buffers. +// The default non-persistent buffers are temp buffers that are not resizable. +// Support of at least one resizable buffer is required. +class INonPersistentBufferAllocator { + public: + INonPersistentBufferAllocator() {} + virtual ~INonPersistentBufferAllocator() {} + + // Allocates a temporary buffer. This buffer is not resizable. + virtual uint8_t* AllocateTemp(size_t size, size_t alignment) = 0; + + // Signals that a temporary buffer is no longer needed. + virtual void DeallocateTemp(uint8_t* buf) = 0; + + // Returns true if all temporary buffers are already deallocated. + virtual bool IsAllTempDeallocated() = 0; + + // Signals that all temporary allocations can be reclaimed. TFLM calls this + // API when it knows that all temporary buffers that it requested has been + // deallocated. The goal of API is to facilitate implementations of + // INonPersistentBufferAllocator can reuse buffer with some reasonable + // complexity. + virtual TfLiteStatus ResetTempAllocations() = 0; + + // Returns a buffer that is resizable viable ResizeBuffer(). + virtual uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) = 0; + + // Resizes a buffer that is previously returned by the + // AllocateResizableBuffer. + virtual TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, + size_t alignment) = 0; + + // Frees up the memory occupied by the resizable buffer. + virtual TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) = 0; + + // Returns a pointer pointing to the start of the overlay memory, which is + // used for activation tensors and scratch buffers by kernels at Invoke stage. + virtual uint8_t* GetOverlayMemoryAddress() const = 0; + + // Reserves the size of the overlay memory. This overlay is reserved for the + // kernels at Invoke stage. This is referred to as the overlay because before + // Invoket state, the same memory can be used for temp buffers. The layout of + // the memory is planned by the memory planner separately at Invoke stage. + virtual TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size, + size_t alignment) = 0; + + // Returns the size of non-persistent buffer in use. + virtual size_t GetNonPersistentUsedBytes() const = 0; + + // Returns the number of bytes available with a given alignment. This number + // takes in account any temporary allocations. + virtual size_t GetAvailableMemory(size_t alignment) const = 0; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h new file mode 100644 index 00000000..8707e5a4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h @@ -0,0 +1,104 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ + +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" + +namespace tflite_micro { + +// Implement INonPersistentBufferAllocator on an arena that is dedicated for +// non-persistent buffers. +class NonPersistentArenaBufferAllocator : public INonPersistentBufferAllocator { + public: + NonPersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); + virtual ~NonPersistentArenaBufferAllocator(); + + // Allocates a temporary buffer. This buffer is not resizable. + uint8_t* AllocateTemp(size_t size, size_t alignment) override; + + // Signals that a temporary buffer is no longer needed. + void DeallocateTemp(uint8_t* buf) override; + + // Returns true if all temporary buffers are already deallocated. + bool IsAllTempDeallocated() override; + + // Signals that all temporary allocations can be reclaimed. TFLM calls this + // API when it knows that all temporary buffers that it requested has been + // deallocated. + TfLiteStatus ResetTempAllocations() override; + + // Returns a buffer that is resizable viable ResizeBuffer(). + uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) override; + + // Resizes a buffer that is previously returned by the + // AllocateResizableBuffer. + TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, + size_t alignment) override; + + // Frees up the memory occupied by the resizable buffer. + TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) override; + + // Returns a pointer pointing to the start of the overlay memory, which is + // used for activation tensors and scratch buffers by kernels at Invoke stage. + uint8_t* GetOverlayMemoryAddress() const override; + + // Reserves the size of the overlay memory. This overlay is reserved for the + // kernels at Invoke stage. This is referred to as the overlay because before + // Invoket state, the same memory can be used for temp buffers. The layout of + // the memory is planned by the memory planner separately at Invoke stage. + TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size, + size_t alignment) override; + + // Returns the size of non-persistent buffer in use. + size_t GetNonPersistentUsedBytes() const override; + + // Returns the number of bytes available with a given alignment. This number + // takes in account any temporary allocations. + size_t GetAvailableMemory(size_t alignment) const override; + + TF_LITE_REMOVE_VIRTUAL_DELETE + + private: + // The memory arena that this allocator manages. + uint8_t* const buffer_head_; + uint8_t* const buffer_tail_; + + // The whole region is split into two parts: + // buffer_head_ to head_temp_ - 1 belongs to the only resizable buffer. + // head_temp_ to buffer_tail_ can be used for (non-resizable) temp buffers. + uint8_t* head_temp_; + + // next_temp_ points to the next available temp buffer allocation address and + // its range is between head_temp_ and buffer_tail_ + uint8_t* next_temp_; + + // XOR Check sum for outstanding temp buffers. + // If all temp buffers are deallocated OR no temp buffers are allocated, + // temp_buffer_ptr_check_sum_ == nullptr. + intptr_t temp_buffer_ptr_check_sum_ = 0; + // Count of outstanding temp buffers. + int temp_buffer_count_ = 0; + bool resizable_buffer_allocated_ = false; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h new file mode 100644 index 00000000..a4ca701c --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h @@ -0,0 +1,58 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ + +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" + +namespace tflite_micro { + +// PersistentArenaBufferAllocator is an implementatation of +// IPersistentBufferAllocator interface on an arena that is dedicated for +// persistent buffers. +class PersistentArenaBufferAllocator : public IPersistentBufferAllocator { + public: + PersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); + virtual ~PersistentArenaBufferAllocator(); + + // Allocates persistent memory. The persistent buffer is never freed. + // Returns nullptr if errors occured. + uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override; + + // Returns the size of all persistent allocations in bytes. + size_t GetPersistentUsedBytes() const override; + + TF_LITE_REMOVE_VIRTUAL_DELETE + private: + // The memory arena that this allocator manages. + uint8_t* const buffer_head_; + uint8_t* const buffer_tail_; + + // The whole region is split into two parts: + // tail_temp_ to buffer_tail_ contains allocated buffers; + // buffer_head_ to tail_temp_ - 1 belongs to still available spaces. + // So in essence, the allocated region grows from the bottom and emulates + // SingleArenaBufferAllocator's persistent part. + uint8_t* tail_temp_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h new file mode 100644 index 00000000..e24530e6 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h @@ -0,0 +1,63 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ + +#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" + +namespace tflite_micro { + +// Utility class used to log allocations of a SingleArenaBufferAllocator. Should +// only be used in debug/evaluation settings or unit tests to evaluate +// allocation usage. +class RecordingSingleArenaBufferAllocator : public SingleArenaBufferAllocator { + public: + RecordingSingleArenaBufferAllocator(uint8_t* buffer_head, size_t buffer_size); + // TODO(b/157615197): Cleanup constructors/destructor and use factory + // functions. + ~RecordingSingleArenaBufferAllocator() override; + + static RecordingSingleArenaBufferAllocator* Create(uint8_t* buffer_head, + size_t buffer_size); + + // Returns the number of bytes requested from the head or tail. + size_t GetRequestedBytes() const; + + // Returns the number of bytes actually allocated from the head or tail. This + // value will be >= to the number of requested bytes due to padding and + // alignment. + size_t GetUsedBytes() const; + + // Returns the number of alloc calls from the head or tail. + size_t GetAllocatedCount() const; + + TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, + size_t alignment) override; + uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override; + + private: + size_t requested_head_bytes_; + size_t requested_tail_bytes_; + size_t used_bytes_; + size_t alloc_count_; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h new file mode 100644 index 00000000..91464c89 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h @@ -0,0 +1,144 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ + +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" + +namespace tflite_micro { + +// TODO(petewarden): This allocator never frees up or reuses any memory, even +// though we have enough information about lifetimes of the tensors to do so. +// This makes it pretty wasteful, so we should use a more intelligent method. +class SingleArenaBufferAllocator : public INonPersistentBufferAllocator, + public IPersistentBufferAllocator { + public: + // TODO(b/157615197): Cleanup constructors/destructor and use factory + // functions. + SingleArenaBufferAllocator(uint8_t* buffer_head, uint8_t* buffer_tail); + SingleArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); + virtual ~SingleArenaBufferAllocator(); + + // Creates a new SingleArenaBufferAllocator from a given buffer head and size. + static SingleArenaBufferAllocator* Create(uint8_t* buffer_head, + size_t buffer_size); + + // Resizes a buffer that is previously returned by the + // AllocateResizableBuffer. In current implementation, it Adjusts the head + // (lowest address and moving upwards) memory allocation to a given size. + // Calls to this method will also invalidate all temporary allocation values + // (it sets the location of temp space at the end of the head section). This + // call will fail if a chain of allocations through AllocateTemp() have not + // been cleaned up with a call to ResetTempAllocations(). + virtual TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, + size_t alignment) override; + + // Returns a buffer that is resizable viable ResizeBuffer(). Only one + // resizable buffer is currently supported. + virtual uint8_t* AllocateResizableBuffer(size_t size, + size_t alignment) override; + + // Frees up the memory occupied by the resizable buffer + virtual TfLiteStatus DeallocateResizableBuffer( + uint8_t* resizable_buf) override; + + // Reserves the non-persistent memory that is planned by the memory planner. + virtual TfLiteStatus ReserveNonPersistentOverlayMemory( + size_t size, size_t alignment) override; + + // Allocates persistent memory starting at the tail of the arena (highest + // address and moving downwards). + virtual uint8_t* AllocatePersistentBuffer(size_t size, + size_t alignment) override; + + // Allocates a temporary buffer from the head of the arena (lowest address and + // moving upwards) but does not update the actual head allocation size or + // position. The returned buffer is guaranteed until either + // ResetTempAllocations() is called or another call to AllocateFromHead(). + // Repeat calls to this function will create a chain of temp allocations. All + // calls to AllocateTemp() must end with a call to ResetTempAllocations(). If + // AllocateFromHead() is called before a call to ResetTempAllocations(), it + // will fail with an error message. + virtual uint8_t* AllocateTemp(size_t size, size_t alignment) override; + + // Signals that a temporary buffer is no longer needed. This is currently for + // book-keeping purpose and the memory region are not immediately available + // for re-use. The deallocated memory region are only reclaimed after + // ResetTempAllocations is called as it is right now. + virtual void DeallocateTemp(uint8_t* buf) override; + + // Returns true if all temporary buffers are already deallocated. + virtual bool IsAllTempDeallocated() override; + + // Resets a chain of temporary allocations back to the current head of the + // arena (lowest address). + virtual TfLiteStatus ResetTempAllocations() override; + + // Returns a pointer to the buffer currently assigned to the head section. + // This buffer is set by calling SetHeadSize(). + uint8_t* GetOverlayMemoryAddress() const override; + + // Returns the size of the head section in bytes. + size_t GetNonPersistentUsedBytes() const override; + + // Returns the size of all allocations in the tail section in bytes. + size_t GetPersistentUsedBytes() const override; + + // Returns the number of bytes available with a given alignment. This number + // takes in account any temporary allocations. + size_t GetAvailableMemory(size_t alignment) const override; + + // Returns the number of used bytes in the allocator. This number takes in + // account any temporary allocations. + size_t GetUsedBytes() const; + + TF_LITE_REMOVE_VIRTUAL_DELETE + + protected: + // Returns a pointer to the current end of the head buffer. + uint8_t* head() const; + + // Returns a pointer to the current end of the tail buffer. + uint8_t* tail() const; + + private: + size_t GetBufferSize() const; + uint8_t* buffer_head_; + uint8_t* buffer_tail_; + uint8_t* head_; + uint8_t* tail_; + uint8_t* temp_; + + // The combination of the checksum of outstanding temporary buffer pointers + // AND the count of outstanding temporary buffer provide a low cost mechanism + // to audit temporary buffers' allocation and deallocation. + // + // XOR Check sum for outstanding temp buffers. + // If all temp buffers are deallocated OR no temp buffers are allocated, + // temp_buffer_ptr_check_sum_ == nullptr. + intptr_t temp_buffer_ptr_check_sum_ = 0; + // Count of outstanding temp buffers. + int temp_buffer_count_ = 0; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h b/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h new file mode 100644 index 00000000..01c06a4a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h @@ -0,0 +1,95 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ +#define TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ + +#include + +#include "tensorflow/lite/micro/micro_log.h" +#include "tensorflow/lite/micro/micro_op_resolver.h" +#include "tensorflow/lite/micro/micro_profiler_interface.h" +#include "tensorflow/lite/micro/micro_resource_variable.h" +#include "tensorflow/lite/micro/micro_time.h" +#include "tensorflow/lite/micro/recording_micro_interpreter.h" + +namespace tflite_micro { + +template +class MicroBenchmarkRunner { + public: + // The lifetimes of model, op_resolver, tensor_arena, profiler must exceed + // that of the created MicroBenchmarkRunner object. + MicroBenchmarkRunner(const uint8_t* model, + const tflite_micro::MicroOpResolver* op_resolver, + uint8_t* tensor_arena, int tensor_arena_size, + MicroProfilerInterface* profiler, + int num_resource_variables = 0) + : allocator_( + RecordingMicroAllocator::Create(tensor_arena, tensor_arena_size)), + interpreter_( + GetModel(model), *op_resolver, allocator_, + MicroResourceVariables::Create(allocator_, num_resource_variables), + profiler) { + interpreter_.AllocateTensors(); + } + + void RunSingleIteration() { + // Run the model on this input and make sure it succeeds. + TfLiteStatus invoke_status = interpreter_.Invoke(); + if (invoke_status == kTfLiteError) { + MicroPrintf("Invoke failed."); + } + } + + int NumInputs() { return interpreter_.inputs().size(); } + + void SetRandomInput(const int random_seed, int input_index = 0) { + // The pseudo-random number generator is initialized to a constant seed + std::srand(random_seed); + TfLiteTensor* input = interpreter_.input(input_index); + + // Pre-populate input tensor with random values. + int input_length = input->bytes / sizeof(inputT); + inputT* input_values = tflite_micro::GetTensorData(input); + for (int i = 0; i < input_length; i++) { + // Pre-populate input tensor with a random value based on a constant seed. + input_values[i] = static_cast( + std::rand() % (std::numeric_limits::max() - + std::numeric_limits::min() + 1)); + } + } + + void SetInput(const inputT* custom_input, int input_index = 0) { + TfLiteTensor* input = interpreter_.input(input_index); + inputT* input_buffer = tflite_micro::GetTensorData(input); + int input_length = input->bytes / sizeof(inputT); + for (int i = 0; i < input_length; i++) { + input_buffer[i] = custom_input[i]; + } + } + + void PrintAllocations() const { + interpreter_.GetMicroAllocator().PrintAllocations(); + } + + private: + tflite_micro::RecordingMicroAllocator* allocator_; + tflite_micro::RecordingMicroInterpreter interpreter_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/compatibility.h b/new_ai_tools/include/tensorflow/lite/micro/compatibility.h new file mode 100644 index 00000000..49acb28f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/compatibility.h @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ +#define TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ + +// C++ will automatically create class-specific delete operators for virtual +// objects, which by default call the global delete function. For embedded +// applications we want to avoid this, and won't be calling new/delete on these +// objects, so we need to override the default implementation with one that does +// nothing to avoid linking in ::delete(). +// This macro needs to be included in all subclasses of a virtual base class in +// the private section. +#ifdef TF_LITE_STATIC_MEMORY +#define TF_LITE_REMOVE_VIRTUAL_DELETE \ + void operator delete(void* p) {} +#else +#define TF_LITE_REMOVE_VIRTUAL_DELETE +#endif + +#endif // TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h b/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h new file mode 100644 index 00000000..c1afd19a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h @@ -0,0 +1,49 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ +#define TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ + +// The application layer must implement and register a callback before calling +// the network in a way similar to +// +// void debug_log_printf(const char* s) +// { +// printf(s); +// } +// +// int main(void) +// { +// // Register callback for printing debug log +// RegisterDebugLogCallback(debug_log_printf); +// +// // now call the network +// TfLiteStatus invoke_status = interpreter->Invoke(); +// } + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +typedef void (*DebugLogCallback)(const char* s); + +// Registers and application-specific callback for debug logging. It must be +// called before the first call to DebugLog(). +void RegisterDebugLogCallback(DebugLogCallback callback); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/debug_log.h b/new_ai_tools/include/tensorflow/lite/micro/debug_log.h new file mode 100644 index 00000000..c0cf6995 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/debug_log.h @@ -0,0 +1,38 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ +#define TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ + +#ifdef __cplusplus +#include +#else +#include +#endif // __cplusplus + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// This function should be implemented by each target platform, and provide a +// way for strings to be output to some text stream. For more information, see +// the tensorflow/lite/micro/debug_log.cc file. This function should support +// standard C/C++ stdio style formatting operations. +void DebugLog(const char* format, va_list args); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h b/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h new file mode 100644 index 00000000..9d5b0462 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h @@ -0,0 +1,37 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ + +// The following values are derived from values used during model training. +// If you change the way you preprocess the input, update all these constants. +constexpr int kAudioSampleFrequency = 16000; +constexpr int kFeatureSize = 40; +constexpr int kFeatureCount = 49; +constexpr int kFeatureElementCount = (kFeatureSize * kFeatureCount); +constexpr int kFeatureStrideMs = 20; +constexpr int kFeatureDurationMs = 30; + +// Variables for the model's output categories. +constexpr int kCategoryCount = 4; +constexpr const char* kCategoryLabels[kCategoryCount] = { + "silence", + "unknown", + "yes", + "no", +}; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h new file mode 100644 index 00000000..3eb62e69 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h @@ -0,0 +1,47 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ + +#ifdef ETHOS_U +static unsigned char expected_output_data[] = {143, 113}; +#else +static unsigned char expected_output_data[] = { + 0x07, 0xf3, 0x16, 0xf3, 0x3a, 0x1f, 0xb1, 0x45, 0xce, 0x12, 0xc2, 0xbb, + 0xf5, 0x47, 0x45, 0x91, 0x13, 0x21, 0xc7, 0x07, 0xa2, 0x2c, 0xf2, 0xf7, + 0xd1, 0xe1, 0x08, 0xf5, 0x0a, 0x58, 0x40, 0xb0, 0xd9, 0xf1, 0x37, 0x1e, + 0xf1, 0x0d, 0xea, 0x05, 0xf9, 0xee, 0xfa, 0xca, 0x37, 0xb9, 0xc4, 0xd8, + 0x15, 0xe3, 0xd7, 0xd3, 0x36, 0x14, 0xb7, 0xed, 0x43, 0xfa, 0x29, 0xbe, + 0xff, 0xdb, 0x38, 0xc7, 0x00, 0xf1, 0x06, 0xbe, 0x31, 0x5b, 0xe9, 0x02, + 0xf0, 0x07, 0x13, 0x25, 0xe1, 0xf0, 0x0e, 0x12, 0x17, 0xd2, 0x51, 0x48, + 0x2a, 0x24, 0xf2, 0x0a, 0x3d, 0x05, 0x0b, 0x67, 0xc3, 0xea, 0x0a, 0xbf, + 0x22, 0x40, 0x20, 0x18, 0x54, 0xd3, 0x31, 0xef, 0xe7, 0x04, 0x3c, 0xf6, + 0xcf, 0xe2, 0x79, 0xed, 0xec, 0xff, 0xfa, 0xcf, 0xc2, 0x42, 0x1f, 0x0d, + 0xf8, 0xc2, 0x36, 0x1d, 0x09, 0x01, 0xd2, 0x09, 0xda, 0xdf, 0xe8, 0x1f, + 0x06, 0xdf, 0xf7, 0x3c, 0x4d, 0xe5, 0xb4, 0xc1, 0xfa, 0x03, 0x22, 0xbc, + 0x08, 0xea, 0x72, 0x3a, 0xc8, 0x0b, 0xe6, 0x1c, 0x00, 0xef, 0xf1, 0x34, + 0x01, 0xbe, 0xf7, 0x92, 0xe6, 0xc1, 0xe3, 0x25, 0xe5, 0x4c, 0xf1, 0xbb, + 0x6f, 0x07, 0x63, 0x80, 0x2a, 0xae, 0x46, 0xf1, 0x3c, 0x01, 0xe8, 0xda, + 0xed, 0x15, 0xfc, 0x56, 0x18, 0x16, 0x16, 0xc1, 0xf5, 0xef, 0x64, 0x03, + 0xa5, 0x0c, 0x15, 0x25, 0xe0, 0x51, 0x23, 0xec, 0x31, 0xfe, 0xe9, 0xd9, + 0x0c, 0x09, 0x42, 0xd8, 0xbc, 0x15, 0xe1, 0x21, 0xe4, 0x09, 0x4f, 0xb5, + 0xd6, 0x0c, 0x04, 0x20, 0xdf, 0x99, 0x2e, 0xe8, 0x06, 0xad, 0xf5, 0x04, + 0xf7, 0x1b, 0x5b, 0x63, 0xaa, 0xf3, 0xf0, 0xfc, 0x05, 0x35, 0xf1, 0x1d, + 0x40, 0x2d, 0xf6, 0xfa, 0xdd, 0x07, 0xfe, 0x32, 0x6d, 0x1b, 0x01, 0x31, + 0x39, 0xcb, 0x22, 0xf8}; +#endif + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h new file mode 100644 index 00000000..460d0ca5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h @@ -0,0 +1,108 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_INPUT_DATA_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_INPUT_DATA_H_ + +static const int input_data_len = 1024; +static const unsigned char input_data[] = { + 0x2c, 0x8a, 0xff, 0x0c, 0xaf, 0x2a, 0x44, 0x17, 0xf5, 0x26, 0x96, 0x37, + 0x40, 0x4c, 0xa1, 0x58, 0xc3, 0x33, 0xce, 0x1a, 0x7b, 0xd2, 0x22, 0x5b, + 0x43, 0xf6, 0xfd, 0x0b, 0xe7, 0xfd, 0x65, 0x58, 0x89, 0x24, 0xf4, 0xec, + 0x53, 0x5e, 0x21, 0x1f, 0x95, 0xd1, 0xd9, 0x25, 0x72, 0x56, 0xe6, 0xe2, + 0xa4, 0x37, 0x85, 0xf0, 0xd7, 0xba, 0xab, 0xcc, 0xc6, 0xbc, 0xcb, 0x64, + 0x58, 0x3d, 0x04, 0x8e, 0xd8, 0x1a, 0x32, 0x76, 0x0c, 0x4d, 0x4c, 0xc5, + 0xba, 0xb9, 0xa9, 0xe2, 0x41, 0xc2, 0xc8, 0xfa, 0x66, 0xfd, 0x2e, 0x4a, + 0xa7, 0xca, 0x6a, 0x4f, 0xd7, 0x28, 0xe5, 0x07, 0x2e, 0x48, 0x5f, 0xfa, + 0xd8, 0xde, 0xeb, 0x11, 0xd1, 0x0b, 0x0d, 0xe4, 0x25, 0x66, 0x73, 0x6c, + 0x99, 0xc2, 0x89, 0x56, 0xcd, 0xeb, 0xaf, 0x92, 0xc8, 0x18, 0xdf, 0xd6, + 0x89, 0x9b, 0xce, 0x96, 0x14, 0x17, 0x6e, 0x25, 0xf3, 0x88, 0xad, 0x85, + 0x50, 0x93, 0xc5, 0xde, 0x73, 0x12, 0xa6, 0x55, 0x45, 0x9e, 0x88, 0x75, + 0x7e, 0xc7, 0xb5, 0x47, 0xcf, 0x87, 0x6e, 0xa3, 0x2f, 0x9d, 0x39, 0x5e, + 0x40, 0x89, 0x3e, 0x5e, 0xd2, 0x3b, 0x06, 0x7a, 0xe3, 0xe0, 0xbe, 0xf9, + 0x58, 0x27, 0x15, 0x4c, 0x31, 0xfc, 0x88, 0x4d, 0x73, 0x02, 0xa3, 0xf6, + 0x9d, 0x83, 0xbe, 0x05, 0x13, 0x42, 0x50, 0x47, 0x13, 0x8c, 0x4b, 0x2d, + 0x0e, 0xbb, 0xc7, 0x9e, 0x27, 0xe1, 0xfb, 0x38, 0xa0, 0x5d, 0x51, 0x23, + 0x41, 0xbf, 0x7f, 0x14, 0x89, 0x4d, 0x32, 0xa4, 0x39, 0xca, 0x11, 0x09, + 0xff, 0x52, 0x55, 0x71, 0xa0, 0x22, 0xb4, 0x42, 0x9f, 0x5a, 0x17, 0x05, + 0x4a, 0x42, 0x9c, 0x9b, 0x74, 0xc8, 0xa7, 0xea, 0x17, 0x60, 0xbd, 0xf9, + 0x23, 0x6a, 0x39, 0xc3, 0x7e, 0xf9, 0x33, 0xaf, 0x4b, 0xad, 0xb4, 0x46, + 0xf2, 0x2b, 0x26, 0x3c, 0x37, 0xc4, 0x46, 0xf4, 0x9c, 0x58, 0xef, 0xb7, + 0xa2, 0x63, 0x04, 0x11, 0x00, 0x28, 0x1d, 0xa2, 0x00, 0xe8, 0x4f, 0x84, + 0x24, 0x67, 0x7a, 0xd2, 0xb5, 0xdc, 0x9d, 0x1e, 0x05, 0x97, 0xd1, 0xa6, + 0xa6, 0xce, 0x32, 0x1c, 0x68, 0x8d, 0x6a, 0xb8, 0x74, 0xe2, 0x6f, 0x1d, + 0x91, 0x71, 0x70, 0xe2, 0xcf, 0xf9, 0x19, 0x71, 0x04, 0x31, 0x90, 0x67, + 0xe9, 0x46, 0x0b, 0x2e, 0xaa, 0x6c, 0xaf, 0xf3, 0x3a, 0x35, 0x88, 0xdc, + 0x9f, 0x3a, 0x71, 0x1c, 0xf8, 0xd3, 0x61, 0xef, 0x81, 0xd1, 0x80, 0x67, + 0xc1, 0x75, 0x17, 0x32, 0x67, 0xef, 0xea, 0x99, 0x29, 0x6f, 0xea, 0x8f, + 0xb9, 0xe8, 0xc5, 0x78, 0xa3, 0xb9, 0x31, 0x2a, 0xe6, 0xaf, 0xb4, 0x27, + 0xf7, 0x6c, 0x2e, 0x2b, 0x8b, 0x3b, 0x40, 0xab, 0x2e, 0x21, 0x5b, 0xb5, + 0xd2, 0x5c, 0x2f, 0xdb, 0xdb, 0xd0, 0x81, 0xa1, 0x00, 0x77, 0xac, 0x40, + 0x0e, 0x69, 0x3f, 0xd0, 0xe3, 0x4a, 0x1a, 0x1b, 0xb5, 0xa6, 0x9c, 0xdd, + 0x0c, 0xcb, 0xb6, 0xd3, 0xf9, 0xea, 0x78, 0x11, 0x2a, 0xbf, 0x56, 0x89, + 0xd4, 0xb0, 0xc8, 0xf0, 0x4b, 0x48, 0xdf, 0x22, 0xc4, 0x91, 0x86, 0x7d, + 0x86, 0x33, 0xfd, 0x75, 0x44, 0x1b, 0x9f, 0x9a, 0xaf, 0xc4, 0x9d, 0x27, + 0xff, 0x72, 0xf8, 0xb5, 0x74, 0xb7, 0x13, 0x22, 0x03, 0x14, 0xcb, 0xa9, + 0x4c, 0x73, 0x4b, 0x7e, 0xe4, 0x9b, 0xb2, 0x27, 0x34, 0xa4, 0x74, 0x14, + 0x68, 0x37, 0x57, 0xc0, 0xce, 0x8f, 0x94, 0xea, 0x0f, 0xa4, 0xe0, 0xf7, + 0x14, 0x46, 0x05, 0xf9, 0x63, 0x2c, 0x12, 0xbe, 0x3a, 0xdb, 0x1e, 0x1f, + 0x97, 0xbb, 0x32, 0xa8, 0x4f, 0x2a, 0x07, 0xd6, 0x0d, 0x0b, 0xc1, 0x9c, + 0xf5, 0x11, 0x3c, 0x2c, 0xd5, 0xb7, 0x85, 0x28, 0xb0, 0xa2, 0xb4, 0xd1, + 0xb1, 0x75, 0xdf, 0xa3, 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0x01, 0xec, + 0x64, 0xbc, 0xea, 0x1e, 0x7b, 0x15, 0xbc, 0x1b, 0x7b, 0x6a, 0x10, 0xf9, + 0xf9, 0x58, 0x69, 0x84, 0xc6, 0x15, 0xda, 0xf8, 0x55, 0x81, 0xb4, 0xcd, + 0x9f, 0x29, 0xd0, 0x37}; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_INPUT_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/network_model.h b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/network_model.h new file mode 100644 index 00000000..e6433ec8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/network_model.h @@ -0,0 +1,166 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ + +alignas(4) const unsigned char network_model[] = { + 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00, + 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, + 0x00, 0x00, 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0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xd8, 0xff, 0xff, 0xff, + 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, + 0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, + 0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x04}; +const unsigned int network_model_len = 1720; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h new file mode 100644 index 00000000..d2d945f4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h @@ -0,0 +1,32 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Provides an interface to take an action based on the output from the person +// detection model. + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ + +#include "tensorflow/lite/c/common.h" + +// Called every time the results of a person detection run are available. The +// `person_score` has the numerical confidence that the captured image contains +// a person, and `no_person_score` has the numerical confidence that the image +// does not contain a person. Typically if person_score > no person score, the +// image is considered to contain a person. This threshold may be adjusted for +// particular applications. +void RespondToDetection(int8_t person_score, int8_t no_person_score); + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h new file mode 100644 index 00000000..f3799922 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h @@ -0,0 +1,38 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ + +#include "tensorflow/lite/c/common.h" + +// This is an abstraction around an image source like a camera, and is +// expected to return 8-bit sample data. The assumption is that this will be +// called in a low duty-cycle fashion in a low-power application. In these +// cases, the imaging sensor need not be run in a streaming mode, but rather can +// be idled in a relatively low-power mode between calls to GetImage(). The +// assumption is that the overhead and time of bringing the low-power sensor out +// of this standby mode is commensurate with the expected duty cycle of the +// application. The underlying sensor may actually be put into a streaming +// configuration, but the image buffer provided to GetImage should not be +// overwritten by the driver code until the next call to GetImage(); +// +// The reference implementation can have no platform-specific dependencies, so +// it just returns a static image. For real applications, you should +// ensure there's a specialized implementation that accesses hardware APIs. +TfLiteStatus GetImage(int image_width, int image_height, int channels, + int8_t* image_data); + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h new file mode 100644 index 00000000..2620097a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h @@ -0,0 +1,37 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ + +// Expose a C friendly interface for main functions. +#ifdef __cplusplus +extern "C" { +#endif + +// Initializes all data needed for the example. The name is important, and needs +// to be setup() for Arduino compatibility. +void setup(); + +// Runs one iteration of data gathering and inference. This should be called +// repeatedly from the application code. The name needs to be loop() for Arduino +// compatibility. +void loop(); + +#ifdef __cplusplus +} +#endif + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h new file mode 100644 index 00000000..f94d58ed --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h @@ -0,0 +1,35 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ + +// Keeping these as constant expressions allow us to allocate fixed-sized arrays +// on the stack for our working memory. + +// All of these values are derived from the values used during model training, +// if you change your model you'll need to update these constants. +constexpr int kNumCols = 96; +constexpr int kNumRows = 96; +constexpr int kNumChannels = 1; + +constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels; + +constexpr int kCategoryCount = 2; +constexpr int kPersonIndex = 1; +constexpr int kNotAPersonIndex = 0; +extern const char* kCategoryLabels[kCategoryCount]; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h b/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h new file mode 100644 index 00000000..cd2f9e6c --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h @@ -0,0 +1,70 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ +#define TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ + +#include "tensorflow/lite/micro/micro_context.h" +#include "tensorflow/lite/micro/micro_graph.h" + +namespace tflite_micro { +// A fake of MicroContext for kernel util tests. +// TODO(b/272759060): FakeMicroContext currently inherits from MicroContext. +// Which allow tests to use functions from MicroContext that weren't added to +// FakeMicroContext in tests. This should be looked into further. + +class FakeMicroContext : public MicroContext { + public: + ~FakeMicroContext() = default; + + FakeMicroContext(TfLiteTensor* tensors, SingleArenaBufferAllocator* allocator, + MicroGraph* micro_graph); + + void* AllocatePersistentBuffer(size_t bytes) override; + TfLiteStatus RequestScratchBufferInArena(size_t bytes, + int* buffer_index) override; + void* GetScratchBuffer(int buffer_index) override; + + TfLiteTensor* AllocateTempTfLiteTensor(int tensor_index) override; + void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override; + bool IsAllTempTfLiteTensorDeallocated(); + + uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override; + void DeallocateTempBuffer(uint8_t* buffer) override; + + TfLiteEvalTensor* GetEvalTensor(int tensor_index) override; + + TfLiteStatus set_external_context(void* external_context_payload) override; + void* external_context() override; + MicroGraph& graph() override; + + private: + static constexpr int kNumScratchBuffers_ = 12; + + MicroGraph& graph_; + int scratch_buffer_count_ = 0; + uint8_t* scratch_buffers_[kNumScratchBuffers_]; + + TfLiteTensor* tensors_; + int allocated_temp_count_ = 0; + + SingleArenaBufferAllocator* allocator_; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h b/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h new file mode 100644 index 00000000..374be3fa --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h @@ -0,0 +1,65 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ +#define THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ + +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { +// Kernels use flexbuffers::Map to pack their init parameters in a tflite file, +// with the parameter names as map keys and the parameter values as the +// corresponding map values. +// Accessing the map values using the flexbuffers:Map class is inline heavy, +// which can cause the code size to bloat beyond what's reasonable for a micro +// application. Use this class instead, when possible. +// FlexbufferWrapper takes advantage of the following properties of +// flexbuffers::Map: +// 1. It can be viewed as a flexbuffers::Vector of the values. +// 2. The values in the vector are ordered alphabetically by their keys. +// 3. All integer and Boolean values are stored as 64-bit numbers. +// 4. All floating point values are stored as double precision numbers. +// The properties are mentioned in the flexbuffers docs, but we rely on +// a unit test to catch design changes. +class FlexbufferWrapper : public flexbuffers::Vector { + public: + // Construct with a serialized flexbuffer 'buffer' of 'size' bytes + explicit FlexbufferWrapper(const uint8_t* buffer, size_t size); + int64_t ElementAsInt64(size_t i) const; + uint64_t ElementAsUInt64(size_t i) const; + int32_t ElementAsInt32(size_t i) const; + bool ElementAsBool(size_t i) const; + double ElementAsDouble(size_t i) const; + float ElementAsFloat(size_t i) const; +}; + +// Return the number of operators in a subgraph tflite +uint32_t NumSubgraphOperators(const SubGraph* subgraph); +uint32_t NumSubgraphOperators(const Model* model, int subgraph_idx); + +// Converts a flatbuffer array to a TfLiteArray. +// TODO(b/188459715): These function convert a const input to a non-const via a +// const_cast. It is unclear exactly why this is required. +TfLiteIntArray* FlatBufferVectorToTfLiteTypeArray( + const flatbuffers::Vector* flatbuffer_array); +TfLiteFloatArray* FlatBufferVectorToTfLiteTypeArray( + const flatbuffers::Vector* flatbuffer_array); + +} // namespace tflite_micro + +#endif // THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h new file mode 100644 index 00000000..5b8c016d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h @@ -0,0 +1,57 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ + +#include +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/cppmath.h" +#include "tensorflow/lite/kernels/internal/max.h" +#include "tensorflow/lite/kernels/internal/min.h" + +namespace tflite_micro { +namespace ops { +namespace micro { + +// Returns the floating point value for a fused activation: +inline float ActivationValFloat(TfLiteFusedActivation act, float a) { + switch (act) { + case kTfLiteActNone: + return a; + case kTfLiteActRelu: + return TfLiteMax(0.0f, a); + case kTfLiteActReluN1To1: + return TfLiteMax(-1.0f, TfLiteMin(a, 1.0f)); + case kTfLiteActRelu6: + return TfLiteMax(0.0f, TfLiteMin(a, 6.0f)); + case kTfLiteActTanh: + return std::tanh(a); + case kTfLiteActSignBit: + return std::signbit(a); + case kTfLiteActSigmoid: + return 1.0f / (1.0f + std::exp(-a)); + } + return 0.0f; // To indicate an unsupported activation (i.e. when a new fused + // activation is added to the enum and not handled here). +} + +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h new file mode 100644 index 00000000..0f83c033 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h @@ -0,0 +1,68 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +extern const int kActivationsInputTensor; +extern const int kActivationsOutputTensor; + +struct ReluOpData { + ReluParams params; +}; + +struct Relu6OpData { + int8_t six_int8; + int8_t zero_int8; +}; + +template +void ReluQuantized(const ReluOpData& data, const RuntimeShape& input_shape, + const RuntimeShape& output_shape, const T* input_data, + T* output_data); + +template +void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output, + ReluOpData* data); + +void ReluFloat(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data); + +void Relu6Float(const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& output_shape, float* output_data); + +void Relu6Quantized(int8_t lower, int8_t upper, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& output_shape, + int8_t* output_data); + +void Relu6Quantized(int16_t lower, int16_t upper, const RuntimeShape& input_shape, + const int16_t* input_data, const RuntimeShape& output_shape, + int16_t* output_data); + +TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h new file mode 100644 index 00000000..201ec9f8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h @@ -0,0 +1,78 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +extern const int kAddInputTensor1; +extern const int kAddInputTensor2; +extern const int kAddOutputTensor; + +struct OpDataAdd { + bool requires_broadcast; + + // These fields are used in both the general 8-bit -> 8bit quantized path, + // and the special 16-bit -> 16bit quantized path + int input1_shift; + int input2_shift; + int32_t output_activation_min; + int32_t output_activation_max; + + // These fields are used only in the general 8-bit -> 8bit quantized path + int32_t input1_multiplier; + int32_t input2_multiplier; + int32_t output_multiplier; + int output_shift; + int left_shift; + int32_t input1_offset; + int32_t input2_offset; + int32_t output_offset; + + // Used only for float evals: + float output_activation_min_f32; + float output_activation_max_f32; +}; + +TfLiteStatus CalculateOpDataAdd(TfLiteContext* context, TfLiteAddParams* params, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteTensor* output, OpDataAdd* data); + +TfLiteStatus AddPrepare(TfLiteContext* context, TfLiteNode* node); + +// Generic must define registration function. +TFLMRegistration Register_ADD(); + +#if defined(CMSIS_NN) +TFLMRegistration Register_ADD_INT8(); + +TFLMRegistration Register_ADD_INT16(); +#else +// Fallback registration +inline TFLMRegistration Register_ADD_INT8() { return Register_ADD(); } + +inline TFLMRegistration Register_ADD_INT16() { return Register_ADD(); } +#endif +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h new file mode 100644 index 00000000..95fb2b2d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h @@ -0,0 +1,141 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "mli_api.h" // NOLINT + +namespace tflite_micro { + +// Convolution specialized function. +typedef mli_status (*conv_func_ptr)(const mli_tensor* /*in*/, + const mli_tensor* /*weights*/, + const mli_tensor* /*bias*/, + const mli_conv2d_cfg* /*cfg*/, + mli_tensor* /*out*/); + +#ifdef MLI_2_0 +conv_func_ptr __attribute__((weak)) +mli_krn_conv2d_hwcn(const mli_tensor* weights) { + int filter_w = weights->shape[KRNL_W_DIM_HWCN]; + int filter_h = weights->shape[KRNL_H_DIM_HWCN]; + + if (filter_w == 1 && filter_h == 1) { + return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k1x1; + } else if (filter_w == 3 && filter_h == 3) { + return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k3x3; + } else if (filter_w == 5 && filter_h == 5) { + return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k5x5; + } else { + return mli_krn_conv2d_hwcn_sa8_sa8_sa32; + } +} +#else +conv_func_ptr __attribute__((weak)) +mli_krn_conv2d_hwcn(const mli_tensor* weights, const mli_conv2d_cfg* cfg) { + return mli_krn_conv2d_nhwc_sa8_sa8_sa32; +} +#endif + +// Depthwise convolution specialized function. +typedef mli_status (*depthwise_func_ptr)(const mli_tensor* /*in*/, + const mli_tensor* /*weights*/, + const mli_tensor* /*bias*/, + const mli_conv2d_cfg* /*cfg*/, + mli_tensor* /*out*/); + +#ifdef MLI_2_0 +depthwise_func_ptr __attribute__((weak)) +mli_krn_depthwise_conv2d(const mli_tensor* weights) { + int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N]; + int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N]; + + if (filter_w == 3 && filter_h == 3) { + return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k3x3; + } else if (filter_w == 5 && filter_h == 5) { + return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k5x5; + } else { + return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32; + } +} +#else +depthwise_func_ptr __attribute__((weak)) +mli_krn_depthwise_conv2d(const mli_tensor* weights, const mli_conv2d_cfg* cfg) { + return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32; +} +#endif + +#ifdef MLI_2_0 +depthwise_func_ptr __attribute__((weak)) +mli_krn_group_conv2d(const mli_tensor* weights) { + int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N]; + int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N]; + + if (filter_w == 3 && filter_h == 3) { + return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32_k3x3; + } else if (filter_w == 5 && filter_h == 5) { + return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32_k5x5; + } else { + return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32; + } +} +#endif + +// Pooling specialized functions. +typedef mli_status (*pooling_func_ptr)(const mli_tensor* /*in*/, + const mli_pool_cfg* /*cfg*/, + mli_tensor* /*out*/); + +#ifdef MLI_2_0 +pooling_func_ptr __attribute__((weak)) +mli_krn_avepool(const mli_pool_cfg* cfg) { + int filter_w = cfg->kernel_width; + int filter_h = cfg->kernel_height; + + if (filter_w == 2 && filter_h == 2) { + return mli_krn_avepool_hwc_sa8_k2x2; + } else if (filter_w == 3 && filter_h == 3) { + return mli_krn_avepool_hwc_sa8_k3x3; + } else { + return mli_krn_avepool_hwc_sa8; + } +} +#else +pooling_func_ptr __attribute__((weak)) +mli_krn_avepool(const mli_pool_cfg* cfg) { + return mli_krn_avepool_hwc_sa8; +} +#endif + +#ifdef MLI_2_0 +pooling_func_ptr __attribute__((weak)) +mli_krn_maxpool(const mli_pool_cfg* cfg) { + int filter_w = cfg->kernel_width; + int filter_h = cfg->kernel_height; + + if (filter_w == 2 && filter_h == 2) { + return mli_krn_maxpool_hwc_sa8_k2x2; + } else if (filter_w == 3 && filter_h == 3) { + return mli_krn_maxpool_hwc_sa8_k3x3; + } else { + return mli_krn_maxpool_hwc_sa8; + } +} +#else +pooling_func_ptr __attribute__((weak)) +mli_krn_maxpool(const mli_pool_cfg* cfg) { + return mli_krn_maxpool_hwc_sa8; +} +#endif + +} // namespace tflite_micro \ No newline at end of file diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h new file mode 100644 index 00000000..1977b2e2 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h @@ -0,0 +1,75 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_INTERFACE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_INTERFACE_H_ + +#include "mli_api.h" // NOLINT +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +namespace tflite_micro { +namespace ops { +namespace micro { + +// Abstracts access to mli_tensor fields to use different versions of MLI +// Library (1.x and 2.x) +// Example: +// ops::micro::MliTensorInterface mli_in = +// ops::micro::MliTensorInterface(static_cast( +// context->AllocatePersistentBuffer(context, sizeof(mli_tensor)))); + +class MliTensorInterface { + public: + // Make sure that lifetime of MliTensorInterface instance isn't bigger than + // related mli_tensor. + MliTensorInterface(mli_tensor* tensor) : tensor_(tensor){}; + MliTensorInterface() = default; + ~MliTensorInterface() = default; + + template + T* Data(); + template + T Scale(); + template + T ZeroPoint(); + template + T ScaleFracBits(); + mli_tensor* MliTensor(); + const mli_tensor* MliTensor() const; + int32_t* Dim(); + uint32_t* Rank(); + uint32_t* Shape(); + const uint32_t* Shape() const; + const uint32_t* DataCapacity() const; + uint32_t* ScaleCapacity(); + mli_element_type* ElType(); + uint32_t* ScaleFracBitsCapacity(); + int32_t* MemStride(); + uint32_t* ZeroPointCapacity(); + + template + void SetData(T* data, uint32_t capacity) const; + void SetScale(float fscale); + void SetScalePerChannel(float* fscale, const int num_channels); + void SetElType(TfLiteType type); + + private: + mli_tensor* tensor_; +}; + +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h new file mode 100644 index 00000000..f92eb98a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h @@ -0,0 +1,56 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ + +#include "mli_api.h" // NOLINT +namespace tflite_micro { +namespace ops { +namespace micro { + +class TensorSlicer { + public: + TensorSlicer(const mli_tensor* full_tensor, int slice_dim, int slice_size, + int padding_pre = 0, int padding_post = 0, int overlap = 0, + bool interleave_mode = false); + ~TensorSlicer() = default; + + void Next(); + bool Done(); + int GetPaddingPre(); + int GetPaddingPost(); + + mli_tensor* Sub(); + + // Default constructor is deleted + TensorSlicer() = delete; + + private: + const mli_tensor* full_tensor_; + mli_tensor sub_tensor_; + mli_sub_tensor_cfg sub_cfg_; + bool done_; + int sliceDim_; + int pad_pre_, pad_post_, overlap_; + int actual_padding_pre, actual_padding_post; + + void ComputeSubTensor(); +}; + +} // namespace micro +} // namespace ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h new file mode 100644 index 00000000..67c84e93 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h @@ -0,0 +1,310 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ + +#include "mli_api.h" // NOLINT +#include "mli_interface.h" +#include "tensorflow/lite/kernels/internal/common.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/micro/kernels/kernel_util.h" +#include "tensorflow/lite/micro/micro_log.h" + +#define KRNL_C_DIM_NHWC 0 // output channels + +namespace tflite_micro { +namespace ops { +namespace micro { + +inline void ConvertToMliTensorData(const TfLiteTensor* tfT, + MliTensorInterface* mliT, + bool is_bias_tensor) { + // Data is NULL until MliTensorAttachBuffer is called. + mliT->SetElType(tfT->type); + if (tfT->type == kTfLiteInt8) { + mliT->SetData(nullptr, tfT->bytes); + } else if (tfT->type == kTfLiteInt32) { + mliT->SetData(nullptr, tfT->bytes); + } else { + MicroPrintf("Wrong data type. Expected int8_t or int32_t."); + TFLITE_ABORT; + } + const int32_t dims_count = GetTensorShape(tfT).DimensionsCount(); + *mliT->Rank() = is_bias_tensor ? 1 : dims_count; + + int mli_tensor_memstride = 1; + if (is_bias_tensor) { + mliT->Shape()[0] = GetTensorShape(tfT).Dims(dims_count - 1); + mliT->MemStride()[0] = mli_tensor_memstride; + } else { + for (int i = dims_count - 1; i >= 0; --i) { + mliT->Shape()[i] = GetTensorShape(tfT).Dims(i); + mliT->MemStride()[i] = mli_tensor_memstride; + mli_tensor_memstride *= GetTensorShape(tfT).Dims(i); + } + } +} + +inline void ConvertToMliQuantParams(const TfLiteTensor* tfT, + MliTensorInterface* mliT) { + *mliT->Dim() = -1; +#ifdef MLI_2_0 + *mliT->ZeroPointCapacity() = 0; +#endif + *mliT->ZeroPoint() = tfT->params.zero_point; + float fscale = tfT->params.scale; + mliT->SetScale(fscale); +} + +inline void ConvertToMliQuantParamsPerChannel(const TfLiteTensor* tfT, + MliTensorInterface* mliT, + bool is_bias_tensor) { + // mli tensor scale and zero_point arrays should be allocated at this point +#ifdef MLI_2_0 + TFLITE_DCHECK_NE(*mliT->Scale(), 0); + TFLITE_DCHECK_NE(*mliT->ZeroPoint(), 0); +#else + TFLITE_DCHECK_NE(*mliT->Scale(), 0); + TFLITE_DCHECK_NE(*mliT->ZeroPoint(), 0); +#endif + + // get per channel quantization parameters + const auto* affine_quantization = + reinterpret_cast(tfT->quantization.params); + int32_t quantized_dimension = + is_bias_tensor ? 0 : affine_quantization->quantized_dimension; + const int num_channels = mliT->Shape()[quantized_dimension]; + + *mliT->Dim() = quantized_dimension; + + // set capacities +#ifdef MLI_2_0 + *mliT->ScaleFracBitsCapacity() = num_channels * sizeof(int8_t); + *mliT->ScaleCapacity() = num_channels * sizeof(int16_t); + *mliT->ZeroPointCapacity() = num_channels * sizeof(int16_t); +#endif + float* fscale = affine_quantization->scale->data; + mliT->SetScalePerChannel(fscale, num_channels); + +#ifdef MLI_2_0 + int16_t* zero_point = *mliT->ZeroPoint(); + for (int i = 0; i < num_channels; i++) { + zero_point[i] = tfT->params.zero_point; + } +#endif +} + +template +inline void MliTensorAttachBuffer(const TfLiteEvalTensor*, + const MliTensorInterface*); + +template <> +inline void MliTensorAttachBuffer(const TfLiteEvalTensor* tfT, + const MliTensorInterface* mliT) { + // "const_cast" here used to attach const data buffer to the initially + // non-const mli_tensor. This is required by current implementation of MLI + // backend and planned for redesign due to this and some other aspects. + mliT->SetData( + const_cast(tflite_micro::micro::GetTensorData(tfT)), + *mliT->DataCapacity()); +} + +template <> +inline void MliTensorAttachBuffer(const TfLiteEvalTensor* tfT, + const MliTensorInterface* mliT) { + // "const_cast" here used to attach const data buffer to the initially + // non-const mli_tensor. This is required by current implementation of MLI + // backend and planned for redesign due to this and some other aspects. + mliT->SetData( + const_cast(tflite_micro::micro::GetTensorData(tfT)), + *mliT->DataCapacity()); +} + +inline void ConvertToMliTensor(const TfLiteTensor* tfT, + MliTensorInterface* mliT) { + ConvertToMliTensorData(tfT, mliT, false); + ConvertToMliQuantParams(tfT, mliT); +} + +inline void ConvertToMliTensorPerChannel(const TfLiteTensor* tfT, + MliTensorInterface* mliT, + bool is_bias_tensor) { + ConvertToMliTensorData(tfT, mliT, is_bias_tensor); + ConvertToMliQuantParamsPerChannel(tfT, mliT, is_bias_tensor); +} + +inline void PrepareLocalTensor(mli_tensor* tensor, mli_tensor* tensor_local) { +#ifdef MLI_2_0 + int8_t* local_data = tensor_local->data.mem.pi8; + *tensor_local = *tensor; + tensor_local->data.mem.pi8 = local_data; +#else + int8_t* local_data = static_cast(tensor_local->data); + *tensor_local = *tensor; + tensor_local->data = local_data; +#endif +} + +inline void AdjustBiasTensor(MliTensorInterface* bias, MliTensorInterface* in, + MliTensorInterface* weights) { + int32_t quantized_dimension = *bias->Dim(); + const int num_channels = + quantized_dimension < 0 ? 1 : bias->Shape()[quantized_dimension]; + for (int i = 0; i < num_channels; i++) { + int32_t adjusted_bias_scale = + (*in->Scale()) * (*weights->Scale())[i]; + int in_shift = *in->ScaleFracBits(); + int w_shift = (*weights->ScaleFracBits())[i]; + int b_shift = (*bias->ScaleFracBits())[i]; + int bias_shift = in_shift + w_shift - b_shift; + (*bias->Scale())[i] = + (int16_t)(adjusted_bias_scale >> bias_shift); + } +} + +#ifdef MLI_2_0_KRNL_TEST +// Reorder an array according to given indexes. If backward is true, order of +// index array must be reversed. +inline static void reorder(uint32_t* arr, const uint8_t index[], + bool backward) { + uint32_t temp[MLI_MAX_RANK]; + for (int8_t i = 0; i < MLI_MAX_RANK; i++) { + if (backward) + temp[index[i]] = arr[i]; + else + temp[i] = arr[index[i]]; + } + for (int8_t i = 0; i < MLI_MAX_RANK; i++) { + arr[i] = temp[i]; + } +} + +// Change shape of mli tensor and recalculate mem strides. +inline void change_shape(mli_tensor* mliT, const uint8_t dim_order[]) { + reorder(mliT->shape, dim_order, false); + + // Calculate strides for new layout + int mli_tensor_memstride = 1; + for (int shape_idx = mliT->rank - 1; shape_idx >= 0; --shape_idx) { + mliT->mem_stride[shape_idx] = mli_tensor_memstride; + mli_tensor_memstride *= mliT->shape[shape_idx]; + } +} + +inline void permute_weights(const mli_tensor* weights_src, + const mli_permute_cfg* permute_cfg, + mli_tensor* weights_dst, + mli_data_container* buffer_data) { + mli_tensor buffer = {}; + buffer.el_params = weights_dst->el_params; + buffer.data = *buffer_data; + // Compare weights tensor size and avaliable buffer capacity. + int buffer_size = buffer_data->capacity; + int weights_size = mli_hlp_count_elem_num(weights_src, 0) * + mli_hlp_tensor_element_size(weights_src); + + // Need to change shape of distanation weights buffer according to permute + // dimensions order to calculate slice sizes + change_shape(weights_dst, permute_cfg->perm_dim); + + if (buffer_size >= weights_size) { + mli_mov_cfg_t copy_config; + mli_mov_cfg_for_copy(©_config); + mli_mov_tensor_sync(weights_src, ©_config, &buffer); + mli_krn_permute_sa8(&buffer, permute_cfg, weights_dst); + } else { + // Weights shape is NHWC and output (buffer) shape is HWC where N_w = C_o. + // Buffer size (H_o * W_o) must be more or equal then the weights size (H_w + // * W_w * C_w). So, this is the reason, why buffer size (output tensor) is + // divided by channel shape. + uint32_t slice_size = buffer_size / weights_src->shape[KRNL_C_DIM_NHWC]; + + mli_mov_cfg_t copy_config = {}; + uint32_t src_offsets[] = {0, 0, 0, 0}; + uint32_t src_sizes[] = {0, 0, 0, 0}; + int dst_mem_stride[] = {0, 0, 0, 0}; + + mli_tensor weights_dst_sub_tensor; + mli_sub_tensor_cfg sub_tensor_cfg = {}; + sub_tensor_cfg.sub_tensor_rank = weights_src->rank; + + // Calculate dimensions for slice accroding to buffer capacity. + // Now, after calling change_shape() function, dst weights buffer has the + // MLI layout (HWCN). This means, the innermost dimension (N) of dst weights + // tensor is equal to the innermost dimension of output tensor (N). + sub_tensor_cfg.size[weights_dst->rank - 1] = + src_sizes[weights_dst->rank - 1] = weights_src->shape[KRNL_C_DIM_NHWC]; + // Now need to calculate other shapes for weights slice. Total slice size is + // H*W*C*N, so to calculate sizes for each axis, avaliable slice size is + // divided by shape for each axis. + uint32_t slice_size_left = slice_size; + for (uint32_t i = 0; i < weights_dst->rank - 1; i++) { + sub_tensor_cfg.size[i] = src_sizes[i] = + slice_size_left / weights_dst->shape[i] > 0 ? weights_dst->shape[i] + : slice_size_left; + slice_size_left /= weights_dst->shape[i]; + slice_size_left = slice_size_left > 0 ? slice_size_left : 1; + } + // Need to reorder src tensor sizes because it is still in TFLM format + // (NHWC) and src_sizes array calculated as (HWCN). + reorder(src_sizes, permute_cfg->perm_dim, true); + + sub_tensor_cfg.offset[KRNL_C_DIM_HWCN] = src_offsets[KRNL_H_DIM_HWCN] = 0; + sub_tensor_cfg.offset[KRNL_H_DIM_HWCN] = src_offsets[KRNL_W_DIM_HWCN] = 0; + sub_tensor_cfg.offset[KRNL_W_DIM_HWCN] = src_offsets[KRNL_D_DIM_HWCN] = 0; + sub_tensor_cfg.offset[KRNL_D_DIM_HWCN] = src_offsets[KRNL_C_DIM_HWCN] = 0; + do { + do { + do { + do { + mli_mov_cfg_for_slice(©_config, (int*)src_offsets, + (int*)src_sizes, dst_mem_stride); + mli_mov_tensor_sync(weights_src, ©_config, &buffer); + + mli_hlp_create_subtensor(weights_dst, &sub_tensor_cfg, + &weights_dst_sub_tensor); + mli_krn_permute_sa8(&buffer, permute_cfg, &weights_dst_sub_tensor); + + // For each axis, it is necessary to recalculate the offsets and + // slice sizes. + sub_tensor_cfg.offset[2] = src_offsets[3] += src_sizes[3]; + src_sizes[3] = + std::min(src_sizes[3], weights_src->shape[3] - src_offsets[3]); + } while (src_offsets[3] < weights_src->shape[3]); + + sub_tensor_cfg.offset[1] = src_offsets[2] += src_sizes[2]; + src_sizes[2] = + std::min(src_sizes[2], weights_src->shape[2] - src_offsets[2]); + } while (src_offsets[2] < weights_src->shape[2]); + + sub_tensor_cfg.offset[0] = src_offsets[1] += src_sizes[1]; + src_sizes[1] = + std::min(src_sizes[1], weights_src->shape[1] - src_offsets[1]); + } while (src_offsets[1] < weights_src->shape[1]); + + sub_tensor_cfg.offset[3] = src_offsets[0] += src_sizes[0]; + src_sizes[0] = + std::min(src_sizes[0], weights_src->shape[0] - src_offsets[0]); + } while (src_offsets[0] < weights_src->shape[0]); + } +} +#endif + +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h new file mode 100644 index 00000000..fd14cf39 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h @@ -0,0 +1,145 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ +#define TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ + +#include "mli_api.h" // NOLINT +#include "mli_interface.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { +namespace ops { +namespace micro { + +/** + * @brief Function to allocate scratch buffers for the convolution tensors + * + * @detail This function will update the data pointers in the 4 tensors with + * pointers to scratch buffers in fast local memory. + * + * @param context [I] pointer to TfLite context (needed for error handling) + * @param in [IO] pointer to the input tensor + * @param weights [IO] pointer to the weights tensor + * @param bias [IO] pointer to the bias tensor + * @param output [IO] pointer to the output tensor + * + * @return Tf Lite status code + */ +TfLiteStatus get_arc_scratch_buffer_for_conv_tensors( + TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* weights, + MliTensorInterface* bias, MliTensorInterface* out); + +/** + * @brief Function to allocate scratch buffers for pooling kernels with only + * input and output buffers + * + * @detail This function will update the data pointers in the 2 tensors with + * pointers to scratch buffers in fast local memory. + * + * @param context [I] pointer to TfLite context (needed for error handling) + * @param in [IO] pointer to the input tensor + * @param output [IO] pointer to the output tensor + * + * @return Tf Lite status code + */ +TfLiteStatus get_arc_scratch_buffer_for_pooling_tensors( + TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* out); + +/** + * @brief Function to allocate scratch buffers for the fully connect tensors + * + * @detail This function will update the data pointers in the 4 tensors with + * pointers to scratch buffers in fast local memory. + * + * @param context [I] pointer to TfLite context (needed for error handling) + * @param in [IO] pointer to the input tensor + * @param weights [IO] pointer to the weights tensor + * @param bias [IO] pointer to the bias tensor + * @param output [IO] pointer to the output tensor + * + * @return Tf Lite status code + */ +TfLiteStatus get_arc_scratch_buffer_for_fully_connect_tensors( + TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* weights, + MliTensorInterface* bias, MliTensorInterface* out); + +/** + * @brief Function to allocate scratch buffers for the eltwise function tensors + * + * @detail This function will update the data pointers in the 3 tensors with + * pointers to scratch buffers in fast local memory. + * + * @param context [I] pointer to TfLite context (needed for error handling) + * @param in1 [IO] pointer to the first input tensor + * @param in2 [IO] pointer to the second input tensor + * @param output [IO] pointer to the output tensor + * + * @return Tf Lite status code + */ +TfLiteStatus get_arc_scratch_buffer_for_eltwise_tensors( + TfLiteContext* context, MliTensorInterface* in1, MliTensorInterface* in2, + MliTensorInterface* out); + +/** + * @brief Function to calculate slice size for io tensors + * + * @detail This function will calculate the slice size in the height dimension + * for input and output tensors. it takes into account the kernel size and the + * padding. the function will look at the capacity filed in the in and out + * tensor to determine the available buffersize. + * + * @param in [I] pointer to the input tensor + * @param out [I] pointer to the output tensor + * @param kernelHeight [I] size of the kernel in height dimension + * @param strideHeight [I] input stride in height dimension + * @param padding_top [I] number of lines with zeros at the top + * @param padding_bot [I] number of lines with zeros at the bottom + * @param inSliceHeight [O] slice size in height dimension for the input + * tensor + * @param outSliceHeight [O] slice size in height dimension for the output + * tensor + * + * @return Tf Lite status code + */ +TfLiteStatus arc_scratch_buffer_calc_slice_size_io( + const MliTensorInterface* in, const MliTensorInterface* out, + const int kernelHeight, const int strideHeight, const int padding_top, + const int padding_bot, int* in_slice_height, int* out_slice_height); + +/** + * @brief Function to calculate slice size for weight slicing + * + * @detail This function will calculate the slice size in the output channel + * dimension for weight and bias tensors. the function will look at the capacity + * filed in the weights and bias tensor to determine the available buffersize. + * + * @param weights [I] pointer to the input tensor + * @param bias [I] pointer to the output tensor + * @param weightOutChDimension [I] dimension of the output channels in the + * weights tensor + * @param sliceChannels [O] slice size in output channel dimension + * + * @return Tf Lite status code + */ +TfLiteStatus arc_scratch_buffer_calc_slice_size_weights( + const MliTensorInterface* weights, const MliTensorInterface* bias, + const int weight_out_ch_dimension, int* slice_channels); + +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h new file mode 100644 index 00000000..0e5dd02b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h @@ -0,0 +1,78 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ +#define TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ + +#include "mli_api.h" // NOLINT +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { +namespace ops { +namespace micro { + +void init_arc_scratch_buffers(void); +void* get_arc_scratch_buffer(int size); // Function to assign fast memory + // from one of 3 scratch buffers. + +void get_arc_scratch_buffer_max_size(int* size); +void get_arc_scratch_buffer_two_max_sizes(int* size1, int* size2); + +static inline bool inside_arc_dccm(void* p) { +#if core_config_dccm_present + return ((unsigned)p >= core_config_dccm_base) && + ((unsigned)p < core_config_dccm_base + core_config_dccm_size); +#else + return false; +#endif +} + +static inline bool inside_arc_xccm(void* p) { +#if core_config_xy + return ((unsigned)p >= core_config_xy_x_base) && + ((unsigned)p < core_config_xy_x_base + core_config_xy_size); +#else + return false; +#endif +} + +static inline bool inside_arc_yccm(void* p) { +#if core_config_xy_size + return ((unsigned)p >= core_config_xy_y_base) && + ((unsigned)p < core_config_xy_y_base + core_config_xy_size); +#else + return false; +#endif +} + +static inline bool inside_arc_vccm(void* p) { +#if core_config_vec_mem_size + return ((unsigned)p >= core_config_vec_mem_base) && + ((unsigned)p < core_config_vec_mem_base + core_config_vec_mem_size); +#else + return false; +#endif +} + +static inline bool inside_arc_ccm(void* p) { + return inside_arc_dccm(p) || inside_arc_xccm(p) || inside_arc_yccm(p) || + inside_arc_vccm(p); +} + +} // namespace micro +} // namespace ops +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h new file mode 100644 index 00000000..dd2dcadd --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h @@ -0,0 +1,24 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_COMMON_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_COMMON_H_ + +#if defined(CEVA_BX1) || defined(CEVA_SP500) +extern int32_t CEVA_TFLM_KERNELS_SCRATCH[]; +extern int32_t CEVA_TFLM_KERNELS_SCRATCH_SIZE_VAL; +#endif + +#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h new file mode 100644 index 00000000..2b404278 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h @@ -0,0 +1,613 @@ + +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// API header for CEVA TFLM optimized kernel library + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ + +#include "tensorflow/lite/micro/kernels/ceva/types.h" + +#if defined(__cplusplus) +extern "C" { +#endif /* __cplusplus */ + +void CEVA_TFLM_ResizeNearestNeighbor_float32( + const bool align_corners, int32_t output_height, int32_t output_width, + int32_t row_offset, int32_t input_height, int32_t input_width, + int32_t col_offset, int32_t depth, const int32_t* input_ptr, + int32_t* output_ptr, const bool half_pixel_centers, int32_t* scratch); +void CEVA_TFLM_ResizeNearestNeighbor_int8( + const bool align_corners, int32_t output_height, int32_t output_width, + int32_t row_offset, int32_t input_height, int32_t input_width, + int32_t col_offset, int32_t depth, const int8_t* input_ptr, + int8_t* output_ptr, const bool half_pixel_centers, int32_t* scratch); + +void CEVA_TFLM_Abs_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Sqrt_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Rsqrt_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Square_Float32(const float* input_data, float* output_data, + int flat_size); + +void CEVA_TFLM_Cos_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Sin_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Tanh_Float32(const float* input_data, float* output_data, + int flat_size); + +void CEVA_TFLM_Sigmoid_Float32(const float* input_data, float* output_data, + int flat_size); +void CEVA_TFLM_Log_Float32(const float* input_data, float* output_data, + int flat_size); + +void CEVA_TFLM_LogicalNot(const bool* input_data, bool* output_data, + int flat_size); + +void CEVA_TFLM_AffineQuantize_Int8(const float_32* input_data, + int8_t* output_data, int flat_size, + float_32 scale, int zero_point); + +void CEVA_TFLM_Softmax_Float32(const float* input_data, float* output_data, + const float beta, const int depth); + +void CEVA_TFLM_Neg_Float32(const float_32* input_data, float_32* output_data, + const int flat_size); + +void CEVA_TFLM_RoundToNearest_asm(const float* input_arr, float* output_arr, + const int size); +float RoundToNearest(float value); + +void CEVA_TFLM_Round_float32(const float* input_data, float* output_data, + const int flat_size); + +void CEVA_TFLM_Softmax_Int8(const int8_t* input_data, int8_t* output_data, + const int32_t input_beta_multiplier, + const int32_t input_beta_left_shift, + const int32_t depth, void* scratch); + +void CEVA_TFLM_Min_Max_Float32(const float* input_data, + const float float_activation_min, + const float float_activation_max, + const int flat_size, float* output_data); + +void CEVA_TFLM_Add_Float32(const void* params_inp, const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastAdd4DSlow_Float32(const void* params_inp, + const float* input1_data, + const float* input2_data, + float* output_data, const int* Dims, + const int* desc1, const int* desc2); + +void CEVA_TFLM_BroadcastSubSlow_Float32( + const void* params_inp, const float* input1_data, const float* input2_data, + float* output_data, const int* strides1, const int* strides2, + const int* output_strides, const int* output_extents); + +void CEVA_TFLM_BroadcastSubSlow_Float32_loop( + const void* params_inp, const float* input1_data, const float* input2_data, + float* output_data, const int* output_extents, const int* strides1, + const int* strides2, const int* output_strides); + +void CEVA_TFLM_SubWithActivation_Float32(const void* params_inp, + const float* input1_data, + const float* input2_data, + float* output_data, + const int flat_size); + +void CEVA_TFLM_MaximumBroadcastSlow_Float32( + const float* input1_data, const float* input2_data, float* output_data, + const int* strides1, const int* strides2, const int* output_strides, + const int* output_extents); +void CEVA_TFLM_MinimumBroadcastSlow_Float32( + const float* input1_data, const float* input2_data, float* output_data, + const int* strides1, const int* strides2, const int* output_strides, + const int* output_extents); + +void CEVA_TFLM_Maximum_Float32(const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); +void CEVA_TFLM_Minimum_Float32(const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); +void CEVA_TFLM_Maximum_Float32_asm(const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); +void CEVA_TFLM_Minimum_Float32_asm(const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); +void CEVA_TFLM_DepthwiseConv_Float32( + // const DepthwiseParams& params, + // const int batches, // always 1 + const int stride_width, const int stride_height, const int pad_width, + const int pad_height, const int depth_multiplier, const int input_height, + const int input_width, const int input_depth, const float* input_data, + const int filter_height, const int filter_width, const int filter_depth, + const float* filter_data, const float* bias_data, const int output_height, + const int output_width, const int output_depth, float* output_data, + const int dilation_width_factor, const int dilation_height_factor, + const float output_activation_min, const float output_activation_max + +); +void CEVA_TFLM_DepthwiseConvPerChannel_int8( + const int stride_width, const int stride_height, const int pad_width, + const int pad_height, const int depth_multiplier_, + const int32_t input_offset_, const int32_t output_offset, + const int32_t* output_multiplier, const int32_t* output_shift, + const int input_height, const int input_width_, const int input_depth_, + const int8_t* input_data, const int filter_height, const int filter_width, + const int filter_depth_, const int8_t* filter_data, + const int32_t* bias_data, const int output_height, const int output_width, + const int output_depth, + + int8_t* output_data, int32_t* scratch_ + + , + const int dilation_width_factor_, const int dilation_height_factor, + const int32_t output_activation_min, const int32_t output_activation_max); + +void CEVA_TFLM_ConvPerChannel_Int8( + const int stride_width, const int stride_height, const int pad_width, + const int pad_height, // const int depth_multiplier, + const int32_t input_offset, const int32_t output_offset, + const int32_t* output_multiplier, const int32_t* output_shift, + const int input_height, const int input_width, const int input_depth_Dims3, + const int input_depth, const int8_t* input_data, const int filter_height, + const int filter_width, const int filter_depth, const int8_t* filter_data, + const int32_t* bias_data, const int output_height, const int output_width, + const int output_depth_Dims3, const int output_depth, int8_t* output_data, + int32_t* scratch, const int dilation_width_factor, + const int dilation_height_factor, const int32_t output_activation_min, + const int32_t output_activation_max); + +void CEVA_TFLM_Conv_Float32( + // const int batches, + const int stride_width, const int stride_height, const int pad_width, + const int pad_height, // const int depth_multiplier, + const int input_height, const int input_width, const int input_depth_Dims3, + const int input_depth, const float* input_data, const int filter_height, + const int filter_width, const int filter_depth, const float* filter_data, + const float* bias_data, const int output_height, const int output_width, + const int output_depth_Dims3, const int output_depth, float* output_data, + const int dilation_width_factor, const int dilation_height_factor, + const float output_activation_min, const float output_activation_max + +); + +/////////////////// +void CEVA_TFLM_MaximumBroadcastSlow_Int8( + const int8_t* input1_data, const int8_t* input2_data, int8_t* output_data, + const int* strides1, const int* strides2, const int* output_strides, + const int* output_extents); +void CEVA_TFLM_MinimumBroadcastSlow_Int8( + const int8_t* input1_data, const int8_t* input2_data, int8_t* output_data, + const int* strides1, const int* strides2, const int* output_strides, + const int* output_extents); + +void CEVA_TFLM_Maximum_Int8(const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); +void CEVA_TFLM_Minimum_Int8(const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastSubSlow_Int8( + const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, const int* strides1, + const int* strides2, const int* output_strides, const int* output_extents); + +void CEVA_TFLM_BroadcastSubSlow_Int8_loop( + const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, const int* output_extents, + const int* strides1, const int* strides2, const int* output_strides); + +void CEVA_TFLM_BroadcastAddSlow_Int8(const void* params_inp, + const int8_t* input1_data, + const int8_t* input2_data, + int8_t* output_data, const int* strides1, + const int* strides2, + const int* output_extents); + +void CEVA_TFLM_BroadcastAddSlow_Int8_loop( + const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, const int* output_extents, + const int* strides1, const int* strides2); + +void CEVA_TFLM_Sub_Int8(const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_Sub_Uint8(const void* params_inp, const uint8_t* input1_data, + const uint8_t* input2_data, uint8_t* output_data, + const int flat_size); + +void CEVA_TFLM_Add_Uint8(const void* params, const uint8_t* input1_data, + const uint8_t* input2_data, uint8_t* output_data, + const int flat_size); + +void CEVA_TFLM_Add_Int8(const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastAdd4DSlow_Uint8(const void* params, + const uint8_t* input1_data, + const uint8_t* input2_data, + uint8_t* output_data, const int* Dims, + const int* desc1, const int* desc2, + const int* dims_data); +void CEVA_TFLM_svdf_Float32(float_32* vector1_ptr, float_32* vector2_ptr, + int32_t num_units, int32_t memory_size_rank, + float_32* output_ptr_batch); +void CEVA_TFLM_svdf_Int8(int n_memory, const int8_t* matrix_ptr, + const int8_t* vector_in_batch_t, + int16_t* result_in_batch, int input_zp, int n_input, + int effective_scale_1_a, int effective_scale_1_b, + int n_filter, int* scratch); +void CEVA_TFLM_AffineQuantize_Int8(const float_32* input_data, + int8_t* output_data, int flat_size, + float_32 scale, int zero_point); + +// int32_t MultiplyByQuantizedMultiplier_t(int32_t x, int32_t +// quantized_multiplier, int shift); int32_t +// MultiplyByQuantizedMultiplier_t1(int32_t x, int32_t quantized_multiplier, int +// shift); + +void CEVA_TFLM_L2Normalization_Float32(const float* input_data, + float* output_data, float epsilon, + const int outer_size, const int depth); +void CEVA_TFLM_L2Normalization_Int8(int32_t input_zero_point, + int32_t outer_size, int32_t depth, + const int8_t* input_data, + int8_t* output_data); + +void CEVA_TFLM_prelu_Float32(const float* in1_data, const int32_t* in1_strides, + const float* in2_data, const int32_t* in2_strides, + float* out_data, const int32_t* out_strides, + const int32_t* dims); + +void CEVA_TFLM_prelu_Int8(const int8_t* in1_data, const int32_t* in1_strides, + const int8_t* alpha_data, + const int32_t* alpha_strides, int8_t* out_data, + const int32_t* out_strides, const int32_t* dims, + const int32_t* params); +void CEVA_TFLM_FullyConnected_Float32( + const void* params_inp, const int input_shape, const float* input_data, + const int weights_shape_DimensionsCount, const int* weights_shape_DimsData, + const float* weights_data, const int bias_shape, const float* bias_data, + const int output_shape_DimensionsCount, const int* output_shape_DimsData, + float* output_data); +void CEVA_TFLM_FullyConnected_int8( + const void* params_inp, const int input_shape, const int8_t* input_data, + const int filter_shape_DimensionsCount, const int* filter_shape_DimsData, + const int8_t* filter_data, const int bias_shape, const int32_t* bias_data, + const int output_shape_DimensionsCount, const int* output_shape_DimsData, + int8_t* output_data, int* scratch); + +void CEVA_TFLM_tanh_Int8(int32_t input_zero_point, int32_t input_range_radius, + int32_t input_multiplier, int32_t input_shift, + int32_t input_size, const int8_t* input_data, + int8_t* output_data); + +void CEVA_TFLM_Logistic_Int8(int32_t input_zero_point, + int32_t input_range_radius, + int32_t input_multiplier, int32_t input_left_shift, + int32_t input_size, const int8_t* input_data, + int8_t* output_data); + +void CEVA_TFLM_Tanh_float32(const float_32* input_data, float_32* output_data, + const int flat_size); +void CEVA_TFLM_Logistic_float32(const float_32* input_data, + float_32* output_data, const int flat_size); + +void CEVA_TFLM_PackImplLoop_float(const float* input_ptr, float* output_ptr, + int outer_size, int copy_size, + int step_vcount_copy_size); +void CEVA_TFLM_PackUnpackImplLoopInitSizes(int* const copy_size, + int* const outer_size, + const int* const outputDimsData, + const int dimensions, int axis); +void CEVA_TFLM_PackImplLoop_Int8(const int8_t* input_ptr, int8_t* output_ptr, + int outer_size, int copy_size, + int step_vcount_copy_size); +void CEVA_TFLM_UnpackImplLoop_float(const float* input_ptr, float* output_ptr, + int outer_size, int copy_size, + int step_vcount_copy_size); +void CEVA_TFLM_UnpackImplLoop_Int8(const int8_t* input_ptr, int8_t* output_ptr, + int outer_size, int copy_size, + int step_vcount_copy_size); + +void CEVA_TFLM_ComparisonEqual_Float32(const float* input1, const float* input2, + bool* output, const int32_t size); +void CEVA_TFLM_ComparisonNotEqual_Float32(const float* input1, + const float* input2, bool* output, + const int32_t size); +void CEVA_TFLM_ComparisonGreater_Float32(const float* input1, + const float* input2, bool* output, + const int32_t size); +void CEVA_TFLM_ComparisonGreaterEqual_Float32(const float* input1, + const float* input2, bool* output, + const int32_t size); +void CEVA_TFLM_ComparisonLess_Float32(const float* input1, const float* input2, + bool* output, const int32_t size); +void CEVA_TFLM_ComparisonLessEqual_Float32(const float* input1, + const float* input2, bool* output, + const int32_t size); + +void CEVA_TFLM_ComparisonEqual_Float32_Broadcast(const float* input1, + const float* input2, + bool* output, + const int32_t* dims, + const int32_t** op_param); + +void CEVA_TFLM_ComparisonNotEqual_Float32_Broadcast(const float* input1, + const float* input2, + bool* output, + const int32_t* dims, + const int32_t** op_param); + +void CEVA_TFLM_ComparisonGreater_Float32_Broadcast(const float* input1, + const float* input2, + bool* output, + const int32_t* dims, + const int32_t** op_param); +void CEVA_TFLM_ComparisonGreaterEqual_Float32_Broadcast( + const float* input1, const float* input2, bool* output, const int32_t* dims, + const int32_t** op_param); + +void CEVA_TFLM_ComparisonLess_Float32_Broadcast(const float* input1, + const float* input2, + bool* output, + const int32_t* dims, + const int32_t** op_param); + +void CEVA_TFLM_ComparisonLessEqual_Float32_Broadcast(const float* input1, + const float* input2, + bool* output, + const int32_t* dims, + const int32_t** op_param); + +void CEVA_TFLM_ComparisonEqual_Int8(const int8_t* input1, const int8_t* input2, + bool* output, const int32_t flatsize, + void* op_params); +void CEVA_TFLM_ComparisonNotEqual_Int8(const int8_t* input1, + const int8_t* input2, bool* output, + const int32_t flatsize, void* op_params); +void CEVA_TFLM_ComparisonGreater_Int8(const int8_t* input1, + const int8_t* input2, bool* output, + const int32_t flatsize, void* op_params); +void CEVA_TFLM_ComparisonGreaterEqual_Int8(const int8_t* input1, + const int8_t* input2, bool* output, + const int32_t flatsize, + void* op_params); +void CEVA_TFLM_ComparisonLess_Int8(const int8_t* input1, const int8_t* input2, + bool* output, const int32_t flatsize, + void* op_params); +void CEVA_TFLM_ComparisonLessEqual_Int8(const int8_t* input1, + const int8_t* input2, bool* output, + const int32_t flatsize, + void* op_params); + +void CEVA_TFLM_ComparisonEqual_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, + bool* output, const int32_t* dims, + void* op_params); +void CEVA_TFLM_ComparisonNotEqual_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, + bool* output, + const int32_t* dims, + void* op_params); +void CEVA_TFLM_ComparisonGreater_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, + bool* output, + const int32_t* dims, + void* op_params); +void CEVA_TFLM_ComparisonGreaterEqual_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, + bool* output, + const int32_t* dims, + void* op_params); +void CEVA_TFLM_ComparisonLess_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, bool* output, + const int32_t* dims, + void* op_params); +void CEVA_TFLM_ComparisonLessEqual_Int8_Broadcast(const int8_t* input1, + const int8_t* input2, + bool* output, + const int32_t* dims, + void* op_params); + +void CEVA_TFLM_Mul_Float32(const void* params_inp, const float* input1_data, + const float* input2_data, float* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastMul4DSlow_Float32(const void* params_inp, + const float* input1_data, + const float* input2_data, + float* output_data, const int* Dims, + const int* desc1, const int* desc2); + +void CEVA_TFLM_AveragePool_Float32(const void* params, const int* input_shape, + const float* input_data, + const int* output_shape, float* output_data); + +void CEVA_TFLM_AveragePool_Int8(const void* params_inp, const int* input_shape, + const int8_t* input_data, + const int* output_shape, int8_t* output_data); + +void CEVA_TFLM_AveragePool_Int8_Loop( + const int* input_shape, const int8_t* input_data, int8_t* output_data, + const int depth, int batch, int in_y, const int filter_y_start, + const int filter_y_end, const int in_x_origin, const int filter_x_start, + const int filter_x_end, int filter_count, int32_t quantized_activation_min, + int32_t quantized_activation_max, int indx_out); + +void CEVA_TFLM_MaxPool_Float32(const void* params_inp, const int* input_shape, + const float* input_data, const int* output_shape, + float* output_data); + +void CEVA_TFLM_MaxPool_Int8(const void* params_inp, const int* input_shape, + const int8_t* input_data, const int* output_shape, + int8_t* output_data); + +void CEVA_TFLM_MaxPool_Int8_Loop( + const int* input_shape, const int8_t* input_data, int8_t* output_data, + const int depth, int batch, int in_y, const int filter_y_start, + const int filter_y_end, const int in_x_origin, const int filter_x_start, + const int filter_x_end, int32_t quantized_activation_min, + int32_t quantized_activation_max, int indx_out); + +void CEVA_TFLM_Mul_Int8(const void* params_inp, const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastMul4DSlow_Int8(const void* params_inp, + const int8_t* input1_data, + const int8_t* input2_data, + int8_t* output_data, const int* Dims, + const int* desc1, const int* desc2); + +void CEVA_TFLM_Dequantize_Float32(const int8_t* input_data, + float_32* output_data, int flat_size, + float_32 scale, int zero_point); + +void CEVA_TFLM_Ceil_Float32(const float* input_data, float* output_data, + const int flat_size); + +void CEVA_TFLM_Logical_And_Int8(const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastLogicalAnd4DSlow_Int8(const int8_t* input1_data, + const int8_t* input2_data, + int8_t* output_data, + const int* Dims, const int* desc1, + const int* desc2); + +void CEVA_TFLM_Logical_Or_Int8(const int8_t* input1_data, + const int8_t* input2_data, int8_t* output_data, + const int flat_size); + +void CEVA_TFLM_BroadcastLogicalOr4DSlow_Int8(const int8_t* input1_data, + const int8_t* input2_data, + int8_t* output_data, + const int* Dims, const int* desc1, + const int* desc2); + +void CEVA_TFLM_SplitLoops_Float32(float** out_ptrs, const int* dataIndex, + const float* input_ptr, int outer_size, + int output_count, int copy_size); +void CEVA_TFLM_SplitLoops_int8(int8_t** out_ptrs, const int* dataIndex, + const int8_t* input_ptr, int outer_size, + int output_count, int copy_size); + +void CEVA_TFLM_Relu_Float32(const float* input_data, float* output_data, + const int flat_size); +void CEVA_TFLM_Relu6_Float32(const float* input_data, float* output_data, + const int flat_size); +void CEVA_TFLM_Relu_int8(const void* params, const int8_t* input_data, + int8_t* output_data, const int flat_size); +void CEVA_TFLM_Relu6_int8(const int8_t lower, const int8_t upper, + const int8_t* input_data, int8_t* output_data, + const int flat_size); +void CEVA_TFLM_Floor_float32(const float* input_data, float* output_data, + const int flat_size); + +void CEVA_TFLM_Concatenation_Float32(const void* params_inp, + const int** input_shape, + const float** input_data, + const int output_shape_DimensionsCount, + const int* output_shape_DimsData, + float* output_data); + +void CEVA_TFLM_Concatenation_int8(const void* params_inp, + const int** input_shape, + const int8_t** input_data, + const int output_shape_DimensionsCount, + const int* output_shape_DimsData, + int8_t* output_data); + +void CEVA_TFLM_Mean4D_Float32(const float* input_data, float* output_data, + const int* Dims, const int* Dims_inp, + const int* dims_data, const int* dims_data_inp); +bool CEVA_TFLM_Mean_Float32(const float* input_data, const int* input_dims, + const int input_num_dims, float* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis, + float* temp_sum); +void CEVA_TFLM_Mean_Float32_loop(float* temp_sum, float* output_data, + int num_elements_in_axis, size_t num_outputs); +void CEVA_TFLM_Mean4D_Int8(int32_t multiplier, int32_t shift, + const int8_t* input_data, int32_t input_zero_point, + int8_t* output_data, int32_t output_zero_point, + int* input_shape, int* output_shape); +bool CEVA_TFLM_Mean_Int8(const int8_t* input_data, const int* input_dims, + const int input_num_dims, int8_t* output_data, + const int* output_dims, const int output_num_dims, + const int* axis, const int num_axis_dimensions, + bool keep_dims, int* temp_index, int* resolved_axis, + int32_t* temp_sum); +void CEVA_TFLM_Mean_Int8_loop(int32_t* temp_sum, int8_t* output_data, + int num_elements_in_axis, size_t num_outputs); +void CEVA_TFLM_StridedSlice_Float32(void* op_params, + int unextended_input_shape_DimensionsCount, + int* unextended_input_shape_DimsData, + float* input_data, + + float* output_data); + +void CEVA_TFLM_StridedSlice_Float32(void* op_params, + int unextended_input_shape_DimensionsCount, + int* unextended_input_shape_DimsData, + float* input_data, float* output_data); + +void CEVA_TFLM_StridedSlice_loop_Float32(float* input_data, float* output_data, + void* params); + +void CEVA_TFLM_StridedSlice_int8(void* op_params, + int unextended_input_shape_DimensionsCount, + int* unextended_input_shape_DimsData, + int8_t* input_data, int8_t* output_data); + +void CEVA_TFLM_StridedSlice_loop_int8(int8_t* input_data, int8_t* output_data, + void* params); + +void CEVA_TFLM_Pad_Float32(void* op_params, int input_shape, int* output_shape, + const float* input_data, const float* pad_value_ptr, + float* output_data); + +void CEVA_TFLM_Pad_Int8(void* op_params, int input_shape, int* output_shape, + const int8_t* input_data, const int8_t* pad_value_ptr, + int8_t* output_data); + +int CEVA_TFLM_ReshapeOutput(int input_type, const int input_size, + const int* input_data, int output_type, + int* output_size, int* output_data, + int node_in_size); + +int CEVA_TFLM_EvalRashape(const int8_t* input, int8_t* output, + unsigned int N_cnt); + +#if defined(__cplusplus) +} +#endif /* __cplusplus */ + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h new file mode 100644 index 00000000..0d51e5ac --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h @@ -0,0 +1,115 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// MCPS measurement macros for CEVA optimized kernels + +#ifndef MCPS_MACROS_ +#define MCPS_MACROS_ + +#ifndef WIN32 +#include +#endif + +#ifdef MCPS_MEASUREMENT + +#ifdef STACK_MEASUREMENT +#if defined(__cplusplus) +extern "C" { +#endif /* __cplusplus */ +void CEVA_BX_Stack_Marking(const int32_t _count); +int32_t CEVA_BX_Stack_Measurement(const int32_t count); +#if defined(__cplusplus) +} +#endif /* __cplusplus */ +#endif + +#define MCPS_CALL_RET_VALUE 4 + +#ifdef STACK_MEASUREMENT +#define MCPS_VARIBLES \ + clock_t c1, c2; \ + int ClockCEVA, Constant_cycles; \ + int StackSize; \ + FILE* f_mcps_report; +#else +#define MCPS_VARIBLES \ + clock_t c1, c2; \ + int ClockCEVA, Constant_cycles; \ + FILE* f_mcps_report; +#endif +#define MCPS_OPEN_FILE f_mcps_report = fopen("mcps_report.txt", "at"); + +#define MCPS_CLOSE_FILE fclose(f_mcps_report); + +#ifdef STACK_MEASUREMENT +#define MCPS_START_CLOCK \ + CEVA_BX_Stack_Marking(0x800); \ + reset_clock(); \ + start_clock(); \ + c1 = clock(); \ + c2 = clock(); \ + Constant_cycles = c2 - c1; \ + c1 = clock(); + +#define MCPS_STOP_AND_LOG(...) \ + c2 = clock(); \ + ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ + StackSize = CEVA_BX_Stack_Measurement(0x800) * 4; \ + fprintf(f_mcps_report, __VA_ARGS__); \ + fprintf(f_mcps_report, ":cycles:%d:Stack:%d\r\n", ClockCEVA, StackSize); + +#else // STACK_MEASUREMENT +#define MCPS_START_CLOCK \ + reset_clock(); \ + start_clock(); \ + c1 = clock(); \ + c2 = clock(); \ + Constant_cycles = c2 - c1; \ + c1 = clock(); + +#define MCPS_STOP_AND_LOG(...) \ + c2 = clock(); \ + ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ + fprintf(f_mcps_report, __VA_ARGS__); \ + fprintf(f_mcps_report, ":cycles:%d\r\n", ClockCEVA); +#endif // STACK_MEASUREMENT + +#define MCPS_STOP_AND_PRINT(...) \ + c2 = clock(); \ + ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ + fprintf(stdout, __VA_ARGS__); \ + fprintf(stdout, ":cycles=%d\n", ClockCEVA); + +#define MCPS_START_ONE \ + MCPS_VARIBLES; \ + MCPS_OPEN_FILE; \ + MCPS_START_CLOCK; +#define MCPS_STOP_ONE(...) \ + MCPS_STOP_AND_LOG(__VA_ARGS__); \ + MCPS_CLOSE_FILE; + +#else +#define MCPS_VARIBLES +#define MCPS_OPEN_FILE +#define MCPS_START_CLOCK +#define MCPS_STOP_AND_LOG(...) +#define MCPS_STOP_AND_PRINT(...) +#define MCPS_CLOSE_FILE + +#define MCPS_START_ONE +#define MCPS_STOP_ONE(...) +#endif + +#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h new file mode 100644 index 00000000..9b766c0d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h @@ -0,0 +1,1286 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef CEVA_TYPES_H_ +#define CEVA_TYPES_H_ + +#include +#include +#include + +// typedef int8_t int8_t; +// typedef int16_t int16; +// typedef int32_t int32_t; +// typedef uint8_t uint8; +// typedef uint16_t uint16; +// typedef uint32_t uint32; + +typedef float float_32; +typedef unsigned long long uint64; +typedef long long int64; + +#if 1 +enum BroadcastableOpCategory { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + +#else +enum class BroadcastableOpCategory : uint8_t { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; +#endif + +struct ArithmeticParams_ceva { + // Shape dependent / common to data / op types. + uint8_t broadcast_category; // BroadcastableOpCategory broadcast_category; + // uint8_t inference params. + int32_t input1_offset; + int32_t input2_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // Add / Sub, not Mul, uint8_t inference params. + int left_shift; + int32_t input1_multiplier; + int input1_shift; + int32_t input2_multiplier; + int input2_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + + // Processed output dimensions. + // Let input "a" be the one that broadcasts in the faster-changing dimension. + // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and + // {b0, b1, b2, b3, b4}, + // broadcast_shape[4] = b0 = a0. + // broadcast_shape[3] = b1; a1 = 1. + // broadcast_shape[2] = b2 = a2. + // broadcast_shape[1] = a3; b3 = 1. + // broadcast_shape[0] = b4 = a4. + int broadcast_shape[5]; +}; + +struct SoftmaxParams_ceva { + // beta is not really used (not a Tensorflow parameter) and not implemented + // for LogSoftmax. + double beta; + // uint8_t inference params. Used even when beta defaults to 1.0. + int32_t input_multiplier; + int32_t input_left_shift; + // Reverse scaling is only used by LogSoftmax. + int32_t reverse_scaling_divisor; + int32_t reverse_scaling_right_shift; + int diff_min; + int32_t zero_point; + float scale; + float* table; + int16_t* exp_lut; + int16_t* one_over_one_plus_x_lut; + uint8_t* uint8_table1; + uint8_t* uint8_table2; +}; + +enum class FusedActivationFunctionType_ceva : uint8_t { + kNone, + kRelu6, + kRelu1, + kRelu +}; +enum class PaddingType_ceva : uint8_t { kNone, kSame, kValid }; + +struct PaddingValues_ceva { + int16_t width; + int16_t height; + // offset is used for calculating "remaining" padding, for example, `width` + // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is + // 1 + 1 = 2. + int16_t width_offset; + // Same as width_offset except it's over the height dimension. + int16_t height_offset; +}; + +struct StridedSliceParams_ceva { + int8_t start_indices_count; + int32_t start_indices[5]; + int8_t stop_indices_count; + int32_t stop_indices[5]; + int8_t strides_count; + int32_t strides[5]; + + int16_t begin_mask; + int16_t ellipsis_mask; + int16_t end_mask; + int16_t new_axis_mask; + int16_t shrink_axis_mask; +}; + +struct PoolParams_ceva { + FusedActivationFunctionType_ceva activation; + PaddingType_ceva padding_type; + PaddingValues_ceva padding_values; + int stride_height; + int stride_width; + int filter_height; + int filter_width; + // uint8, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +inline size_t ReducedOutputOffset(const int num_dims, const int* dims, + const int* index, const int num_axis, + const int* axis) { + if (num_dims == 0) { + return 0; + } + // TFLITE_DCHECK(dims != nullptr); + // TFLITE_DCHECK(index != nullptr); + size_t offset = 0; + for (int idx = 0; idx < num_dims; ++idx) { + // if we need to skip this axis + bool is_axis = false; + if (axis != nullptr) { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { + if (idx == axis[axis_idx]) { + is_axis = true; + break; + } + } + } + if (!is_axis) { + offset = offset * static_cast(dims[idx]) + + static_cast(index[idx]); + } + } + return offset; +} +inline bool NextIndex(const int num_dims, const int* dims, int* current) { + if (num_dims == 0) { + return false; + } + // TFLITE_DCHECK(dims != nullptr); + // TFLITE_DCHECK(current != nullptr); + int carry = 1; + for (int idx = num_dims - 1; idx >= 0; --idx) { + int current_val = current[idx] + carry; + // TFLITE_DCHECK_GE(dims[idx], current_val); + if (dims[idx] == current_val) { + current[idx] = 0; + } else { + current[idx] = current_val; + carry = 0; + break; + } + } + return (carry == 0); +} + +#if 0 +#include +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" + +namespace tflite_micro { + +enum class FusedActivationFunctionType : uint8_t { kNone, kRelu6, kRelu1, kRelu }; +enum class PaddingType : uint8_t { kNone, kSame, kValid }; + +struct PaddingValues { + int16 width; + int16 height; + // offset is used for calculating "remaining" padding, for example, `width` + // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is + // 1 + 1 = 2. + int16 width_offset; + // Same as width_offset except it's over the height dimension. + int16 height_offset; +}; + +// This enumeration allows for non-default formats for the weights array +// of a fully-connected operator, allowing the use of special optimized +// runtime paths. +enum class FullyConnectedWeightsFormat : uint8_t { + // Default format (flat 2D layout, the inner contiguous dimension + // is input_depth, the outer non-contiguous dimension is output_depth) + kDefault, + // Summary: optimized layout for fast CPU runtime implementation, + // aimed specifically at ARM CPUs at the moment, and specialized for + // 8-bit quantized layers. + // + // The use case we're concerned with here is: 8-bit quantization, + // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in + // a key application that drove this), very small batch size (e.g. 1 -- 4). + // + // Even with 8-bit quantization of weights, the performance of memory + // accesses to the weights can become the dominant issue when + // the batch size is small, so each weight value is used in only a few + // arithmetic ops, i.e. the fully-connected node has a low arithmetic + // intensity. The specific issues that arise are of three kinds: + // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory + // bound. That's the "good" issue to run into. + // (2) One may run into sub-optimal pre-fetching: the data hasn't been + // prefetched into the cache by the time we need it. + // (3) One may run into cache aliasing: multiple values that are + // pre-fetched, alias each other in the L1 cache (which typically + // has only 4-way set associativity in ARM CPUs) and thus evict + // each other before we get to using them. + // + // The point of this shuffling is to avoid issues (2) and (3) so that + // we get as fast as possible given only the hard constraint (1). + // This is achieved by turning the difficulty into a solution: the + // difficulty, that each value loaded from memory is used only in + // one kernel iteration, making this operation memory-intensive, hints at + // the solution, of shuffling the weights so that they are stored in the + // exact order as the kernel needs to load them, so that the memory + // accesses made by the kernel are trivial. This solves (2) because the + // trivial memory access pattern allows the CPU's automatic prefetching + // to perform very well (no need even for preload instructions), and this + // solves (3) because the values being loaded concurrently are now + // contiguous in the address space, thus don't alias each other in the cache. + // + // On ARM, we typically want our kernel to process a 4x16 block of weights + // at a time, because: + // - 16 is the number of bytes in a NEON register. + // - 4 is how many rows we need to handle concurrently in the kernel in + // order to have sufficient mutual independence of instructions to + // maximize arithmetic throughput. + // + // Finally, the 'int8_t' part in the name refers to the fact that this + // weights format has each weights value encoded as a signed int8_t value, + // even if the data type of the weights buffer is uint8_t. This is intended + // to save runtime kernels the effort to have to XOR the top bit of these + // bytes before using them in signed arithmetic, see this file for more + // explanations on the 'signed int8_t trick' in matrix multiplication kernels: + // + // tensorflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc + // + kShuffled4x16Int8, +}; + +// Quantization parameters, determining the mapping of quantized values +// to real values (i.e. determining how quantized values are mathematically +// interpreted). +// +// The correspondence is as follows: +// +// real_value = scale * (quantized_value - zero_point); +// +// In other words, zero_point designates which quantized value corresponds to +// the real 0 value, and scale designates the difference between the real values +// corresponding to consecutive quantized values differing by 1. +struct QuantizationParams { + int32_t zero_point = 0; + double scale = 0.0; +}; + +inline bool operator==(const QuantizationParams& qp1, + const QuantizationParams& qp2) { + return qp1.zero_point == qp2.zero_point && qp1.scale == qp2.scale; +} + +template +struct Dims { + int sizes[N]; + int strides[N]; +}; + +class RuntimeShape { + public: + // Shapes with dimensions up to 5 are stored directly in the structure, while + // larger shapes are separately allocated. + static constexpr int kMaxSmallSize = 5; + + RuntimeShape& operator=(RuntimeShape const&) = delete; + + RuntimeShape() : size_(0) {} + + explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { + if (dimensions_count > kMaxSmallSize) { +#ifdef TF_LITE_STATIC_MEMORY + TFLITE_CHECK(false && "No shape resizing supported on this platform"); +#else // TF_LITE_STATIC_MEMORY + dims_pointer_ = new int32_t[dimensions_count]; +#endif // TF_LITE_STATIC_MEMORY + } + } + + RuntimeShape(int shape_size, int32_t value) : size_(0) { + Resize(shape_size); + for (int i = 0; i < shape_size; ++i) { + SetDim(i, value); + } + } + + RuntimeShape(int dimensions_count, const int32_t* dims_data) : size_(0) { + ReplaceWith(dimensions_count, dims_data); + } + + RuntimeShape(const std::initializer_list init_list) : size_(0) { + BuildFrom(init_list); + } + + // Avoid using this constructor. We should be able to delete it when C++17 + // rolls out. + RuntimeShape(RuntimeShape const& other) : size_(other.DimensionsCount()) { + if (size_ > kMaxSmallSize) { + dims_pointer_ = new int32_t[size_]; + } + std::memcpy(DimsData(), other.DimsData(), sizeof(int32_t) * size_); + } + + bool operator==(const RuntimeShape& comp) const { + return this->size_ == comp.size_ && + std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32_t)) == 0; + } + + ~RuntimeShape() { + if (size_ > kMaxSmallSize) { +#ifdef TF_LITE_STATIC_MEMORY + TFLITE_CHECK(false && "No shape resizing supported on this platform"); +#else // TF_LITE_STATIC_MEMORY + delete[] dims_pointer_; +#endif // TF_LITE_STATIC_MEMORY + } + } + + inline int32_t DimensionsCount() const { return size_; } + inline int32_t Dims(int i) const { + TFLITE_DCHECK_GE(i, 0); + TFLITE_DCHECK_LT(i, size_); + return size_ > kMaxSmallSize ? dims_pointer_[i] : dims_[i]; + } + inline void SetDim(int i, int32_t val) { + TFLITE_DCHECK_GE(i, 0); + TFLITE_DCHECK_LT(i, size_); + if (size_ > kMaxSmallSize) { + dims_pointer_[i] = val; + } else { + dims_[i] = val; + } + } + + inline int32_t* DimsData() { + return size_ > kMaxSmallSize ? dims_pointer_ : dims_; + } + inline const int32_t* DimsData() const { + return size_ > kMaxSmallSize ? dims_pointer_ : dims_; + } + // The caller must ensure that the shape is no bigger than 5-D. + inline const int32_t* DimsDataUpTo5D() const { return dims_; } + + inline void Resize(int dimensions_count) { + if (size_ > kMaxSmallSize) { +#ifdef TF_LITE_STATIC_MEMORY + TFLITE_CHECK(false && "No shape resizing supported on this platform"); +#else // TF_LITE_STATIC_MEMORY + delete[] dims_pointer_; +#endif // TF_LITE_STATIC_MEMORY + } + size_ = dimensions_count; + if (dimensions_count > kMaxSmallSize) { +#ifdef TF_LITE_STATIC_MEMORY + TFLITE_CHECK(false && "No shape resizing supported on this platform"); +#else // TF_LITE_STATIC_MEMORY + dims_pointer_ = new int32_t[dimensions_count]; +#endif // TF_LITE_STATIC_MEMORY + } + } + + inline void ReplaceWith(int dimensions_count, const int32_t* dims_data) { + Resize(dimensions_count); + int32_t* dst_dims = DimsData(); + std::memcpy(dst_dims, dims_data, dimensions_count * sizeof(int32_t)); + } + + template + inline void BuildFrom(const T& src_iterable) { + const int dimensions_count = + std::distance(src_iterable.begin(), src_iterable.end()); + Resize(dimensions_count); + int32_t* data = DimsData(); + for (auto it : src_iterable) { + *data = it; + ++data; + } + } + + // This will probably be factored out. Old code made substantial use of 4-D + // shapes, and so this function is used to extend smaller shapes. Note that + // (a) as Dims<4>-dependent code is eliminated, the reliance on this should be + // reduced, and (b) some kernels are stricly 4-D, but then the shapes of their + // inputs should already be 4-D, so this function should not be needed. + inline static RuntimeShape ExtendedShape(int new_shape_size, + const RuntimeShape& shape) { + return RuntimeShape(new_shape_size, shape, 1); + } + + inline void BuildFrom(const std::initializer_list init_list) { + BuildFrom>(init_list); + } + + // Returns the total count of elements, that is the size when flattened into a + // vector. + inline int FlatSize() const { + int buffer_size = 1; + const int* dims_data = reinterpret_cast(DimsData()); + for (int i = 0; i < size_; i++) { + buffer_size *= dims_data[i]; + } + return buffer_size; + } + + bool operator!=(const RuntimeShape& comp) const { return !((*this) == comp); } + + private: + // For use only by ExtendedShape(), written to guarantee (return-value) copy + // elision in C++17. + // This creates a shape padded to the desired size with the specified value. + RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) + : size_(0) { + // If the following check fails, it is likely because a 4D-only kernel is + // being used with an array of larger dimension count. + TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); + Resize(new_shape_size); + const int size_increase = new_shape_size - shape.DimensionsCount(); + for (int i = 0; i < size_increase; ++i) { + SetDim(i, pad_value); + } + std::memcpy(DimsData() + size_increase, shape.DimsData(), + sizeof(int32_t) * shape.DimensionsCount()); + } + + int32_t size_; + union { + int32_t dims_[kMaxSmallSize]; + int32_t* dims_pointer_; + }; +}; + +// Converts inference-style shape to legacy tflite_micro::Dims<4>. +inline tflite_micro::Dims<4> ToRuntimeDims(const tflite_micro::RuntimeShape& array_shape) { + tflite_micro::Dims<4> result; + const int dimensions_count = array_shape.DimensionsCount(); + TFLITE_CHECK_LE(dimensions_count, 4); + int cum_prod = 1; + for (int i = 0; i < 4; i++) { + const int new_dim = + (i < dimensions_count) ? array_shape.Dims(dimensions_count - 1 - i) : 1; + result.sizes[i] = new_dim; + result.strides[i] = cum_prod; + cum_prod *= new_dim; + } + return result; +} + +// TODO(b/80418076): Move to legacy ops file, update invocations. +inline RuntimeShape DimsToShape(const tflite_micro::Dims<4>& dims) { + return RuntimeShape( + {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); +} + +// Gets next index to iterate through a multidimensional array. +inline bool NextIndex(const int num_dims, const int* dims, int* current) { + if (num_dims == 0) { + return false; + } + TFLITE_DCHECK(dims != nullptr); + TFLITE_DCHECK(current != nullptr); + int carry = 1; + for (int idx = num_dims - 1; idx >= 0; --idx) { + int current_val = current[idx] + carry; + TFLITE_DCHECK_GE(dims[idx], current_val); + if (dims[idx] == current_val) { + current[idx] = 0; + } else { + current[idx] = current_val; + carry = 0; + break; + } + } + return (carry == 0); +} + +// Gets offset of index if reducing on axis. When reducing, the flattened offset +// will not change, if the input index changes on the given axis. For example, +// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, +// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened +// offset. +// TODO(kanlig): uses Dims to represent dimensions. +inline size_t ReducedOutputOffset(const int num_dims, const int* dims, + const int* index, const int num_axis, + const int* axis) { + if (num_dims == 0) { + return 0; + } + TFLITE_DCHECK(dims != nullptr); + TFLITE_DCHECK(index != nullptr); + size_t offset = 0; + for (int idx = 0; idx < num_dims; ++idx) { + // if we need to skip this axis + bool is_axis = false; + if (axis != nullptr) { + for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { + if (idx == axis[axis_idx]) { + is_axis = true; + break; + } + } + } + if (!is_axis) { + offset = offset * static_cast(dims[idx]) + + static_cast(index[idx]); + } + } + return offset; +} + +inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), 4); + const int* dims_data = reinterpret_cast(shape.DimsDataUpTo5D()); + TFLITE_DCHECK(i0 >= 0 && i0 < dims_data[0]); + TFLITE_DCHECK(i1 >= 0 && i1 < dims_data[1]); + TFLITE_DCHECK(i2 >= 0 && i2 < dims_data[2]); + TFLITE_DCHECK(i3 >= 0 && i3 < dims_data[3]); + return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; +} + +inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); + TFLITE_DCHECK(i1 >= 0 && i1 < dims.sizes[1]); + TFLITE_DCHECK(i2 >= 0 && i2 < dims.sizes[2]); + TFLITE_DCHECK(i3 >= 0 && i3 < dims.sizes[3]); + return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + + i3 * dims.strides[3]; +} + +inline int Offset(const Dims<4>& dims, int* index) { + return Offset(dims, index[0], index[1], index[2], index[3]); +} + +inline int Offset(const RuntimeShape& shape, int* index) { + return Offset(shape, index[0], index[1], index[2], index[3]); +} + +// Get array size, DCHECKing that the dim index is in range. +// +// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() +// already performs this check. +template +int ArraySize(const Dims& array, int index) { + TFLITE_DCHECK(index >= 0 && index < N); + return array.sizes[index]; +} + +// Get common array size, DCHECKing that they all agree. +template +int MatchingArraySize(const ArrayType1& array1, int index1, + const ArrayType2& array2, int index2) { + TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); + return ArraySize(array1, index1); +} + +template +int MatchingArraySize(const ArrayType1& array1, int index1, + const ArrayType2& array2, int index2, Args... args) { + TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); + return MatchingArraySize(array1, index1, args...); +} + +// Get common shape dim, DCHECKing that they all agree. +inline int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return shape1.Dims(index1); +} + +template +int MatchingDim(const RuntimeShape& shape1, int index1, + const RuntimeShape& shape2, int index2, Args... args) { + TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); + return MatchingDim(shape1, index1, args...); +} + +// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). +template +inline int FlatSize(const Dims& dims) { + int flat_size = 1; + for (int i = 0; i < N; ++i) { + flat_size *= dims.sizes[i]; + } + return flat_size; +} + +TFLITE_DEPRECATED("Prefer FlatSize.") +inline int RequiredBufferSizeForDims(const Dims<4>& dims) { + return FlatSize(dims); +} + +inline int MatchingElementsSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + const int size_1 = shape.FlatSize(); + const int size_2 = check_shape_0.FlatSize(); + TFLITE_CHECK_EQ(size_1, size_2); + return size_1; +} + +inline int MatchingElementsSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int size_1 = shape.FlatSize(); + const int size_2 = check_shape_0.FlatSize(); + const int size_3 = check_shape_1.FlatSize(); + TFLITE_CHECK_EQ(size_1, size_2); + TFLITE_CHECK_EQ(size_2, size_3); + return size_1; +} + +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return shape.FlatSize(); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSize(const RuntimeShape& shape, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); +} + +// Flat size calculation, checking that dimensions match with one or more other +// arrays. +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return FlatSize(dims); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1, check_dims_2); +} + +template +inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2, + const Dims& check_dims_3) { + for (int i = 0; i < N; ++i) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); +} + +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +template +inline int FlatSizeSkipDim(const Dims& dims, int skip_dim) { + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < N); + int flat_size = 1; + for (int i = 0; i < N; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims.sizes[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return FlatSizeSkipDim(dims, skip_dim); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2); +} + +template +inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, + const Dims& check_dims_0, + const Dims& check_dims_1, + const Dims& check_dims_2, + const Dims& check_dims_3) { + for (int i = 0; i < N; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); + } + } + return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2, + check_dims_3); +} + +// Data is required to be contiguous, and so many operators can use either the +// full array flat size or the flat size with one dimension skipped (commonly +// the depth). +inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { + const int dims_count = shape.DimensionsCount(); + TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); + const auto* dims_data = shape.DimsData(); + int flat_size = 1; + for (int i = 0; i < dims_count; ++i) { + flat_size *= (i == skip_dim) ? 1 : dims_data[i]; + } + return flat_size; +} + +// A combination of MatchingFlatSize() and FlatSizeSkipDim(). +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return FlatSizeSkipDim(shape, skip_dim); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); +} + +inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, + const RuntimeShape& check_shape_0, + const RuntimeShape& check_shape_1, + const RuntimeShape& check_shape_2, + const RuntimeShape& check_shape_3) { + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) { + if (i != skip_dim) { + TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, + check_shape_3); +} + +template +bool IsPackedWithoutStrides(const Dims& dims) { + int expected_stride = 1; + for (int d = 0; d < N; d++) { + if (dims.strides[d] != expected_stride) return false; + expected_stride *= dims.sizes[d]; + } + return true; +} + +template +void ComputeStrides(Dims* dims) { + dims->strides[0] = 1; + for (int d = 1; d < N; d++) { + dims->strides[d] = dims->strides[d - 1] * dims->sizes[d - 1]; + } +} + +enum class BroadcastableOpCategory : uint8_t { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + +struct MinMax { + float min; + float max; +}; +static_assert(sizeof(MinMax) == 8, ""); + +struct ActivationParams { + FusedActivationFunctionType activation_type; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; +}; + +struct ReluParams : public ActivationParams { + int32_t input_offset; + int32_t output_offset; + int32_t output_multiplier; + int32_t output_shift; +}; + +// Styles of resizing op usages. For example, kImageStyle can be used with a Pad +// op for pattern-specific optimization. +enum class ResizingCategory : uint8_t { + kNone, + kImageStyle, // 4D, operating on inner dimensions, say {0, a, b, 0}. + kGenericResize, +}; + +// For Add, Sub, Mul ops. + + +struct ConcatenationParams { + int8_t axis; + const int32_t* input_zeropoint; + const float* input_scale; + uint16 inputs_count; + int32_t output_zeropoint; + float output_scale; +}; + +struct ComparisonParams { + // uint8_t inference params. + int left_shift; + int32_t input1_offset; + int32_t input1_multiplier; + int input1_shift; + int32_t input2_offset; + int32_t input2_multiplier; + int input2_shift; + // Shape dependent / common to inference types. + bool is_broadcast; +}; + +struct ConvParams { + PaddingType padding_type; + PaddingValues padding_values; + // TODO(starka): This was just "stride", so check that width+height is OK. + int16 stride_width; + int16 stride_height; + int16 dilation_width_factor; + int16 dilation_height_factor; + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +struct DepthToSpaceParams { + int32_t block_size; +}; + +struct DepthwiseParams { + PaddingType padding_type; + PaddingValues padding_values; + int16 stride_width; + int16 stride_height; + int16 dilation_width_factor; + int16 dilation_height_factor; + int16 depth_multiplier; + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + const int32_t* output_multiplier_per_channel; + const int32_t* output_shift_per_channel; +}; + +struct DequantizationParams { + double scale; + int32_t zero_point; +}; + +struct PerChannelDequantizationParams { + const float* scale; + const int32_t* zero_point; + int32_t quantized_dimension; +}; + +struct FakeQuantParams { + MinMax minmax; + int32_t num_bits; +}; + +struct FullyConnectedParams { + // uint8_t inference params. + // TODO(b/65838351): Use smaller types if appropriate. + int32_t input_offset; + int32_t weights_offset; + int32_t output_offset; + int32_t output_multiplier; + int output_shift; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + // Mark the operands as cacheable if they are unchanging, e.g. weights. + bool lhs_cacheable; + bool rhs_cacheable; + FullyConnectedWeightsFormat weights_format; +}; + +struct GatherParams { + int16 axis; +}; + +struct L2NormalizationParams { + // uint8_t inference params. + int32_t input_zero_point; +}; + +struct LocalResponseNormalizationParams { + int32_t range; + double bias; + double alpha; + double beta; +}; + +struct HardSwishParams { + // zero_point of the input activations. + int16_t input_zero_point; + // zero_point of the output activations. + int16_t output_zero_point; + // 16bit fixed-point component of the multiplier to apply to go from the + // "high-res input scale", which is the input scale multiplied by 2^7, to the + // "relu-ish scale", which 3.0/32768. + // See the implementation of HardSwishPrepare. + int16_t reluish_multiplier_fixedpoint_int16; + // exponent/bit-shift component of the aforementioned multiplier. + int reluish_multiplier_exponent; + // 16bit fixed-point component of the multiplier to apply to go from the + // "high-res input scale", which is the input scale multiplied by 2^7, to the + // output scale. + // See the implementation of HardSwishPrepare. + int16_t output_multiplier_fixedpoint_int16; + // exponent/bit-shift component of the aforementioned multiplier. + int output_multiplier_exponent; +}; + +struct LogisticParams { + // uint8_t inference params. + int32_t input_zero_point; + int32_t input_range_radius; + int32_t input_multiplier; + int input_left_shift; +}; + +struct LstmCellParams { + int32_t weights_zero_point; + int32_t accum_multiplier; + int accum_shift; + int state_integer_bits; +}; + +struct MeanParams { + int8_t axis_count; + int16 axis[4]; +}; + +struct PackParams { + int8_t axis; + const int32_t* input_zeropoint; + const float* input_scale; + uint16 inputs_count; + int32_t output_zeropoint; + float output_scale; +}; + +struct PadParams { + int8_t left_padding_count; + int32_t left_padding[4]; + int8_t right_padding_count; + int32_t right_padding[4]; + ResizingCategory resizing_category; +}; + +struct PreluParams { + int32_t input_offset; + int32_t alpha_offset; + int32_t output_offset; + int32_t output_multiplier_1; + int32_t output_shift_1; + int32_t output_multiplier_2; + int32_t output_shift_2; +}; + +struct PoolParams { + FusedActivationFunctionType activation; + PaddingType padding_type; + PaddingValues padding_values; + int stride_height; + int stride_width; + int filter_height; + int filter_width; + // uint8_t, etc, activation params. + int32_t quantized_activation_min; + int32_t quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; +}; + +struct ReshapeParams { + int8_t shape_count; + int32_t shape[4]; +}; + +struct ResizeBilinearParams { + bool align_corners; + // half_pixel_centers assumes pixels are of half the actual dimensions, and + // yields more accurate resizes. Corresponds to the same argument for the + // original TensorFlow op in TF2.0. + bool half_pixel_centers; +}; + +struct ResizeNearestNeighborParams { + bool align_corners; + bool half_pixel_centers; +}; + +struct SliceParams { + int8_t begin_count; + int32_t begin[4]; + int8_t size_count; + int32_t size[4]; +}; + +struct SoftmaxParams { + // beta is not really used (not a Tensorflow parameter) and not implemented + // for LogSoftmax. + double beta; + // uint8_t inference params. Used even when beta defaults to 1.0. + int32_t input_multiplier; + int32_t input_left_shift; + // Reverse scaling is only used by LogSoftmax. + int32_t reverse_scaling_divisor; + int32_t reverse_scaling_right_shift; + int diff_min; + int32_t zero_point; + float scale; + float* table; + int16_t* exp_lut; + int16_t* one_over_one_plus_x_lut; + uint8_t* uint8_table1; + uint8_t* uint8_table2; +}; + +struct SpaceToBatchParams { + // "Zero" padding for uint8_t means padding with the output offset. + int32_t output_offset; +}; + +struct SpaceToDepthParams { + int32_t block_size; +}; + +struct SplitParams { + // Graphs that split into, say, 2000 nodes are encountered. The indices in + // OperatorEdges are of type uint16. + uint16 num_split; + int16 axis; +}; + +struct SqueezeParams { + int8_t squeeze_dims_count; + int32_t squeeze_dims[4]; +}; + +struct StridedSliceParams { + int8_t start_indices_count; + int32_t start_indices[5]; + int8_t stop_indices_count; + int32_t stop_indices[5]; + int8_t strides_count; + int32_t strides[5]; + + int16 begin_mask; + int16 ellipsis_mask; + int16 end_mask; + int16 new_axis_mask; + int16 shrink_axis_mask; +}; + +struct TanhParams { + int32_t input_zero_point; + int32_t input_range_radius; + int32_t input_multiplier; + int input_left_shift; +}; + +struct TransposeParams { + int8_t perm_count; + int32_t perm[5]; +}; + +struct UnpackParams { + uint16 num_split; + int16 axis; +}; + +struct LeakyReluParams { + float alpha; + int32_t input_offset; + int32_t output_offset; + int32_t output_multiplier_alpha; + int32_t output_shift_alpha; + int32_t output_multiplier_identity; + int32_t output_shift_identity; +}; + +template +inline void SetActivationParams(float min, float max, P* params) { + params->float_activation_min = min; + params->float_activation_max = max; +} + +template +inline void SetActivationParams(int32_t min, int32_t max, P* params) { + params->quantized_activation_min = min; + params->quantized_activation_max = max; +} + +template +inline void GetActivationParams(const P& params, int32_t* min, int32_t* max) { + *min = params.quantized_activation_min; + *max = params.quantized_activation_max; +} + +template +inline void GetActivationParams(const P& params, float* min, float* max) { + *min = params.float_activation_min; + *max = params.float_activation_max; +} + +} // namespace tflite_micro +#endif +#endif // CEVA_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h new file mode 100644 index 00000000..dd3ffcb2 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h @@ -0,0 +1,45 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +// The CircularBuffer op has one input and one output tensor. +extern const int kCircularBufferInputTensor; +extern const int kCircularBufferOutputTensor; + +// Indices into the init flexbuffer's vector. +// The parameter's name is in the comment that follows. +// Elements in the vectors are ordered alphabetically by parameter name. +extern const int kCircularBufferCyclesMaxIndex; // 'cycles_max' + +// These fields control the stride period of a strided streaming model. This op +// returns kTfLiteAbort until cycles_until_run-- is zero. At this time, +// cycles_until_run is reset to cycles_max. +struct OpDataCircularBuffer { + int cycles_until_run; + int cycles_max; +}; + +TfLiteStatus CircularBufferPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h new file mode 100644 index 00000000..2fbf4fe9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h @@ -0,0 +1,22 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H +#define TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H + +extern const int g_gen_data_size_circular_buffer_config; +extern const unsigned char g_gen_data_circular_buffer_config[]; + +#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h new file mode 100644 index 00000000..45f3b406 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h @@ -0,0 +1,117 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +struct OpDataConv { + TfLitePaddingValues padding; + + // Cached tensor zero point values for quantized operations. + int32_t input_zero_point; + int32_t filter_zero_point; + int32_t output_zero_point; + + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t output_multiplier; + int output_shift; + + // Per channel output multiplier and shift. + int32_t* per_channel_output_multiplier; + int32_t* per_channel_output_shift; + + // The range of the fused activation layer. For example for kNone and + // uint8_t these would be 0 and 255. + int32_t output_activation_min; + int32_t output_activation_max; + + // A buffer used to store unpacked filter values. This is used if the source + // tensor is of n-bit precision that cannot be easily processed by kernels. + int filter_buffer_index; +}; + +extern const int kConvInputTensor; +extern const int kConvWeightsTensor; +extern const int kConvBiasTensor; +extern const int kConvOutputTensor; +extern const int kConvQuantizedDimension; + +// Returns a ConvParams struct with all the parameters needed for a +// float computation. +ConvParams ConvParamsFloat(const TfLiteConvParams& params, + const OpDataConv& data); + +// Returns a ConvParams struct with all the parameters needed for a +// quantized computation. +ConvParams ConvParamsQuantized(const TfLiteConvParams& params, + const OpDataConv& data); + +TfLiteStatus CalculateOpDataConv(TfLiteContext* context, TfLiteNode* node, + const TfLiteConvParams& params, int width, + int height, int filter_width, + int filter_height, int out_width, + int out_height, const TfLiteType data_type, + OpDataConv* data); + +void* ConvInit(TfLiteContext* context, const char* buffer, size_t length); + +TfLiteStatus ConvPrepare(TfLiteContext* context, TfLiteNode* node); + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +TFLMRegistration Register_CONV_2D(); + +#if defined(XTENSA) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 activations and int8 weights and always calls the reference +// implementation. +TFLMRegistration Register_CONV_2D_INT8REF(); + +#else +inline TFLMRegistration Register_CONV_2D_INT8REF() { + return Register_CONV_2D(); +} +#endif // defined(XTENSA) + +#if defined(CMSIS_NN) || defined(XTENSA) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 activations and int8 weights and uses the latency optimized +// implementations. +TFLMRegistration Register_CONV_2D_INT8(); + +// Returns a TFLMRegistration struct for kernel variant that only supports +// int16 activations and int8 weights and uses the latency optimized +// implementations. +TFLMRegistration Register_CONV_2D_INT16(); + +#else +inline TFLMRegistration Register_CONV_2D_INT8() { return Register_CONV_2D(); } + +inline TFLMRegistration Register_CONV_2D_INT16() { return Register_CONV_2D(); } +#endif // defined(CMSIS_NN) || defined(XTENSA) + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h new file mode 100644 index 00000000..d23f18c9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h @@ -0,0 +1,94 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/kernel_runner.h" +#include "tensorflow/lite/micro/kernels/micro_ops.h" +#include "tensorflow/lite/micro/test_helpers.h" +#include "tensorflow/lite/micro/testing/micro_test.h" + +namespace tflite_micro { +namespace testing { + +TfLiteStatus InvokeConv(TfLiteTensor* tensors, int tensors_size, + int output_length, TfLiteConvParams* conv_params, + TFLMRegistration registration, float* output_data); + +TfLiteStatus InvokeConv(TfLiteTensor* tensors, int tensors_size, + int output_length, TfLiteConvParams* conv_params, + TFLMRegistration registration, int8_t* output_data); + +TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size, + const float* expected_output_data, + int output_length, + TfLiteConvParams* conv_params, + TFLMRegistration registration, + float* output_data, float tolerance = 1e-5); + +TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size, + const int8_t* expected_output_data, + int output_length, + TfLiteConvParams* conv_params, + TFLMRegistration registration, + int8_t* output_data, float tolerance = 1e-5); + +TfLiteStatus TestConvFloat(int* input_dims_data, const float* input_data, + int* filter_dims_data, const float* filter_data, + int* bias_dims_data, const float* bias_data, + int* output_dims_data, + const float* expected_output_data, + TfLiteConvParams* conv_params, + TFLMRegistration registration, float* output_data); + +TfLiteStatus TestConvQuantizedPerChannel( + int* input_dims_data, const float* input_data, int8_t* input_quantized, + float input_scale, int input_zero_point, int* filter_dims_data, + const float* filter_data, int8_t* filter_data_quantized, + int* bias_dims_data, const float* bias_data, int32_t* bias_data_quantized, + float* bias_scales, int* bias_zero_points, int* output_dims_data, + const float* expected_output_data, int8_t* expected_output_data_quantized, + float output_scale, int output_zero_point, TfLiteConvParams* conv_params, + TFLMRegistration registration, int8_t* output_data, + TfLiteType tensor_weight_type = kTfLiteNoType); + +TfLiteStatus TestConvQuantizedPerChannel( + int* input_dims_data, const float* input_data, int16_t* input_quantized, + float input_scale, int input_zero_point, int* filter_dims_data, + const float* filter_data, int8_t* filter_data_quantized, + int* bias_dims_data, const float* bias_data, + std::int64_t* bias_data_quantized, float* bias_scales, + int* bias_zero_points, int* output_dims_data, + const float* expected_output_data, int16_t* expected_output_data_quantized, + float output_scale, int output_zero_point, TfLiteConvParams* conv_params, + TFLMRegistration registration, int16_t* output_data); + +TfLiteStatus TestConvQuantizedPerChannel( + int* input_dims_data, const float* input_data, int16_t* input_quantized, + float input_scale, int input_zero_point, int* filter_dims_data, + const float* filter_data, int8_t* filter_data_quantized, + int* bias_dims_data, const float* bias_data, int32_t* bias_data_quantized, + float* bias_scales, int* bias_zero_points, int* output_dims_data, + const float* expected_output_data, int16_t* expected_output_data_quantized, + float output_scale, int output_zero_point, TfLiteConvParams* conv_params, + TFLMRegistration registration, int16_t* output_data); + +} // namespace testing +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h new file mode 100644 index 00000000..a035d580 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h @@ -0,0 +1,80 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/conv.h" + +namespace tflite_micro { + +extern const int kDepthwiseConvInputTensor; +extern const int kDepthwiseConvWeightsTensor; +extern const int kDepthwiseConvBiasTensor; +extern const int kDepthwiseConvOutputTensor; +extern const int kDepthwiseConvQuantizedDimension; + +// Returns a DepthwiseParams struct with all the parameters needed for a +// float computation. +DepthwiseParams DepthwiseConvParamsFloat( + const TfLiteDepthwiseConvParams& params, const OpDataConv& data); + +// Returns a DepthwiseParams struct with all the parameters needed for a +// quantized computation. +DepthwiseParams DepthwiseConvParamsQuantized( + const TfLiteDepthwiseConvParams& params, const OpDataConv& data); + +TfLiteStatus CalculateOpDataDepthwiseConv( + TfLiteContext* context, TfLiteNode* node, + const TfLiteDepthwiseConvParams& params, int width, int height, + int filter_width, int filter_height, int out_width, int out_height, + const TfLiteType data_type, OpDataConv* data); + +TfLiteStatus DepthwiseConvPrepare(TfLiteContext* context, TfLiteNode* node); + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +TFLMRegistration Register_DEPTHWISE_CONV_2D(); + +#if defined(CMSIS_NN) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 activations and int8 weights and uses the latency optimized +// implementations. +TFLMRegistration Register_DEPTHWISE_CONV_2D_INT8(); + +// Returns a TFLMRegistration struct for kernel variant that only supports +// int16 activations and int8 weights and uses the latency optimized +// implementations. +TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16(); + +#else +inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT8() { + return Register_DEPTHWISE_CONV_2D(); +} + +inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16() { + return Register_DEPTHWISE_CONV_2D(); +} +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h new file mode 100644 index 00000000..5915cea1 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h @@ -0,0 +1,38 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +struct DequantizeOpData { + tflite_micro::DequantizationParams quantization_params; + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t output_multiplier; + int output_shift; + int32_t output_zero_point; +}; + +TfLiteStatus DequantizePrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h new file mode 100644 index 00000000..f5b9eae0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h @@ -0,0 +1,25 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H +#define TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H + +extern const int g_gen_data_size_none_regular_nms; +extern const unsigned char g_gen_data_none_regular_nms[]; + +extern const int g_gen_data_size_regular_nms; +extern const unsigned char g_gen_data_regular_nms[]; + +#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h new file mode 100644 index 00000000..da91066b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h @@ -0,0 +1,28 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +TFLMRegistration* Register_ETHOSU(); + +const char* GetString_ETHOSU(); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h new file mode 100644 index 00000000..9a7adb8a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h @@ -0,0 +1,112 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +struct OpDataFullyConnected { + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t output_multiplier; + int output_shift; + // The range of the fused activation layer. For example for kNone and + // uint8_t these would be 0 and 255. + int32_t output_activation_min; + int32_t output_activation_max; + // The index of the temporary tensor where the quantized inputs are cached. + int input_quantized_index; + // Cached zero point values of tensors. + int32_t input_zero_point; + int32_t filter_zero_point; + int32_t output_zero_point; + +// TODO(b/258710417): enable by default once optimized fully-connected works for +// all targets. +#if !defined(HEXAGON) + // A buffer used to store unpacked filter values. This is used if the source + // tensor is of n-bit precision that cannot be easily processed by kernels. + int filter_buffer_index; +#endif +}; + +extern const int kFullyConnectedInputTensor; +extern const int kFullyConnectedWeightsTensor; +extern const int kFullyConnectedBiasTensor; +extern const int kFullyConnectedOutputTensor; + +// Returns a FullyConnectedParams struct with all the parameters needed for a +// float computation. +FullyConnectedParams FullyConnectedParamsFloat( + TfLiteFusedActivation activation); + +// Returns a FullyConnectedParams struct with all the parameters needed for a +// quantized computation. +FullyConnectedParams FullyConnectedParamsQuantized( + const OpDataFullyConnected& op_data); + +TfLiteStatus CalculateOpDataFullyConnected( + TfLiteContext* context, TfLiteFusedActivation activation, + TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output, OpDataFullyConnected* data); + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +TFLMRegistration Register_FULLY_CONNECTED(); + +#if defined(CMSIS_NN) || defined(HEXAGON) || defined(XTENSA) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8. +TFLMRegistration Register_FULLY_CONNECTED_INT8(); + +#else +// Note that while this block gets used for both reference and optimized kernels +// that do not have any specialized implementations, the only goal here is to +// define fallback implementation that allow reference kernels to still be used +// from applications that call a more specific kernel variant. + +inline TFLMRegistration Register_FULLY_CONNECTED_INT8() { + return Register_FULLY_CONNECTED(); +} + +#endif + +#if defined(CMSIS_NN) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int16. +TFLMRegistration Register_FULLY_CONNECTED_INT16(); + +#else +// Note that while this block gets used for both reference and optimized kernels +// that do not have any specialized implementations, the only goal here is to +// define fallback implementation that allow reference kernels to still be used +// from applications that call a more specific kernel variant. + +inline TFLMRegistration Register_FULLY_CONNECTED_INT16() { + return Register_FULLY_CONNECTED(); +} + +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h new file mode 100644 index 00000000..18bbf3bb --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h @@ -0,0 +1,30 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +extern const int kHardSwishInputTensor; +extern const int kHardSwishOutputTensor; + +TfLiteStatus HardSwishPrepare(TfLiteContext* context, TfLiteNode* node); +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h new file mode 100644 index 00000000..a0be324f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h @@ -0,0 +1,86 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" +#include "tensorflow/lite/micro/fake_micro_context.h" +#include "tensorflow/lite/micro/mock_micro_graph.h" + +namespace tflite_micro { +namespace micro { + +// Helper class to perform a simulated kernel (i.e. TFLMRegistration) +// lifecycle (init, prepare, invoke). All internal allocations are handled by +// this class. Simply pass in the registration, list of required tensors, inputs +// array, outputs array, and any pre-builtin data. Calling Invoke() will +// automatically walk the kernel and outputs will be ready on the TfLiteTensor +// output provided during construction. +class KernelRunner { + public: + KernelRunner(const TFLMRegistration& registration, TfLiteTensor* tensors, + int tensors_size, TfLiteIntArray* inputs, + TfLiteIntArray* outputs, const void* builtin_data, + TfLiteIntArray* intermediates = nullptr); + + // Calls init and prepare on the kernel (i.e. TFLMRegistration) struct. + // Any exceptions will be DebugLog'd and returned as a status code. + TfLiteStatus InitAndPrepare(const char* init_data = nullptr, + size_t length = 0); + + // Calls invoke on a given TFLMRegistration pointer. After successful + // invoke, results will be available in the output tensor as passed into the + // constructor of this class. + TfLiteStatus Invoke(); + + // Calls Free on a given TFLMRegistration pointer(if it's implemented). + // After successful Free, kTfLiteOk status will be returned. If Free is not + // implemented for a given kernel kTfLiteError will be returned. + TfLiteStatus Free(); + + // Calls Reset on a given TFLMRegistration pointer(if it's implemented). + // After successful Reset, kTfLiteOk status will be returned. If Free is not + // implemented for a given kernel kTfLiteError will be returned. + TfLiteStatus Reset(); + + // Returns a pointer to the internal MockMicroGraph which KernelRunner uses + // to stub out MicroGraph methods and track invocations on each subgraph. + MockMicroGraph* GetMockGraph() { return &mock_micro_graph_; } + + // Returns true if all temp buffer in tests are deallocated. + // TODO(b/209453859): move this function to private after deallocation checks + // are enabled for all kernel tests. + bool ValidateTempBufferDeallocated(); + + private: + static constexpr int kKernelRunnerBufferSize_ = 10000; + static uint8_t kKernelRunnerBuffer_[kKernelRunnerBufferSize_]; + + TfLiteContext context_ = {}; + TfLiteNode node_ = {}; + const TFLMRegistration& registration_; + + SingleArenaBufferAllocator* allocator_; + MockMicroGraph mock_micro_graph_; + FakeMicroContext fake_micro_context_; +}; + +} // namespace micro +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h new file mode 100644 index 00000000..f8a534fa --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h @@ -0,0 +1,150 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/micro_context.h" + +namespace tflite_micro { +namespace micro { + +TFLMRegistration RegisterOp( + void* (*init)(TfLiteContext* context, const char* buffer, size_t length), + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node), + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node), + void (*free)(TfLiteContext* context, void* buffer) = nullptr, + void (*reset)(TfLiteContext* context, void* buffer) = nullptr); + +TFLMInferenceRegistration RegisterOp( + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node), + void (*reset)(TfLiteContext* context, void* buffer) = nullptr); + +// Prints out n bytes in a int8_t buffer as hex +void PrintNBytes(const int8_t* tensor_data, int n_bytes, + const char* prefix = nullptr); + +// Prints out the n bytes in a TfLiteEvalTensor as hex +void PrintNBytes(const TfLiteEvalTensor* tensor, int n_bytes, + const char* prefix = nullptr); + +// Prints out n bytes in a TfLiteTensor as hex +void PrintNBytes(const TfLiteTensor* tensor, int n_bytes, + const char* prefix = nullptr); + +// Returns a mutable tensor for a given input index. is_variable must be checked +// during prepare when the full TfLiteTensor is available. +TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context, + const TfLiteNode* node, int index); + +// Returns the TfLiteEvalTensor struct for a given input index in a node. +const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context, + const TfLiteNode* node, int index); + +// Returns the TfLiteEvalTensor struct for a given output index in a node. +TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context, + const TfLiteNode* node, int index); + +// Returns data for a TfLiteEvalTensor struct that are expected to exist. +template +T* GetTensorData(TfLiteEvalTensor* tensor) { + TFLITE_DCHECK(tensor != nullptr); + return reinterpret_cast(tensor->data.raw); +} + +// Returns const data for a TfLiteEvalTensor struct that are expected to exist. +template +const T* GetTensorData(const TfLiteEvalTensor* tensor) { + TFLITE_DCHECK(tensor != nullptr); + return reinterpret_cast(tensor->data.raw); +} + +// Returns data for a TfLiteEvalTensor struct that could be null. +template +T* GetOptionalTensorData(TfLiteEvalTensor* tensor) { + return tensor == nullptr ? nullptr : reinterpret_cast(tensor->data.raw); +} + +// Returns const data for a TfLiteEvalTensor struct that could be null. +template +const T* GetOptionalTensorData(const TfLiteEvalTensor* tensor) { + return tensor == nullptr ? nullptr + : reinterpret_cast(tensor->data.raw); +} + +// Returns the shape of a TfLiteEvalTensor struct. +const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor); + +// Return true if the given tensors have the same shape. +bool HaveSameShapes(const TfLiteEvalTensor* input1, + const TfLiteEvalTensor* input2); + +PaddingType RuntimePaddingType(TfLitePadding padding); + +// Relocate tensor dims from FlatBuffer to the persistent storage arena. +// The old dims data is copied to the new storage area. +// The tensor and eval_tensor must be the same tensor. +// Only use during Prepare phase. +TfLiteStatus CreateWritableTensorDimsWithCopy(TfLiteContext* context, + TfLiteTensor* tensor, + TfLiteEvalTensor* eval_tensor); + +// Copy all op input tensors to op output tensors. Requires all op input tensor +// shapes and types to be identical to op output tensor shapes and types. +TfLiteStatus CopyOpInputsToOpOutputs(TfLiteContext* context, TfLiteNode* node); + +// Copy all op input tensors to subgraph input tensors. Requires all op input +// tensor shapes and types to be identical to subgraph input tensor shapes and +// types. +TfLiteStatus CopyOpInputsToSubgraphInputs(TfLiteContext* context, + TfLiteNode* node, + MicroGraph* graph_info, + int subgraph_idx, + int first_tensor_idx); + +// Copy all op output tensors to subgraph input tensors. Requires all op output +// tensor shapes and types to be identical to subgraph input tensor shapes and +// types. +TfLiteStatus CopyOpOutputsToSubgraphInputs(TfLiteContext* context, + TfLiteNode* node, + MicroGraph* graph_info, + int subgraph_idx); + +// Copy all subgraph output tensors to op outputs. Requires all subgraph output +// tensor shapes and types to be identical to op output tensor shapes and types. +TfLiteStatus CopySubgraphOutputsToOpOutputs(TfLiteContext* context, + TfLiteNode* node, + MicroGraph* graph_info, + int subgraph_idx); + +// If tensor is INT4, make a new TfLiteEvalTensor with data unpacked into +// a scratch buffer. The returned tensor will have the kTfLiteInt8 type. +// Assume scratch buffer is previously requested in Prepare, and +// scratch_buffer_index can be used to retrieve that buffer. +// If the tensor is not INT4, a shallow copy is returned. +TfLiteEvalTensor MakeUnpackedInt4Tensor(TfLiteContext* context, + int scratch_buffer_index, + const TfLiteEvalTensor* tensor); +} // namespace micro +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h new file mode 100644 index 00000000..5a05869a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h @@ -0,0 +1,43 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +// Input/output tensor index. +extern const int kInputTensor; +extern const int kOutputTensor; + +struct LeakyReluOpData { + // quantization parameters + int32_t output_multiplier_alpha; + int32_t output_shift_alpha; + int32_t output_multiplier_identity; + int32_t output_shift_identity; + int32_t input_zero_point; + int32_t output_zero_point; +}; + +TfLiteStatus CalculateOpDataLeakyRelu(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus LeakyReluPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h new file mode 100644 index 00000000..a2b59ae3 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h @@ -0,0 +1,35 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { +// Input/output tensor index. +extern const int kLogicalInputTensor1; +extern const int kLogicalInputTensor2; +extern const int kLogicalOutputTensor; + +TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node, + bool (*func)(bool, bool)); + +bool LogicalOr(bool x, bool y); +bool LogicalAnd(bool x, bool y); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h new file mode 100644 index 00000000..8c7a8ce8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h @@ -0,0 +1,42 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { +extern const int kLogisticInputTensor; +extern const int kLogisticOutputTensor; + +struct OpDataLogistic { + int32_t input_zero_point; + int32_t input_range_radius; + int32_t input_multiplier; + int input_left_shift; +}; + +TfLiteStatus CalculateArithmeticOpDataLogistic(TfLiteContext* context, + TfLiteNode* node, + OpDataLogistic* data); + +TfLiteStatus LogisticPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h new file mode 100644 index 00000000..8db0971f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h @@ -0,0 +1,541 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Functions to perform integer evaulation for standard LSTM (e.g., defined in +// the keras lstm layer, no peephole etc.). Currently used by the 16 bits +// activation case only + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_ +#include +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/kernel_util.h" +#include "tensorflow/lite/micro/kernels/lstm_shared.h" +#include "tensorflow/lite/micro/micro_log.h" + +namespace tflite_micro { + +// Interface to access all the TempTfLiteTensors of the LSTM kernel during the +// preparation phase. Can only be constructed through the constructor to avoid +// memory leakage. All TempTfLiteTensors will be deallocated through the +// destructor. +class LstmTensors { + public: + LstmTensors(const LstmTensors& other) = delete; + LstmTensors& operator=(const LstmTensors& other) = delete; + + LstmTensors(TfLiteContext* context, TfLiteNode* node); + ~LstmTensors(); + + // Verify the LSTM internal tensor properties (e.g., type checks) + // Input/output/states/fc weights tensors are required for kernel evaulation. + // The state tensors should be variables. Variants of the standard LSTM + // are not supported here, therefore their corresponding tensors should be + // invalid + TfLiteStatus ValidateTensorStatus(TfLiteContext* context) const; + + // Internal tensors. see lstm_shared.h for tensor names + const TfLiteTensor* GetInternalTensor(const int tensor_index) const { + return internal_tensors_[tensor_index]; + } + + const TfLiteTensor* HiddenStateTensor() const { + return internal_tensors_[kLstmOutputStateTensor]; + } + const TfLiteTensor* CellStateTensor() const { + return internal_tensors_[kLstmCellStateTensor]; + } + const TfLiteTensor* OutputTensor() const { return output_tensor_; } + + private: + // see lstm_shared.h for tensor names + MicroContext* micro_context_; + TfLiteTensor* internal_tensors_[24]; + TfLiteTensor* output_tensor_; +}; + +// Deduce the size information (Batch (B), Time Steps (T), Input dimension (I), +// State dimension (S)) that defines the LSTM using the input and hidden state +// tensor +LstmSizeInfo CreateLstmSizeInfo( + const bool time_major, const TfLiteIntArray* input_tensor_shape, + const TfLiteIntArray* hidden_state_tensor_shape); + +TfLiteStatus ValidateWeightTensorSize(TfLiteContext* context, + const TfLiteTensor* tensor, int dim1_size, + int dim2_size); + +TfLiteStatus ValidateBiasTensorSize(TfLiteContext* context, + const TfLiteTensor* tensor, int size); + +// Go through every tensors and make sure their shape match the kernel +// configuration +TfLiteStatus ValidateTensorSize(TfLiteContext* context, + const LstmTensors& tensors, + const LstmSizeInfo& size_info); + +// Wrapper function to create gate parameters for the four internal LSTM gates +TfLiteStatus CreateGateParams( + TfLiteContext* context, + /*Input tensors*/ + const TfLiteTensor* input, const TfLiteTensor* input_weight, + const TfLiteTensor* input_bias, + /*Hidden state tensors*/ + const TfLiteTensor* hidden_state, const TfLiteTensor* hidden_state_weight, + const TfLiteTensor* hidden_state_bias, + /*Scale of the fc output (input to non-linear activation)*/ + const float nonlinear_activation_input_scale, const TfLiteType cell_type, + const tflite_micro::GateParameters& gate_params); + +// Create parameters for element wise multiplication that happens in a) cell +// state update ; b) hidden state update +// Note that all the output of gates are symmetrically quantized so only scales +// are required for input. However, during the hidden state update phase, the +// output is the updated hidden state, which is asymmetrically quantized. Thus +// output may require zero point +tflite_micro::ArithmeticParams CreateInterGateMulParams(const float input1_scale, + const float input2_scale, + const float output_scale, + const TfLiteType output_type, + const int output_zp = 0); + +// Create the additional information about the cell state, which include: +// cell_state_scale_power: used in integer nonlinear function (e.g., tanh) +// quantized_cell_clip: quantized cell clip range +CellStateInfo CreateLstmCellStateInfo(const float cell_state_scale, + const float cell_clip); + +CellStateInfo CreateLstmCellStateInfoFloat(const float cell_clip); +tflite_micro::FullyConnectedParams CreateFCParamsFloat(); + +tflite_micro::GateParameters CreateGateParamsFloat(); + +tflite_micro::ArithmeticParams CreateInterGateMulParamsFloat(); + +TfLiteStatus PrepareGateParametersFloat(TfLiteContext* context, + const LstmTensors& lstm_tensors, + OpDataLSTM* op_data_lstm); + +TfLiteStatus PrepareGateParametersInteger(TfLiteContext* context, + const LstmTensors& lstm_tensors, + OpDataLSTM* op_data_lstm); + +LSTMKernelContents CreateLSTMKernelContent(TfLiteContext* context, + TfLiteNode* node); + +template +LSTMBuffers CreateLSTMBuffers(TfLiteContext* context, + const int* buffer_indices) { + LSTMBuffers buffers; + buffers.buffer0 = reinterpret_cast( + context->GetScratchBuffer(context, buffer_indices[0])); + buffers.buffer1 = reinterpret_cast( + context->GetScratchBuffer(context, buffer_indices[1])); + buffers.buffer2 = reinterpret_cast( + context->GetScratchBuffer(context, buffer_indices[2])); + buffers.buffer3 = reinterpret_cast( + context->GetScratchBuffer(context, buffer_indices[3])); + return buffers; +} + +// Since LSTM includes multiple intermediate stages, introducing the internal +// namespace to expose them for testing +namespace lstm_internal { + +void Sigmoid(const RuntimeShape& data_shape, int16_t* data); + +void Sigmoid(const RuntimeShape& data_shape, float* data); + +void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape, + int16_t* input_data, const RuntimeShape& output_data_shape, + int16_t* output_data); + +void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape, + float* input_data, const RuntimeShape& output_data_shape, + float* output_data); + +void Mul(const RuntimeShape& shape, const ArithmeticParams& params, + const int16_t* input1_data, const int16_t* input2_data, + int8_t* output_data); + +void Mul(const RuntimeShape& shape, const ArithmeticParams& params, + const int16_t* input1_data, const int16_t* input2_data, + int16_t* output_data); + +void Mul(const RuntimeShape& shape, const ArithmeticParams& params, + const float* input1_data, const float* input2_data, + float* output_data); + +void FullyConnected(const FullyConnectedParams& params, + const RuntimeShape& input_shape, const int8_t* input_data, + const RuntimeShape& filter_shape, const int8_t* filter_data, + const RuntimeShape& bias_shape, const int32_t* bias_data, + const RuntimeShape& output_shape, int16_t* output_data); + +void FullyConnected(const FullyConnectedParams& params, + const RuntimeShape& input_shape, const int16_t* input_data, + const RuntimeShape& filter_shape, const int8_t* filter_data, + const RuntimeShape& bias_shape, const int64_t* bias_data, + const RuntimeShape& output_shape, int16_t* output_data); + +void FullyConnected(const FullyConnectedParams& params, + const RuntimeShape& input_shape, const float* input_data, + const RuntimeShape& filter_shape, const float* filter_data, + const RuntimeShape& bias_shape, const float* bias_data, + const RuntimeShape& output_shape, float* output_data); + +void AddElementWise(const int16_t* input_1, const int16_t* input_2, int n_batch, + int n_input, int16_t* output); + +void AddElementWise(const float* input_1, const float* input_2, int n_batch, + int n_input, float* output); + +void Clipping(const int v_size, const CellStateInfo& cell_state_info, + int16_t* vector); + +void Clipping(const int v_size, const CellStateInfo& cell_state_info, + float* vector); + +// Manages the slice position (offset), slice length (sliced tensor shape), +// and update rules for input/output/hidden state/cell state tensors at each +// time step. +class LstmStepManager { + public: + LstmStepManager() = delete; + // Does not take any ownership, and all pointers must refer to valid objects + // that outlive the one constructed. + explicit LstmStepManager(const LstmSizeInfo* size_info) + : size_info_(*size_info) {} + + void UpdateTime(); + void UpdateBatch(); + + void ResetTime() { current_time_ = 0; } + RuntimeShape InputShape() const; + RuntimeShape StateShape() const; + + int InputOffset() const { return input_offset_; } + int OutputOffset() const { return output_offset_; } + int HiddenStateOffset() const { return hidden_state_offset_; } + int CellStateOffset() const { return cell_state_offset_; } + + private: + int current_time_ = 0; + int current_batch_ = 0; + int input_offset_ = 0; + int output_offset_ = 0; + int hidden_state_offset_ = 0; + int cell_state_offset_ = 0; + // Sizeinfo is from LstmOpData, which reside in the memory arena + // (guarante to outlast LSTMStepManager, which reside in stack) + const LstmSizeInfo& size_info_; +}; + +// Calculates a single LSTM gate. +// Implements the following formula: +// gate = activate(FC(input) + FC(recurrent)) +// Activation is sigmoid except for the "cell" gate (configurable, usually tanh) +template +void CalculateLstmGate( + const LstmStepManager& step_info, const GateParameters& gate_params, + // Input FC + const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight, + const TfLiteEvalTensor* input_bias, + // Recurrent FC + const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight, + const TfLiteEvalTensor* recurrent_bias, + // Output + CellType* gate_output, + // Scratch arrays + CellType* fc_output_buffer, const TfLiteFusedActivation activation) { + const auto gate_output_shape = step_info.StateShape(); + // Check offset validity to avoid memory overflow + TFLITE_DCHECK_LE(step_info.InputOffset() + step_info.InputShape().FlatSize(), + tflite_micro::micro::GetTensorShape(input).FlatSize()); + TFLITE_DCHECK_LE( + step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(), + tflite_micro::micro::GetTensorShape(recurrent).FlatSize()); + + // Input FC + FullyConnected(gate_params.input_fc_params, step_info.InputShape(), + tflite_micro::micro::GetTensorData(input) + + step_info.InputOffset(), + micro::GetTensorShape(input_weight), + tflite_micro::micro::GetTensorData(input_weight), + tflite_micro::micro::GetTensorShape(input_bias), + tflite_micro::micro::GetOptionalTensorData(input_bias), + gate_output_shape, gate_output); + + // Recurrent FC + FullyConnected(gate_params.recurrent_fc_params, step_info.StateShape(), + tflite_micro::micro::GetTensorData(recurrent) + + step_info.HiddenStateOffset(), + tflite_micro::micro::GetTensorShape(recurrent_weight), + tflite_micro::micro::GetTensorData(recurrent_weight), + tflite_micro::micro::GetTensorShape(recurrent_bias), + tflite_micro::micro::GetOptionalTensorData(recurrent_bias), + gate_output_shape, fc_output_buffer); + + AddElementWise(gate_output, fc_output_buffer, + /*n_batch=*/gate_output_shape.DimsData()[0], + /*n_state=*/gate_output_shape.DimsData()[1], gate_output); + // Apply activation + switch (activation) { + case kTfLiteActSigmoid: + Sigmoid(gate_output_shape, gate_output); + break; + case kTfLiteActTanh: { + // Set the scale power to -12 to avoid shift + Tanh(/*cell_state_scale_power=*/-12, gate_output_shape, gate_output, + gate_output_shape, gate_output); + } break; + default: + // Only Sigmoid or Tanh is used. + TFLITE_ASSERT_FALSE; + } +} + +// Update the cell state using the output from the forget gate, input gate, and +// cell gate Formula: updated_cell_state = forget_gate_output*cell_state + +// input_gate_output * cell_gate_output, where * denotes element wise +// multiplication +template +void UpdateLstmCell(const LstmStepManager& step_info, + TfLiteEvalTensor* cell_state, + // Gate outputs + CellType* forget_gate_output, + const CellType* input_gate_output, + const CellType* cell_gate_output, + // Mul parameters + const ArithmeticParams& forget_cell_mul_params, + const ArithmeticParams& input_mul_params, + const CellStateInfo& cell_state_info, CellType* buffer) { + // Check offset validity to avoid memory overflow + TFLITE_DCHECK_LE( + step_info.CellStateOffset() + step_info.StateShape().FlatSize(), + tflite_micro::micro::GetTensorShape(cell_state).FlatSize()); + + auto cell_state_shape = step_info.StateShape(); + // Forget Gate x Cell State + Mul(cell_state_shape, forget_cell_mul_params, forget_gate_output, + tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset(), + tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset()); + // Input Gate x Cell Gate + Mul(cell_state_shape, input_mul_params, input_gate_output, cell_gate_output, + buffer); + + // Update the cell state + AddElementWise(tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset(), + buffer, + /*n_batch=*/cell_state_shape.DimsData()[0], + /*n_state=*/cell_state_shape.DimsData()[1], + tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset()); + + if (cell_state_info.cell_clip > 0) { + Clipping(cell_state_shape.FlatSize(), cell_state_info, + tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset()); + } +} + +// Update the hidden state of the LSTM kernel using the following formula: +// updated_hidden_state = Tanh(updated_cell_state) * output_gate_output, * means +// element wise multiplication +template +void UpdateLstmHidden(const LstmStepManager& step_info, + TfLiteEvalTensor* cell_state, + TfLiteEvalTensor* hidden_state, + const CellType* output_gate_output, + const ArithmeticParams& mul_params, + int32_t cell_state_scale_power, CellType* buffer) { + // Check offset validity to avoid memory overflow + TFLITE_DCHECK_LE( + step_info.CellStateOffset() + step_info.StateShape().FlatSize(), + tflite_micro::micro::GetTensorShape(cell_state).FlatSize()); + TFLITE_DCHECK_LE( + step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(), + tflite_micro::micro::GetTensorShape(hidden_state).FlatSize()); + + auto cell_state_shape = step_info.StateShape(); + CellType* cell_state_data = + tflite_micro::micro::GetTensorData(cell_state) + + step_info.CellStateOffset(); + // Tanh(cell_state) + Tanh(cell_state_scale_power, cell_state_shape, cell_state_data, + cell_state_shape, buffer); + // Update the hidden state + Mul(cell_state_shape, mul_params, buffer, output_gate_output, + tflite_micro::micro::GetTensorData(hidden_state) + + step_info.HiddenStateOffset()); +} + +template +void LstmStep(const LstmStepManager& step_info, const OpDataLSTM& op_data, + LSTMKernelContents& kernel_content, + const LSTMBuffers& buffers) { + /*Step1: Calculate gate outputs to prepare cell state update*/ + CellType* gate_internal_buffer = buffers.buffer3; + CellType* forget_gate_output = buffers.buffer0; + CalculateLstmGate( + step_info, op_data.forget_gate_parameters, + // Input FC + kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmInputToForgetWeightsTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmForgetGateBiasTensor), + // Recurrent FC + kernel_content.HiddenStateTensor(), + kernel_content.GetInternalTensor( + tflite_micro::kLstmRecurrentToForgetWeightsTensor), + /*recurrent_bias*/ nullptr, + // Output + forget_gate_output, + // Scratch arrays + gate_internal_buffer, kTfLiteActSigmoid); + + // Input Gate calculation; + CellType* input_gate_output = buffers.buffer1; + CalculateLstmGate( + step_info, op_data.input_gate_parameters, + // Input FC + kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmInputToInputWeightsTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmInputGateBiasTensor), + // Recurrent FC + kernel_content.HiddenStateTensor(), + kernel_content.GetInternalTensor( + tflite_micro::kLstmRecurrentToInputWeightsTensor), + /*recurrent_bias*/ nullptr, + // Output + input_gate_output, + // Scratch arrays + gate_internal_buffer, kTfLiteActSigmoid); + + // Cell Gate calculation + CellType* cell_gate_output = buffers.buffer2; + CalculateLstmGate( + step_info, op_data.cell_gate_parameters, + // Input FC + kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmInputToCellWeightsTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmCellGateBiasTensor), + // Recurrent FC + kernel_content.HiddenStateTensor(), + kernel_content.GetInternalTensor( + tflite_micro::kLstmRecurrentToCellWeightsTensor), + /*recurrent_bias*/ nullptr, + // Output + cell_gate_output, + // Scratch arrays + gate_internal_buffer, op_data.cell_gate_nonlinear_type); + + /*Step2: update the cell state */ + const InterGateParameters& inter_gate_params = op_data.inter_gate_parameters; + CellType* updated_input_buffer = buffers.buffer1; // reuse buffer + + UpdateLstmCell(step_info, kernel_content.CellStateTensor(), + forget_gate_output, input_gate_output, + cell_gate_output, + inter_gate_params.forget_cell_mul_params, + inter_gate_params.input_mul_params, + op_data.cell_state_info, updated_input_buffer); + + /*Step3: update the hidden state */ + CellType* output_gate_output = buffers.buffer1; // reuse buffer + CalculateLstmGate( + step_info, op_data.output_gate_parameters, + // Input FC + kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmInputToOutputWeightsTensor), + kernel_content.GetInternalTensor(tflite_micro::kLstmOutputGateBiasTensor), + // Recurrent FC + kernel_content.HiddenStateTensor(), + kernel_content.GetInternalTensor( + tflite_micro::kLstmRecurrentToOutputWeightsTensor), + /*recurrent_bias*/ nullptr, + // Output + output_gate_output, + // Scratch arrays + gate_internal_buffer, kTfLiteActSigmoid); + + CellType* tanh_activated_cell_buffer = buffers.buffer0; // reuse buffer + tflite_micro::lstm_internal::UpdateLstmHidden( + step_info, kernel_content.CellStateTensor(), + kernel_content.HiddenStateTensor(), output_gate_output, + inter_gate_params.output_mul_params, + op_data.cell_state_info.cell_state_scale_power, + tanh_activated_cell_buffer); + + /*Step4: copy the update the hidden state to output*/ + // Check offset validity to avoid memory overflow + TFLITE_DCHECK_LE( + step_info.OutputOffset() + step_info.StateShape().FlatSize(), + tflite_micro::micro::GetTensorShape(kernel_content.output_tensor).FlatSize()); + // record the output (from the updated hidden state) + ActivationType* output_ptr = tflite_micro::micro::GetTensorData( + kernel_content.output_tensor); + const auto* hidden_state = kernel_content.HiddenStateTensor(); + std::memcpy(output_ptr + step_info.OutputOffset(), + tflite_micro::micro::GetTensorData(hidden_state) + + step_info.HiddenStateOffset(), + step_info.StateShape().FlatSize() * sizeof(ActivationType)); +} + +} // namespace lstm_internal + +// Evaulate the LSTM kernel with (potential) multi-steps and multi-batch input +// Since +template +TfLiteStatus EvalLstm(const OpDataLSTM& op_data, + LSTMKernelContents& kernel_content, + const LSTMBuffers& buffers) { + lstm_internal::LstmStepManager step_info(&op_data.size_info); + const auto& size_info = op_data.size_info; + // time is the first dimention, enable batch computation + if (size_info.time_major) { + for (int t = 0; t < size_info.time_steps; t++) { + lstm_internal::LstmStep( + step_info, op_data, kernel_content, buffers); + // prepare for the next time step + step_info.UpdateTime(); + } + } else { + // batch first, unable to size the input data. single batch inference + for (int b = 0; b < size_info.batch_size; b++) { + for (int t = 0; t < size_info.time_steps; t++) { + lstm_internal::LstmStep( + step_info, op_data, kernel_content, buffers); + // prepare for the next time step + step_info.UpdateTime(); + } + // prepare for the next batch + step_info.UpdateBatch(); + step_info.ResetTime(); + } + } + return kTfLiteOk; +} +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_16ACT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h new file mode 100644 index 00000000..10e443de --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h @@ -0,0 +1,817 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ + +#include +#include + +#include "tensorflow/lite/micro/kernels/lstm_eval.h" +#include "tensorflow/lite/micro/kernels/testdata/lstm_test_data.h" +#include "tensorflow/lite/micro/test_helpers.h" +#include "tensorflow/lite/micro/testing/micro_test.h" + +namespace tflite_micro { +namespace testing { + +/*Helper Functions (mainly about mimicking the kernel preparation)*/ + +// Create fully connected parameters using quantization settings of input and +// weight tensors. +// Since TfLiteContext is not available during the kernel test, here we mimic +// (put into stack memory) CalculateOpDataFullyConnected in +// tensorflow/lite/micro/kernels/fully_connected_common.cc +template +tflite_micro::FullyConnectedParams CreateFCParams( + const TensorQuantizationParameters& input_quant_params, + const TensorQuantizationParameters& weight_quant_params, + const float nonlinear_activation_input_scale) { + OpDataFullyConnected data; + const double input_product_scale = + input_quant_params.scale * weight_quant_params.scale; + double effective_scale = + input_product_scale / + static_cast(nonlinear_activation_input_scale); + + QuantizeMultiplier(effective_scale, &data.output_multiplier, + &data.output_shift); + + data.input_zero_point = input_quant_params.zero_point; + + data.filter_zero_point = 0; // symmetrically quantized + data.output_zero_point = 0; // symmetrically quantized + + data.output_activation_min = std::numeric_limits::min(); + data.output_activation_max = std::numeric_limits::max(); + + return tflite_micro::FullyConnectedParamsQuantized(data); +} + +inline tflite_micro::FullyConnectedParams CreateFCParamsFloat() { + FullyConnectedParams op_params; + CalculateActivationRange(kTfLiteActNone, &op_params.float_activation_min, + &op_params.float_activation_max); + return op_params; +} + +// Wrapper function to create gate parameters for the four internal LSTM gates +template +tflite_micro::GateParameters CreateGateParams( + const TensorQuantizationParameters& input_quant_params, + const TensorQuantizationParameters& hidden_state_quant_params, + const GateQuantizationParameters& gate_quantization_settings, + const float nonlinear_activation_input_scale) { + tflite_micro::GateParameters gate_params = {}; + gate_params.input_fc_params = CreateFCParams( + input_quant_params, gate_quantization_settings.activation_weight, + nonlinear_activation_input_scale); + gate_params.recurrent_fc_params = CreateFCParams( + hidden_state_quant_params, gate_quantization_settings.recurrent_weight, + nonlinear_activation_input_scale); + return gate_params; +} + +inline tflite_micro::GateParameters CreateGateParamsFloat() { + tflite_micro::GateParameters gate_params = {}; + gate_params.input_fc_params = CreateFCParamsFloat(); + gate_params.recurrent_fc_params = CreateFCParamsFloat(); + return gate_params; +} +// Create parameters for element wise multiplication that happens in a) cell +// state update ; b) hidden state update +// Note that all the output of gates are symmetrically quantized so only scales +// are required for input. However, during the hidden state update phase, the +// output is the updated hidden state, which is asymmetrically quantized. Thus +// output may require zero point +template +tflite_micro::ArithmeticParams CreateInterGateMulParams(const float input1_scale, + const float input2_scale, + const float output_scale, + const int output_zp = 0) { + tflite_micro::ArithmeticParams op_params = {}; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + op_params.input1_offset = 0; + op_params.input2_offset = 0; + op_params.output_offset = output_zp; + + const double input_product_scale = + static_cast(input1_scale) * static_cast(input2_scale); + double effective_scale = + input_product_scale / static_cast(output_scale); + + QuantizeMultiplier(effective_scale, &op_params.output_multiplier, + &op_params.output_shift); + return op_params; +} + +inline tflite_micro::ArithmeticParams CreateInterGateMulParamsFloat() { + tflite_micro::ArithmeticParams op_params = {}; + CalculateActivationRange(kTfLiteActNone, &op_params.float_activation_min, + &op_params.float_activation_max); + return op_params; +} + +// Create the additional information about the cell state, which include: +// cell_state_scale_power: used in integer nonlinear function (e.g., tanh) +// quantized_cell_clip: quantized cell clip range +CellStateInfo CreateLstmCellStateInfo(const float cell_state_scale, + const float cell_clip) { + CellStateInfo cell_state_info; + // cell_state_scale_power: 2^-cell_state_scale_power = cell state scale + int buffer; + tflite_micro::CheckedLog2(cell_state_scale, &buffer); + cell_state_info.cell_state_scale_power = buffer; + // Cell state specifics + cell_state_info.cell_clip = cell_clip; + cell_state_info.quantized_cell_clip = static_cast( + std::min(std::max(static_cast(cell_clip) / + static_cast(cell_state_scale), + -32768.0), + 32767.0)); + return cell_state_info; +} + +// Create LSTMKernelContents from LstmNodeContent by copying TfLiteEvalTensor +// pointers +template +LSTMKernelContents CreateLSTMKernelContent( + LstmNodeContent& + node_contents) { + LSTMKernelContents kernel_content; + // Point to correct tensors + kernel_content.internal_tensors[kLstmInputTensor] = + node_contents.GetEvalTensor(kLstmInputTensor); + kernel_content.internal_tensors[kLstmInputToInputWeightsTensor] = + node_contents.GetEvalTensor(kLstmInputToInputWeightsTensor); + kernel_content.internal_tensors[kLstmInputToForgetWeightsTensor] = + node_contents.GetEvalTensor(kLstmInputToForgetWeightsTensor); + kernel_content.internal_tensors[kLstmInputToCellWeightsTensor] = + node_contents.GetEvalTensor(kLstmInputToCellWeightsTensor); + kernel_content.internal_tensors[kLstmInputToOutputWeightsTensor] = + node_contents.GetEvalTensor(kLstmInputToOutputWeightsTensor); + kernel_content.internal_tensors[kLstmRecurrentToInputWeightsTensor] = + node_contents.GetEvalTensor(kLstmRecurrentToInputWeightsTensor); + kernel_content.internal_tensors[kLstmRecurrentToForgetWeightsTensor] = + node_contents.GetEvalTensor(kLstmRecurrentToForgetWeightsTensor); + kernel_content.internal_tensors[kLstmRecurrentToCellWeightsTensor] = + node_contents.GetEvalTensor(kLstmRecurrentToCellWeightsTensor); + kernel_content.internal_tensors[kLstmRecurrentToOutputWeightsTensor] = + node_contents.GetEvalTensor(kLstmRecurrentToOutputWeightsTensor); + kernel_content.internal_tensors[kLstmInputGateBiasTensor] = + node_contents.GetEvalTensor(kLstmInputGateBiasTensor); + kernel_content.internal_tensors[kLstmForgetGateBiasTensor] = + node_contents.GetEvalTensor(kLstmForgetGateBiasTensor); + kernel_content.internal_tensors[kLstmCellGateBiasTensor] = + node_contents.GetEvalTensor(kLstmCellGateBiasTensor); + kernel_content.internal_tensors[kLstmOutputGateBiasTensor] = + node_contents.GetEvalTensor(kLstmOutputGateBiasTensor); + kernel_content.internal_tensors[kLstmOutputStateTensor] = + node_contents.GetEvalTensor(kLstmOutputStateTensor); + kernel_content.internal_tensors[kLstmOutputGateBiasTensor] = + node_contents.GetEvalTensor(kLstmOutputGateBiasTensor); + kernel_content.internal_tensors[kLstmCellStateTensor] = + node_contents.GetEvalTensor(kLstmCellStateTensor); + // Not used internal tensors + kernel_content.internal_tensors[kLstmCellToInputWeightsTensor] = nullptr; + kernel_content.internal_tensors[kLstmCellToForgetWeightsTensor] = nullptr; + kernel_content.internal_tensors[kLstmCellToOutputWeightsTensor] = nullptr; + kernel_content.internal_tensors[kLstmProjectionWeightsTensor] = nullptr; + kernel_content.internal_tensors[kLstmProjectionBiasTensor] = nullptr; + kernel_content.internal_tensors[kLstmInputLayerNormCoefficientsTensor] = + nullptr; + kernel_content.internal_tensors[kLstmForgetLayerNormCoefficientsTensor] = + nullptr; + kernel_content.internal_tensors[kLstmInputLayerNormCoefficientsTensor] = + nullptr; + kernel_content.internal_tensors[kLstmCellLayerNormCoefficientsTensor] = + nullptr; + kernel_content.internal_tensors[kLstmOutputLayerNormCoefficientsTensor] = + nullptr; + // Output tensor + kernel_content.output_tensor = node_contents.OutputEvalTensor(); + return kernel_content; +} + +// Deduce the size information (Batch (B), Time Steps (T), Input dimension (I), +// State dimension (S)) that defines the LSTM using the input and hidden state +// tensor +LstmSizeInfo CreateLstmSizeInfo( + const bool time_major, const TfLiteIntArray* input_tensor_shape, + const TfLiteIntArray* hidden_state_tensor_shape) { + LstmSizeInfo size_info; + size_info.time_major = time_major; + size_info.batch_size = + time_major ? input_tensor_shape->data[1] : input_tensor_shape->data[0]; + size_info.time_steps = + time_major ? input_tensor_shape->data[0] : input_tensor_shape->data[1]; + size_info.input_dimension = input_tensor_shape->data[2]; + size_info.state_dimension = hidden_state_tensor_shape->data[1]; + return size_info; +} + +// Create the LstmOpData using the LstmNodeContent and +// NodeQuantizationParameters (defined in test_data/lstm_test_data) During the +// actual inference phase, OpDataLSTM is created using information from the +// flatbuffer file. The test divide the complete LSTM node information into +// LstmNodeContent and NodeQuantizationParameters for easy construction +// purposes +template +OpDataLSTM CreateLstmOpData( + LstmNodeContent& + node_contents) { + const auto& builtin_data = node_contents.BuiltinData(); + const auto& quantization_settings = node_contents.QuantizationSettings(); + OpDataLSTM op_data; + + op_data.cell_gate_nonlinear_type = builtin_data.activation; + op_data.size_info = + CreateLstmSizeInfo(builtin_data.time_major, + node_contents.GetEvalTensor(kLstmInputTensor)->dims, + node_contents.HiddenStateEvalTensor()->dims); + + op_data.cell_state_info = CreateLstmCellStateInfo( + quantization_settings.cell_state.scale, builtin_data.cell_clip); + + // Gate Parameters + op_data.forget_gate_parameters = CreateGateParams( + quantization_settings.input, quantization_settings.hidden_state, + quantization_settings.forget_gate, + quantization_settings.nonlinear_activation_input_scale); + op_data.input_gate_parameters = CreateGateParams( + quantization_settings.input, quantization_settings.hidden_state, + quantization_settings.input_gate, + quantization_settings.nonlinear_activation_input_scale); + op_data.cell_gate_parameters = CreateGateParams( + quantization_settings.input, quantization_settings.hidden_state, + quantization_settings.cell_gate, + quantization_settings.nonlinear_activation_input_scale); + op_data.output_gate_parameters = CreateGateParams( + quantization_settings.input, quantization_settings.hidden_state, + quantization_settings.output_gate, + quantization_settings.nonlinear_activation_input_scale); + // Inter gate multiplication parameters + op_data.inter_gate_parameters.forget_cell_mul_params = + CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.cell_state.scale, + quantization_settings.cell_state.scale); + op_data.inter_gate_parameters.input_mul_params = + CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.cell_state.scale); + op_data.inter_gate_parameters.output_mul_params = + CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point); + return op_data; +} + +template +OpDataLSTM CreateLstmOpDataFloat( + LstmNodeContent& node_contents) { + const auto& builtin_data = node_contents.BuiltinData(); + OpDataLSTM op_data; + + op_data.cell_gate_nonlinear_type = builtin_data.activation; + op_data.size_info = + CreateLstmSizeInfo(builtin_data.time_major, + node_contents.GetEvalTensor(kLstmInputTensor)->dims, + node_contents.HiddenStateEvalTensor()->dims); + op_data.cell_state_info.cell_clip = builtin_data.cell_clip; + op_data.cell_state_info.quantized_cell_clip = 0; // No quantization + op_data.cell_state_info.cell_state_scale_power = 0; // No quantization + + // Gate Parameters + op_data.forget_gate_parameters = CreateGateParamsFloat(); + op_data.input_gate_parameters = CreateGateParamsFloat(); + op_data.cell_gate_parameters = CreateGateParamsFloat(); + op_data.output_gate_parameters = CreateGateParamsFloat(); + // Inter gate multiplication parameters + op_data.inter_gate_parameters.forget_cell_mul_params = + CreateInterGateMulParamsFloat(); + op_data.inter_gate_parameters.input_mul_params = + CreateInterGateMulParamsFloat(); + op_data.inter_gate_parameters.output_mul_params = + CreateInterGateMulParamsFloat(); + return op_data; +} + +/*Test Functions Below Here*/ +template +void ValidateResultGoldens(const T* golden, const T* output_data, + const int output_len, const float tolerance) { + for (int i = 0; i < output_len; ++i) { + TF_LITE_MICRO_EXPECT_NEAR(golden[i], output_data[i], tolerance); + } +} + +template +void TestCalculateLstmGateFloat(const TfLiteEvalTensor* input, + const TfLiteEvalTensor* input_weight, + const TfLiteEvalTensor* input_bias, + // Recurrent FC + const TfLiteEvalTensor* recurrent, + const TfLiteEvalTensor* recurrent_weight, + const TfLiteEvalTensor* recurrent_bias, + // Result comparison + TfLiteFusedActivation nonlinear_type, + const float* expected_vals, float tolerance) { + float gate_output[batch_size * state_dimension] = {}; + float fc_output_buffer[batch_size * state_dimension] = {}; + + tflite_micro::GateParameters gate_params = CreateGateParamsFloat(); + + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, input->dims, recurrent->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + tflite_micro::lstm_internal::CalculateLstmGate( + step_info, gate_params, + // Input FC + input, input_weight, input_bias, + // Recurrent FC + recurrent, recurrent_weight, recurrent_bias, + // Output + gate_output, + // Scratch arrays + fc_output_buffer, nonlinear_type); + + ValidateResultGoldens(expected_vals, gate_output, + batch_size * state_dimension, tolerance); +} + +template +void TestCalculateLstmGateInteger( + const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight, + const TfLiteEvalTensor* input_bias, + // Recurrent FC + const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight, + const TfLiteEvalTensor* recurrent_bias, + // Quantization settings + const NodeQuantizationParameters& node_quantization_settings, + const GateQuantizationParameters& gate_quantization_settings, + // Result comparison + TfLiteFusedActivation nonlinear_type, const float* expected_vals, + float tolerance) { + CellType gate_output[batch_size * state_dimension] = {}; + CellType fc_output_buffer[batch_size * state_dimension] = {}; + + tflite_micro::GateParameters gate_params = CreateGateParams( + node_quantization_settings.input, node_quantization_settings.hidden_state, + gate_quantization_settings, + node_quantization_settings.nonlinear_activation_input_scale); + + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, input->dims, recurrent->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + // only int8 weight is supported now + tflite_micro::lstm_internal::CalculateLstmGate( + step_info, gate_params, + // Input FC + input, input_weight, input_bias, + // Recurrent FC + recurrent, recurrent_weight, recurrent_bias, + // Output + gate_output, + // Scratch arrays + fc_output_buffer, nonlinear_type); + + float gate_output_float[batch_size * state_dimension] = {}; + Dequantize(gate_output, batch_size * state_dimension, + node_quantization_settings.nonlinear_activation_output_scale, 0, + gate_output_float); + + ValidateResultGoldens(expected_vals, gate_output_float, + batch_size * state_dimension, tolerance); +} + +template +void TestUpdateLstmCellFloat( + const GateOutputCheckData& gate_output_data, + LstmNodeContent& node_content, + const float tolerance) { + float buffer[batch_size * state_dimension] = {}; + + auto forget_cell_mul_params = CreateInterGateMulParamsFloat(); + auto input_mul_params = CreateInterGateMulParamsFloat(); + + auto cell_state = node_content.CellStateEvalTensor(); + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, + node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, + node_content.HiddenStateEvalTensor()->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + // copy the data since it will be updated + float forget_gate[batch_size * state_dimension] = {}; + std::memcpy(forget_gate, gate_output_data.expected_forget_gate_output, + batch_size * state_dimension * sizeof(float)); + + CellStateInfo cell_state_info; + cell_state_info.cell_clip = node_content.BuiltinData().cell_clip; + // Call the function to be tested + tflite_micro::lstm_internal::UpdateLstmCell( + step_info, cell_state, forget_gate, + gate_output_data.expected_input_gate_output, + gate_output_data.expected_cell_gate_output, forget_cell_mul_params, + input_mul_params, cell_state_info, buffer); + + ValidateResultGoldens(gate_output_data.expected_updated_cell, + tflite_micro::micro::GetTensorData(cell_state), + batch_size * state_dimension, tolerance); +} + +template +void TestUpdateLstmCellInteger( + const GateOutputCheckData& gate_output_data, + LstmNodeContent& node_content, + const float tolerance) { + const auto& quantization_settings = node_content.QuantizationSettings(); + CellType quantized_forget_gate[batch_size * state_dimension] = {}; + tflite_micro::Quantize(gate_output_data.expected_forget_gate_output, + quantized_forget_gate, batch_size * state_dimension, + quantization_settings.nonlinear_activation_output_scale, 0); + + CellType quantized_input_gate[batch_size * state_dimension] = {}; + tflite_micro::Quantize(gate_output_data.expected_input_gate_output, + quantized_input_gate, batch_size * state_dimension, + quantization_settings.nonlinear_activation_output_scale, 0); + + CellType quantized_cell_gate[batch_size * state_dimension] = {}; + tflite_micro::Quantize(gate_output_data.expected_cell_gate_output, + quantized_cell_gate, batch_size * state_dimension, + quantization_settings.nonlinear_activation_output_scale, 0); + + CellType buffer[batch_size * state_dimension] = {}; + + auto forget_cell_mul_params = CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.cell_state.scale, + quantization_settings.cell_state.scale); + auto input_mul_params = CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.cell_state.scale); + + auto cell_state_info = + CreateLstmCellStateInfo(quantization_settings.cell_state.scale, + node_content.BuiltinData().cell_clip); + + auto cell_state = node_content.CellStateEvalTensor(); + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, + node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, + node_content.HiddenStateEvalTensor()->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + // Call the function to be tested + tflite_micro::lstm_internal::UpdateLstmCell( + step_info, cell_state, quantized_forget_gate, quantized_input_gate, + quantized_cell_gate, forget_cell_mul_params, input_mul_params, + cell_state_info, buffer); + + float cell_state_float[batch_size * state_dimension] = {}; + Dequantize(tflite_micro::micro::GetTensorData(cell_state), + batch_size * state_dimension, + quantization_settings.cell_state.scale, + quantization_settings.cell_state.zero_point, cell_state_float); + + ValidateResultGoldens(gate_output_data.expected_updated_cell, + cell_state_float, batch_size * state_dimension, + tolerance); +} + +template +void TestUpdateLstmHiddenFloat( + const GateOutputCheckData& gate_output_data, + LstmNodeContent& node_content, + const float tolerance) { + float buffer[batch_size * state_dimension] = {}; + + auto mul_params = CreateInterGateMulParamsFloat(); + + int32_t cell_state_scale_power = 0; + + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, + node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, + node_content.HiddenStateEvalTensor()->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + auto cell_state = node_content.CellStateEvalTensor(); + auto hidden_state = node_content.HiddenStateEvalTensor(); + + tflite_micro::lstm_internal::UpdateLstmHidden( + step_info, cell_state, hidden_state, + gate_output_data.expected_output_gate_output, mul_params, + cell_state_scale_power, buffer); + + ValidateResultGoldens(gate_output_data.expected_updated_hidden, + tflite_micro::micro::GetTensorData(hidden_state), + batch_size * state_dimension, tolerance); +} + +template +void TestUpdateLstmHiddenInteger( + const GateOutputCheckData& gate_output_data, + LstmNodeContent& node_content, + const float tolerance) { + const auto& quantization_settings = node_content.QuantizationSettings(); + CellType quantized_output_gate[batch_size * state_dimension] = {}; + tflite_micro::Quantize(gate_output_data.expected_output_gate_output, + quantized_output_gate, batch_size * state_dimension, + quantization_settings.nonlinear_activation_output_scale, 0); + + CellType buffer[batch_size * state_dimension] = {}; + + auto mul_params = CreateInterGateMulParams( + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.nonlinear_activation_output_scale, + quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point); + + int cell_state_scale_power_buffer; + tflite_micro::CheckedLog2(quantization_settings.cell_state.scale, + &cell_state_scale_power_buffer); + int32_t cell_state_scale_power = cell_state_scale_power_buffer; + + // Create step information: only one time step, no need to update + auto size_info = tflite_micro::testing::CreateLstmSizeInfo( + /*time_major*/ false, + node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, + node_content.HiddenStateEvalTensor()->dims); + // revise time_major = true to enable batch inference + size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); + + auto cell_state = node_content.CellStateEvalTensor(); + auto hidden_state = node_content.HiddenStateEvalTensor(); + + tflite_micro::lstm_internal::UpdateLstmHidden( + step_info, cell_state, hidden_state, quantized_output_gate, mul_params, + cell_state_scale_power, buffer); + + float hidden_state_float[batch_size * state_dimension] = {}; + Dequantize(tflite_micro::micro::GetTensorData(hidden_state), + batch_size * state_dimension, + quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point, hidden_state_float); + + ValidateResultGoldens(gate_output_data.expected_updated_hidden, + hidden_state_float, batch_size * state_dimension, + tolerance); +} + +template +void TestLstmStepFloat( + const GateOutputCheckData& gate_output_data, + const float hidden_state_tolerance, const float cell_state_tolerance, + /*can not be const, state will be updated*/ + LstmNodeContent& node_contents) { + // Mimicking the kernel preparation phase, node_contents approximate the + LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); + LSTMBuffers buffers; + // Scratch buffers on the stack + float buffer0[batch_size * state_dimension] = {}; + buffers.buffer0 = buffer0; + float buffer1[batch_size * state_dimension] = {}; + buffers.buffer1 = buffer1; + float buffer2[batch_size * state_dimension] = {}; + buffers.buffer2 = buffer2; + float buffer3[batch_size * state_dimension] = {}; + buffers.buffer3 = buffer3; + + OpDataLSTM op_data = CreateLstmOpDataFloat(node_contents); + // set time_major to true to test batch inference + op_data.size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&op_data.size_info); + tflite_micro::lstm_internal::LstmStep( + step_info, op_data, kernel_content, buffers); + + ValidateResultGoldens( + gate_output_data.expected_updated_hidden, + tflite_micro::micro::GetTensorData(kernel_content.HiddenStateTensor()), + batch_size * state_dimension, hidden_state_tolerance); + ValidateResultGoldens( + gate_output_data.expected_updated_cell, + tflite_micro::micro::GetTensorData(kernel_content.CellStateTensor()), + batch_size * state_dimension, cell_state_tolerance); +} + +template +void TestLstmStepInteger( + const GateOutputCheckData& gate_output_data, + const float hidden_state_tolerance, const float cell_state_tolerance, + /*can not be const, state will be updated*/ + LstmNodeContent& + node_contents) { + // Mimicking the kernel preparation phase, node_contents approximate the + LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); + LSTMBuffers buffers; + + // Scratch buffers on the stack + CellType buffer0[batch_size * state_dimension] = {}; + buffers.buffer0 = buffer0; + CellType buffer1[batch_size * state_dimension] = {}; + buffers.buffer1 = buffer1; + CellType buffer2[batch_size * state_dimension] = {}; + buffers.buffer2 = buffer2; + CellType buffer3[batch_size * state_dimension] = {}; + buffers.buffer3 = buffer3; + + OpDataLSTM op_data = CreateLstmOpData(node_contents); + // set time_major to true to test batch inference + op_data.size_info.time_major = true; + tflite_micro::lstm_internal::LstmStepManager step_info(&op_data.size_info); + tflite_micro::lstm_internal::LstmStep(step_info, op_data, kernel_content, + buffers); + + const auto& quantization_settings = node_contents.QuantizationSettings(); + float dequantized_hidden_state[batch_size * state_dimension] = {}; + Dequantize( + tflite_micro::micro::GetTensorData( + kernel_content.HiddenStateTensor()), + batch_size * state_dimension, quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point, dequantized_hidden_state); + + float dequantized_cell_state[batch_size * state_dimension] = {}; + Dequantize( + tflite_micro::micro::GetTensorData(kernel_content.CellStateTensor()), + batch_size * state_dimension, quantization_settings.cell_state.scale, + quantization_settings.cell_state.zero_point, dequantized_cell_state); + + ValidateResultGoldens(gate_output_data.expected_updated_hidden, + dequantized_hidden_state, batch_size * state_dimension, + hidden_state_tolerance); + ValidateResultGoldens(gate_output_data.expected_updated_cell, + dequantized_cell_state, batch_size * state_dimension, + cell_state_tolerance); +} + +template +void TestEvalLstmFloat( + const LstmEvalCheckData< + batch_size * time_steps * input_dimension, batch_size * state_dimension, + batch_size * state_dimension * time_steps>& eval_check_data, + const float hidden_state_tolerance, const float cell_state_tolerance, + LstmNodeContent& node_contents) { + // Mimicking the kernel preparation phase, node_contents approximate the node + LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); + // Scratch buffers on the stack + LSTMBuffers buffers; + float buffer0[batch_size * state_dimension] = {}; + buffers.buffer0 = buffer0; + float buffer1[batch_size * state_dimension] = {}; + buffers.buffer1 = buffer1; + float buffer2[batch_size * state_dimension] = {}; + buffers.buffer2 = buffer2; + float buffer3[batch_size * state_dimension] = {}; + buffers.buffer3 = buffer3; + + OpDataLSTM op_data = CreateLstmOpDataFloat(node_contents); + + tflite_micro::EvalLstm(op_data, kernel_content, + buffers); + + ValidateResultGoldens(eval_check_data.expected_hidden_state, + node_contents.GetHiddenStateData(), + batch_size * state_dimension, hidden_state_tolerance); + + ValidateResultGoldens(eval_check_data.expected_cell_state, + node_contents.GetCellStateData(), + batch_size * state_dimension, cell_state_tolerance); + + ValidateResultGoldens(eval_check_data.expected_output, + node_contents.GetOutputData(), + batch_size * state_dimension, hidden_state_tolerance); +} + +template +void TestEvalLstmInteger( + const LstmEvalCheckData< + batch_size * time_steps * input_dimension, batch_size * state_dimension, + batch_size * state_dimension * time_steps>& eval_check_data, + const float hidden_state_tolerance, const float cell_state_tolerance, + LstmNodeContent& + node_contents) { + // Mimicking the kernel preparation phase, node_contents approximate the node + LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); + // Scratch buffers on the stack + LSTMBuffers buffers; + CellType buffer0[batch_size * state_dimension] = {}; + buffers.buffer0 = buffer0; + CellType buffer1[batch_size * state_dimension] = {}; + buffers.buffer1 = buffer1; + CellType buffer2[batch_size * state_dimension] = {}; + buffers.buffer2 = buffer2; + CellType buffer3[batch_size * state_dimension] = {}; + buffers.buffer3 = buffer3; + + OpDataLSTM op_data = CreateLstmOpData(node_contents); + + tflite_micro::EvalLstm( + op_data, kernel_content, buffers); + + const auto& quantization_settings = node_contents.QuantizationSettings(); + float dequantized_hidden_state[batch_size * state_dimension] = {}; + Dequantize(node_contents.GetHiddenStateData(), batch_size * state_dimension, + quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point, + dequantized_hidden_state); + + ValidateResultGoldens(eval_check_data.expected_hidden_state, + dequantized_hidden_state, batch_size * state_dimension, + hidden_state_tolerance); + + float dequantized_cell_state[batch_size * state_dimension] = {}; + Dequantize(node_contents.GetCellStateData(), batch_size * state_dimension, + quantization_settings.cell_state.scale, + quantization_settings.cell_state.zero_point, + dequantized_cell_state); + ValidateResultGoldens(eval_check_data.expected_cell_state, + dequantized_cell_state, batch_size * state_dimension, + cell_state_tolerance); + + float dequantized_output[batch_size * state_dimension * time_steps] = {}; + Dequantize(node_contents.GetOutputData(), + batch_size * state_dimension * time_steps, + quantization_settings.output.scale, + quantization_settings.output.zero_point, dequantized_output); + ValidateResultGoldens(eval_check_data.expected_output, dequantized_output, + batch_size * state_dimension, hidden_state_tolerance); +} + +} // namespace testing +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h new file mode 100644 index 00000000..d30728b9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h @@ -0,0 +1,150 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +// Input Tensors of size {n_batch, n_input} +constexpr int kLstmInputTensor = 0; + +// Input weight tensors of size: {n_cell, n_input} +constexpr int kLstmInputToInputWeightsTensor = 1; // Optional +constexpr int kLstmInputToForgetWeightsTensor = 2; +constexpr int kLstmInputToCellWeightsTensor = 3; +constexpr int kLstmInputToOutputWeightsTensor = 4; + +// Recurrent weight tensors of size {n_cell, n_output} +constexpr int kLstmRecurrentToInputWeightsTensor = 5; // Optional +constexpr int kLstmRecurrentToForgetWeightsTensor = 6; +constexpr int kLstmRecurrentToCellWeightsTensor = 7; +constexpr int kLstmRecurrentToOutputWeightsTensor = 8; + +// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. +constexpr int kLstmCellToInputWeightsTensor = 9; // Optional +constexpr int kLstmCellToForgetWeightsTensor = 10; // Optional +constexpr int kLstmCellToOutputWeightsTensor = 11; // Optional + +// Gates bias tensors of size {n_cell} +constexpr int kLstmInputGateBiasTensor = 12; // Optional +constexpr int kLstmForgetGateBiasTensor = 13; +constexpr int kLstmCellGateBiasTensor = 14; +constexpr int kLstmOutputGateBiasTensor = 15; + +// Projection weight tensor of size {n_output, n_cell} +constexpr int kLstmProjectionWeightsTensor = 16; // Optional +// Projection bias tensor of size {n_output} +constexpr int kLstmProjectionBiasTensor = 17; // Optional + +// These state tensors are defined as variable tensors, and will be modified by +// this op. +constexpr int kLstmOutputStateTensor = 18; +constexpr int kLstmCellStateTensor = 19; + +// Layer norm coefficient tensors of size {n_cell}, representing a diagonal +// matrix. +constexpr int kLstmInputLayerNormCoefficientsTensor = 20; // Optional +constexpr int kLstmForgetLayerNormCoefficientsTensor = 21; // Optional +constexpr int kLstmCellLayerNormCoefficientsTensor = 22; // Optional +constexpr int kLstmOutputLayerNormCoefficientsTensor = 23; // Optional + +// Output tensors. +constexpr int kLstmOutputTensor = 0; + +// Parameters for the two fully conncted computation inside each gate +struct GateParameters { + FullyConnectedParams input_fc_params; + FullyConnectedParams recurrent_fc_params; +}; + +// Paramaters for the element wise multiplications between gate outputs +struct InterGateParameters { + ArithmeticParams forget_cell_mul_params; + ArithmeticParams input_mul_params; + ArithmeticParams output_mul_params; +}; + +// Size information about the LSTM kernel, which is deduced from tensors stored +// in the flat buffer file. +struct LstmSizeInfo { + bool time_major; + int batch_size; + int time_steps; + int input_dimension; + int state_dimension; +}; + +// Contains information about the cell state tensor +struct CellStateInfo { + float cell_clip; + // clipping range for cell state only 16 bits cell is supported (could be + // generalized through templatation) + int16_t quantized_cell_clip; + // 2^-cell_state_scale_power = cell state scale, required by integer tanh + // computation + int32_t cell_state_scale_power; +}; + +// Contains required computation information for LSTM kernel evaluation. +// Specifically, it includes shape and quantization settings for the LSTM +// internal operations. Formatted to support operations defined in the +// tensorflow/lite/kernels/internal/reference/integer_ops +// Should be constructed during the preparation phase +struct OpDataLSTM { + LstmSizeInfo size_info; + CellStateInfo cell_state_info; + TfLiteFusedActivation cell_gate_nonlinear_type; + GateParameters forget_gate_parameters; + GateParameters input_gate_parameters; + GateParameters cell_gate_parameters; + GateParameters output_gate_parameters; + InterGateParameters inter_gate_parameters; + int buffer_indices[4]; // TFLM only +}; + +// Provide an interface to access the internal tensors and buffers used for LSTM +// invocation. Constructed during the invocation phase +struct LSTMKernelContents { + public: + // Internal tensors, fixed (const). see lstm_shared.h for tensor names + const TfLiteEvalTensor* GetInternalTensor(const int tensor_index) const { + return internal_tensors[tensor_index]; + } + // Variable tensors (will be changed, can not be const) + TfLiteEvalTensor* HiddenStateTensor() { + return internal_tensors[kLstmOutputStateTensor]; + } + TfLiteEvalTensor* CellStateTensor() { + return internal_tensors[kLstmCellStateTensor]; + } + // Node internal tensors with indexes defined at the beginning of the file + TfLiteEvalTensor* internal_tensors[24]; + TfLiteEvalTensor* output_tensor; +}; + +template +struct LSTMBuffers { + // TFLM buffers requires buffer index from LstmOpData. + CellType* buffer0; + CellType* buffer1; + CellType* buffer2; + CellType* buffer3; +}; + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h new file mode 100644 index 00000000..52798128 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h @@ -0,0 +1,158 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ + +#include "signal/micro/kernels/irfft.h" +#include "signal/micro/kernels/rfft.h" +#include "tensorflow/lite/c/common.h" + +// Forward declaration of all micro op kernel registration methods. These +// registrations are included with the standard `BuiltinOpResolver`. +// +// This header is particularly useful in cases where only a subset of ops are +// needed. In such cases, the client can selectively add only the registrations +// their model requires, using a custom `(Micro)MutableOpResolver`. Selective +// registration in turn allows the linker to strip unused kernels. + +namespace tflite_micro { + +// TFLM is incrementally moving towards a flat tflite namespace +// (https://abseil.io/tips/130). Any new ops (or cleanup of existing ops should +// have their Register function declarations in the tflite namespace. + +TFLMRegistration Register_ABS(); +TFLMRegistration Register_ADD(); +TFLMRegistration Register_ADD_N(); +TFLMRegistration Register_ARG_MAX(); +TFLMRegistration Register_ARG_MIN(); +TFLMRegistration Register_ASSIGN_VARIABLE(); +TFLMRegistration Register_AVERAGE_POOL_2D(); +TFLMRegistration Register_BATCH_MATMUL(); +TFLMRegistration Register_BATCH_TO_SPACE_ND(); +TFLMRegistration Register_BROADCAST_ARGS(); +TFLMRegistration Register_BROADCAST_TO(); +TFLMRegistration Register_CALL_ONCE(); +TFLMRegistration Register_CAST(); +TFLMRegistration Register_CEIL(); +// TODO(b/160234179): Change custom OPs to also return by value. +TFLMRegistration* Register_CIRCULAR_BUFFER(); +TFLMRegistration Register_CONCATENATION(); +TFLMRegistration Register_CONV_2D(); +TFLMRegistration Register_COS(); +TFLMRegistration Register_CUMSUM(); +TFLMRegistration Register_DEPTH_TO_SPACE(); +TFLMRegistration Register_DEPTHWISE_CONV_2D(); +TFLMRegistration Register_DEQUANTIZE(); +TFLMRegistration Register_DIV(); +TFLMRegistration Register_ELU(); +TFLMRegistration Register_EMBEDDING_LOOKUP(); +TFLMRegistration Register_EQUAL(); +TFLMRegistration* Register_ETHOSU(); +TFLMRegistration Register_EXP(); +TFLMRegistration Register_EXPAND_DIMS(); +TFLMRegistration Register_FILL(); +TFLMRegistration Register_FLOOR(); +TFLMRegistration Register_FLOOR_DIV(); +TFLMRegistration Register_FLOOR_MOD(); +TFLMRegistration Register_FULLY_CONNECTED(); +TFLMRegistration Register_GATHER(); +TFLMRegistration Register_GATHER_ND(); +TFLMRegistration Register_GREATER(); +TFLMRegistration Register_GREATER_EQUAL(); +TFLMRegistration Register_HARD_SWISH(); +TFLMRegistration Register_IF(); +TFLMRegistration Register_L2_NORMALIZATION(); +TFLMRegistration Register_L2_POOL_2D(); +TFLMRegistration Register_LEAKY_RELU(); +TFLMRegistration Register_LESS(); +TFLMRegistration Register_LESS_EQUAL(); +TFLMRegistration Register_LOG(); +TFLMRegistration Register_LOG_SOFTMAX(); +TFLMRegistration Register_LOGICAL_AND(); +TFLMRegistration Register_LOGICAL_NOT(); +TFLMRegistration Register_LOGICAL_OR(); +TFLMRegistration Register_LOGISTIC(); +TFLMRegistration Register_MAX_POOL_2D(); +TFLMRegistration Register_MAXIMUM(); +TFLMRegistration Register_MEAN(); +TFLMRegistration Register_MINIMUM(); +TFLMRegistration Register_MIRROR_PAD(); +TFLMRegistration Register_MUL(); +TFLMRegistration Register_NEG(); +TFLMRegistration Register_NOT_EQUAL(); +TFLMRegistration Register_PACK(); +TFLMRegistration Register_PAD(); +TFLMRegistration Register_PADV2(); +TFLMRegistration Register_PRELU(); +TFLMRegistration Register_QUANTIZE(); +TFLMRegistration Register_READ_VARIABLE(); +TFLMRegistration Register_REDUCE_MAX(); +TFLMRegistration Register_RELU(); +TFLMRegistration Register_RELU6(); +TFLMRegistration Register_RESHAPE(); +TFLMRegistration Register_RESIZE_BILINEAR(); +TFLMRegistration Register_RESIZE_NEAREST_NEIGHBOR(); +TFLMRegistration Register_ROUND(); +TFLMRegistration Register_RSQRT(); +TFLMRegistration Register_SELECT_V2(); +TFLMRegistration Register_SHAPE(); +TFLMRegistration Register_SIN(); +TFLMRegistration Register_SLICE(); +TFLMRegistration Register_SOFTMAX(); +TFLMRegistration Register_SPACE_TO_BATCH_ND(); +TFLMRegistration Register_SPACE_TO_DEPTH(); +TFLMRegistration Register_SPLIT(); +TFLMRegistration Register_SPLIT_V(); +TFLMRegistration Register_SQRT(); +TFLMRegistration Register_SQUARE(); +TFLMRegistration Register_SQUARED_DIFFERENCE(); +TFLMRegistration Register_SQUEEZE(); +TFLMRegistration Register_STRIDED_SLICE(); +TFLMRegistration Register_SUB(); +TFLMRegistration Register_SUM(); +TFLMRegistration Register_SVDF(); +TFLMRegistration Register_TANH(); +TFLMRegistration Register_TRANSPOSE(); +TFLMRegistration Register_TRANSPOSE_CONV(); +// TODO(b/230666079): resolve conflict with xtensa implementation +TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); +TFLMRegistration Register_UNPACK(); +TFLMRegistration Register_VAR_HANDLE(); +TFLMRegistration Register_WHILE(); +TFLMRegistration Register_ZEROS_LIKE(); + +// TODO(b/295174388): Add the rest of inference only registration functions. +TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED(); + +// TODO(b/160234179): Change custom OPs to also return by value. +namespace tflm_signal { +TFLMRegistration* Register_DELAY(); +TFLMRegistration* Register_FFT_AUTO_SCALE(); +TFLMRegistration* Register_FILTER_BANK(); +TFLMRegistration* Register_FILTER_BANK_LOG(); +TFLMRegistration* Register_FILTER_BANK_SPECTRAL_SUBTRACTION(); +TFLMRegistration* Register_FILTER_BANK_SQUARE_ROOT(); +TFLMRegistration* Register_ENERGY(); +TFLMRegistration* Register_FRAMER(); +TFLMRegistration* Register_OVERLAP_ADD(); +TFLMRegistration* Register_PCAN(); +TFLMRegistration* Register_STACKER(); +TFLMRegistration* Register_WINDOW(); +} // namespace tflm_signal + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h new file mode 100644 index 00000000..6e2d328f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h @@ -0,0 +1,56 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This file and the associated .cc file is branched from +// tensorflow/lite/kernels/internal/reference/portable_tensor_utils* +// TFLM needs to create its own because the original files are coupled with +// the tensor_utils module, which we cannot reuse due to its use of the +// Eigen library. + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ + +#include +#include +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" + +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + +namespace tflite_micro { + +// Not all backends support CpuBackendContext usage, so forward declare to avoid +// pulling in its implementation. +// TODO(b/230666277): consider removing this since micro does not utilize it +class CpuBackendContext; + +// Apply sigmoid to elements of a vector. +void PortableApplySigmoidToVector(const float* vector, int v_size, + float* result); +// Apply tanh to elements of a vector +void PortableApplyTanhToVector(const float* vector, int v_size, float* result); +// Apply appropriate activation function to elements of a vector. +void PortableApplyActivationToVector(const float* vector, int v_size, + TfLiteFusedActivation activation, + float* result); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ \ No newline at end of file diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h new file mode 100644 index 00000000..a97049e0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h @@ -0,0 +1,74 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +extern const int kMulInput1Tensor; +extern const int kMulInput2Tensor; +extern const int kMulOutputTensor; + +struct OpDataMul { + int32_t input1_zero_point; + int32_t input2_zero_point; + + int32_t output_activation_min; + int32_t output_activation_max; + int32_t output_zero_point; + int32_t output_multiplier; + int output_shift; + + float output_activation_min_f32; + float output_activation_max_f32; +}; + +void* MulInit(TfLiteContext* context, const char* buffer, size_t length); + +TfLiteStatus CalculateOpDataMul(TfLiteContext* context, TfLiteNode* node, + TfLiteMulParams* params, OpDataMul* data); + +TfLiteStatus MulPrepare(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus EvalMulQuantizedReference(TfLiteContext* context, TfLiteNode* node, + const OpDataMul* data, + const TfLiteEvalTensor* input1, + const TfLiteEvalTensor* input2, + TfLiteEvalTensor* output); + +void EvalMulFloatReference(TfLiteContext* context, TfLiteNode* node, + TfLiteMulParams* params, const OpDataMul* data, + const TfLiteEvalTensor* input1, + const TfLiteEvalTensor* input2, + TfLiteEvalTensor* output); + +// Generic must define registration function. +TFLMRegistration Register_MUL(); + +#if defined(CMSIS_NN) +TFLMRegistration Register_MUL_INT8(); +#else +// Fallback registration +inline TFLMRegistration Register_MUL_INT8() { return Register_MUL(); } +#endif +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h new file mode 100644 index 00000000..265700a0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h @@ -0,0 +1,27 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +TfLiteStatus PadPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h new file mode 100644 index 00000000..2c0aa0f0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h @@ -0,0 +1,142 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h" +#include "tensorflow/lite/kernels/internal/reference/pooling.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/kernels/kernel_util.h" +#include "tensorflow/lite/kernels/padding.h" +#include "tensorflow/lite/micro/kernels/kernel_util.h" +#include "tensorflow/lite/micro/kernels/micro_ops.h" +#include "tensorflow/lite/micro/micro_log.h" + +namespace tflite_micro { + +extern const int kPoolingInputTensor; +extern const int kPoolingOutputTensor; + +struct OpDataPooling { + TfLitePaddingValues padding; + int32_t activation_min; + int32_t activation_max; + float activation_min_f32; + float activation_max_f32; +}; + +TfLiteStatus CalculateOpDataPooling(const TfLiteContext* context, + const TfLitePoolParams* params, + const TfLiteTensor* input, + const TfLiteTensor* output, + OpDataPooling* data); + +TfLiteStatus PoolingPrepare(TfLiteContext* context, TfLiteNode* node); + +void AveragePoolingEvalFloat(const TfLiteContext* context, + const TfLiteNode* node, + const TfLitePoolParams* params, + const OpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); + +template +void AveragePoolingEvalQuantized(TfLiteContext* context, const TfLiteNode* node, + const TfLitePoolParams* params, + const OpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output) { + TFLITE_DCHECK(input->type == kTfLiteInt8 || input->type == kTfLiteInt16); + + PoolParams op_params; + op_params.stride_height = params->stride_height; + op_params.stride_width = params->stride_width; + op_params.filter_height = params->filter_height; + op_params.filter_width = params->filter_width; + op_params.padding_values.height = data->padding.height; + op_params.padding_values.width = data->padding.width; + op_params.quantized_activation_min = data->activation_min; + op_params.quantized_activation_max = data->activation_max; + + reference_integer_ops::AveragePool(op_params, + tflite_micro::micro::GetTensorShape(input), + tflite_micro::micro::GetTensorData(input), + tflite_micro::micro::GetTensorShape(output), + tflite_micro::micro::GetTensorData(output)); +} + +void MaxPoolingEvalFloat(TfLiteContext* context, TfLiteNode* node, + TfLitePoolParams* params, const OpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); + +template +void MaxPoolingEvalQuantized(TfLiteContext* context, TfLiteNode* node, + TfLitePoolParams* params, + const OpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output) { + TFLITE_DCHECK(input->type == kTfLiteInt8 || input->type == kTfLiteInt16); + + tflite_micro::PoolParams op_params; + op_params.stride_height = params->stride_height; + op_params.stride_width = params->stride_width; + op_params.filter_height = params->filter_height; + op_params.filter_width = params->filter_width; + op_params.padding_values.height = data->padding.height; + op_params.padding_values.width = data->padding.width; + op_params.quantized_activation_min = data->activation_min; + op_params.quantized_activation_max = data->activation_max; + + reference_integer_ops::MaxPool(op_params, + tflite_micro::micro::GetTensorShape(input), + tflite_micro::micro::GetTensorData(input), + tflite_micro::micro::GetTensorShape(output), + tflite_micro::micro::GetTensorData(output)); +} + +#if defined(CMSIS_NN) || defined(XTENSA) +TFLMRegistration Register_AVERAGE_POOL_2D_INT8(); + +TFLMRegistration Register_MAX_POOL_2D_INT8(); + +TFLMRegistration Register_AVERAGE_POOL_2D_INT16(); + +TFLMRegistration Register_MAX_POOL_2D_INT16(); +#else +inline TFLMRegistration Register_AVERAGE_POOL_2D_INT8() { + return tflite_micro::Register_AVERAGE_POOL_2D(); +} + +inline TFLMRegistration Register_MAX_POOL_2D_INT8() { + return tflite_micro::Register_MAX_POOL_2D(); +} + +inline TFLMRegistration Register_AVERAGE_POOL_2D_INT16() { + return tflite_micro::Register_AVERAGE_POOL_2D(); +} + +inline TFLMRegistration Register_MAX_POOL_2D_INT16() { + return tflite_micro::Register_MAX_POOL_2D(); +} +#endif +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h new file mode 100644 index 00000000..e7bfbf17 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h @@ -0,0 +1,39 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +TfLiteStatus CalculatePreluParams(const TfLiteTensor* input, + const TfLiteTensor* alpha, + TfLiteTensor* output, PreluParams* params); + +void BroadcastPrelu4DSlowFloat(const RuntimeShape& unextended_input1_shape, + const float* input1_data, + const RuntimeShape& unextended_input2_shape, + const float* input2_data, + const RuntimeShape& unextended_output_shape, + float* output_data); + +TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h new file mode 100644 index 00000000..e61074ac --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h @@ -0,0 +1,37 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +struct OpDataQuantizeReference { + tflite_micro::QuantizationParams quantization_params; + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t requantize_output_multiplier; + int requantize_output_shift; + + int32_t input_zero_point; +}; + +TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node); +TfLiteStatus PrepareQuantizeReference(TfLiteContext* context, TfLiteNode* node); +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h new file mode 100644 index 00000000..9f70bd6c --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h @@ -0,0 +1,65 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +extern const int kMaxNumberOfAxis; +extern const int kMaxNumberOfReducedAxis; + +struct OpDataReduce { + int32_t multiplier; + int shift; + int temp_buffer_idx; + int resolved_axis_idx; + int input_zp; + float input_scale; + int output_zp; + float output_scale; + int num_output_elements; + int num_axis; +}; + +TfLiteStatus PrepareMaxHelper(TfLiteContext* context, TfLiteNode* node, + OpDataReduce* op_data); + +TfLiteStatus PrepareMeanOrSumHelper(TfLiteContext* context, TfLiteNode* node, + OpDataReduce* op_data); + +TfLiteStatus EvalMaxHelper(TfLiteContext* context, TfLiteNode* node, + OpDataReduce* op_data); +TfLiteStatus EvalMeanHelper(TfLiteContext* context, TfLiteNode* node, + OpDataReduce* op_data); +TfLiteStatus EvalSumHelper(TfLiteContext* context, TfLiteNode* node, + OpDataReduce* op_data); + +void ReduceResolveAxis(const int* axis_data, int axis_count, + MeanParams* op_params); + +TFLMRegistration Register_MEAN(); +TFLMRegistration Register_REDUCE_MAX(); +TFLMRegistration Register_SUM(); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h new file mode 100644 index 00000000..bcb3e056 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h @@ -0,0 +1,26 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +constexpr int kReshapeInputTensor = 0; +constexpr int kReshapeOutputTensor = 0; + +TfLiteStatus PrepareReshapeReference(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h new file mode 100644 index 00000000..137158c4 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h @@ -0,0 +1,67 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length); + +// Common helper function to SoftmaxPrepare. +TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, + const TfLiteTensor* input, + TfLiteTensor* output, + const TfLiteSoftmaxParams* params, + SoftmaxParams* op_data); + +TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node); + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +TFLMRegistration Register_SOFTMAX(); + +#if defined(XTENSA) || defined(CMSIS_NN) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 input and int16 output. +TFLMRegistration Register_SOFTMAX_INT8_INT16(); +#else +inline TFLMRegistration Register_SOFTMAX_INT8_INT16() { + return Register_SOFTMAX(); +} +#endif + +#if defined(CMSIS_NN) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 input/output and uses the latency optimized implementations. +TFLMRegistration Register_SOFTMAX_INT8(); + +// Returns a TFLMRegistration struct for kernel variant that only supports +// int16 input/output and uses the latency optimized implementations. +TFLMRegistration Register_SOFTMAX_INT16(); + +#else +inline TFLMRegistration Register_SOFTMAX_INT8() { return Register_SOFTMAX(); } + +inline TFLMRegistration Register_SOFTMAX_INT16() { return Register_SOFTMAX(); } +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h new file mode 100644 index 00000000..b62cf746 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h @@ -0,0 +1,40 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +constexpr int kStridedSliceInputTensor = 0; +constexpr int kStridedSliceBeginTensor = 1; +constexpr int kStridedSliceEndTensor = 2; +constexpr int kStridedSliceStridesTensor = 3; +constexpr int kStridedSliceOutputTensor = 0; + +void* StridedSliceInit(TfLiteContext* context, const char* buffer, + size_t length); + +TfLiteStatus StridedSlicePrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h new file mode 100644 index 00000000..130eddfe --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h @@ -0,0 +1,60 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +extern const int kSubInputTensor1; +extern const int kSubInputTensor2; +extern const int kSubOutputTensor; + +struct OpDataSub { + bool requires_broadcast; + + // These fields are used in both the general 8-bit -> 8bit quantized path, + // and the special 16-bit -> 16bit quantized path + int input1_shift; + int input2_shift; + int32_t output_activation_min; + int32_t output_activation_max; + + // These fields are used only in the general 8-bit -> 8bit quantized path + int32_t input1_multiplier; + int32_t input2_multiplier; + int32_t output_multiplier; + int output_shift; + int left_shift; + int32_t input1_offset; + int32_t input2_offset; + int32_t output_offset; +}; + +TfLiteStatus CalculateOpDataSub(TfLiteContext* context, TfLiteSubParams* params, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteTensor* output, OpDataSub* data); + +TfLiteStatus SubPrepare(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h new file mode 100644 index 00000000..469f8c57 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h @@ -0,0 +1,100 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/micro/micro_common.h" + +namespace tflite_micro { + +struct OpDataSvdf { + int32_t effective_scale_1_a; + int32_t effective_scale_2_a; + // b versions of each scale are kept at int since the numbers are just the + // shift value - typically between [-32, 32]. + int effective_scale_1_b; + int effective_scale_2_b; + int scratch_tensor_index; + int scratch_output_tensor_index; + + // Cached tensor zero point values for quantized operations. + int input_zero_point; + int output_zero_point; + int activation_state_zero_point; +}; + +// Input tensors. +extern const int kSvdfInputTensor; +extern const int kSvdfWeightsFeatureTensor; +extern const int kSvdfWeightsTimeTensor; +extern const int kSvdfBiasTensor; +// This is a variable tensor, and will be modified by this op. +extern const int kSvdfInputActivationStateTensor; + +// Output tensor. +extern const int kSvdfOutputTensor; + +void EvalInt8SvdfReference(TfLiteContext* context, TfLiteNode* node, + const TfLiteEvalTensor* input_tensor, + const TfLiteEvalTensor* weights_feature_tensor, + const TfLiteEvalTensor* weights_time_tensor, + const TfLiteEvalTensor* bias_tensor, + const TfLiteSVDFParams* params, + TfLiteEvalTensor* activation_state_tensor, + TfLiteEvalTensor* output_tensor, + const OpDataSvdf& data); + +// TODO(#523): remove 16-bit code when no longer needed. +void EvalInt16SvdfReference(TfLiteContext* context, TfLiteNode* node, + const TfLiteEvalTensor* input_tensor, + const TfLiteEvalTensor* weights_feature_tensor, + const TfLiteEvalTensor* weights_time_tensor, + const TfLiteEvalTensor* bias_tensor, + const TfLiteSVDFParams* params, + TfLiteEvalTensor* activation_state_tensor, + TfLiteEvalTensor* output_tensor, + const OpDataSvdf& data); + +void EvalFloatSvdfReference( + TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input, + const TfLiteEvalTensor* weights_feature, + const TfLiteEvalTensor* weights_time, const TfLiteEvalTensor* bias, + const TfLiteSVDFParams* params, int scratch_tensor_index, + TfLiteEvalTensor* activation_state, TfLiteEvalTensor* output); + +TfLiteStatus PrepareSvdf(TfLiteContext* context, TfLiteNode* node); + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +TFLMRegistration Register_SVDF(); + +#if defined(HEXAGON) || defined(CMSIS_NN) || defined(XTENSA) + +TFLMRegistration Register_SVDF_INT8(); + +#else +// Note that while this block gets used for both reference and optimized kernels +// that do not have any specialized implementations, the only goal here is to +// define fallback implementation that allow reference kernels to still be used +// from applications that call a more specific kernel variant. + +inline TFLMRegistration Register_SVDF_INT8() { return Register_SVDF(); } + +#endif +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h new file mode 100644 index 00000000..4f7ef613 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h @@ -0,0 +1,37 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { +extern const int8_t kConvInput1x32x32x3[]; +extern const int8_t kConvFilter8x3x3x3[]; +extern const int32_t kConvBiasQuantized8[]; +extern const int8_t kConvGoldenOutput1x16x16x8[]; + +// Kernel Conv Test Cases: Int8Filter1x3x3x1ShouldMatchGolden +extern const int8_t kConvInput1x4x4x1[]; +extern const int8_t kConvInput1x5x5x1[]; +extern const int8_t kConvFilter1x3x3x1[]; +extern const int32_t kConvZeroBias[]; +extern const int8_t kConvGoldenOutput4x4InputPaddingSame2x2[]; +extern const int8_t kConvGoldenOutput5x5InputPaddingSame3x3[]; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h new file mode 100644 index 00000000..671bbb00 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h @@ -0,0 +1,579 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" +#include "tensorflow/lite/micro/kernels/lstm_shared.h" +#include "tensorflow/lite/micro/test_helpers.h" + +namespace tflite_micro { +namespace testing { +// Data structure to store all the data used to check output of internal gates +// of one time step +// input_size = batch_size*input_dimension (size of the input array) +// gate_output_size = batch_size*state_dimension (size of the gate output) +template +struct GateOutputCheckData { + float input_data[input_size]; + float hidden_state[gate_output_size]; + float cell_state[gate_output_size]; + float expected_forget_gate_output[gate_output_size]; + float expected_input_gate_output[gate_output_size]; + float expected_output_gate_output[gate_output_size]; + float expected_cell_gate_output[gate_output_size]; + float expected_updated_cell[gate_output_size]; + float expected_updated_hidden[gate_output_size]; +}; + +// Data structure to store all the data used to check the output of the kernel +// of multiple batch, multiple timesteps +// input_size = batch_size*time_steps*input_dimension (size of the input array) +// gate_output_size = batch_size*state_dimension (size of the gate output) +// output_size = time_steps*gate_output_size (size of the output from the +// kernel) +template +struct LstmEvalCheckData { + float input_data[input_size]; + float hidden_state[gate_output_size]; + float expected_output[output_size]; + float expected_hidden_state[gate_output_size]; + float expected_cell_state[gate_output_size]; +}; + +// Struct that holds the weight/bias information for a standard gate (i.e. no +// modification such as layer normalization, peephole, etc.) +// Every gate is defined by the type and size of the weights (bias included) +// inside. +// Specifically, types are weight type and bias type (normally the same +// type of MatMul accumulator). +// activation_weight has shape (hidden state dimension * input tensor dimension) +// recurrent_weight has shape (hidden state dimension * hidden state dimension) +// bias has shape (hidden state dimension, 1) +template +struct GateData { + WeightType activation_weight[state_dimension * input_dimension]; + WeightType recurrent_weight[state_dimension * state_dimension]; + BiasType fused_bias[state_dimension]; + // Quantized model folded the zero point of activations into biases: + // bias + zero_point * weight. + // Note: folded bias is only required for the legacy 8x8->16 pass. Therefore + // the data type is fixed here to avoid compilation errors (the computation of + // folding does not support other types) + int32_t activation_zp_folded_bias[state_dimension]; + int32_t recurrent_zp_folded_bias[state_dimension]; +}; + +// A struct that holds quantization parameters for a LSTM Tensor +struct TensorQuantizationParameters { + double scale; + int zero_point; + bool symmetry; +}; + +// A struct that holds quantization parameters for an internal gate, which is +// defined by activation/recurrent weight and bias (assuming no internal layer +// normalization) +struct GateQuantizationParameters { + TensorQuantizationParameters activation_weight; + TensorQuantizationParameters recurrent_weight; + TensorQuantizationParameters bias; +}; + +// A struct that holds the quantization settings for the LSTM node. Data +// members can be grouped into five parts. +// 1. Data types (activation,weight, cell, bias) +// 2. Non-linear activation (i.e., tanh and sigmoid) fixed point +// calculation settings +// 3. Input/output tensor quantization settings +// 4. Internal state (hidden and cell) quantization settings +// 5. Internal gate (forget, input, cell, output) settings +struct NodeQuantizationParameters { + TfLiteType activation_type; + TfLiteType weight_type; + TfLiteType cell_type; + TfLiteType bias_type; + // Fixed point setting for integer nonlinear activation calculation + double nonlinear_activation_input_scale; + double nonlinear_activation_output_scale; + // Quantization parameters for input/output + TensorQuantizationParameters input; + TensorQuantizationParameters output; + // Quantization parameters for internal states + TensorQuantizationParameters hidden_state; + TensorQuantizationParameters cell_state; + // Quantization parameters for gates + GateQuantizationParameters forget_gate; + GateQuantizationParameters input_gate; + GateQuantizationParameters cell_gate; + GateQuantizationParameters output_gate; +}; + +// Data structure that holds all the information to evaluate a LSTM kernel +// (mimic the LSTM node). +// Tensor Types: +// ActivationType defines the data type of input/output of the layer. The hidden +// state has the ActivationType as well since it is the layer output of the +// previous time. +// WeightType defines the weight data type inside the internal gates. +// BiasType defines the bias data type inside the internal gates. (normally the +// same type of MatMul accumulator). +// Tensor Shapes: +// The input to the layer has shape (batch_size,time_steps,input_dimension). +// Both the hidden state and cell state has shape (state_dimension, 1) +// The output of the layer has shape (batch_size,time_steps,state_dimension) +// Note: state values can change through calls (stateful) +template +class LstmNodeContent { + public: + LstmNodeContent(const LstmNodeContent& other) = default; + LstmNodeContent& operator=(const LstmNodeContent& other) = default; + // Use the general model setting (builtin data) and the four gates data to + // construct the node content. Note the input, hidden state, and cell state + // data is provided later for flexible testing (initialize as zero now) + LstmNodeContent( + const TfLiteUnidirectionalSequenceLSTMParams builtin_data, + const GateData + forget_gate_params, + const GateData + input_gate_params, + const GateData + cell_gate_params, + const GateData + output_gate_params) + : builtin_data_(builtin_data), + forget_gate_data_(forget_gate_params), + input_gate_data_(input_gate_params), + cell_gate_data_(cell_gate_params), + output_gate_data_(output_gate_params) { + InitializeTensors(); + } + + // Add quantization parameters (scale, zero point) to tensors + // Only required for the integer kernel + void AddQuantizationParameters( + const NodeQuantizationParameters& quantization_params) { + quantization_settings_ = quantization_params; + // Input Tensor + SetTensorQuantizationParam(kLstmInputTensor, quantization_params.input); + // Forget Gate Tensors + const auto& forget_gate_quant_param = quantization_params.forget_gate; + SetTensorQuantizationParam(kLstmInputToForgetWeightsTensor, + forget_gate_quant_param.activation_weight); + SetTensorQuantizationParam(kLstmRecurrentToForgetWeightsTensor, + forget_gate_quant_param.recurrent_weight); + SetTensorQuantizationParam(kLstmForgetGateBiasTensor, + forget_gate_quant_param.bias); + // Input Gate Tensors + const auto& input_gate_quant_param = quantization_params.input_gate; + SetTensorQuantizationParam(kLstmInputToInputWeightsTensor, + input_gate_quant_param.activation_weight); + SetTensorQuantizationParam(kLstmRecurrentToInputWeightsTensor, + input_gate_quant_param.recurrent_weight); + SetTensorQuantizationParam(kLstmInputGateBiasTensor, + input_gate_quant_param.bias); + // Cell Gate Tensors + const auto& cell_gate_quant_param = quantization_params.cell_gate; + SetTensorQuantizationParam(kLstmInputToCellWeightsTensor, + cell_gate_quant_param.activation_weight); + SetTensorQuantizationParam(kLstmRecurrentToCellWeightsTensor, + cell_gate_quant_param.recurrent_weight); + SetTensorQuantizationParam(kLstmCellGateBiasTensor, + cell_gate_quant_param.bias); + // Output Gate Tensors + const auto& output_gate_quant_param = quantization_params.output_gate; + SetTensorQuantizationParam(kLstmInputToOutputWeightsTensor, + output_gate_quant_param.activation_weight); + SetTensorQuantizationParam(kLstmRecurrentToOutputWeightsTensor, + output_gate_quant_param.recurrent_weight); + SetTensorQuantizationParam(kLstmOutputGateBiasTensor, + output_gate_quant_param.bias); + // State Tensors + SetTensorQuantizationParam(kLstmOutputStateTensor, + quantization_params.hidden_state); + SetTensorQuantizationParam(kLstmCellStateTensor, + quantization_params.cell_state); + // Output Tensor + SetTensorQuantizationParam(24, quantization_params.output); + } + + // Provide interface to set the input tensor values for flexible testing + void SetInputData(const ActivationType* data) { + std::memcpy( + input_, data, + batch_size * input_dimension * time_steps * sizeof(ActivationType)); + SetTensor(kLstmInputTensor, input_, input_size_); + } + const ActivationType* GetInputData() const { return input_; } + + // Provide interface to set the hidden state tensor values for flexible + // testing + void SetHiddenStateData(const ActivationType* data) { + std::memcpy(hidden_state_, data, + batch_size * state_dimension * sizeof(ActivationType)); + } + ActivationType* GetHiddenStateData() { return hidden_state_; } + + // Provide interface to set the cell state tensor values for flexible + // testing + void SetCellStateData(const CellType* data) { + std::memcpy(cell_state_, data, + batch_size * state_dimension * sizeof(CellType)); + } + CellType* GetCellStateData() { return cell_state_; } + ActivationType* GetOutputData() { return output_; } + + // Internal tensors, see lstm_shared.h for tensor names + TfLiteEvalTensor* GetEvalTensor(const int tensor_index) { + auto valid_index = input_tensor_indices_[tensor_index + 1]; + if (valid_index < 0) { + return nullptr; + } + return &eval_tensors_[tensor_index]; + } + + TfLiteTensor* GetTensors() { return tensors_; } + + // Required by the kernel runner + TfLiteIntArray* KernelInputs() { + return IntArrayFromInts(input_tensor_indices_); + } + // Required by the kernel runner + TfLiteIntArray* KernelOutputs() { + return IntArrayFromInts(output_tensor_indices_); + } + + // Variable tensors (will be changed, can not be const) + TfLiteEvalTensor* HiddenStateEvalTensor() { + return &eval_tensors_[kLstmOutputStateTensor]; + } + TfLiteEvalTensor* CellStateEvalTensor() { + return &eval_tensors_[kLstmCellStateTensor]; + } + TfLiteEvalTensor* OutputEvalTensor() { return &eval_tensors_[24]; } + + const GateData& + ForgetGateData() const { + return forget_gate_data_; + } + const GateData& + InputGateData() const { + return input_gate_data_; + } + const GateData& + CellGateData() const { + return cell_gate_data_; + } + const GateData& + OutputGateData() const { + return output_gate_data_; + } + + const TfLiteUnidirectionalSequenceLSTMParams& BuiltinData() const { + return builtin_data_; + } + + const NodeQuantizationParameters& QuantizationSettings() const { + return quantization_settings_; + } + + private: + void InitializeTensors() { + // Invalid all the input tensors untill we set it + input_tensor_indices_[0] = 24; // tot elements + for (size_t i = 1; i < 25; i++) { + input_tensor_indices_[i] = kTfLiteOptionalTensor; + } + // Input Tensor + SetTensor(kLstmInputTensor, input_, input_size_); + // Forget Gate Tensors + SetTensor(kLstmInputToForgetWeightsTensor, + forget_gate_data_.activation_weight, activation_weight_size_); + SetTensor(kLstmRecurrentToForgetWeightsTensor, + forget_gate_data_.recurrent_weight, recurrent_weight_size_); + SetTensor(kLstmForgetGateBiasTensor, forget_gate_data_.fused_bias, + bias_size_); + // Input Gate Tensors + SetTensor(kLstmInputToInputWeightsTensor, + input_gate_data_.activation_weight, activation_weight_size_); + SetTensor(kLstmRecurrentToInputWeightsTensor, + input_gate_data_.recurrent_weight, recurrent_weight_size_); + SetTensor(kLstmInputGateBiasTensor, input_gate_data_.fused_bias, + bias_size_); + // Cell Gate Tensors + SetTensor(kLstmInputToCellWeightsTensor, cell_gate_data_.activation_weight, + activation_weight_size_); + SetTensor(kLstmRecurrentToCellWeightsTensor, + cell_gate_data_.recurrent_weight, recurrent_weight_size_); + SetTensor(kLstmCellGateBiasTensor, cell_gate_data_.fused_bias, bias_size_); + // Output Gate Tensors + SetTensor(kLstmInputToOutputWeightsTensor, + output_gate_data_.activation_weight, activation_weight_size_); + SetTensor(kLstmRecurrentToOutputWeightsTensor, + output_gate_data_.recurrent_weight, recurrent_weight_size_); + SetTensor(kLstmOutputGateBiasTensor, output_gate_data_.fused_bias, + bias_size_); + // State Tensors + SetTensor(kLstmOutputStateTensor, hidden_state_, state_size_, + /*is_variable=*/true); + SetTensor(kLstmCellStateTensor, cell_state_, state_size_, + /*is_variable=*/true); + // // Output Tensor + SetTensor(24, output_, output_size_, /*is_variable=*/true); + } + + template + void SetTensor(const int index, const T* data, int* dims, + const bool is_variable = false) { + // Lite tensors for kernel level testing + tensors_[index].data.data = const_cast(data); + tensors_[index].dims = IntArrayFromInts(dims); + tensors_[index].type = typeToTfLiteType(); + tensors_[index].is_variable = is_variable; + // Eval tensors for internal computation testing + eval_tensors_[index].data.data = const_cast(data); + eval_tensors_[index].dims = IntArrayFromInts(dims); + eval_tensors_[index].type = typeToTfLiteType(); + // update the index + if (index < 24) { + input_tensor_indices_[index + 1] = index; + } + } + + void SetTensorQuantizationParam( + const int index, const TensorQuantizationParameters& quant_param) { + tensors_[index].params.scale = quant_param.scale; + tensors_[index].params.zero_point = quant_param.zero_point; + } + + const TfLiteUnidirectionalSequenceLSTMParams builtin_data_; + GateData + forget_gate_data_; + GateData + input_gate_data_; + GateData + cell_gate_data_; + GateData + output_gate_data_; + + // Keep to ease the testing process (although all quantization information can + // be obtained from individual tensors, they are well organized here and light + // weighted) + NodeQuantizationParameters quantization_settings_; + + // Not const since IntArrayFromInts takes int *; the first element of the + // array must be the size of the array + int input_size_[4] = {3, batch_size, time_steps, input_dimension}; + int output_size_[4] = {3, batch_size, time_steps, state_dimension}; + // weight tensor has C-style "row-major" memory ordering + int activation_weight_size_[3] = {2, state_dimension, input_dimension}; + int recurrent_weight_size_[3] = {2, state_dimension, state_dimension}; + int bias_size_[2] = {1, state_dimension}; + int state_size_[3] = {2, batch_size, state_dimension}; + + // see lstm_shared.h for tensor names, the last tensor is the output tensor + TfLiteTensor tensors_[24 + 1]; + // Use for internel kernel testing + TfLiteEvalTensor eval_tensors_[24 + 1]; + // indices for the tensors inside the node (required by kernel runner) + int input_tensor_indices_[1 + 24] = {}; + // single output (last in the tensors array) + int output_tensor_indices_[2] = {1, 24}; + + // tennsor data + // states are initialized to zero + ActivationType hidden_state_[batch_size * state_dimension] = {}; + CellType cell_state_[batch_size * state_dimension] = {}; + // input is defined in the ModelContent (const across all derived models) + ActivationType input_[batch_size * input_dimension * time_steps] = {}; + ActivationType output_[batch_size * state_dimension * time_steps] = {}; +}; + +// Converts floating point gate parameters to the corresponding quantized +// version +template +GateData +CreateQuantizedGateData( + const GateData& + gate_parameters, + const TensorQuantizationParameters& input_quantization_params, + const TensorQuantizationParameters& output_quantization_params, + const GateQuantizationParameters& gate_quantization_params, + const bool fold_zero_point) { + GateData + quantized_gate_params; + tflite_micro::SymmetricQuantize(gate_parameters.activation_weight, + quantized_gate_params.activation_weight, + state_dimension * input_dimension, + gate_quantization_params.activation_weight.scale); + tflite_micro::SymmetricQuantize(gate_parameters.recurrent_weight, + quantized_gate_params.recurrent_weight, + state_dimension * state_dimension, + gate_quantization_params.recurrent_weight.scale); + tflite_micro::SymmetricQuantize(gate_parameters.fused_bias, + quantized_gate_params.fused_bias, state_dimension, + gate_quantization_params.bias.scale); + // Note: steps below are not required for the generalized LSTM evaluation + // (e.g., 16bits activation) + if (fold_zero_point) { + // Copy the bias values to prepare zero_point folded + // bias precomputation. bias has same scale as + // input_scale*input_weight_scale) + std::memcpy(quantized_gate_params.activation_zp_folded_bias, + quantized_gate_params.fused_bias, 2 * sizeof(int32_t)); + // Pre-calculate bias - zero_point * weight (a constant). + tflite_micro::tensor_utils::MatrixScalarMultiplyAccumulate( + quantized_gate_params.activation_weight, + -1 * input_quantization_params.zero_point, 2, 2, + quantized_gate_params.activation_zp_folded_bias); + + // Initialize the folded bias to zeros for accumulation + for (size_t i = 0; i < 2; i++) { + quantized_gate_params.recurrent_zp_folded_bias[i] = 0; + } + // Calculate : -zero_point * weight since it is a constant + tflite_micro::tensor_utils::MatrixScalarMultiplyAccumulate( + quantized_gate_params.recurrent_weight, + -1 * output_quantization_params.zero_point, 2, 2, + quantized_gate_params.recurrent_zp_folded_bias); + } + return quantized_gate_params; +} + +// Create integer LSTM node content from the float node contents and +// quantization settings +// Note: fold_zero_point folds the zero point into the bias (precomputation), +// which is not required for the generalized integer inference (16 bits act +// LSTM). +template +LstmNodeContent +CreateIntegerNodeContents( + const NodeQuantizationParameters& quantization_settings, + const bool fold_zero_point, + LstmNodeContent& float_node_contents) { + const auto quantized_forget_gate_data = + CreateQuantizedGateData( + float_node_contents.ForgetGateData(), quantization_settings.input, + quantization_settings.output, quantization_settings.forget_gate, + fold_zero_point); + const auto quantized_input_gate_data = + CreateQuantizedGateData( + float_node_contents.InputGateData(), quantization_settings.input, + quantization_settings.output, quantization_settings.input_gate, + fold_zero_point); + const auto quantized_cell_gate_data = + CreateQuantizedGateData( + float_node_contents.CellGateData(), quantization_settings.input, + quantization_settings.output, quantization_settings.cell_gate, + fold_zero_point); + const auto quantized_output_gate_params = + CreateQuantizedGateData( + float_node_contents.OutputGateData(), quantization_settings.input, + quantization_settings.output, quantization_settings.output_gate, + fold_zero_point); + LstmNodeContent + quantized_node_content( + float_node_contents.BuiltinData(), quantized_forget_gate_data, + quantized_input_gate_data, quantized_cell_gate_data, + quantized_output_gate_params); + + // Quantize the floating point input + ActivationType quantized_input[batch_size * input_dimension * time_steps] = + {}; + Quantize(float_node_contents.GetInputData(), quantized_input, + batch_size * input_dimension * time_steps, + quantization_settings.input.scale, + quantization_settings.input.zero_point); + quantized_node_content.SetInputData(quantized_input); + // Quantize the floating point hidden state + ActivationType quantized_hidden_state[batch_size * state_dimension] = {}; + Quantize(float_node_contents.GetHiddenStateData(), quantized_hidden_state, + batch_size * state_dimension, + quantization_settings.hidden_state.scale, + quantization_settings.hidden_state.zero_point); + quantized_node_content.SetHiddenStateData(quantized_hidden_state); + // Quantize the floating point cell state + CellType quantized_cell_state[batch_size * state_dimension] = {}; + Quantize(float_node_contents.GetCellStateData(), quantized_cell_state, + batch_size * state_dimension, quantization_settings.cell_state.scale, + quantization_settings.cell_state.zero_point); + quantized_node_content.SetCellStateData(quantized_cell_state); + + // Add scale and zero point to tensors + quantized_node_content.AddQuantizationParameters(quantization_settings); + return quantized_node_content; +} + +// Get the gate output data (one time step) for a simple 2X2 model +// batch_size = 2; time_steps = 1; input_dimension = 2; state_dimension = 2 +// input_size = batch_size*time_steps*input_dimension = 4 +// gate_output_size = batch_size*state_dimension = 4 +GateOutputCheckData<4, 4> Get2X2GateOutputCheckData(); + +// Get the kernel output data for a simple 2X2 model +// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 +// input_size = batch_size*time_steps*input_dimension = 12 +// gate_output_size = batch_size*state_dimension = 4 +// output_size = time_steps*gate_output_size = 12 +LstmEvalCheckData<12, 4, 12> Get2X2LstmEvalCheckData(); + +// Create a 2x2 float node content +// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 +LstmNodeContent +Create2x3x2X2FloatNodeContents(const float* input_data = nullptr, + const float* hidden_state = nullptr, + const float* cell_state = nullptr); + +// Get the quantization settings for the 2X2 model +NodeQuantizationParameters Get2X2Int8LstmQuantizationSettings(); + +// Create int8 (activation) x int8 (weight) -> int16 (cell) node +// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 +// input is in float format since the source of truth is always the float +// configuration +LstmNodeContent +Create2x3x2X2Int8NodeContents(const float* input_data = nullptr, + const float* hidden_state = nullptr, + const float* cell_state = nullptr); + +// Create int16 (activation) x int8 (weight) -> int16 (cell) node +// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 +// input is in float format since the source of truth is always the float +// configuration +LstmNodeContent +Create2x3x2X2Int16NodeContents(const float* input_data = nullptr, + const float* hidden_state = nullptr, + const float* cell_state = nullptr); + +} // namespace testing +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h new file mode 100644 index 00000000..a66c7566 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h @@ -0,0 +1,47 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +// This is the most generic TFLMRegistration. The actual supported types +// may still be target dependent. The only requirement is that every +// implementation (reference or optimized) must define this function. +// TODO(b/230666079): resolve conflict with xtensa implementation +TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); + +#if defined(CMSIS_NN) +// Returns a TFLMRegistration struct for kernel variant that only supports +// int8 activations and int8 weights and uses the latency optimized +// implementations. +TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8(); + +#else +inline TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8() { + return Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); +} +#endif + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h new file mode 100644 index 00000000..7d814717 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h @@ -0,0 +1,139 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ + +#if defined(HIFIMINI) +#include + +#include +#include +#include + +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h" + +namespace tflite_micro { + +// INT24 MIN/MAX +#define INT24_MIN -8388608 +#define INT24_MAX 8388607 + +// Multiply 24bit value by a quantized multiplier (w/ shift) and returns a 48bit +// aligned value in the QR register. +inline ae_q56s MultiplyByQuantizedMultiplier(ae_p24x2s x_24x2, + int32_t quantized_multiplier, + int shift) { + // A value with 1 sign bit, N integer bits and M fractional bits is + // represented as QN+1.M since the sign bit is included in the integer bits. + // + // The Q notation in this method explains the values represented in each + // variable, along with an implicit division since the quantized_multiplier + // represents a value between 0.5 and 1.0 (Q1.X-1 where X is the bit precision + // of the type). + // + // Load the quantized multiplier into the PR register. + // NOTE: This method assumes that this param has been calculated for 24bit + // space - not 32bits. + // Q32.0 / 2^23 -> Q24.0 / 2^23 representing a Q1.23 multiplier. + ae_p24x2s quantized_multiplier_24x2 = AE_MOVPA24(quantized_multiplier); + // Shift right by 23 - 16 bits minus the specified shift. This is because we + // keep 16 fractional bits until the end to perform rounding. Subtract shift + // since shift is a left shift, and the 23-16 is a right shift. + int shift_amount = 7 - shift; + + // Find the product of x and the quantized_multiplier. + // Q24.0 / 2^23 * Q24.0 = Q48.0 / 2^23 + // Q48.0 / 2^23 >> 7 = Q48.0 / 2^16 + ae_q56s result_56 = AE_MULP24S_HH(x_24x2, quantized_multiplier_24x2); + + // Shift right if shift amount is positive, left if shift amount is negative. + if (shift_amount >= 0) { + result_56 = AE_Q56S_SRA(result_56, shift_amount); + } else { + result_56 = AE_Q56S_SLA(result_56, -shift_amount); + } + + // Round off the bottom 16 bits. + // Q48.0 / 2^16 -> Q32.0 aligned to 48 bits. + result_56 = AE_ROUNDSQ32SYM(result_56); + return result_56; +} + +// Multiply 32bit value by a quantized multiplier (w/ shift) and returns a 48bit +// aligned value in the QR register. +inline ae_q56s MultiplyByQuantizedMultiplierResult48Bit( + int32_t x, int32_t quantized_multiplier, int shift) { + // Convert x into a 2x24bit PR register file. If x is outside the numerical + // limits of a 24bit integer, the "fractional" or lower 8bits are discarded. + // If x is within the range of a 24 bit integer, the "signed" or upper 8bits + // are discarded. + ae_p24x2s x_24x2; + if (x > INT24_MIN && x < INT24_MAX) { + x_24x2 = AE_MOVPA24(x); + } else { + x_24x2 = static_cast(*reinterpret_cast(&x)); + shift += 8; + } + + return MultiplyByQuantizedMultiplier(x_24x2, quantized_multiplier, shift); +} + +// Calculate quantization params for 24bit runtimes. +inline void QuantizeMultiplierForInt24(float multiplier, + int32_t* quantized_multiplier, + int* shift) { + if (multiplier == 0.0f) { + *quantized_multiplier = 0; + *shift = 0; + return; + } + + // Special cased to 24bit: + const float q = std::frexp(multiplier, shift); + auto q_fixed = static_cast(std::round(q * (1 << 23))); + + TFLITE_CHECK(q_fixed <= (1 << 23)); + if (q_fixed == (1 << 23)) { + q_fixed /= 2; + ++*shift; + } + TFLITE_CHECK_LE(q_fixed, INT24_MAX); + + // Ensure shift does not exceed 24-bit range. + TFLITE_CHECK_LE(*shift, 23); + if (*shift < -23) { + *shift = 0; + q_fixed = 0; + } + *quantized_multiplier = static_cast(q_fixed); +} + +// Convert a floating point number to a Q representation for 24 bit integers. +inline int CreateQConstantForInt24(int integer_bits, float f) { + const float min_bounds = static_cast(INT24_MIN); + const float max_bounds = static_cast(INT24_MAX); + + int fractional_bits = 23 - integer_bits; + float raw = std::round(f * static_cast(1 << fractional_bits)); + raw = std::max(raw, min_bounds); + raw = std::min(raw, max_bounds); + return static_cast(raw); +} + +} // namespace tflite_micro +#endif // defined(HIFIMINI) +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h new file mode 100644 index 00000000..ebf9700d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h @@ -0,0 +1,216 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ +#define TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ + +#include +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" +#include "tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h" +#include "tensorflow/lite/micro/kernels/kernel_util.h" + +namespace tflite_micro { +namespace ops { +namespace micro { +namespace lstm_eval { + +#if defined(HIFI5) +void calc_cell_state_without_cifg(int16_t* cell_state, + const int16_t* forget_gate, + const int16_t* cell_gate, + const int16_t* input_gate, int shift1, + int shift2, int clip, int num_elms); + +void calc_cell_state_with_cifg(int16_t* cell_state, const int16_t* forget_gate, + const int16_t* cell_gate, int shift1, int shift2, + int clip, int num_elms); + +void xa_nn_elm_mul_16x16_asym8s(int8_t* output, const int16_t* input_1, + const int16_t* input_2, int32_t multiplier, + int32_t shift, int32_t zero_point, + int num_elms); +#endif // defined(HIFI5) + +// Pamameters for integer LSTM. +// Consider split this into two Integer Parameters if more fields are added. +struct IntegerLstmParameter { + int32_t effective_input_to_input_scale_a; + int effective_input_to_input_scale_b; + int32_t effective_recurrent_to_input_scale_a; + int effective_recurrent_to_input_scale_b; + int32_t effective_cell_to_input_scale_a; + int effective_cell_to_input_scale_b; + int32_t effective_input_to_forget_scale_a; + int effective_input_to_forget_scale_b; + int32_t effective_recurrent_to_forget_scale_a; + int effective_recurrent_to_forget_scale_b; + int32_t effective_cell_to_forget_scale_a; + int effective_cell_to_forget_scale_b; + int32_t effective_input_to_cell_scale_a; + int effective_input_to_cell_scale_b; + int32_t effective_recurrent_to_cell_scale_a; + int effective_recurrent_to_cell_scale_b; + int32_t effective_input_to_output_scale_a; + int effective_input_to_output_scale_b; + int32_t effective_recurrent_to_output_scale_a; + int effective_recurrent_to_output_scale_b; + int32_t effective_cell_to_output_scale_a; + int effective_cell_to_output_scale_b; + int32_t effective_proj_scale_a; + int effective_proj_scale_b; + int32_t effective_hidden_scale_a; + int effective_hidden_scale_b; + int32_t layer_norm_input_scale_a; + int layer_norm_input_scale_b; + int32_t layer_norm_forget_scale_a; + int layer_norm_forget_scale_b; + int32_t layer_norm_cell_scale_a; + int layer_norm_cell_scale_b; + int32_t layer_norm_output_scale_a; + int layer_norm_output_scale_b; + // Quantized clip value for cell and projection. Zero value means no clipping. + int16_t quantized_cell_clip; + int8_t quantized_proj_clip; + int32_t hidden_zp; + int32_t cell_scale; + + int32_t input_variance_guard; + int32_t forget_variance_guard; + int32_t cell_variance_guard; + int32_t output_variance_guard; + + // Pre-calculate bias + zero_point * weight. + // Unabled to use temporary tensors since those are used in Prepare() and + // scratch buffer is only allocated after Preapre(). + std::unique_ptr input_to_forget_effective_bias; + std::unique_ptr recurrent_to_forget_effective_bias; + std::unique_ptr input_to_cell_effective_bias; + std::unique_ptr recurrent_to_cell_effective_bias; + std::unique_ptr input_to_output_effective_bias; + std::unique_ptr recurrent_to_output_effective_bias; + std::unique_ptr input_to_input_effective_bias; + std::unique_ptr recurrent_to_input_effective_bias; + std::unique_ptr projection_effective_bias; + + // Scale and zero point for intermediate tensors. + // Used only in the 8x8_8 case. + int32_t intermediate_scale_a[8]; + int32_t intermediate_scale_b[8]; + int32_t intermediate_zp[12]; +}; + +TfLiteStatus EvalFloat(const TfLiteEvalTensor* input, + const TfLiteEvalTensor* input_to_input_weights, + const TfLiteEvalTensor* input_to_forget_weights, + const TfLiteEvalTensor* input_to_cell_weights, + const TfLiteEvalTensor* input_to_output_weights, + const TfLiteEvalTensor* recurrent_to_input_weights, + const TfLiteEvalTensor* recurrent_to_forget_weights, + const TfLiteEvalTensor* recurrent_to_cell_weights, + const TfLiteEvalTensor* recurrent_to_output_weights, + const TfLiteEvalTensor* cell_to_input_weights, + const TfLiteEvalTensor* cell_to_forget_weights, + const TfLiteEvalTensor* cell_to_output_weights, + const TfLiteEvalTensor* input_layer_norm_coefficients, + const TfLiteEvalTensor* forget_layer_norm_coefficients, + const TfLiteEvalTensor* cell_layer_norm_coefficients, + const TfLiteEvalTensor* output_layer_norm_coefficients, + const TfLiteEvalTensor* aux_input, + const TfLiteEvalTensor* aux_input_to_input_weights, + const TfLiteEvalTensor* aux_input_to_forget_weights, + const TfLiteEvalTensor* aux_input_to_cell_weights, + const TfLiteEvalTensor* aux_input_to_output_weights, + const TfLiteEvalTensor* input_gate_bias, + const TfLiteEvalTensor* forget_gate_bias, + const TfLiteEvalTensor* cell_gate_bias, + const TfLiteEvalTensor* output_gate_bias, + const TfLiteEvalTensor* projection_weights, + const TfLiteEvalTensor* projection_bias, + const TfLiteLSTMParams* params, bool forward_sequence, + bool time_major, int output_offset, + TfLiteEvalTensor* scratch_buffer, + TfLiteEvalTensor* output_state, + TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output); + +TfLiteStatus EvalInteger8x8_16( + TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input, + const TfLiteEvalTensor* input_to_input_weights, + const TfLiteEvalTensor* input_to_forget_weights, + const TfLiteEvalTensor* input_to_cell_weights, + const TfLiteEvalTensor* input_to_output_weights, + const TfLiteEvalTensor* recurrent_to_input_weights, + const TfLiteEvalTensor* recurrent_to_forget_weights, + const TfLiteEvalTensor* recurrent_to_cell_weights, + const TfLiteEvalTensor* recurrent_to_output_weights, + const TfLiteEvalTensor* cell_to_input_weights, + const TfLiteEvalTensor* cell_to_forget_weights, + const TfLiteEvalTensor* cell_to_output_weights, + const TfLiteEvalTensor* input_layer_norm_coefficients, + const TfLiteEvalTensor* forget_layer_norm_coefficients, + const TfLiteEvalTensor* cell_layer_norm_coefficients, + const TfLiteEvalTensor* output_layer_norm_coefficients, + const TfLiteEvalTensor* input_gate_bias, + const TfLiteEvalTensor* forget_gate_bias, + const TfLiteEvalTensor* cell_gate_bias, + const TfLiteEvalTensor* output_gate_bias, + const TfLiteEvalTensor* projection_weights, + const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, + bool forward_sequence, bool time_major, + const lstm_eval::IntegerLstmParameter* integer_lstm_param, + TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, + TfLiteEvalTensor* output, TfLiteEvalTensor* scratch0, + TfLiteEvalTensor* scratch1, TfLiteEvalTensor* scratch2, + TfLiteEvalTensor* scratch3, TfLiteEvalTensor* scratch4, + TfLiteEvalTensor* scratch5); + +TfLiteStatus EvalInteger8x8_8( + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* input_to_input_weights, + const TfLiteEvalTensor* input_to_forget_weights, + const TfLiteEvalTensor* input_to_cell_weights, + const TfLiteEvalTensor* input_to_output_weights, + const TfLiteEvalTensor* recurrent_to_input_weights, + const TfLiteEvalTensor* recurrent_to_forget_weights, + const TfLiteEvalTensor* recurrent_to_cell_weights, + const TfLiteEvalTensor* recurrent_to_output_weights, + const TfLiteEvalTensor* cell_to_input_weights, + const TfLiteEvalTensor* cell_to_forget_weights, + const TfLiteEvalTensor* cell_to_output_weights, + const TfLiteEvalTensor* input_layer_norm_coefficients, + const TfLiteEvalTensor* forget_layer_norm_coefficients, + const TfLiteEvalTensor* cell_layer_norm_coefficients, + const TfLiteEvalTensor* output_layer_norm_coefficients, + const TfLiteEvalTensor* input_gate_bias, + const TfLiteEvalTensor* forget_gate_bias, + const TfLiteEvalTensor* cell_gate_bias, + const TfLiteEvalTensor* output_gate_bias, + const TfLiteEvalTensor* projection_weights, + const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, + TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, + TfLiteEvalTensor* output, + const lstm_eval::IntegerLstmParameter* integer_lstm_param, + TfLiteEvalTensor* scratch0, TfLiteEvalTensor* scratch1, + TfLiteEvalTensor* scratch2, TfLiteEvalTensor* scratch3, + TfLiteEvalTensor* scratch4, TfLiteEvalTensor* scratch5, + TfLiteEvalTensor* scratch6, TfLiteEvalTensor* scratch7); + +} // namespace lstm_eval +} // namespace micro +} // namespace ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h new file mode 100644 index 00000000..4d854c68 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h @@ -0,0 +1,78 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ +#define TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ + +namespace tflite_micro { +namespace ops { +namespace micro { +namespace lstm { +// For full inputs kernel (24-inputs). +// Please note the 20-input full kernel is deprecated and only kept +// here for backward compatibility. +namespace full { + +// Input Tensors of size {n_batch, n_input} +constexpr int kInputTensor = 0; + +// Input weight tensors of size: {n_cell, n_input} +constexpr int kInputToInputWeightsTensor = 1; // Optional +constexpr int kInputToForgetWeightsTensor = 2; +constexpr int kInputToCellWeightsTensor = 3; +constexpr int kInputToOutputWeightsTensor = 4; + +// Recurrent weight tensors of size {n_cell, n_output} +constexpr int kRecurrentToInputWeightsTensor = 5; // Optional +constexpr int kRecurrentToForgetWeightsTensor = 6; +constexpr int kRecurrentToCellWeightsTensor = 7; +constexpr int kRecurrentToOutputWeightsTensor = 8; + +// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. +constexpr int kCellToInputWeightsTensor = 9; // Optional +constexpr int kCellToForgetWeightsTensor = 10; // Optional +constexpr int kCellToOutputWeightsTensor = 11; // Optional + +// Gates bias tensors of size {n_cell} +constexpr int kInputGateBiasTensor = 12; // Optional +constexpr int kForgetGateBiasTensor = 13; +constexpr int kCellGateBiasTensor = 14; +constexpr int kOutputGateBiasTensor = 15; + +// Projection weight tensor of size {n_output, n_cell} +constexpr int kProjectionWeightsTensor = 16; // Optional +// Projection bias tensor of size {n_output} +constexpr int kProjectionBiasTensor = 17; // Optional + +// These state tensors are defined as variable tensors, and will be modified by +// this op. +constexpr int kOutputStateTensor = 18; +constexpr int kCellStateTensor = 19; + +// Layer norm coefficient tensors of size {n_cell}, representing a diagonal +// matrix. +constexpr int kInputLayerNormCoefficientsTensor = 20; // Optional +constexpr int kForgetLayerNormCoefficientsTensor = 21; // Optional +constexpr int kCellLayerNormCoefficientsTensor = 22; // Optional +constexpr int kOutputLayerNormCoefficientsTensor = 23; // Optional + +// Output tensors. +constexpr int kOutputTensor = 0; +} // namespace full + +} // namespace lstm +} // namespace micro +} // namespace ops +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h new file mode 100644 index 00000000..604736dd --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h @@ -0,0 +1,38 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ + +#if defined(HIFIMINI) +#include + +#include "tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h" +#endif // defined(HIFMINI) + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) +#include "include/nnlib/xa_nnlib_api.h" +#include "include/nnlib/xa_nnlib_standards.h" + +#define ALIGNED_SIZE(x, bytes) (((x) + (bytes - 1)) & (~(bytes - 1))) +#define ALIGN_PTR(x, bytes) ((((unsigned)(x)) + (bytes - 1)) & (~(bytes - 1))) +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) +#include "utils.h" +#include "vision_api.h" +#endif // defined(VISION_P6) + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h new file mode 100644 index 00000000..20142fa5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h @@ -0,0 +1,48 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/add.h" +namespace tflite_micro { + +struct XtensaAddOpData { + OpDataAdd reference_op_data; +#if defined(VISION_P6) + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // VISION_P6 +}; + +#if defined(VISION_P6) + +TfLiteStatus AddPrepareVision(TfLiteContext* context, TfLiteNode* node); +TfLiteStatus AddEvalQuantizedVision(TfLiteContext* context, TfLiteNode* node, + const TfLiteAddParams& params, + const XtensaAddOpData& data, + const TfLiteEvalTensor* input1, + const TfLiteEvalTensor* input2, + TfLiteEvalTensor* output); + +#endif // VISION_P6 + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h new file mode 100644 index 00000000..a7ba3450 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h @@ -0,0 +1,89 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/conv.h" + +namespace tflite_micro { +struct XtensaConvOpData { + OpDataConv reference_op_data; + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + int scratch_tensor_index; +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) + int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and + // biases. + uint32_t reorder_coefficient_bias_size; + int8_t* per_channel_output_shift_int8; + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; + bool is_per_channel_quantized; +#endif // VISION_P6 +}; + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) +TfLiteStatus ConvPrepareHifi(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus ConvEvalHifiInt8(TfLiteContext* context, TfLiteNode* node, + const TfLiteConvParams& params, + const XtensaConvOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); + +#if defined(HIFI3) || defined(HIFI4) +TfLiteStatus ConvEvalHifiInt16(TfLiteContext* context, TfLiteNode* node, + const TfLiteConvParams& params, + const XtensaConvOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); +#endif // defined(HIFI3) || defined(HIFI4) + +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) + +TfLiteStatus ConvPrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus ConvEvalVision(TfLiteContext* context, TfLiteNode* node, + const TfLiteConvParams& params, + const XtensaConvOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); + +#endif // VISION_P6 + +TfLiteStatus ConvReferenceEvalInt8(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus ConvReferenceEvalInt16(TfLiteContext* context, TfLiteNode* node); + +void* ConvInitXtensa(TfLiteContext* context, const char* buffer, size_t length); +TfLiteStatus ConvPrepareXtensa(TfLiteContext* context, TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h new file mode 100644 index 00000000..8aca9de3 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h @@ -0,0 +1,74 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/depthwise_conv.h" + +namespace tflite_micro { +struct XtensaDepthwiseConvOpData { + OpDataConv reference_op_data; + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + int scratch_tensor_index; +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) + int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and + // biases. + uint32_t reorder_coefficient_bias_size; + int8_t* per_channel_output_shift_int8; + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // VISION_P6 +}; + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) +TfLiteStatus DepthwiseConvPrepareHifi(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus DepthwiseConvEvalHifi(TfLiteContext* context, TfLiteNode* node, + const TfLiteDepthwiseConvParams& params, + const XtensaDepthwiseConvOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); + +TfLiteStatus DepthwiseConvReferenceEvalInt8(TfLiteContext* context, + TfLiteNode* node); +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) + +TfLiteStatus DepthwiseConvPrepareVision(TfLiteContext* context, + TfLiteNode* node); + +TfLiteStatus DepthwiseConvEvalVision(TfLiteContext* context, TfLiteNode* node, + const TfLiteDepthwiseConvParams& params, + const XtensaDepthwiseConvOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); + +#endif // VISION_P6 + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h new file mode 100644 index 00000000..fcedf829 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h @@ -0,0 +1,78 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/fully_connected.h" +#include "tensorflow/lite/micro/micro_log.h" + +namespace tflite_micro { +struct XtensaFullyConnectedOpData { + OpDataFullyConnected reference_op_data; + +#if defined(VISION_P6) + int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and + // biases. + uint32_t reorder_coefficient_bias_size; + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // VISION_P6 +}; + +#if defined(HIFIMINI) +void FullyConnectedEvalHifimini( + const FullyConnectedParams& params, const RuntimeShape& input_shape, + const int8_t* input_data, const RuntimeShape& filter_shape, + const int8_t* filter_data, const RuntimeShape& bias_shape, + const int32_t* bias_data, const RuntimeShape& output_shape, + int8_t* output_data); +#endif // defined(HIFIMINI) + +#if defined(VISION_P6) +TfLiteStatus FullyConnectedPrepareVision(TfLiteContext* context, + TfLiteNode* node); + +TfLiteStatus FullyConnectedEvalVision(TfLiteContext* context, TfLiteNode* node, + const TfLiteConvParams& params, + const XtensaFullyConnectedOpData& data, + const TfLiteEvalTensor* input, + const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, + TfLiteEvalTensor* output); +#endif // VISION_P6 + +void* XtensaInitFullyConnected(TfLiteContext* context, const char* buffer, + size_t length); + +TfLiteStatus XtensaEvalFullyConnectedQuantizedInt8( + TfLiteContext* context, TfLiteNode* node, const OpDataFullyConnected& data, + const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter, + const TfLiteEvalTensor* bias, TfLiteEvalTensor* output); + +TfLiteStatus XtensaCalculateOpDataFullyConnected( + TfLiteContext* context, TfLiteFusedActivation activation, + TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output, OpDataFullyConnected* data); + +TfLiteStatus XtensaPrepareFullyConnected(TfLiteContext* context, + TfLiteNode* node); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h new file mode 100644 index 00000000..101fc403 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h @@ -0,0 +1,49 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +namespace tflite_micro { + +struct OpDataPad { + PadParams params; + int32_t output_zero_point; +}; + +struct XtensaPadData { + OpDataPad reference_op_data; + +#if defined(VISION_P6) + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // VISION_P6 +}; + +#if defined(VISION_P6) + +TfLiteStatus PadPrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus PadEvalVision(const XtensaPadData& data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); +#endif // VISION_P6 + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h new file mode 100644 index 00000000..1086f716 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h @@ -0,0 +1,76 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ + +#include + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/pooling.h" +namespace tflite_micro { + +struct XtensaOpDataPooling { + OpDataPooling reference_op_data; + +#if defined(VISION_P6) + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // defined(VISION_P6) + +#if defined(HIFI5) + int scratch_tensor_index; +#endif // defined(HIFI5) +}; + +#if defined(VISION_P6) + +TfLiteStatus AvgPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus MaxPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus PoolEvalVision(TfLiteContext* context, TfLiteNode* node, + const TfLitePoolParams& params, + const XtensaOpDataPooling& data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); +#endif + +#if defined(HIFI5) + +TfLiteStatus AveragePrepareHifi(TfLiteContext* context, TfLiteNode* node); +TfLiteStatus AverageEvalQuantizedHifi(TfLiteContext* context, + const TfLiteNode* node, + const TfLitePoolParams* params, + const XtensaOpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); + +TfLiteStatus MaxPrepareHifi(TfLiteContext* context, TfLiteNode* node); +TfLiteStatus MaxEvalQuantizedHifi(TfLiteContext* context, TfLiteNode* node, + TfLitePoolParams* params, + const XtensaOpDataPooling* data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); + +#endif // defined(HIFI5) + +void* XtensaPoolingInit(TfLiteContext* context, const char* buffer, + size_t length); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h new file mode 100644 index 00000000..a1db4fe2 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h @@ -0,0 +1,47 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/reduce.h" + +namespace tflite_micro { + +struct XtensaReduceOpData { + OpDataReduce reference_op_data; + +#if defined(VISION_P6) + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +#endif // VISION_P6 +}; + +#if defined(VISION_P6) + +TfLiteStatus ReducePrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus ReduceEvalVision(const XtensaReduceOpData& data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); + +#endif // VISION_P6 + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h new file mode 100644 index 00000000..9dce2f61 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h @@ -0,0 +1,44 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" + +namespace tflite_micro { + +#if defined(VISION_P6) + +struct XtensaReshapeData { + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +}; +#endif // VISION_P6 + +#if defined(VISION_P6) + +TfLiteStatus ReshapePrepareVision(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus ReshapeEvalVision(const XtensaReshapeData& data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); +#endif // VISION_P6 + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h new file mode 100644 index 00000000..1e8e393a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h @@ -0,0 +1,58 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ + +#include "tensorflow/lite/c/builtin_op_data.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/softmax.h" + +namespace tflite_micro { + +#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) +struct XtensaSoftmaxOpData { + SoftmaxParams params; + int scratch_tensor_index; +}; +#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) + +#if defined(VISION_P6) +struct XtensaSoftmaxOpData { + SoftmaxParams params; + uint8_t* p_context; // persistent lib context for this instance saved here + uint32_t context_size; +}; +#endif // defined(VISION_P6) + +void* XtensaInitSoftmax(TfLiteContext* context, const char* buffer, + size_t length); + +TfLiteStatus XtensaPrepareSoftmax(TfLiteContext* context, TfLiteNode* node); + +TfLiteStatus XtensaEvalSoftmaxInt8Int16(TfLiteContext* context, + TfLiteNode* node); + +#if defined(VISION_P6) +TfLiteStatus SoftmaxPrepareVision(TfLiteContext* context, TfLiteNode* node); +TfLiteStatus SoftmaxEvalVision(TfLiteContext* context, TfLiteNode* node, + const XtensaSoftmaxOpData& data, + const TfLiteEvalTensor* input, + TfLiteEvalTensor* output); +#endif // defined(VISION_P6) + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h new file mode 100644 index 00000000..002f1496 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h @@ -0,0 +1,39 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ +#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/types.h" +#include "tensorflow/lite/micro/kernels/svdf.h" + +namespace tflite_micro { +#if defined(HIFIMINI) +TfLiteStatus EvalIntegerSvdfHifimini( + TfLiteContext* context, TfLiteNode* node, + const TfLiteEvalTensor* input_tensor, + const TfLiteEvalTensor* weights_feature_tensor, + const TfLiteEvalTensor* weights_time_tensor, + const TfLiteEvalTensor* bias_tensor, const TfLiteSVDFParams* params, + TfLiteEvalTensor* activation_state_tensor, TfLiteEvalTensor* output_tensor, + OpDataSvdf data); +#endif // HIFIMINI + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h b/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h new file mode 100644 index 00000000..51186064 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h @@ -0,0 +1,64 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ +#define TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ + +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// Returns the next pointer address aligned to the given alignment. +uint8_t* AlignPointerUp(uint8_t* data, size_t alignment); + +// Returns the previous pointer address aligned to the given alignment. +uint8_t* AlignPointerDown(uint8_t* data, size_t alignment); + +// Returns an increased size that's a multiple of alignment. +size_t AlignSizeUp(size_t size, size_t alignment); + +// Templated version of AlignSizeUp +// Returns an increased size that's a multiple of alignment. +template +size_t AlignSizeUp(size_t count = 1) { + return AlignSizeUp(sizeof(T) * count, alignof(T)); +} + +// Returns size in bytes for a given TfLiteType. +TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size); + +// How many bytes are needed to hold a tensor's contents. +TfLiteStatus BytesRequiredForTensor(const tflite_micro::Tensor& flatbuffer_tensor, + size_t* bytes, size_t* type_size); + +// How many bytes are used in a TfLiteEvalTensor instance. The byte length is +// returned in out_bytes. +TfLiteStatus TfLiteEvalTensorByteLength(const TfLiteEvalTensor* eval_tensor, + size_t* out_bytes); + +// Deduce output dimensions from input and allocate given size. +// Useful for operators with two inputs where the largest input should equal the +// output dimension. +TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context, + const TfLiteTensor* input1, + const TfLiteTensor* input2, + TfLiteTensor* output); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h new file mode 100644 index 00000000..954798c8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h @@ -0,0 +1,172 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ +#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" + +namespace tflite_micro { + +constexpr int kOnlinePlannedBuffer = -1; +// We are using this to mark a buffer explicitly as externally offline allocated +constexpr int kXcoreOfflinePlannedBuffer = -111; + +// A memory planner that uses a greedy algorithm to arrange buffers in memory +// to minimize the overall arena size needed. +// +// The algorithm works like this: +// - The client enters the buffer information through AddBuffer(). +// - When a function like GetOffsetForBuffer() is called, the +// CalculateOffsetsIfNeeded() method is invoked. +// - If an up to date plan is not already present, one will be calculated. +// - The buffers are sorted in descending order of size. +// - The largest buffer is placed at offset zero. +// - The rest of the buffers are looped through in descending size order. +// - The other buffers that need to be in memory at the same time are found. +// - The first gap between simultaneously active buffers that the current +// buffer fits into will be used. +// - If no large-enough gap is found, the current buffer is placed after the +// last buffer that's simultaneously active. +// - This continues until all buffers are placed, and the offsets stored. +// +// This is not guaranteed to produce the best placement, since that's an +// NP-Complete problem, but in practice it should produce one that's decent. +class GreedyMemoryPlanner : public MicroMemoryPlanner { + public: + GreedyMemoryPlanner(); + ~GreedyMemoryPlanner() override; + + // You need to pass in an area of memory to be used for planning. The client + // should ensure the validity of the memory when it needs to use this object. + // This memory isn't owned by this object, so management should be handled by + // the client. This is so it can be stack or globally allocated if necessary + // on devices without dynamic memory allocation. How many buffers can be + // planned for will depend on the size of this scratch memory, so you should + // enlarge it if you see an error when calling AddBuffer(). The memory can be + // reused once you're done with the planner, as long as you copy the + // calculated offsets to another location. Each buffer requires about 36 bytes + // of scratch. + TfLiteStatus Init(unsigned char* scratch_buffer, + int scratch_buffer_size) override; + + // Record details of a buffer we want to place. + TfLiteStatus AddBuffer(int size, int first_time_used, + int last_time_used) override; + + // Record details of an offline planned buffer offset we want to place. + // offline_offset is the buffer offset from the start of the arena. + TfLiteStatus AddBuffer(int size, int first_time_used, int last_time_used, + int offline_offset) override; + + // Returns the high-water mark of used memory. This is the minimum size of a + // memory arena you'd need to allocate to hold these buffers. + size_t GetMaximumMemorySize() override; + + // How many buffers have been recorded. + int GetBufferCount() override; + + // Where a given buffer should be placed in the memory arena. + // This information is stored in the memory arena itself, so once the arena + // is used for inference, it will be overwritten. + TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) override; + + // Prints an ascii-art diagram of the buffer layout plan. + void PrintMemoryPlan() override; + + // Debug method to check whether any buffer allocations are overlapping. This + // is an O(N^2) complexity operation, so only use for testing. + bool DoAnyBuffersOverlap(); + + // Used to store a list of buffers ordered by their offset. + struct ListEntry { + int offset; + int requirements_index; + int next_entry_index; + }; + + // Number of bytes required in order to plan a buffer. + static size_t per_buffer_size() { + const int per_buffer_size = + sizeof(BufferRequirements) + // requirements_ + sizeof(int) + // buffer_sizes_sorted_ + sizeof(int) + // buffer_ids_sorted_ + sizeof(ListEntry) + // buffers_sorted_by_offset_ + sizeof(int); // buffer_offsets_; + return per_buffer_size; + } + + // Returns False because the GreedyMemoryPlanner doesn't preserves all tensors + // after invocation. Do to the fact that tensors that tensor data for tensors + // that aren't being used during a phase of invocation are overwritten. + bool preserves_all_tensors() const override { return false; } + + TF_LITE_REMOVE_VIRTUAL_DELETE + + private: + // Whether a buffer is active in a given time range. + bool DoesEntryOverlapInTime(const ListEntry* entry, const int first_time_used, + const int last_time_used) const; + + // Walks the list to return the next buffer that is active in a given time + // range, or a null pointer if there are none. + ListEntry* NextSimultaneouslyActiveBuffer(const ListEntry* start, + const int first_time_used, + const int last_time_used); + + // If there isn't an up to date plan, calculate a new one. + void CalculateOffsetsIfNeeded(); + + // How many buffers we can plan for, based on the arena size we're given in + // the constructor. + int max_buffer_count_; + + // The number of buffers added so far. + int buffer_count_; + + // Records the client-provided information about each buffer. + struct BufferRequirements { + int size; + int offline_offset; + int first_time_used; + int last_time_used; + }; + + // Working arrays used during the layout algorithm. + BufferRequirements* requirements_; + // buffer_sizes_sorted_ and buffer_ids_sorted_ are sorted according to: + // { + // offline planned buffers, + // online planned buffers sorted by size + // } + int* buffer_sizes_sorted_; + int* buffer_ids_sorted_; + ListEntry* buffers_sorted_by_offset_; + int next_free_entry_; // Index of the next free entry of + // buffers_sorted_by_offset_ + int first_entry_index_; // Index of the first entry (smallest offset) of + // buffers_sorted_by_offset_ + + // Stores the outcome of the plan, the location of each buffer in the arena. + int* buffer_offsets_; + + // Whether buffers have been added since the last plan was calculated. + bool need_to_calculate_offsets_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h new file mode 100644 index 00000000..c701ceef --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h @@ -0,0 +1,53 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ +#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" + +namespace tflite_micro { + +// The simplest possible memory planner that just lays out all buffers at +// increasing offsets without trying to reuse memory. +class LinearMemoryPlanner : public MicroMemoryPlanner { + public: + LinearMemoryPlanner(); + ~LinearMemoryPlanner() override; + + TfLiteStatus AddBuffer(int size, int first_time_used, + int last_time_used) override; + + size_t GetMaximumMemorySize() override; + int GetBufferCount() override; + TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) override; + + // Returns True because the LinearMemoryPlanner preserves all tensors after + // invocation. + bool preserves_all_tensors() const override { return true; } + + private: + static constexpr int kMaxBufferCount = 1024; + size_t buffer_offsets_[kMaxBufferCount]; + int current_buffer_count_; + size_t next_free_offset_; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h new file mode 100644 index 00000000..3ca7ab9f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h @@ -0,0 +1,73 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ +#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ + +#include +#include + +#include "tensorflow/lite/micro/micro_utils.h" + +namespace tflite_micro { + +// This is an experimental feature and subjected to change. +// More description is available at +// tensorflow/lite/micro/docs/offline_memory_plan.md. + +// Describes a buffer's layout inside an arena. This struct should be kept as +// small as possible for memory footprint sensitive applications and should use +// only primitive fields, making it easy to adjust offline. +struct BufferDescriptor { + // Starting offset inside an arena for this buffer. + // Offset is the minimum information needed for the buffer. The user knows + // the model and the size of each buffer in order to lay out a valid buffer + // plan. + int32_t offset; +}; + +// A structure describing the lay out of buffers inside an arena. +struct BufferPlan { + // Number of buffers described in this plan. + int32_t buffer_count; + + // Each element describes one buffer. + // Buffer index is implicit by the order of AddBuffer() call. + // Specifically, indices of activation tensors are 0 … N-1 where N is the + // number of activation tensors. + // The rest are based on the order of OP requests. + // + // This is a flexible array member and should ideally be + // arena_entries[]; However, in order to support a variety + // of compilers (and without needing to add ifdef's), we + // are implementing the flexible array member with an array of + // length 1 as the last member of the struct. When the size of a BufferPlan + // is needed, use the provided SizeOfBufferPlan(buffer_count) that + // accounts for this implemenatation caveat. + BufferDescriptor buffer_plan_entries[1]; +}; + +// Returns size of a BufferPlan given a buffer count. This size is compile time +// known if buffer_count is a compile time constant. +constexpr size_t SizeOfBufferPlan(int32_t buffer_count) { + // Minus 1 because a BufferPlan struct have a BufferDescriptor already. + // Max to provide a lower bound for the corner case of buffer_count = 0. + return sizeof(BufferPlan) + + sizeof(BufferDescriptor) * Max(buffer_count - 1, 0); +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h new file mode 100644 index 00000000..f3e84f6a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h @@ -0,0 +1,95 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +// Interface class for planning the layout of memory buffers during the +// execution of a graph. +// It's designed to be used by a client that iterates in any order through the +// buffers it wants to lay out, and then calls the getter functions for +// information about the calculated layout. For example: +// +// SomeMemoryPlanner planner; +// planner.AddBuffer(100, 0, 1); // Buffer 0 +// planner.AddBuffer(50, 2, 3); // Buffer 1 +// planner.AddBuffer(50, 2, 3); // Buffer 2 +// +// int offset0; +// TF_EXPECT_OK(planner.GetOffsetForBuffer(0, &offset0)); +// int offset1; +// TF_EXPECT_OK(planner.GetOffsetForBuffer(1, &offset1)); +// int offset2; +// TF_EXPECT_OK(planner.GetOffsetForBuffer(2, &offset2)); +// const int arena_size_needed = planner.GetMaximumMemorySize(); +// +// The goal is for applications to be able to experiment with different layout +// strategies without changing their client code, by swapping out classes that +// implement this interface.= +class MicroMemoryPlanner { + public: + MicroMemoryPlanner() {} + virtual ~MicroMemoryPlanner() {} + + // Pass information about a buffer's size and lifetime to the layout + // algorithm. The order this is called implicitly assigns an index to the + // result, so the buffer information that's passed into the N-th call of + // this method will be used as the buffer_index argument to + // GetOffsetForBuffer(). + virtual TfLiteStatus AddBuffer(int size, int first_time_used, + int last_time_used) = 0; + + // Record details of an offline planned buffer offset we want to place. + // offline_offset is the buffer offset from the start of the arena. + // This is to support offline memory planning from the flatbuffer metadata. + // By default, it returns an error. + virtual TfLiteStatus AddBuffer(int size, int first_time_used, + int last_time_used, int offline_offset) { + return kTfLiteError; + } + + // The largest contiguous block of memory that's needed to hold the layout. + virtual size_t GetMaximumMemorySize() = 0; + // How many buffers have been added to the planner. + virtual int GetBufferCount() = 0; + // Calculated layout offset for the N-th buffer added to the planner. + virtual TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) = 0; + + // Provides the scratch buffer in case that the memory planner needs it. + // The lifetime of scratch buffers lifetime lasts until the static memory plan + // is committed. + // The default implementation is for the memory planner that does not need + // scratch buffer and simply returns ok. + virtual TfLiteStatus Init(unsigned char* scratch_buffer, + int scratch_buffer_size) { + return kTfLiteOk; + } + + // Method will return True if the MicroMemoryPlanner preserves all tensors + // after invocation, and False if it doesn't. + virtual bool preserves_all_tensors() const = 0; + + virtual void PrintMemoryPlan() { + // Default does nothing. + } +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h new file mode 100644 index 00000000..4d4eb31a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h @@ -0,0 +1,133 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ +#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/memory_planner/memory_plan_struct.h" +#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" + +namespace tflite_micro { + +/* This is an experimental feature and subjected to change. + * +The NonPersistentMemoryPlannerShim enables TFLM to work with an external tooling +that can plan the offset of each non persistent buffer for the Model within the +TFLM arena. + +If the NonPersistentMemoryPlannerShim is used, then the final binary does not +have any of the symbols associated with the GreedyMemoryPlanner which results in +a reduced memory footprint. + +Additionally, the offline planning of the non-persistent buffers can be used to +have a more efficient utilization compared to the GreedyMemoryPlanner. + +For example, consider the following hypothetical model: + +A1(400) A2(401) +──┬─────────┐ ┌─────────── + │ │ │ + │ │ │ + │ ▼ ▼ + │ ┌────────┐ + │ │ OP1 │ + │ └───┬────┘ A4(201) + │ A3(10) │ │ + │ │ │ + │ │ │ + │ ┌───┴────┐ │ + │ │ OP2 │◄────────┤ + │ └───┬────┘ │ + │ A5(11) │ A6(202) │ + │ │ │ │ + │ ▼ │ │ + │ ┌────────┐ │ │ + │ │ OP3 │◄─┘ │ + │ └───┬────┘ │ + │ │ A8(200) │ + │ A7(12) │ │ │ + │ │ │ │ + │ ┌───┴────┐◄──┘ │ + └──────►│ OP4 │ │ + └───┬────┘◄────────┘ + │ + A9(13) │ + ▼ + +The GreedyMemoryPlanner will give the following memory layout that requires 1012 +bytes of scratch arena size: + +┌─────────────────────────────────────────┬──────────────────────────┬────────┬───────┐ +│ A2(401) │ A1(400) │ A4(201)│ +A3(10)│ +└─────────────────────────────────────────┴──────────────────────────┴────────┴───────┘ + +┌───────────┬──────┬──────┐ +│ A6(202) │A5(11)│A7(12)│ +└───────────┴──────┴──────┘ + +┌──────────┬───────┐ +│ A8(200) │A9(13) │ +└──────────┴───────┘ + +But a more efficient offline memory plan that requires only 826 bytes of scratch +arena size can be + +┌──────────────────────────────────────┬─────────────────────────────┬───────┬──────┐ +│ A1(400) │ A2(401) │ +A3(10)│A5(11)│ +└──────────────────────────────────────┴─────────────────────────────┴───────┴──────┘ + + ┌────────────────┬────────────┬────────┬───────┐ + │A4(201) │ A8(200) │A9(13) +│A7(12) │ └────────────────┴────────────┴────────┴───────┘ + + ┌─────────────┐ + │ A6(202) │ + └─────────────┘ + +*/ +class NonPersistentMemoryPlannerShim : public MicroMemoryPlanner { + public: + // Does not take ownership of buffer_plan, which must refer to a valid + // BufferPlan that outlives this object. + explicit NonPersistentMemoryPlannerShim(const BufferPlan* buffer_plan); + ~NonPersistentMemoryPlannerShim() override; + + TfLiteStatus GetOffsetForBuffer(int buffer_request_index, + int* offset) override; + + TfLiteStatus AddBuffer(int size, int first_time_used, + int last_time_used) override; + size_t GetMaximumMemorySize() override; + int GetBufferCount() override; + + // Returns False because the NonPersistentMemoryPlannerShim doesn't preserves + // all tensors after invocation. + bool preserves_all_tensors() const override { return false; } + + private: + const BufferPlan* buffer_plan_; // not owned, can't be null + + // The number of buffers requested so far. Used for error checking. + int buffer_request_count_; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h b/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h new file mode 100644 index 00000000..2cb7c5fa --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h @@ -0,0 +1,138 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/flatbuffer_utils.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// Used to hold information used during allocation calculations. +struct AllocationInfo { + size_t bytes; + void** output_ptr; + int first_created; + int last_used; + int32_t offline_offset; + bool needs_allocating; +}; + +// Used to hold the allocation info list and related metadata for the entire +// graph (including subgraphs). Since all subgraphs are planned together, the +// allocation info list contains allocations for all subgraphs. Track the offset +// into this list for each subgraph then reserve space to track all allocations. +// +// The AllocationInfo list is a contiguous list of allocations across all +// subgraphs and scratch buffers. Each element here is marked as +// st. The following is a possible +// AllocationInfo list: +// [s0t0, s0t1, s1t0, s2t1, s1t2, s3t0, s3t1, scratch0, scratch1, scratch2] +// +// For this example, the subgraph offsets would be [0, 2, 5] and the scratch +// offset would be 7. +struct GraphAllocationInfo { + AllocationInfo* allocation_info; + size_t allocation_info_count; + size_t* subgraph_offsets; + size_t scratch_offset; + size_t tensor_count; + size_t scratch_buffer_count; +}; + +// A helper class to construct AllocationInfo array. This array contains the +// lifetime of tensors / scratch_buffer and will be used to calculate the memory +// plan. Methods need to be called in order from `Create`, Init`, `Add*`, to +// `Finish`. +class AllocationInfoBuilder { + public: + AllocationInfoBuilder(const Model* model, + INonPersistentBufferAllocator* non_persistent_allocator) + : model_(model), non_persistent_allocator_(non_persistent_allocator) {} + + // Check if model contains offline planned buffer offsets. + // - If there's no metadata available, offline_planner_offsets is not set + // - If there's metadata available, offline_planner_offsets will point to the + // first offset in the metadata buffer list. + TfLiteStatus GetOfflinePlannedOffsets( + const int32_t** offline_planner_offsets); + + // Allocate memory for the allocation info array as well as offsets into that + // array for each subgraph. + TfLiteStatus CreateAllocationInfo(int scratch_buffer_request_count); + + // Release memory used for the allocation info array. + TfLiteStatus FreeAllocationInfo(); + + // Initialize AllocationInfo for all tensors and scratch buffers in the graph. + TfLiteStatus InitializeAllocationInfo(const int32_t* offline_offsets, + SubgraphAllocations* allocations); + + // Mark the scope of each tensor and scratch buffer across the graph. Enter + // all possible subgraphs invoked by each control flow operator. This method + // marks the maximum lifetime of each buffer so that tensors are correctly + // planned for all valid invocation flows. + TfLiteStatus MarkAllocationLifetimes( + int subgraph_idx, internal::ScratchBufferRequest* scratch_buffer_request, + ScratchBufferHandle* scratch_buffer_handles, + SubgraphAllocations* allocations); + + // Identify control flow operators and recursively mark all subgraphs which + // that operator can invoke. The lifetime of all tensors within a subgraph + // can only be extended. The order of subgraph invocation does not matter + // since subgraphs within the same control flow operator are executed + // within their own allocation scope (planned buffers in a subgraph cannot + // persist beyond the end of that subgraph's invocation). + TfLiteStatus MarkSubgraphLifetimesIfNecessary( + const Operator* op, + internal::ScratchBufferRequest* scratch_buffer_requests, + ScratchBufferHandle* scratch_buffer_handles, + SubgraphAllocations* allocations); + + // Returns the number of allocations. + int AllocationCount() const { return info_.allocation_info_count; } + + // Returns a pointer to the built AllocationInfo array. + AllocationInfo* Finish() const { return info_.allocation_info; } + + private: + // Mark the given Allocation info as first created at the specified allocation + // scope count. Only the first creation must be recorded since the allocation + // scope count monotonically increases throughout the lifetime marking + // process. + void UpdateFirstCreated(AllocationInfo* current, int allocation_scope_count); + + // Mark the given AllocationInfo as last used at the specified allocation + // scope + // count. Update the last used marker every time, since the allocation scope + // count monotonically increases through the lifetime marking process. + void UpdateLastUsed(AllocationInfo* current, int allocation_scope_count); + + // Validate if a subgraph satisfies assumptions. + TfLiteStatus ValidateSubgraph(const SubGraph* subgraph, + TfLiteEvalTensor* eval_tensors); + + const tflite_micro::Model* model_ = nullptr; + INonPersistentBufferAllocator* non_persistent_allocator_ = nullptr; + GraphAllocationInfo info_; + int allocation_scope_count_ = 0; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h new file mode 100644 index 00000000..9f57f580 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h @@ -0,0 +1,351 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ + +#include +#include + +#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/flatbuffer_utils.h" +#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" +#include "tensorflow/lite/micro/micro_common.h" +#include "tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// TODO(b/199402574): rename to tflite_internal or just remove internal +// namespace. +namespace internal { + +// Sets up all of the data structure members for a TfLiteTensor based on the +// contents of a serialized tensor in the flatbuffer. +// TODO(b/162311891): Drop this method when the interpreter has an API for +// returning buffers on TfLiteEvalTensor. +TfLiteStatus InitializeTfLiteTensorFromFlatbuffer( + IPersistentBufferAllocator* persistent_buffer_allocator, + INonPersistentBufferAllocator* non_persistent_buffer_allocator, + bool allocate_temp, const tflite_micro::Tensor& flatbuffer_tensor, + const flatbuffers::Vector>* buffers, + TfLiteTensor* result); + +// Holds placeholder information for a scratch buffer request from a kernel. +// This struct is only used during the model prepare stage. Each request from a +// kernel is stored in the head section. During the prepare stage, the head +// section will at least hold kMaxScratchBuffersPerOp number of requests plus +// any requests from previous kernel requests. +// +// When the memory plan is finalized, these structs are no longer used in favor +// of a sequential, array of ScratchBufferHandle allocations in the tail +// section. These allocations are indexed by the request API defined in the +// TfLiteContext struct. +struct ScratchBufferRequest { + // Number of bytes required by the buffer. The actual allocated size might be + // greater than `bytes` due to buffer alignment. + size_t bytes; + // Node where the buffer is allocated for. This provides useful information to + // determine the lifetime of the buffer. In AllocationInfo, this buffer will + // have `before` = node_idx and `after` = node_idx. + int node_idx; + int subgraph_idx; +}; + +} // namespace internal + +// Enum used to keep track of which MemoryPlanner is being used for +// MicroAllocater::Create(); +enum class MemoryPlannerType { + kGreedy, + kLinear, +}; + +struct NodeAndRegistration { + TfLiteNode node; + const TFLMRegistration* registration; +}; + +// Holds a pointer to a buffer for a scratch buffer requested by a kernel during +// the model prepare stage. This struct is allocated in-place and allows for +// quick pointer-indexed lookup for speed during model inference. +struct ScratchBufferHandle { + // Pointer to location of the scratch buffer: + uint8_t* data; +}; + +// Stores all per-subgraph allocations. This includes the node and registration +// array, and tensor list for each subgraph. +struct SubgraphAllocations { + NodeAndRegistration* node_and_registrations; + TfLiteEvalTensor* tensors; +}; + +// Allocator responsible for allocating memory for all intermediate tensors +// necessary to invoke a model. +// +// The lifetime of the model, tensor arena and error reporter must be at +// least as long as that of the allocator object, since the allocator needs +// them to be accessible during its entire lifetime. +// +// The MicroAllocator simply plans out additional allocations that are required +// to standup a model for inference in TF Micro. This class currently relies on +// an additional allocator - SingleArenaBufferAllocator - for all allocations +// from an arena. These allocations are divided into head (non-persistent) and +// tail (persistent) regions: +// +// Memory layout to help understand how it works +// This information could change in the future version. +// ************** .memory_allocator->GetBuffer() +// Tensors/Scratch buffers (head) +// ************** .head_watermark +// unused memory +// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() +// - ->GetDataSize() +// persistent area (tail) +// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() +class MicroAllocator { + public: + // Creates a MicroAllocator instance from a given tensor arena. This arena + // will be managed by the created instance. The GreedyMemoryPlanner will + // by default be used and created on the arena. + // Note: Please use alignas(16) to make sure tensor_arena is 16 + // bytes aligned, otherwise some head room will be wasted. + // TODO(b/157615197): Cleanup constructor + factory usage. + static MicroAllocator* Create( + uint8_t* tensor_arena, size_t arena_size, + MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy); + + // Creates a MicroAllocator instance from a given tensor arena and a given + // MemoryPlanner. This arena will be managed by the created instance. Note: + // Please use alignas(16) to make sure tensor_arena is 16 bytes + // aligned, otherwise some head room will be wasted. + static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size, + MicroMemoryPlanner* memory_planner); + + // Creates a MicroAllocator instance using the provided + // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator + // instance will use the SingleArenaBufferAllocator instance to manage + // allocations internally. + static MicroAllocator* Create(SingleArenaBufferAllocator* memory_allocator, + MicroMemoryPlanner* memory_planner); + + // Creates a MicroAllocator instance using the provided + // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator + // instance will use the SingleArenaBufferAllocator instance to manage + // allocations internally. + static MicroAllocator* Create( + uint8_t* persistent_tensor_arena, size_t persistent_arena_size, + uint8_t* non_persistent_tensor_arena, size_t non_persistent_arena_size, + MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy); + + // Returns the fixed amount of memory overhead of MicroAllocator. + static size_t GetDefaultTailUsage(bool is_memory_planner_given); + + // Returns True if the MicroAllocator uses a LinearMemoryPlanner(is compatible + // with the PerserveAllTensors flag / feature ) and False otherwise. + bool preserves_all_tensor() const { + return memory_planner_->preserves_all_tensors(); + }; + + // Allocates internal resources required for model inference for each subgraph + // from the arena. + // + // This method will run through the flatbuffer data supplied in the model to + // properly allocate tensor, node, and op registration data. This method is + // expected to be followed with a call to FinishModelAllocation() Returns a + // pointer to an array of SubgraphAllocations (also stored in the tail of the + // arena) where each index corresponds to a different subgraph in the model. + // Return value is nullptr if the allocations failed. + SubgraphAllocations* StartModelAllocation(const Model* model); + + // Finish allocating internal resources required for model inference. + // + // -Plan the memory for activation tensors and scratch buffers. + // -Update eval tensors for each subgraph based on planned offsets. + // -Allocate scratch buffer handles array and update based on planned offsets. + // + // This method should be called after assigning model resources + // in StartModelAllocation(). The subgraph_allocations pointer should be the + // value passed into this class during StartModelAllocation(). Scratch buffer + // handles are stored in the out-param `scratch_buffer_handles` array which is + // allocated in this method. This value will be used in `GetScratchBuffer` + // call to retrieve scratch buffers. + TfLiteStatus FinishModelAllocation( + const Model* model, SubgraphAllocations* subgraph_allocations, + ScratchBufferHandle** scratch_buffer_handles); + + // Allocates a TfLiteTensor struct and populates the returned value with + // properties from the model flatbuffer. This struct is allocated from + // persistent arena memory is only guaranteed for the lifetime of the + // application. The eval_tensors pointer should be the value passed into this + // class during StartModelAllocation() and contains the source-of-truth for + // buffers. + virtual TfLiteTensor* AllocatePersistentTfLiteTensor( + const Model* model, const SubgraphAllocations* subgraph_allocations, + int tensor_index, int subgraph_index); + + // Allocates a TfLiteTensor struct and populates the returned value with + // properties from the model flatbuffer. This struct is allocated from + // temporary arena memory is only guaranteed until a call is made to + // ResetTempAllocations(). Subgraph_allocations contains the array of + // TfLiteEvalTensors. If the newly allocated temp at the specified subgraph + // and tensor index is already present int the TfLiteEvalTensor array, its + // data buffer will be re-used. + virtual TfLiteTensor* AllocateTempTfLiteTensor( + const Model* model, const SubgraphAllocations* subgraph_allocations, + int tensor_index, int subgraph_index); + + virtual void DeallocateTempTfLiteTensor(TfLiteTensor*); + + // Returns a pointer to a buffer from the temporary arena memory and is only + // guaranteed until a call is made to ResetTempAllocations(). + virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment); + + // Signals that the temporary buffer no longer needed. + virtual void DeallocateTempBuffer(uint8_t* buffer); + + // Resets all temporary allocations. This method should be called after a + // chain of temp allocations (e.g. chain of TfLiteTensor objects via + // AllocateTfLiteTensor()). + virtual TfLiteStatus ResetTempAllocations(); + + // Returns true if all temporary buffers including temp TfLiteTensor are + // already deallocated. + virtual bool IsAllTempDeallocated(); + + // Allocates persistent buffer which has the same life time as the allocator. + // The memory is immediately available and is allocated from the tail of the + // arena. + virtual void* AllocatePersistentBuffer(size_t bytes); + + // Register a scratch buffer of size `bytes` for Node with `node_id`. + // This method only requests a buffer with a given size to be used after a + // model has finished allocation via FinishModelAllocation(). All requested + // buffers will be accessible by the out-param in that method. + TfLiteStatus RequestScratchBufferInArena(size_t bytes, int subgraph_idx, + int* buffer_idx); + + // Finish allocating a specific NodeAndRegistration prepare block (kernel + // entry for a model) with a given node ID. This call ensures that any scratch + // buffer requests and temporary allocations are handled and ready for the + // next node prepare block. + TfLiteStatus FinishPrepareNodeAllocations(int node_id); + + // Returns the arena usage in bytes, only available after + // `FinishModelAllocation`. Otherwise, it will return 0. + size_t used_bytes() const; + + TfLiteBridgeBuiltinDataAllocator* GetBuiltinDataAllocator(); + + MicroMemoryPlanner* memory_planner() { return memory_planner_;} + + // Returns the pointer for the array of ScratchBufferRequest allocations in + // the head section. + internal::ScratchBufferRequest* GetScratchBufferRequests(); + + size_t GetScratchBufferRequestCount() { return scratch_buffer_request_count_;} + + protected: + MicroAllocator(SingleArenaBufferAllocator* memory_allocator, + MicroMemoryPlanner* memory_planner); + MicroAllocator(IPersistentBufferAllocator* persistent_buffer_allocator, + INonPersistentBufferAllocator* non_persistent_buffer_allocator, + MicroMemoryPlanner* memory_planner); + virtual ~MicroAllocator(); + + // Allocates an array in the arena to hold pointers to the node and + // registration pointers required to represent the inference graph of the + // model. + virtual TfLiteStatus AllocateNodeAndRegistrations( + const Model* model, SubgraphAllocations* subgraph_allocations); + + // Allocates the list of persistent TfLiteEvalTensors that are used for the + // "eval" phase of model inference. These structs will be the source of truth + // for all tensor buffers. + virtual TfLiteStatus AllocateTfLiteEvalTensors( + const Model* model, SubgraphAllocations* subgraph_allocations); + + // Allocates persistent tensor buffers for variable tensors in the subgraph. + // Online and offline variable tensors are handled differently hence the + // offline_planner_offsets parameter is needed. + virtual TfLiteStatus AllocateVariables( + const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors, + const int32_t* offline_planner_offsets); + + // Allocate and return a persistent TfLiteTensor. + // TODO(b/162311891): Drop this method when the interpreter has an API for + // accessing TfLiteEvalTensor structs. + virtual TfLiteTensor* AllocatePersistentTfLiteTensorInternal(); + + // Populates a TfLiteTensor struct with data from the model flatbuffer. Any + // quantization data is allocated from either the tail (persistent) or temp + // sections of the arena based on the allocation flag. + virtual TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model, + TfLiteTensor* tensor, + int tensor_index, + int subgraph_idx, + bool allocate_temp); + + private: + // Commits a memory plan for all non-persistent buffer allocations in the + // 'head' section of the memory arena. The eval_tensors pointer is the list of + // pre-allocated TfLiteEvalTensor structs that will point to the buffers that + // will be allocated into the head section in this function call. The + // scratch_buffer_handles pointer is the array of pre-allocated + // ScratchBufferHandle structs that will point to allocated buffers also in + // the head section. + virtual TfLiteStatus CommitStaticMemoryPlan( + const Model* model, SubgraphAllocations* allocations, + ScratchBufferHandle* scratch_buffer_handles); + + // Allocates an array of ScratchBufferHandle structs in the tail section for a + // given number of handles. + virtual TfLiteStatus AllocateScratchBufferHandles( + ScratchBufferHandle** scratch_buffer_handles, size_t handle_count); + + // Clears all internal scratch buffer request counts and resets the head to + // prepare for kernels to request scratch buffer data when a model is + // preparing. + TfLiteStatus InitScratchBufferData(); + + // A simple memory allocator that always allocate from the arena tail or head. + INonPersistentBufferAllocator* non_persistent_buffer_allocator_; + IPersistentBufferAllocator* persistent_buffer_allocator_; + + // Allocator used to allocate persistent builtin data. + TfLiteBridgeBuiltinDataAllocator* builtin_data_allocator_; + + // Activation buffer memory planner. + MicroMemoryPlanner* memory_planner_; + + bool model_is_allocating_; + + // Holds the number of ScratchBufferRequest instances stored in the head + // section when a model is allocating. + size_t scratch_buffer_request_count_ = 0; + + // Holds ScratchBufferRequest when a model is allocating + uint8_t* scratch_buffer_head_ = nullptr; + + // Holds the byte length of the memory plan with the largest head usage. Used + // to ensure that multi-tenant allocations can share the head for buffers. + size_t max_head_buffer_usage_ = 0; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h b/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h new file mode 100644 index 00000000..96c109e8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h @@ -0,0 +1,28 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ + +namespace tflite_micro { + +// The default buffer alignment requirement. +// We align tensor buffers to 16-byte boundaries, since this is a common +// requirement for SIMD extensions. +constexpr int MicroArenaBufferAlignment() { return 16; } + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_common.h b/new_ai_tools/include/tensorflow/lite/micro/micro_common.h new file mode 100644 index 00000000..9ab427f5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_common.h @@ -0,0 +1,38 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ +#define THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ + +#include "tensorflow/lite/c/common.h" + +// TFLMRegistration defines the API that TFLM kernels need to implement. +// This will be replacing the current TfLiteRegistration_V1 struct with +// something more compatible Embedded enviroment TFLM is used in. +struct TFLMRegistration { + void* (*init)(TfLiteContext* context, const char* buffer, size_t length); + void (*free)(TfLiteContext* context, void* buffer); + TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + void (*reset)(TfLiteContext* context, void* buffer); + int32_t builtin_code; + const char* custom_name; +}; + +struct TFLMInferenceRegistration { + TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); + void (*reset)(TfLiteContext* context, void* buffer); +}; + +#endif // THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_context.h b/new_ai_tools/include/tensorflow/lite/micro/micro_context.h new file mode 100644 index 00000000..40490ac0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_context.h @@ -0,0 +1,176 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_graph.h" + +#define XCORE_TFLITE_MICRO_PATCHED + +#ifdef NO_INTERPRETER + +namespace tflite_micro { + const TfLiteStatus kTfLiteAbort = static_cast(15); + + struct MicroContext{ + TfLiteTensor* (*AllocateTempInputTensor)(const TfLiteNode* node, int index); + TfLiteTensor* (*AllocateTempOutputTensor)(const TfLiteNode* node, int index); + void (*DeallocateTempTfLiteTensor)(TfLiteTensor* tensor); + void* (*external_context)(); + MicroGraph& (*graph)(); + }; + static inline MicroContext* GetMicroContext(const struct TfLiteContext* context){ + return reinterpret_cast(context->impl_); + } +} + +#else + +namespace tflite_micro { +// TODO(b/149795762): kTfLiteAbort cannot be part of the tflite TfLiteStatus. +const TfLiteStatus kTfLiteAbort = static_cast(15); + +// MicroContext is eventually going to become the API between TFLM and the +// kernels, replacing all the functions in TfLiteContext. The end state is code +// kernels to have code like: +// +// MicroContext* micro_context = GetMicroContext(context); +// micro_context-> +class MicroContext { + public: + virtual ~MicroContext() = default; + + // Allocate persistent buffer which has the same life time as the interpreter. + // Returns nullptr on failure. + // The memory is allocated from the tail. + // This method is only available in Init or Prepare stage. + virtual void* AllocatePersistentBuffer(size_t bytes) = 0; + + // Request a scratch buffer in the arena through static memory planning. + // This method is only available in Prepare stage and the buffer is allocated + // by the interpreter between Prepare and Eval stage. In Eval stage, + // GetScratchBuffer API can be used to fetch the address. + virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes, + int* buffer_idx) = 0; + + // Get the scratch buffer pointer. + // This method is only available in Eval stage. + virtual void* GetScratchBuffer(int buffer_idx) = 0; + + // Returns a temporary TfLiteTensor struct for a given index. + virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) = 0; + virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx, int sg){return nullptr;} + + // Returns a temporary TfLiteTensor struct for the specified input tensor of a + // given mode. This is the recommended API over the deprecated + // GetInput/GetInputSafe to get a temp input tensor. The returned tensor shall + // be freed via calling DeallocateTempTfLiteTensor. + TfLiteTensor* AllocateTempInputTensor(const TfLiteNode* node, int index); + + // Returns a temporary TfLiteTensor struct for the specified output tensor of + // a given mode. This is the recommended API over the deprecated + // GetOutput/GetOutputSafe to get a temp output tensor. The returned tensor + // shall be freed via calling DeallocateTempTfLiteTensor. + TfLiteTensor* AllocateTempOutputTensor(const TfLiteNode* node, int index); + + // Returns a temporary TfLiteTensor struct for the specified intermediate + // tensor of a given mode. This is the recommended API over the deprecated + // GetIntermediates/GetIntermediatesSafe to get a temp intermediate tensor. + // The returned tensor shall be freed via calling DeallocateTempTfLiteTensor. + TfLiteTensor* AllocateTempIntermediateTensor(const TfLiteNode* node, + int index); + + // Deallocates a temp TfLiteTensor. + virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) = 0; + + // Returns a pointer to a temporary buffer (from the arena). + // This API is only valid from the kernel's Prepare function and + // the buffer's lifetime is also that of the Prepare function. + virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) = 0; + + // Signals that the temporary buffer is no longer needed. + virtual void DeallocateTempBuffer(uint8_t* buffer) = 0; + + // Returns a TfLiteEvalTensor struct for a given index. + virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) = 0; + virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx, int sg){return nullptr;} + + // Does not take ownership of the pointer and the pointer must refer to valid + // an object that outlive this class instance. + // This can only be called once to set one external context. + virtual TfLiteStatus set_external_context(void* external_context_payload) = 0; + + virtual void* external_context() = 0; + + virtual MicroGraph& graph() = 0; + + private: + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +inline MicroContext* GetMicroContext(const struct TfLiteContext* context) { + return reinterpret_cast(context->impl_); +} + +// Deprecated API. Prefer to using the MicroContext API directly from the +// kernels. +// TODO(b/213010668): migrate all existing kernels to use MicroContext, delete +// these functions, and remove corresponding members from the TfLiteContext +// struct for TFLM. +inline void* MicroContextAllocatePersistentBuffer(TfLiteContext* ctx, + size_t bytes) { + return GetMicroContext(ctx)->AllocatePersistentBuffer(bytes); +} +inline TfLiteStatus MicroContextRequestScratchBufferInArena(TfLiteContext* ctx, + size_t bytes, + int* buffer_idx) { + return GetMicroContext(ctx)->RequestScratchBufferInArena(bytes, buffer_idx); +} +inline void* MicroContextGetScratchBuffer(TfLiteContext* ctx, int buffer_idx) { + return GetMicroContext(ctx)->GetScratchBuffer(buffer_idx); +} +inline TfLiteTensor* MicroContextGetTensor(const struct TfLiteContext* context, + int tensor_idx) { + return GetMicroContext(context)->AllocateTempTfLiteTensor(tensor_idx); +} +inline TfLiteTensor* MicroContextGetTensor(const struct TfLiteContext* context, + int tensor_idx, int sg) { + return GetMicroContext(context)->AllocateTempTfLiteTensor(tensor_idx, sg); +} +inline TfLiteEvalTensor* MicroContextGetEvalTensor( + const struct TfLiteContext* context, int tensor_idx) { + return GetMicroContext(context)->GetEvalTensor(tensor_idx); +} +inline TfLiteEvalTensor* MicroContextGetEvalTensor( + const struct TfLiteContext* context, int tensor_idx, int sg) { + return GetMicroContext(context)->GetEvalTensor(tensor_idx, sg); +} +inline TfLiteExternalContext* MicroContextGetExternalContext( + TfLiteContext* context, TfLiteExternalContextType unused) { + return reinterpret_cast( + GetMicroContext(context)->external_context()); +} + +// Requests that an error be reported with format string msg. +void MicroContextReportOpError(struct TfLiteContext* context, + const char* format, ...); + +} // namespace tflite_micro + +#endif // NO_INTERPRETER + +#endif // TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h b/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h new file mode 100644 index 00000000..3bd12792 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h @@ -0,0 +1,79 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/micro_common.h" +#include "tensorflow/lite/micro/micro_resource_variable.h" + +#ifdef NO_INTERPRETER + +namespace tflite_micro { + struct MicroGraph{ + int (*NumSubgraphs)(); + size_t (*NumSubgraphInputs)(int subgraph_idx); + size_t (*NumSubgraphOutputs)(int subgraph_idx); + TfLiteEvalTensor* (*GetSubgraphInput)(int subgraph_idx, int i); + TfLiteEvalTensor* (*GetSubgraphOutput)(int subgraph_idx, int i); + TfLiteStatus (*InvokeSubgraph)(int subgraph_idx); + }; +} + +#else + +namespace tflite_micro { + +// Abstracts the details of interacting with the graph from the kernels +// +// Provides methods to invoke any subgraph in the tflite_micro::Graph. +class MicroGraph { + public: + virtual ~MicroGraph() = default; + + // Calls TFLMRegistration->Invoke for every operator in a single subgraph + // in the model. + virtual TfLiteStatus InvokeSubgraph(int subgraph_idx) = 0; + + // Number of tensor inputs to a specified subgraph in the model. + virtual size_t NumSubgraphInputs(int subgraph_idx) = 0; + + // Get the specified input tensor of a specified subgraph in the model. + virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, + int input_idx) = 0; + + // Number of tensor outputs from a specified subgraph in the model. + virtual size_t NumSubgraphOutputs(int subgraph_idx) = 0; + + // Get the specified output tensor of a specified subgraph in the model. + virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, + int output_idx) = 0; + + // Number of subgraphs in the model. + virtual int NumSubgraphs() = 0; + + // Get the resource variables for this TFLM graph. + virtual MicroResourceVariables* GetResourceVariables() = 0; + + private: + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // NO_INTERPRETER + +#endif // TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h new file mode 100644 index 00000000..abe0f83f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h @@ -0,0 +1,189 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ + +#include +#include + +#include "flatbuffers/flatbuffers.h" // from @flatbuffers +#include "tensorflow/lite/c/c_api_types.h" +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/core/api/error_reporter.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_interpreter_context.h" +#include "tensorflow/lite/micro/micro_interpreter_graph.h" +#include "tensorflow/lite/micro/micro_op_resolver.h" +#include "tensorflow/lite/micro/micro_profiler_interface.h" +#include "tensorflow/lite/portable_type_to_tflitetype.h" +#include "tensorflow/lite/schema/schema_generated.h" + +/// Copied from tensorflow/lite/version.h to avoid a dependency chain into +// tensorflow/core. +#define TFLITE_SCHEMA_VERSION (3) + +namespace tflite_micro { + +class MicroInterpreter { + public: + // The lifetime of the model, op resolver, tensor arena, error reporter, + // resource variables, and profiler must be at least as long as that of the + // interpreter object, since the interpreter may need to access them at any + // time. This means that you should usually create them with the same scope as + // each other, for example having them all allocated on the stack as local + // variables through a top-level function. The interpreter doesn't do any + // deallocation of any of the pointed-to objects, ownership remains with the + // caller. + MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, + uint8_t* tensor_arena, size_t tensor_arena_size, + MicroResourceVariables* resource_variables = nullptr, + MicroProfilerInterface* profiler = nullptr, + bool preserve_all_tensors = false); + + // Create an interpreter instance using an existing MicroAllocator instance. + // This constructor should be used when creating an allocator that needs to + // have allocation handled in more than one interpreter or for recording + // allocations inside the interpreter. The lifetime of the allocator must be + // as long as that of the interpreter object. + MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, + MicroAllocator* allocator, + MicroResourceVariables* resource_variables = nullptr, + MicroProfilerInterface* profiler = nullptr); + + ~MicroInterpreter(); + + // Runs through the model and allocates all necessary input, output and + // intermediate tensors. + TfLiteStatus AllocateTensors(); + + // In order to support partial graph runs for strided models, this can return + // values other than kTfLiteOk and kTfLiteError. + // TODO(b/149795762): Add this to the TfLiteStatus enum. + TfLiteStatus Invoke(); + + // This is the recommended API for an application to pass an external payload + // pointer as an external context to kernels. The life time of the payload + // pointer should be at least as long as this interpreter. TFLM supports only + // one external context. + TfLiteStatus SetMicroExternalContext(void* external_context_payload); + + TfLiteTensor* input(size_t index); + size_t inputs_size() const { + return model_->subgraphs()->Get(0)->inputs()->size(); + } + const flatbuffers::Vector& inputs() const { + return *model_->subgraphs()->Get(0)->inputs(); + } + TfLiteTensor* input_tensor(size_t index) { return input(index); } + template + T* typed_input_tensor(int tensor_index) { + if (TfLiteTensor* tensor_ptr = input_tensor(tensor_index)) { + if (tensor_ptr->type == typeToTfLiteType()) { + return GetTensorData(tensor_ptr); + } + } + return nullptr; + } + + TfLiteTensor* output(size_t index); + size_t outputs_size() const { + return model_->subgraphs()->Get(0)->outputs()->size(); + } + const flatbuffers::Vector& outputs() const { + return *model_->subgraphs()->Get(0)->outputs(); + } + TfLiteTensor* output_tensor(size_t index) { return output(index); } + template + T* typed_output_tensor(int tensor_index) { + if (TfLiteTensor* tensor_ptr = output_tensor(tensor_index)) { + if (tensor_ptr->type == typeToTfLiteType()) { + return GetTensorData(tensor_ptr); + } + } + return nullptr; + } + + // Returns a pointer to the tensor for the corresponding tensor_index + TfLiteEvalTensor* GetTensor(int tensor_index, int subgraph_index = 0); + + // Reset the state to be what you would expect when the interpreter is first + // created. i.e. after Init and Prepare is called for the very first time. + TfLiteStatus Reset(); + + TfLiteStatus initialization_status() const { return initialization_status_; } + + // Populates node and registration pointers representing the inference graph + // of the model from values inside the flatbuffer (loaded from the TfLiteModel + // instance). Persistent data (e.g. operator data) is allocated from the + // arena. + TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer(); + + // For debugging only. + // Returns the actual used arena in bytes. This method gives the optimal arena + // size. It's only available after `AllocateTensors` has been called. + // Note that normally `tensor_arena` requires 16 bytes alignment to fully + // utilize the space. If it's not the case, the optimial arena size would be + // arena_used_bytes() + 16. + size_t arena_used_bytes() const { return allocator_.used_bytes(); } + + // Returns True if all Tensors are being preserves + // TODO(b/297106074) : revisit making C++ example or test for + // preserve_all_tesnors + bool preserve_all_tensors() const { + return allocator_.preserves_all_tensor(); + } + + size_t operators_size(int sg) const { return model_->subgraphs()->Get(sg)->operators()->size(); } + + // For debugging only. + const NodeAndRegistration node_and_registration(int node_index, int sg) { + return graph_.GetAllocations()[sg].node_and_registrations[node_index]; + } + + protected: + const MicroAllocator& allocator() const { return allocator_; } + const TfLiteContext& context() const { return context_; } + + private: + // TODO(b/158263161): Consider switching to Create() function to enable better + // error reporting during initialization. + void Init(MicroProfilerInterface* profiler); + + // Gets the current subgraph index used from within context methods. + int get_subgraph_index() { return graph_.GetCurrentSubgraphIndex(); } + + const Model* model_; + const MicroOpResolver& op_resolver_; + TfLiteContext context_ = {}; + MicroAllocator& allocator_; + MicroInterpreterGraph graph_; + bool tensors_allocated_; + + TfLiteStatus initialization_status_; + + ScratchBufferHandle* scratch_buffer_handles_ = nullptr; + + // TODO(b/162311891): Clean these pointers up when this class supports buffers + // from TfLiteEvalTensor. + TfLiteTensor** input_tensors_; + TfLiteTensor** output_tensors_; + + MicroInterpreterContext micro_context_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h new file mode 100644 index 00000000..0681bba6 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h @@ -0,0 +1,125 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_context.h" +#include "tensorflow/lite/micro/micro_interpreter_graph.h" +#include "tensorflow/lite/micro/micro_log.h" + +namespace tflite_micro { + +// A full implementation of the MicroContext, to be used by the +// MicroInterpreter. Kernels should not depend on this directly. Instead they +// should only depend on the MicroContext. +class MicroInterpreterContext : public MicroContext { + public: + // Enum that allows MicroContext to keep track of the stages different memory + // planning APIs are available to kernels. + enum class InterpreterState { + kInit, + kPrepare, + kMemoryPlanning, + kInvoke, + }; + + // Does not take any ownership, and all pointers must refer to valid objects + // that outlive the one constructed. + MicroInterpreterContext(MicroAllocator* allocator, const Model* model, + MicroInterpreterGraph* graph); + virtual ~MicroInterpreterContext(); + + // Allocate persistent buffer which has the same life time as the interpreter. + // Returns nullptr on failure. + // The memory is allocated from the tail. + // This method is only available in Init or Prepare stage. + // Virtual so that it can be faked for kernel tests. + virtual void* AllocatePersistentBuffer(size_t bytes) override; + + // Request a scratch buffer in the arena through static memory planning. + // This method is only available in Prepare stage and the buffer is allocated + // by the interpreter between Prepare and Eval stage. In Eval stage, + // GetScratchBuffer API can be used to fetch the address. + // Virtual so that it can be faked for kernel tests. + virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes, + int* buffer_idx) override; + + // Get the scratch buffer pointer. + // This method is only available in Eval stage. + // Virtual so that it can be faked for kernel tests. + virtual void* GetScratchBuffer(int buffer_idx) override; + + // Returns a temporary TfLiteTensor struct for a given index. + // Virtual so that it can be faked for kernel tests. + virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) override; + virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx, int sg) override; + + // Deallocates a temp TfLiteTensor. + // Virtual so that it can be faked for kernel tests. + virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override; + + // Returns a pointer to a temporary buffer (from the arena). + // This API is only valid from the kernel's Prepare function and + // the buffer's lifetime is also that of the Prepare function. + // Virtual so that it can be faked for kernel tests. + virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override; + + // Signals that the temporary buffer is no longer needed. + // Virtual so that it can be faked for kernel tests. + virtual void DeallocateTempBuffer(uint8_t* buffer) override; + + // Returns a TfLiteEvalTensor struct for a given index. + // Virtual so that it can be faked for kernel tests. + virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) override; + virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx, int sg) override; + + // Sets the State of MemoryPlanning MicroInterpreterContext + void SetInterpreterState(InterpreterState state); + + // Sets the State of MemoryPlanning MicroInterpreterContext + InterpreterState GetInterpreterState() const; + + // Does not take ownership of the pointer and the pointer must refer to valid + // an object that outlive this class instance. + // This can only be called once to set one external context. + TfLiteStatus set_external_context(void* external_context_payload) override; + + void* external_context() override { return external_context_payload_; } + + MicroGraph& graph() override { return graph_; } + + // Sets the pointer to a list of ScratchBufferHandle instances. + // Not API between TFLM and kernels. Primarily used by the framework for + // housekeeping in MicroInterpreterContext. + void SetScratchBufferHandles(ScratchBufferHandle* scratch_buffer_handles); + + private: + MicroAllocator& allocator_; + MicroInterpreterGraph& graph_; + const Model* model_; + InterpreterState state_; + + ScratchBufferHandle* scratch_buffer_handles_ = nullptr; + void* external_context_payload_ = nullptr; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h new file mode 100644 index 00000000..b3de4c97 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h @@ -0,0 +1,110 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ + +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_common.h" +#include "tensorflow/lite/micro/micro_graph.h" +#include "tensorflow/lite/micro/micro_resource_variable.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// Abstracts the details of interacting with the tflite_micro::Model. +// +// Provides methods to access, initialize, prepare, invoke and free any +// subgraph in the tflite_micro::Graph. +class MicroInterpreterGraph : public MicroGraph { + public: + // The lifetime of the context, model, allocator and resource_variables must + // be at least as long as that of the graph object, since the this class may + // need to access them at any time. If resource_variables is a nullptr, + // GetResourceVariables will return a nullptr. + MicroInterpreterGraph(TfLiteContext* context, const Model* model, + MicroAllocator* allocator, + MicroResourceVariables* resource_variables); + virtual ~MicroInterpreterGraph(); + + // Sets up builtin data and calls TFLMRegistration->Init for every + // operator in every subgraph in the model. + virtual TfLiteStatus InitSubgraphs(); + + // Calls TFLMRegistration->Prepare for every operator in every subgraph + // in the model. + virtual TfLiteStatus PrepareSubgraphs(); + + // Calls TFLMRegistration->Reset for every operator in every subgraph in + // the model. + virtual TfLiteStatus ResetSubgraphs(); + + // Calls TFLMRegistration->Free for every operator in every subgraph in + // the model. + virtual TfLiteStatus FreeSubgraphs(); + + // Calls TFLMRegistration->Invoke for every operator in a single subgraph + // in the model. + virtual TfLiteStatus InvokeSubgraph(int subgraph_idx); + + // Zeros out all variable tensors in all subgraphs in the model. + virtual TfLiteStatus ResetVariableTensors(); + + // Number of tensor inputs to a specified subgraph in the model. + virtual size_t NumSubgraphInputs(int subgraph_idx); + + // Get the specified input tensor of a specified subgraph in the model. + virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int input_idx); + + // Number of tensor outputs from a specified subgraph in the model. + virtual size_t NumSubgraphOutputs(int subgraph_idx); + + // Get the specified output tensor of a specified subgraph in the model. + virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, int output_idx); + + // Number of subgraphs in the model. + virtual int NumSubgraphs(); + + // Hook to pass in subgraph allocations tracked within the interpreter, + // allowing MicroInterpreterGraph to init / prepare / invoke subgraphs in the + // model. + void SetSubgraphAllocations(SubgraphAllocations* subgraph_allocations); + + // Get the current subgraph index. Within an on operator, this is guaranteed + // to be the subgraph of that operator. + int GetCurrentSubgraphIndex() { return current_subgraph_index_; } + + // Gets the list of alloctions for each subgraph. This is the source of truth + // for all per-subgraph allocation data. + SubgraphAllocations* GetAllocations() { return subgraph_allocations_; } + + // Get the resource variables for this TFLM graph. + MicroResourceVariables* GetResourceVariables() { return resource_variables_; } + + private: + TfLiteContext* context_; + const Model* model_; + MicroAllocator* allocator_; + SubgraphAllocations* subgraph_allocations_ = nullptr; + int current_subgraph_index_; + MicroResourceVariables* resource_variables_; + const flatbuffers::Vector>* subgraphs_; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_log.h b/new_ai_tools/include/tensorflow/lite/micro/micro_log.h new file mode 100644 index 00000000..c9db1e44 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_log.h @@ -0,0 +1,42 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ + +#if !defined(TF_LITE_STRIP_ERROR_STRINGS) +#include +// These functions can be used independent of the MicroErrorReporter to get +// printf-like functionalitys and are common to all target platforms. +void MicroPrintf(const char* format, ...); +void VMicroPrintf(const char* format, va_list args); +#else +// We use a #define to ensure that the strings are completely stripped, to +// prevent an unnecessary increase in the binary size. +#define MicroPrintf(...) tflite_micro::Unused(__VA_ARGS__) +#define VMicroPrintf(...) tflite_micro::Unused(__VA_ARGS__) +#endif + +namespace tflite_micro { + +// From +// https://stackoverflow.com/questions/23235910/variadic-unused-function-macro +template +void Unused(Args&&... args) { + (void)(sizeof...(args)); +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h new file mode 100644 index 00000000..2790bba6 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h @@ -0,0 +1,708 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ + +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/core/api/flatbuffer_conversions.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/op_macros.h" +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/kernels/add.h" +#include "tensorflow/lite/micro/kernels/conv.h" +#include "tensorflow/lite/micro/kernels/depthwise_conv.h" +#include "tensorflow/lite/micro/kernels/ethosu.h" +#include "tensorflow/lite/micro/kernels/fully_connected.h" +#include "tensorflow/lite/micro/kernels/micro_ops.h" +#include "tensorflow/lite/micro/kernels/pooling.h" +#include "tensorflow/lite/micro/kernels/reduce.h" +#include "tensorflow/lite/micro/kernels/softmax.h" +#include "tensorflow/lite/micro/micro_log.h" +#include "tensorflow/lite/micro/micro_op_resolver.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { +TFLMRegistration* Register_DETECTION_POSTPROCESS(); + +template +class MicroMutableOpResolver : public MicroOpResolver { + public: + TF_LITE_REMOVE_VIRTUAL_DELETE + + explicit MicroMutableOpResolver() {} + + const TFLMRegistration* FindOp(tflite_micro::BuiltinOperator op) const override { + if (op == BuiltinOperator_CUSTOM) return nullptr; + + for (unsigned int i = 0; i < registrations_len_; ++i) { + const TFLMRegistration& registration = registrations_[i]; + if (registration.builtin_code == op) { + return ®istration; + } + } + return nullptr; + } + + const TFLMRegistration* FindOp(const char* op) const override { + for (unsigned int i = 0; i < registrations_len_; ++i) { + const TFLMRegistration& registration = registrations_[i]; + if ((registration.builtin_code == BuiltinOperator_CUSTOM) && + (strcmp(registration.custom_name, op) == 0)) { + return ®istration; + } + } + return nullptr; + } + + TfLiteBridgeBuiltinParseFunction GetOpDataParser( + BuiltinOperator op) const override { + TFLITE_DCHECK(num_buitin_ops_ <= tOpCount); + for (unsigned int i = 0; i < num_buitin_ops_; ++i) { + if (builtin_codes_[i] == op) return builtin_parsers_[i]; + } + return nullptr; + } + + // Registers a Custom Operator with the MicroOpResolver. + // + // Only the first call for a given name will be successful. i.e. if this + // function is called again for a previously added Custom Operator, the + // MicroOpResolver will be unchanged and this function will return + // kTfLiteError. + TfLiteStatus AddCustom(const char* name, + const TFLMRegistration* registration) { + if (registrations_len_ >= tOpCount) { + MicroPrintf( + "Couldn't register custom op '%s', resolver size is too" + "small (%d)", + name, tOpCount); + return kTfLiteError; + } + + if (FindOp(name) != nullptr) { + MicroPrintf("Calling AddCustom for the same op more than once "); + MicroPrintf("is not supported (Op: %s).", name); + return kTfLiteError; + } + + TFLMRegistration* new_registration = ®istrations_[registrations_len_]; + registrations_len_ += 1; + + *new_registration = *registration; + new_registration->builtin_code = BuiltinOperator_CUSTOM; + new_registration->custom_name = name; + return kTfLiteOk; + } + + // The Add* functions below add the various Builtin operators to the + // MicroMutableOpResolver object. + + TfLiteStatus AddAbs() { + return AddBuiltin(BuiltinOperator_ABS, Register_ABS(), ParseAbs); + } + + TfLiteStatus AddAdd(const TFLMRegistration& registration = Register_ADD()) { + return AddBuiltin(BuiltinOperator_ADD, registration, ParseAdd); + } + + TfLiteStatus AddAddN() { + return AddBuiltin(BuiltinOperator_ADD_N, tflite_micro::Register_ADD_N(), + ParseAddN); + } + + TfLiteStatus AddArgMax() { + return AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX(), ParseArgMax); + } + + TfLiteStatus AddArgMin() { + return AddBuiltin(BuiltinOperator_ARG_MIN, Register_ARG_MIN(), ParseArgMin); + } + + TfLiteStatus AddAssignVariable() { + return AddBuiltin(BuiltinOperator_ASSIGN_VARIABLE, + tflite_micro::Register_ASSIGN_VARIABLE(), ParseAssignVariable); + } + + TfLiteStatus AddAveragePool2D( + const TFLMRegistration& registration = Register_AVERAGE_POOL_2D()) { + return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, registration, ParsePool); + } + + TfLiteStatus AddBatchMatMul() { + return AddBuiltin(BuiltinOperator_BATCH_MATMUL, + tflite_micro::Register_BATCH_MATMUL(), ParseBatchMatMul); + } + + TfLiteStatus AddBatchToSpaceNd() { + return AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, + Register_BATCH_TO_SPACE_ND(), ParseBatchToSpaceNd); + } + + TfLiteStatus AddBroadcastArgs() { + return AddBuiltin(BuiltinOperator_BROADCAST_ARGS, Register_BROADCAST_ARGS(), + ParseBroadcastArgs); + } + + TfLiteStatus AddBroadcastTo() { + return AddBuiltin(BuiltinOperator_BROADCAST_TO, Register_BROADCAST_TO(), + ParseBroadcastTo); + } + + TfLiteStatus AddCallOnce() { + return AddBuiltin(BuiltinOperator_CALL_ONCE, Register_CALL_ONCE(), + ParseCallOnce); + } + + TfLiteStatus AddCast() { + return AddBuiltin(BuiltinOperator_CAST, Register_CAST(), ParseCast); + } + + TfLiteStatus AddCeil() { + return AddBuiltin(BuiltinOperator_CEIL, Register_CEIL(), ParseCeil); + } + + TfLiteStatus AddCircularBuffer() { + return AddCustom("CIRCULAR_BUFFER", tflite_micro::Register_CIRCULAR_BUFFER()); + } + + TfLiteStatus AddConcatenation() { + return AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION(), + ParseConcatenation); + } + + TfLiteStatus AddConv2D( + const TFLMRegistration& registration = Register_CONV_2D()) { + return AddBuiltin(BuiltinOperator_CONV_2D, registration, ParseConv2D); + } + + TfLiteStatus AddCos() { + return AddBuiltin(BuiltinOperator_COS, tflite_micro::Register_COS(), ParseCos); + } + + TfLiteStatus AddCumSum() { + return AddBuiltin(BuiltinOperator_CUMSUM, tflite_micro::Register_CUMSUM(), + ParseCumsum); + } + + TfLiteStatus AddDelay() { + // TODO(b/286250473): change back name to "Delay" and remove namespace + return AddCustom("SignalDelay", tflite_micro::tflm_signal::Register_DELAY()); + } + + TfLiteStatus AddDepthToSpace() { + return AddBuiltin(BuiltinOperator_DEPTH_TO_SPACE, + tflite_micro::Register_DEPTH_TO_SPACE(), ParseDepthToSpace); + } + + TfLiteStatus AddDepthwiseConv2D( + const TFLMRegistration& registration = Register_DEPTHWISE_CONV_2D()) { + return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, registration, + ParseDepthwiseConv2D); + } + + TfLiteStatus AddDequantize() { + return AddBuiltin(BuiltinOperator_DEQUANTIZE, tflite_micro::Register_DEQUANTIZE(), + ParseDequantize); + } + + TfLiteStatus AddDetectionPostprocess() { + return AddCustom("TFLite_Detection_PostProcess", + tflite_micro::Register_DETECTION_POSTPROCESS()); + } + + TfLiteStatus AddDiv() { + return AddBuiltin(BuiltinOperator_DIV, tflite_micro::Register_DIV(), ParseDiv); + } + + TfLiteStatus AddEmbeddingLookup() { + return AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, + Register_EMBEDDING_LOOKUP(), ParseEmbeddingLookup); + } + + TfLiteStatus AddEnergy() { + // TODO(b/286250473): change back name to "Energy" and remove namespace + return AddCustom("SignalEnergy", tflite_micro::tflm_signal::Register_ENERGY()); + } + + TfLiteStatus AddElu() { + return AddBuiltin(BuiltinOperator_ELU, tflite_micro::Register_ELU(), ParseElu); + } + + TfLiteStatus AddEqual() { + return AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL(), ParseEqual); + } + + TfLiteStatus AddEthosU() { + TFLMRegistration* registration = tflite_micro::Register_ETHOSU(); + if (registration) { + return AddCustom(tflite_micro::GetString_ETHOSU(), registration); + } + return kTfLiteOk; + } + + TfLiteStatus AddExp() { + return AddBuiltin(BuiltinOperator_EXP, Register_EXP(), ParseExp); + } + + TfLiteStatus AddExpandDims() { + return AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS(), + ParseExpandDims); + } + + TfLiteStatus AddFftAutoScale() { + // TODO(b/286250473): change back name and remove namespace + return AddCustom("SignalFftAutoScale", + tflite_micro::tflm_signal::Register_FFT_AUTO_SCALE()); + } + + TfLiteStatus AddFill() { + return AddBuiltin(BuiltinOperator_FILL, tflite_micro::Register_FILL(), ParseFill); + } + + TfLiteStatus AddFilterBank() { + // TODO(b/286250473): change back name to "FilterBank" and remove namespace + return AddCustom("SignalFilterBank", + tflite_micro::tflm_signal::Register_FILTER_BANK()); + } + TfLiteStatus AddFilterBankLog() { + // TODO(b/286250473): change back name to "FilterBankLog" and remove + // namespace + return AddCustom("SignalFilterBankLog", + tflite_micro::tflm_signal::Register_FILTER_BANK_LOG()); + } + TfLiteStatus AddFilterBankSquareRoot() { + // TODO(b/286250473): change back name to "FilterBankSquareRoot" and remove + // namespace + return AddCustom("SignalFilterBankSquareRoot", + tflite_micro::tflm_signal::Register_FILTER_BANK_SQUARE_ROOT()); + } + TfLiteStatus AddFilterBankSpectralSubtraction() { + // TODO(b/286250473): change back name to "FilterBankSpectralSubtraction" + // and remove namespace + return AddCustom( + "SignalFilterBankSpectralSubtraction", + tflite_micro::tflm_signal::Register_FILTER_BANK_SPECTRAL_SUBTRACTION()); + } + + TfLiteStatus AddFloor() { + return AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR(), ParseFloor); + } + + TfLiteStatus AddFloorDiv() { + return AddBuiltin(BuiltinOperator_FLOOR_DIV, tflite_micro::Register_FLOOR_DIV(), + ParseFloorDiv); + } + + TfLiteStatus AddFloorMod() { + return AddBuiltin(BuiltinOperator_FLOOR_MOD, tflite_micro::Register_FLOOR_MOD(), + ParseFloorMod); + } + + TfLiteStatus AddFramer() { + // TODO(b/286250473): change back name to "Framer" and remove namespace + return AddCustom("SignalFramer", tflite_micro::tflm_signal::Register_FRAMER()); + } + + TfLiteStatus AddFullyConnected( + const TFLMRegistration& registration = Register_FULLY_CONNECTED()) { + return AddBuiltin(BuiltinOperator_FULLY_CONNECTED, registration, + ParseFullyConnected); + } + + TfLiteStatus AddGather() { + return AddBuiltin(BuiltinOperator_GATHER, tflite_micro::Register_GATHER(), + ParseGather); + } + + TfLiteStatus AddGatherNd() { + return AddBuiltin(BuiltinOperator_GATHER_ND, tflite_micro::Register_GATHER_ND(), + ParseGatherNd); + } + + TfLiteStatus AddGreater() { + return AddBuiltin(BuiltinOperator_GREATER, Register_GREATER(), + ParseGreater); + } + + TfLiteStatus AddGreaterEqual() { + return AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL(), + ParseGreaterEqual); + } + + TfLiteStatus AddHardSwish() { + return AddBuiltin(BuiltinOperator_HARD_SWISH, tflite_micro::Register_HARD_SWISH(), + ParseHardSwish); + } + + TfLiteStatus AddIf() { + return AddBuiltin(BuiltinOperator_IF, tflite_micro::Register_IF(), ParseIf); + } + + TfLiteStatus AddIrfft(const TFLMRegistration* registration = + tflite::tflm_signal::Register_IRFFT()) { + // TODO(b/286250473): change back name and remove namespace + return AddCustom("SignalIrfft", registration); + } + + TfLiteStatus AddL2Normalization() { + return AddBuiltin(BuiltinOperator_L2_NORMALIZATION, + Register_L2_NORMALIZATION(), ParseL2Normalization); + } + + TfLiteStatus AddL2Pool2D() { + return AddBuiltin(BuiltinOperator_L2_POOL_2D, tflite_micro::Register_L2_POOL_2D(), + ParsePool); + } + + TfLiteStatus AddLeakyRelu() { + return AddBuiltin(BuiltinOperator_LEAKY_RELU, tflite_micro::Register_LEAKY_RELU(), + ParseLeakyRelu); + } + + TfLiteStatus AddLess() { + return AddBuiltin(BuiltinOperator_LESS, Register_LESS(), ParseLess); + } + + TfLiteStatus AddLessEqual() { + return AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL(), + ParseLessEqual); + } + + TfLiteStatus AddLog() { + return AddBuiltin(BuiltinOperator_LOG, Register_LOG(), ParseLog); + } + + TfLiteStatus AddLogicalAnd() { + return AddBuiltin(BuiltinOperator_LOGICAL_AND, + tflite_micro::Register_LOGICAL_AND(), ParseLogicalAnd); + } + + TfLiteStatus AddLogicalNot() { + return AddBuiltin(BuiltinOperator_LOGICAL_NOT, Register_LOGICAL_NOT(), + ParseLogicalNot); + } + + TfLiteStatus AddLogicalOr() { + return AddBuiltin(BuiltinOperator_LOGICAL_OR, tflite_micro::Register_LOGICAL_OR(), + ParseLogicalOr); + } + + TfLiteStatus AddLogistic() { + return AddBuiltin(BuiltinOperator_LOGISTIC, tflite_micro::Register_LOGISTIC(), + ParseLogistic); + } + + TfLiteStatus AddLogSoftmax() { + return AddBuiltin(BuiltinOperator_LOG_SOFTMAX, + tflite_micro::Register_LOG_SOFTMAX(), ParseLogSoftmax); + } + + TfLiteStatus AddMaximum() { + return AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM(), + ParseMaximum); + } + + TfLiteStatus AddMaxPool2D( + const TFLMRegistration& registration = Register_MAX_POOL_2D()) { + return AddBuiltin(BuiltinOperator_MAX_POOL_2D, registration, ParsePool); + } + + TfLiteStatus AddMirrorPad() { + return AddBuiltin(BuiltinOperator_MIRROR_PAD, tflite_micro::Register_MIRROR_PAD(), + ParseMirrorPad); + } + + TfLiteStatus AddMean() { + return AddBuiltin(BuiltinOperator_MEAN, Register_MEAN(), ParseReducer); + } + + TfLiteStatus AddMinimum() { + return AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM(), + ParseMinimum); + } + + TfLiteStatus AddMul(const TFLMRegistration& registration = Register_MUL()) { + return AddBuiltin(BuiltinOperator_MUL, registration, ParseMul); + } + + TfLiteStatus AddNeg() { + return AddBuiltin(BuiltinOperator_NEG, Register_NEG(), ParseNeg); + } + + TfLiteStatus AddNotEqual() { + return AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL(), + ParseNotEqual); + } + + TfLiteStatus AddOverlapAdd() { + // TODO(b/286250473): change back name to "OverlapAdd" and remove namespace + return AddCustom("SignalOverlapAdd", + tflite_micro::tflm_signal::Register_OVERLAP_ADD()); + } + + TfLiteStatus AddPack() { + return AddBuiltin(BuiltinOperator_PACK, Register_PACK(), ParsePack); + } + + TfLiteStatus AddPad(const TFLMRegistration& registration = Register_PAD()) { + return AddBuiltin(BuiltinOperator_PAD, registration, ParsePad); + } + + TfLiteStatus AddPadV2() { + return AddBuiltin(BuiltinOperator_PADV2, Register_PADV2(), ParsePadV2); + } + + TfLiteStatus AddPCAN() { + // TODO(b/286250473): change back name to "PCAN" and remove namespace + return AddCustom("SignalPCAN", tflite_micro::tflm_signal::Register_PCAN()); + } + + TfLiteStatus AddPrelu() { + return AddBuiltin(BuiltinOperator_PRELU, tflite_micro::Register_PRELU(), + ParsePrelu); + } + + TfLiteStatus AddQuantize() { + return AddBuiltin(BuiltinOperator_QUANTIZE, Register_QUANTIZE(), + ParseQuantize); + } + + TfLiteStatus AddReadVariable() { + return AddBuiltin(BuiltinOperator_READ_VARIABLE, + tflite_micro::Register_READ_VARIABLE(), ParseReadVariable); + } + + TfLiteStatus AddReduceMax() { + return AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX(), + ParseReducer); + } + + TfLiteStatus AddRelu() { + return AddBuiltin(BuiltinOperator_RELU, tflite_micro::Register_RELU(), ParseRelu); + } + + TfLiteStatus AddRelu6() { + return AddBuiltin(BuiltinOperator_RELU6, tflite_micro::Register_RELU6(), + ParseRelu6); + } + + TfLiteStatus AddReshape() { + return AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE(), + ParseReshape); + } + + TfLiteStatus AddResizeBilinear() { + return AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, + Register_RESIZE_BILINEAR(), ParseResizeBilinear); + } + + TfLiteStatus AddResizeNearestNeighbor() { + return AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + Register_RESIZE_NEAREST_NEIGHBOR(), + ParseResizeNearestNeighbor); + } + + TfLiteStatus AddRfft(const TFLMRegistration* registration = + tflite::tflm_signal::Register_RFFT()) { + // TODO(b/286250473): change back name and remove namespace + return AddCustom("SignalRfft", registration); + } + + TfLiteStatus AddRound() { + return AddBuiltin(BuiltinOperator_ROUND, Register_ROUND(), ParseRound); + } + + TfLiteStatus AddRsqrt() { + return AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT(), ParseRsqrt); + } + + TfLiteStatus AddSelectV2() { + return AddBuiltin(BuiltinOperator_SELECT_V2, Register_SELECT_V2(), + ParseSelectV2); + } + + TfLiteStatus AddShape() { + return AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE(), ParseShape); + } + + TfLiteStatus AddSin() { + return AddBuiltin(BuiltinOperator_SIN, Register_SIN(), ParseSin); + } + + TfLiteStatus AddSlice() { + return AddBuiltin(BuiltinOperator_SLICE, Register_SLICE(), ParseSlice); + } + + TfLiteStatus AddSoftmax( + const TFLMRegistration& registration = Register_SOFTMAX()) { + return AddBuiltin(BuiltinOperator_SOFTMAX, registration, ParseSoftmax); + } + + TfLiteStatus AddSpaceToBatchNd() { + return AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, + Register_SPACE_TO_BATCH_ND(), ParseSpaceToBatchNd); + } + + TfLiteStatus AddSpaceToDepth() { + return AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH(), + ParseSpaceToDepth); + } + + TfLiteStatus AddSplit() { + return AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT(), ParseSplit); + } + + TfLiteStatus AddSplitV() { + return AddBuiltin(BuiltinOperator_SPLIT_V, Register_SPLIT_V(), ParseSplitV); + } + + TfLiteStatus AddSqueeze() { + return AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE(), + ParseSqueeze); + } + + TfLiteStatus AddSqrt() { + return AddBuiltin(BuiltinOperator_SQRT, Register_SQRT(), ParseSqrt); + } + + TfLiteStatus AddSquare() { + return AddBuiltin(BuiltinOperator_SQUARE, Register_SQUARE(), ParseSquare); + } + + TfLiteStatus AddSquaredDifference() { + return AddBuiltin(BuiltinOperator_SQUARED_DIFFERENCE, + tflite_micro::Register_SQUARED_DIFFERENCE(), + ParseSquaredDifference); + } + + TfLiteStatus AddStridedSlice() { + return AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE(), + ParseStridedSlice); + } + + TfLiteStatus AddStacker() { + // TODO(b/286250473): change back name to "Stacker" and remove namespace + return AddCustom("SignalStacker", tflite_micro::tflm_signal::Register_STACKER()); + } + + TfLiteStatus AddSub() { + return AddBuiltin(BuiltinOperator_SUB, tflite_micro::Register_SUB(), ParseSub); + } + + TfLiteStatus AddSum() { + return AddBuiltin(BuiltinOperator_SUM, Register_SUM(), ParseReducer); + } + + TfLiteStatus AddSvdf(const TFLMRegistration& registration = Register_SVDF()) { + return AddBuiltin(BuiltinOperator_SVDF, registration, ParseSvdf); + } + + TfLiteStatus AddTanh() { + return AddBuiltin(BuiltinOperator_TANH, Register_TANH(), ParseTanh); + } + + TfLiteStatus AddTransposeConv() { + return AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, + tflite_micro::Register_TRANSPOSE_CONV(), ParseTransposeConv); + } + + TfLiteStatus AddTranspose() { + return AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE(), + ParseTranspose); + } + + TfLiteStatus AddUnpack() { + return AddBuiltin(BuiltinOperator_UNPACK, Register_UNPACK(), ParseUnpack); + } + + TfLiteStatus AddUnidirectionalSequenceLSTM( + const TFLMRegistration& registration = + Register_UNIDIRECTIONAL_SEQUENCE_LSTM()) { + return AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, + registration, ParseUnidirectionalSequenceLSTM); + } + + TfLiteStatus AddVarHandle() { + return AddBuiltin(BuiltinOperator_VAR_HANDLE, Register_VAR_HANDLE(), + ParseVarHandle); + } + + TfLiteStatus AddWhile() { + return AddBuiltin(BuiltinOperator_WHILE, Register_WHILE(), ParseWhile); + } + + TfLiteStatus AddWindow() { + // TODO(b/286250473): change back name to "Window" and remove namespace + return AddCustom("SignalWindow", tflite_micro::tflm_signal::Register_WINDOW()); + } + + TfLiteStatus AddZerosLike() { + return AddBuiltin(BuiltinOperator_ZEROS_LIKE, Register_ZEROS_LIKE(), + ParseZerosLike); + } + + unsigned int GetRegistrationLength() { return registrations_len_; } + + private: + TfLiteStatus AddBuiltin(tflite_micro::BuiltinOperator op, + const TFLMRegistration& registration, + TfLiteBridgeBuiltinParseFunction parser) { + if (op == BuiltinOperator_CUSTOM) { + MicroPrintf("Invalid parameter BuiltinOperator_CUSTOM to the "); + MicroPrintf("AddBuiltin function."); + return kTfLiteError; + } + + if (FindOp(op) != nullptr) { + MicroPrintf("Calling AddBuiltin with the same op more than "); + MicroPrintf("once is not supported (Op: #%d).", op); + return kTfLiteError; + } + + if (registrations_len_ >= tOpCount) { + MicroPrintf("Couldn't register builtin op #%d, resolver size ", op); + MicroPrintf("is too small (%d).", tOpCount); + return kTfLiteError; + } + + registrations_[registrations_len_] = registration; + // Strictly speaking, the builtin_code is not necessary for TFLM but filling + // it in regardless. + registrations_[registrations_len_].builtin_code = op; + registrations_len_++; + + builtin_codes_[num_buitin_ops_] = op; + builtin_parsers_[num_buitin_ops_] = parser; + num_buitin_ops_++; + + return kTfLiteOk; + } + + TFLMRegistration registrations_[tOpCount]; + unsigned int registrations_len_ = 0; + + // Arrays (and counter) to store the builtin codes and their corresponding + // parse functions as these are registered with the Op Resolver. + BuiltinOperator builtin_codes_[tOpCount]; + TfLiteBridgeBuiltinParseFunction builtin_parsers_[tOpCount]; + unsigned int num_buitin_ops_ = 0; +}; + +}; // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h new file mode 100644 index 00000000..af4e4d26 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h @@ -0,0 +1,62 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_common.h" +#include "tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// This is an interface for the OpResolver for TFLiteMicro. The differences from +// the TFLite OpResolver base class are to: +// * explicitly remove support for Op versions +// * allow for finer grained registration of the Builtin Ops to reduce code +// size for TFLiteMicro. +// +// We need an interface class instead of directly using MicroMutableOpResolver +// because MicroMutableOpResolver is a class template with the number of +// registered Ops as the template parameter. +class MicroOpResolver { + public: + // Returns the Op registration struct corresponding to the enum code from the + // flatbuffer schema. Returns nullptr if the op is not found or if op == + // BuiltinOperator_CUSTOM. + virtual const TFLMRegistration* FindOp(BuiltinOperator op) const = 0; + + // Returns the Op registration struct corresponding to the custom operator by + // name. + virtual const TFLMRegistration* FindOp(const char* op) const = 0; + + // Returns the operator specific parsing function for the OpData for a + // BuiltinOperator (if registered), else nullptr. + virtual TfLiteBridgeBuiltinParseFunction GetOpDataParser( + BuiltinOperator op) const = 0; + + virtual ~MicroOpResolver() {} +}; + +// Handles the logic for converting between an OperatorCode structure extracted +// from a flatbuffer and information about a registered operator +// implementation. +TfLiteStatus GetRegistrationFromOpCode(const OperatorCode* opcode, + const MicroOpResolver& op_resolver, + const TFLMRegistration** registration); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h new file mode 100644 index 00000000..fe8e586e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h @@ -0,0 +1,140 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ + +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/micro_profiler_interface.h" + +namespace tflite_micro { + +// MicroProfiler creates a common way to gain fine-grained insight into runtime +// performance. Bottleck operators can be identified along with slow code +// sections. This can be used in conjunction with running the relevant micro +// benchmark to evaluate end-to-end performance. +class MicroProfiler : public MicroProfilerInterface { + public: + MicroProfiler() = default; + virtual ~MicroProfiler() = default; + + // Marks the start of a new event and returns an event handle that can be used + // to mark the end of the event via EndEvent. The lifetime of the tag + // parameter must exceed that of the MicroProfiler. + virtual uint32_t BeginEvent(const char* tag) override; + + // Marks the end of an event associated with event_handle. It is the + // responsibility of the caller to ensure than EndEvent is called once and + // only once per event_handle. + // + // If EndEvent is called more than once for the same event_handle, the last + // call will be used as the end of event marker.If EndEvent is called 0 times + // for a particular event_handle, the duration of that event will be 0 ticks. + virtual void EndEvent(uint32_t event_handle) override; + + // Clears all the events that have been currently profiled. + void ClearEvents() { num_events_ = 0; } + + // Returns the sum of the ticks taken across all the events. This number + // is only meaningful if all of the events are disjoint (the end time of + // event[i] <= start time of event[i+1]). + uint32_t GetTotalTicks() const; + + // Prints the profiling information of each of the events in human readable + // form. + void Log() const; + + // Prints the profiling information of each of the events in CSV (Comma + // Separated Value) form. + void LogCsv() const; + + // Prints total ticks for each unique tag in CSV format. + // Output will have one row for each unique tag along with the + // total ticks summed across all events with that particular tag. + void LogTicksPerTagCsv(); + + private: + // Maximum number of events that this class can keep track of. If we call + // AddEvent more than kMaxEvents number of times, then the oldest event's + // profiling information will be overwritten. + static constexpr int kMaxEvents = 4096; + + const char* tags_[kMaxEvents]; + uint32_t start_ticks_[kMaxEvents]; + uint32_t end_ticks_[kMaxEvents]; + int num_events_ = 0; + + struct TicksPerTag { + const char* tag; + uint32_t ticks; + }; + // In practice, the number of tags will be much lower than the number of + // events. But it is theoretically possible that each event to be unique and + // hence we allow total_ticks_per_tag to have kMaxEvents entries. + TicksPerTag total_ticks_per_tag[kMaxEvents] = {}; + + int FindExistingOrNextPosition(const char* tag_name); + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +#if defined(TF_LITE_STRIP_ERROR_STRINGS) +// For release builds, the ScopedMicroProfiler is a noop. +// +// This is done because the ScipedProfiler is used as part of the +// MicroInterpreter and we want to ensure zero overhead for the release builds. +class ScopedMicroProfiler { + public: + explicit ScopedMicroProfiler(const char* tag, + MicroProfilerInterface* profiler) {} +}; + +#else + +// This class can be used to add events to a MicroProfiler object that span the +// lifetime of the ScopedMicroProfiler object. +// Usage example: +// +// MicroProfiler profiler(); +// ... +// { +// ScopedMicroProfiler scoped_profiler("custom_tag", profiler); +// work_to_profile(); +// } +class ScopedMicroProfiler { + public: + explicit ScopedMicroProfiler(const char* tag, + MicroProfilerInterface* profiler) + : profiler_(profiler) { + if (profiler_ != nullptr) { + event_handle_ = profiler_->BeginEvent(tag); + } + } + + ~ScopedMicroProfiler() { + if (profiler_ != nullptr) { + profiler_->EndEvent(event_handle_); + } + } + + private: + uint32_t event_handle_ = 0; + MicroProfilerInterface* profiler_ = nullptr; +}; +#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS) + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h new file mode 100644 index 00000000..a879501b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h @@ -0,0 +1,38 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ + +#include + +namespace tflite_micro { + +// Interface class that the TFLM framework relies on for profiling. +class MicroProfilerInterface { + public: + virtual ~MicroProfilerInterface() {} + + // Marks the start of a new event and returns an event handle that can be used + // to mark the end of the event via EndEvent. + virtual uint32_t BeginEvent(const char* tag) = 0; + + // Marks the end of an event associated with event_handle. + virtual void EndEvent(uint32_t event_handle) = 0; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h b/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h new file mode 100644 index 00000000..44bf92d0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h @@ -0,0 +1,89 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ +#define TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_allocator.h" + +namespace tflite_micro { + +class MicroResourceVariables { + public: + // Create + static MicroResourceVariables* Create(MicroAllocator* allocator, + int num_variables); + + // Creates a resource variable if none is available for the given container + // and shared name pair. Returns the resource ID corresponding to the + // container and shared name pair. If allocation fails, the returned resource + // ID will be negative. The the container and shared_name must outlive this + // class. + int CreateIdIfNoneFound(const char* container, const char* shared_name); + + // Read the resource buffer associated with the given ID into the given + // tensor. + TfLiteStatus Read(int id, const TfLiteEvalTensor* tensor); + + // Allocates the resource buffer if none has been allocated, based on the + // length of the input tensor. Copies input tensor contents to the resource + // buffer. + TfLiteStatus Allocate(int id, TfLiteContext* context, + const TfLiteTensor* tensor); + + // Copies input tensor contents to the resource buffer. + // AllocateResourceVariable with a TFLite tensor must have been called first + // in order to allocate the resource buffer. + TfLiteStatus Assign(int id, const TfLiteEvalTensor* tensor); + + // Zeros out all resource buffers. + TfLiteStatus ResetAll(); + + private: + int FindId(const char* container, const char* shared_name); + + // Micro resource contains the mapping between resource container/name strings + // and resouce IDs. Each resource ID corresponds to a resource buffer pointer. + // The resouce ID is created during the VAR_HANDLE operator preparation stage. + // The resource buffer pointer is created during ASSIGN_VARIABLE preparation + // stage based on the size of the TFLiteTensor being assigned. + struct MicroResourceVariable { + const char* container; + const char* shared_name; + void* resource_buffer; + + // This is only for verifying read size. + size_t bytes; + // Initialization default value + int8_t default_value; + }; + + MicroResourceVariables(MicroResourceVariable* variables, + int max_variable_count) + : resource_variables_(variables), + max_variable_count_(max_variable_count), + num_resource_variables_(0) {} + + MicroResourceVariable* resource_variables_; + int max_variable_count_; + int num_resource_variables_; +}; + +} // namespace tflite_micro + +#endif // TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_time.h b/new_ai_tools/include/tensorflow/lite/micro/micro_time.h new file mode 100644 index 00000000..27e7292d --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_time.h @@ -0,0 +1,36 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ + +#include + +namespace tflite_micro { + +// These functions should be implemented by each target platform, and provide an +// accurate tick count along with how many ticks there are per second. +uint32_t ticks_per_second(); + +// Return time in ticks. The meaning of a tick varies per platform. +uint32_t GetCurrentTimeTicks(); + +inline uint32_t TicksToMs(int32_t ticks) { + return static_cast(1000.0f * static_cast(ticks) / + static_cast(ticks_per_second())); +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h b/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h new file mode 100644 index 00000000..ad53ffdb --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h @@ -0,0 +1,162 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ +#define TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ + +#include +#include +#include +#include + +#include "tensorflow/lite/c/common.h" + +namespace tflite_micro { + +// Returns number of elements in the shape array. + +int ElementCount(const TfLiteIntArray& dims); + +size_t EvalTensorBytes(const TfLiteEvalTensor* tensor); + +// C++11 does not support constexpr max; hence, use ternary conditional to +// create our own constexpr Max function. +constexpr int Max(int a, int b) { return a >= b ? a : b; } + +// Converts a float value into a quantized value. Note that large values (close +// to max int and min int) may see significant error due to a lack of floating +// point granularity for large values. +template +T FloatToQuantizedType(const float value, const float scale, int zero_point) { + int32_t result = round(value / scale) + zero_point; + result = + std::max(static_cast(std::numeric_limits::min()), result); + result = + std::min(static_cast(std::numeric_limits::max()), result); + return result; +} + +template +T FloatToSymmetricQuantizedType(const float value, const float scale) { + // 64-bit values are required since 8x16 conv accumulates to int64, meaning + // an int64 bias is required. + std::int64_t result = round(value / scale); + result = std::max( + static_cast(std::numeric_limits::min() + 1), result); + result = std::min(static_cast(std::numeric_limits::max()), + result); + return result; +} + +// Helper methods to quantize arrays of floats to the desired format. +// +// There are several key flavors of quantization in TfLite: +// asymmetric symmetric per channel +// int8_t | X | X | X | +// uint8_t | X | X | | +// int16_t | X | | | +// int32_t | | X | X | +// +// The per-op quantization spec can be found here: +// https://www.tensorflow.org/lite/performance/quantization_spec +template +void Quantize(const float* input, T* output, int num_elements, float scale, + int zero_point) { + for (int i = 0; i < num_elements; i++) { + output[i] = FloatToQuantizedType(input[i], scale, zero_point); + } +} + +template +void SymmetricQuantize(const float* input, T* output, int num_elements, + float scale) { + for (int i = 0; i < num_elements; i++) { + output[i] = FloatToSymmetricQuantizedType(input[i], scale); + } +} + +template +void SymmetricPerChannelQuantize(const float* input, T* output, + int num_elements, int num_channels, + float* scales) { + int elements_per_channel = num_elements / num_channels; + for (int i = 0; i < num_channels; i++) { + for (int j = 0; j < elements_per_channel; j++) { + output[i * elements_per_channel + j] = FloatToSymmetricQuantizedType( + input[i * elements_per_channel + j], scales[i]); + } + } +} + +void SignedSymmetricPerChannelQuantize(const float* values, + TfLiteIntArray* dims, + int quantized_dimension, + int8_t* quantized_values, + float* scaling_factor, + TfLiteType type = kTfLiteNoType); + +// Quantizes inputs based on the values provided, choosing the smallest range +// which includes all input values. +template +void SymmetricQuantizeCalculateScales(const float* values, TfLiteIntArray* dims, + T* output, float* scale) { + int input_size = ElementCount(*dims); + + float min = 0; + float max = 0; + for (int i = 0; i < input_size; i++) { + min = fminf(min, values[i]); + max = fmaxf(max, values[i]); + } + *scale = fmaxf(std::abs(min), std::abs(max)) / std::numeric_limits::max(); + for (int i = 0; i < input_size; i++) { + const int32_t quantized_value = + static_cast(roundf(values[i] / *scale)); + // Clamp: just in case some odd numeric offset. + quantized_value = fminf(std::numeric_limits::max(), quantized_value); + quantized_value = fmaxf(std::numeric_limits::min() + 1, quantized_value); + output[i] = quantized_value; + } +} + +template +void Dequantize(const T* values, const int size, const float scale, + int zero_point, float* dequantized_values) { + for (int i = 0; i < size; ++i) { + dequantized_values[i] = (values[i] - zero_point) * scale; + } +} + +// based on TfLiteType passed in to these functions the corresponding max / min +// int for that type are returned +inline int QMinFromTfLiteType(TfLiteType type) { + if (type == kTfLiteInt4) { + return -8; + } else { + return std::numeric_limits::min(); + } +} + +inline int QMaxFromTfLiteType(TfLiteType type) { + if (type == kTfLiteInt4) { + return 7; + } else { + return std::numeric_limits::max(); + } +} + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h b/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h new file mode 100644 index 00000000..219b6b4b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h @@ -0,0 +1,60 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ +#define TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/micro_graph.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// MockMicroGraph stubs out all MicroGraph methods used during invoke. A count +// of the number of calls to invoke for each subgraph is maintained for +// validation of control flow operators. +class MockMicroGraph : public MicroGraph { + public: + explicit MockMicroGraph(SingleArenaBufferAllocator* allocator); + TfLiteStatus InvokeSubgraph(int subgraph_idx) override; + size_t NumSubgraphInputs(int subgraph_idx) override; + TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int tensor_idx) override; + size_t NumSubgraphOutputs(int subgraph_idx) override; + TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, + int tensor_idx) override; + int NumSubgraphs() override; + MicroResourceVariables* GetResourceVariables() override; + int get_init_count() const { return init_count_; } + int get_prepare_count() const { return prepare_count_; } + int get_free_count() const { return free_count_; } + int get_invoke_count(int subgraph_idx) const { + return invoke_counts_[subgraph_idx]; + } + + private: + static constexpr int kMaxSubgraphs = 10; + SingleArenaBufferAllocator* allocator_; + TfLiteEvalTensor* mock_tensor_; + int init_count_; + int prepare_count_; + int free_count_; + int invoke_counts_[kMaxSubgraphs]; + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h new file mode 100644 index 00000000..8d27aee9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h @@ -0,0 +1,21 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ +#define TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ + +// TODO(b/286456378): remove once this shim is no longer needed. +#include "python/tflite_micro/python_ops_resolver.h" + +#endif // TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h new file mode 100644 index 00000000..f65d8bb6 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h @@ -0,0 +1,30 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ +#define TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ + +#include + +#include "flatbuffers/flatbuffers.h" +#include "flatbuffers/util.h" + +namespace tflite_micro { + +std::string FlatBufferSizeToJsonString( + const uint8_t* buffer, const flatbuffers::TypeTable* type_table); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h new file mode 100644 index 00000000..96df3291 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h @@ -0,0 +1,33 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ +#define TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ + +#include + +#include + +namespace tflite_micro { + +class FlatbufferSizeWrapper { + public: + FlatbufferSizeWrapper(); + ~FlatbufferSizeWrapper(); + + std::string ConvertToJsonString(const char* in_flatbuffer); +}; + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h new file mode 100644 index 00000000..e585f0e8 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h @@ -0,0 +1,125 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ + +#include "tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h" +#include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/micro_allocator.h" + +namespace tflite_micro { + +// List of buckets currently recorded by this class. Each type keeps a list of +// allocated information during model initialization. +// TODO(b/169834511): Add tracking for scratch buffer allocations. +enum class RecordedAllocationType { + kTfLiteEvalTensorData, + kPersistentTfLiteTensorData, + kPersistentTfLiteTensorQuantizationData, + kPersistentBufferData, + kTfLiteTensorVariableBufferData, + kNodeAndRegistrationArray, + kOpData, +}; + +// Container for holding information about allocation recordings by a given +// type. Each recording contains the number of bytes requested, the actual bytes +// allocated (can defer from requested by alignment), and the number of items +// allocated. +struct RecordedAllocation { + size_t requested_bytes; + size_t used_bytes; + size_t count; +}; + +// Utility subclass of MicroAllocator that records all allocations +// inside the arena. A summary of allocations can be logged through the +// ErrorReporter by invoking LogAllocations(). This special allocator requires +// an instance of RecordingSingleArenaBufferAllocator to capture allocations in +// the head and tail. Arena allocation recording can be retrieved by type +// through the GetRecordedAllocation() function. This class should only be used +// for auditing memory usage or integration testing. +class RecordingMicroAllocator : public MicroAllocator { + public: + static RecordingMicroAllocator* Create(uint8_t* tensor_arena, + size_t arena_size); + + // Returns the fixed amount of memory overhead of RecordingMicroAllocator. + static size_t GetDefaultTailUsage(); + + // Returns the recorded allocations information for a given allocation type. + RecordedAllocation GetRecordedAllocation( + RecordedAllocationType allocation_type) const; + + const RecordingSingleArenaBufferAllocator* GetSimpleMemoryAllocator() const; + + // Logs out through the ErrorReporter all allocation recordings by type + // defined in RecordedAllocationType. + void PrintAllocations() const; + + void* AllocatePersistentBuffer(size_t bytes) override; + + protected: + TfLiteStatus AllocateNodeAndRegistrations( + const Model* model, SubgraphAllocations* subgraph_allocations) override; + TfLiteStatus AllocateTfLiteEvalTensors( + const Model* model, SubgraphAllocations* subgraph_allocations) override; + TfLiteStatus AllocateVariables( + const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors, + const int32_t* offline_planner_offsets) override; + // TODO(b/162311891): Once all kernels have been updated to the new API drop + // this method. It is only used to record TfLiteTensor persistent allocations. + TfLiteTensor* AllocatePersistentTfLiteTensorInternal() override; + + // TODO(b/162311891): Once all kernels have been updated to the new API drop + // this function since all allocations for quantized data will take place in + // the temp section. + TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model, + TfLiteTensor* tensor, + int tensor_index, + int subgraph_index, + bool allocate_temp) override; + + private: + RecordingMicroAllocator(RecordingSingleArenaBufferAllocator* memory_allocator, + MicroMemoryPlanner* memory_planner); + + void PrintRecordedAllocation(RecordedAllocationType allocation_type, + const char* allocation_name, + const char* allocation_description) const; + + RecordedAllocation SnapshotAllocationUsage() const; + void RecordAllocationUsage(const RecordedAllocation& snapshotted_allocation, + RecordedAllocation& recorded_allocation); + + const RecordingSingleArenaBufferAllocator* recording_memory_allocator_; + + RecordedAllocation recorded_tflite_eval_tensor_data_ = {}; + RecordedAllocation recorded_persistent_tflite_tensor_data_ = {}; + RecordedAllocation recorded_persistent_tflite_tensor_quantization_data_ = {}; + RecordedAllocation recorded_persistent_buffer_data_ = {}; + RecordedAllocation recorded_tflite_tensor_variable_buffer_data_ = {}; + RecordedAllocation recorded_node_and_registration_array_data_ = {}; + + // TODO(b/187993291): Re-enable OpData allocating tracking. + RecordedAllocation recorded_op_data_ = {}; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h new file mode 100644 index 00000000..f53d0335 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h @@ -0,0 +1,69 @@ +/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ +#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ + +#include "tensorflow/lite/micro/micro_interpreter.h" +#include "tensorflow/lite/micro/micro_profiler_interface.h" +#include "tensorflow/lite/micro/recording_micro_allocator.h" + +namespace tflite_micro { + +// Utility subclass that enables internal recordings of the MicroInterpreter. +// This class should be used to audit and analyze memory arena usage for a given +// model and interpreter. +// +// After construction and the first Invoke() or AllocateTensors() call - the +// memory usage is recorded and available through the GetMicroAllocator() +// function. See RecordingMicroAlloctor for more details on what is currently +// recorded from arena allocations. +// +// It is recommended for users to increase the tensor arena size by at least 1kb +// to ensure enough additional memory is available for internal recordings. +class RecordingMicroInterpreter : public MicroInterpreter { + public: + RecordingMicroInterpreter(const Model* model, + const MicroOpResolver& op_resolver, + uint8_t* tensor_arena, size_t tensor_arena_size, + MicroResourceVariables* resource_variable = nullptr, + MicroProfilerInterface* profiler = nullptr) + : MicroInterpreter( + model, op_resolver, + RecordingMicroAllocator::Create(tensor_arena, tensor_arena_size), + resource_variable, profiler), + recording_micro_allocator_( + static_cast(allocator())) {} + + RecordingMicroInterpreter(const Model* model, + const MicroOpResolver& op_resolver, + RecordingMicroAllocator* allocator, + MicroResourceVariables* resource_variable = nullptr, + MicroProfilerInterface* profiler = nullptr) + : MicroInterpreter(model, op_resolver, allocator, resource_variable, + profiler), + recording_micro_allocator_(*allocator) {} + + const RecordingMicroAllocator& GetMicroAllocator() const { + return recording_micro_allocator_; + } + + private: + const RecordingMicroAllocator& recording_micro_allocator_; +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/system_setup.h b/new_ai_tools/include/tensorflow/lite/micro/system_setup.h new file mode 100644 index 00000000..f01d27e9 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/system_setup.h @@ -0,0 +1,27 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ +#define TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ + +namespace tflite_micro { + +// This should called during initialization of TFLM binaries and tests. It can +// be specialized if there is a need for custom target-specific intialization. +// For more information, see tensorflow/lite/micro/system_setup.cc. +void InitializeTarget(); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h b/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h new file mode 100644 index 00000000..aab4aca1 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h @@ -0,0 +1,49 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ +#define TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ + +#include +#include + +#include "flatbuffers/flatbuffers.h" // from @flatbuffers +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/micro/micro_common.h" +#include "tensorflow/lite/micro/micro_utils.h" +#include "tensorflow/lite/portable_type_to_tflitetype.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { +namespace testing { + +class PackerOp { + public: + static const TFLMRegistration* getRegistration(); + static TFLMRegistration* GetMutableRegistration(); + static void* Init(TfLiteContext* context, const char* buffer, size_t length); + static void Free(TfLiteContext* context, void* buffer); + static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); + static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); + + private: + static bool freed_; +}; + +} // namespace testing +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h b/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h new file mode 100644 index 00000000..58888614 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h @@ -0,0 +1,334 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ +#define TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ + +#include +#include +#include +#include + +#include "flatbuffers/flatbuffers.h" // from @flatbuffers +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/kernels/internal/compatibility.h" +#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" +#include "tensorflow/lite/micro/micro_utils.h" +#include "tensorflow/lite/portable_type_to_tflitetype.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { +namespace testing { + +constexpr int kOfflinePlannerHeaderSize = 3; +using TestingOpResolver = tflite_micro::MicroMutableOpResolver<10>; + +struct NodeConnection_ { + std::initializer_list input; + std::initializer_list output; +}; +typedef struct NodeConnection_ NodeConnection; + +// A simple operator that returns the median of the input with the number of +// times the kernel was invoked. The implementation below is deliberately +// complicated, just to demonstrate how kernel memory planning works. +class SimpleStatefulOp { + static constexpr int kBufferNotAllocated = 0; + // Inputs: + static constexpr int kInputTensor = 0; + // Outputs: + static constexpr int kMedianTensor = 0; + static constexpr int kInvokeCount = 1; + struct OpData { + int* invoke_count = nullptr; + int sorting_buffer = kBufferNotAllocated; + }; + + public: + static const TFLMRegistration* getRegistration(); + static TFLMRegistration* GetMutableRegistration(); + static void* Init(TfLiteContext* context, const char* buffer, size_t length); + static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); + static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); +}; + +class MockCustom { + public: + static const TFLMRegistration* getRegistration(); + static TFLMRegistration* GetMutableRegistration(); + static void* Init(TfLiteContext* context, const char* buffer, size_t length); + static void Free(TfLiteContext* context, void* buffer); + static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); + static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); + + static bool freed_; +}; + +// A simple operator with the purpose of testing multiple inputs. It returns +// the sum of the inputs. +class MultipleInputs { + public: + static const TFLMRegistration* getRegistration(); + static TFLMRegistration* GetMutableRegistration(); + static void* Init(TfLiteContext* context, const char* buffer, size_t length); + static void Free(TfLiteContext* context, void* buffer); + static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); + static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); + + static bool freed_; +}; + +// A simple no-op operator. +class NoOp { + public: + static const TFLMRegistration* getRegistration(); + static TFLMRegistration* GetMutableRegistration(); + static void* Init(TfLiteContext* context, const char* buffer, size_t length); + static void Free(TfLiteContext* context, void* buffer); + static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); + static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); + + static bool freed_; +}; + +// Returns an Op Resolver that can be used in the testing code. +TfLiteStatus GetTestingOpResolver(TestingOpResolver& op_resolver); + +// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input, +// 1 layer of weights, 1 output Tensor, and 1 operator. +const Model* GetSimpleMockModel(); + +// Returns a flatbuffer TensorFlow Lite model with more inputs, variable +// tensors, and operators. +const Model* GetComplexMockModel(); + +// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input, +// 1 layer of weights, 1 output Tensor, and 1 operator. +// The size of all three tensors is 256 x 256, which is larger than what other +// models provide from this test helper. +const Model* GetModelWith256x256Tensor(); + +// Returns a simple flatbuffer model with two branches. +const Model* GetSimpleModelWithBranch(); + +// Returns a simple example flatbuffer TensorFlow Lite model. Contains 3 inputs, +// 1 output Tensor, and 1 operator. +const Model* GetSimpleMultipleInputsModel(); + +// Returns a simple flatbuffer model with offline planned tensors +// @param[in] num_tensors Number of tensors in the model. +// @param[in] metadata_buffer Metadata for offline planner. +// @param[in] node_con List of connections, i.e. operators +// in the model. +// @param[in] num_conns Number of connections. +// @param[in] num_subgraph_inputs How many of the input tensors are in +// the subgraph inputs. The default value +// of 0 means all of the input tensors +// are in the subgraph input list. There +// must be at least 1 input tensor in the +// subgraph input list. +const Model* GetModelWithOfflinePlanning(int num_tensors, + const int32_t* metadata_buffer, + NodeConnection* node_conn, + int num_conns, + int num_subgraph_inputs = 0); + +// Returns a flatbuffer with a single operator, two inputs (one unused) and one +// output. +const Model* GetModelWithUnusedInputs(); + +// Returns a flatbuffer with a single operator, zero inputs and two outputs +// (one unused). +const Model* GetModelWithUnusedOperatorOutputs(); + +// Returns a flatbuffer model with `simple_stateful_op` +const Model* GetSimpleStatefulModel(); + +// Returns a flatbuffer model with "if" and two subgraphs. +const Model* GetSimpleModelWithSubgraphsAndIf(); + +// Returns a flatbuffer model with "if" and two subgraphs one of which is empty. +const Model* GetSimpleModelWithIfAndEmptySubgraph(); + +// Returns a flatbuffer model with "while" and three subgraphs. +const Model* GetSimpleModelWithSubgraphsAndWhile(); + +// Returns a flatbuffer model with "if" and two subgraphs and the input tensor 1 +// of "if" subgraph overlaps with the input tensor 2 of subgraph 1. +const Model* GetModelWithIfAndSubgraphInputTensorOverlap(); + +// Returns a flatbuffer model with null subgraph/operator inputs and outputs. +const Model* GetSimpleModelWithNullInputsAndOutputs(); + +// Builds a one-dimensional flatbuffer tensor of the given size. +const Tensor* Create1dFlatbufferTensor(int size, bool is_variable = false); + +// Builds a one-dimensional flatbuffer tensor of the given size with +// quantization metadata. +const Tensor* CreateQuantizedFlatbufferTensor(int size); + +// Creates a one-dimensional tensor with no quantization metadata. +const Tensor* CreateMissingQuantizationFlatbufferTensor(int size); + +// Creates a vector of flatbuffer buffers. +const flatbuffers::Vector>* +CreateFlatbufferBuffers(); + +// Performs a simple string comparison without requiring standard C library. +int TestStrcmp(const char* a, const char* b); + +void PopulateContext(TfLiteTensor* tensors, int tensors_size, + TfLiteContext* context); + +// Create a TfLiteIntArray from an array of ints. The first element in the +// supplied array must be the size of the array expressed as an int. +TfLiteIntArray* IntArrayFromInts(const int* int_array); + +// Create a TfLiteFloatArray from an array of floats. The first element in the +// supplied array must be the size of the array expressed as a float. +TfLiteFloatArray* FloatArrayFromFloats(const float* floats); + +// Assumes that `src_tensor` is a buffer where each element is a 4-bit value +// stored in 8-bit. +// Returns a new buffer that is packed densely with 2 4-bit values in a byte. +// The packing format is low-bits-first, i.e. the lower nibble of a byte is +// filled first, followed by the upper nibble. +void PackInt4ValuesDenselyInPlace(uint8_t* src_buffer, int buffer_size); + +template +TfLiteTensor CreateTensor(const T* data, TfLiteIntArray* dims, + const bool is_variable = false, + TfLiteType type = kTfLiteNoType) { + TfLiteTensor result; + result.dims = dims; + result.params = {}; + result.quantization = {kTfLiteNoQuantization, nullptr}; + result.is_variable = is_variable; + result.allocation_type = kTfLiteMemNone; + result.data.data = const_cast(data); + result.bytes = ElementCount(*dims) * sizeof(T); + result.data.data = const_cast(data); + + if (type == kTfLiteInt4) { + result.type = kTfLiteInt4; + PackInt4ValuesDenselyInPlace(tflite_micro::GetTensorData(&result), + ElementCount(*dims)); + result.bytes = ((ElementCount(*dims) + 1) / 2); + } else { + // Const cast is used to allow passing in const and non-const arrays within + // a single CreateTensor method. A Const array should be used for immutable + // input tensors and non-const array should be used for mutable and output + // tensors. + result.type = typeToTfLiteType(); + } + return result; +} + +template +TfLiteTensor CreateQuantizedTensor(const T* data, TfLiteIntArray* dims, + const float scale, const int zero_point = 0, + const bool is_variable = false, + TfLiteType type = kTfLiteNoType) { + TfLiteTensor result = CreateTensor(data, dims, is_variable, type); + result.params = {scale, zero_point}; + result.quantization = {kTfLiteAffineQuantization, nullptr}; + return result; +} + +template +TfLiteTensor CreateQuantizedTensor(const float* input, T* quantized, + TfLiteIntArray* dims, float scale, + int zero_point, bool is_variable = false, + TfLiteType type = kTfLiteNoType) { + int input_size = ElementCount(*dims); + tflite_micro::Quantize(input, quantized, input_size, scale, zero_point); + return CreateQuantizedTensor(quantized, dims, scale, zero_point, is_variable, + type); +} + +TfLiteTensor CreateQuantizedBiasTensor(const float* data, int16_t* quantized, + TfLiteIntArray* dims, float input_scale, + float weights_scale, + bool is_variable = false); + +TfLiteTensor CreateQuantizedBiasTensor(const float* data, int32_t* quantized, + TfLiteIntArray* dims, float input_scale, + float weights_scale, + bool is_variable = false); + +TfLiteTensor CreateQuantizedBiasTensor(const float* data, + std::int64_t* quantized, + TfLiteIntArray* dims, float input_scale, + float weights_scale, + bool is_variable = false); + +// Quantizes int32_t bias tensor with per-channel weights determined by input +// scale multiplied by weight scale for each channel. +TfLiteTensor CreatePerChannelQuantizedBiasTensor( + const float* input, int32_t* quantized, TfLiteIntArray* dims, + float input_scale, float* weight_scales, float* scales, int* zero_points, + TfLiteAffineQuantization* affine_quant, int quantized_dimension, + bool is_variable = false); + +// Quantizes int64_t bias tensor with per-channel weights determined by input +// scale multiplied by weight scale for each channel. +TfLiteTensor CreatePerChannelQuantizedBiasTensor( + const float* input, std::int64_t* quantized, TfLiteIntArray* dims, + float input_scale, float* weight_scales, float* scales, int* zero_points, + TfLiteAffineQuantization* affine_quant, int quantized_dimension, + bool is_variable = false); + +TfLiteTensor CreateSymmetricPerChannelQuantizedTensor( + const float* input, int8_t* quantized, TfLiteIntArray* dims, float* scales, + int* zero_points, TfLiteAffineQuantization* affine_quant, + int quantized_dimension, bool is_variable = false, + TfLiteType tensor_weight_type = kTfLiteNoType); + +// Returns the number of tensors in the default subgraph for a tflite_micro::Model. +size_t GetModelTensorCount(const Model* model); + +// Derives the asymmetric quantization scaling factor from a min and max range. +template +inline float ScaleFromMinMax(const float min, const float max) { + return (max - min) / + static_cast((std::numeric_limits::max() * 1.0) - + std::numeric_limits::min()); +} + +// Derives the symmetric quantization scaling factor from a min and max range. +template +inline float SymmetricScaleFromMinMax(const float min, const float max) { + const int32_t kScale = + std::numeric_limits::type>::max(); + const float range = std::max(std::abs(min), std::abs(max)); + if (range == 0) { + return 1.0f; + } else { + return range / kScale; + } +} + +// Derives the quantization zero point from a min and max range. +template +inline int ZeroPointFromMinMax(const float min, const float max) { + return static_cast(std::numeric_limits::min()) + + static_cast(-min / ScaleFromMinMax(min, max) + 0.5f); +} + +} // namespace testing +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h b/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h new file mode 100644 index 00000000..a5595fe5 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h @@ -0,0 +1,267 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// An ultra-lightweight testing framework designed for use with microcontroller +// applications. This is designed to be usable even +// when no standard C or C++ libraries are available, and without any dynamic +// memory allocation or reliance on global constructors. +// +// To build a test, you use syntax similar to gunit, but with some extra +// decoration to create a hidden 'main' function containing each of the tests to +// be run. Your code should look something like: +// ---------------------------------------------------------------------------- +// #include "path/to/this/header" +// +// TF_LITE_MICRO_TESTS_BEGIN +// +// TF_LITE_MICRO_TEST(SomeTest) { +// TF_LITE_LOG_EXPECT_EQ(true, true); +// } +// +// TF_LITE_MICRO_TESTS_END +// ---------------------------------------------------------------------------- +// If you compile this for your platform, you'll get a normal binary that you +// should be able to run. Executing it will output logging information like this +// to stderr: +// ---------------------------------------------------------------------------- +// Testing SomeTest +// 1/1 tests passed +// ~~~ALL TESTS PASSED~~~ +// ---------------------------------------------------------------------------- +// This is designed to be human-readable, so you can just run tests manually, +// but the string "~~~ALL TESTS PASSED~~~" should only appear if all of the +// tests do pass. This makes it possible to integrate with automated test +// systems by scanning the output logs and looking for that magic value. +// +// This framework is intended to be a rudimentary alternative to no testing at +// all on systems that struggle to run more conventional approaches, so use with +// caution! + +#ifndef TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ +#define TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ +#include +#include + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_log.h" +#include "tensorflow/lite/micro/system_setup.h" + +namespace micro_test { +extern int tests_passed; +extern int tests_failed; +extern bool is_test_complete; +extern bool did_test_fail; +} // namespace micro_test + +namespace tflite_micro { + +// This additional helper function is used (instead of directly calling +// tflite_micro::InitializeTarget from the TF_LITE_MICRO_TESTS_BEGIN macro) to avoid +// adding a dependency from every bazel test target to micro:system_setp (which +// is the target that implements InitializeTarget(). +// +// The underlying issue here is that the use of the macros results in +// dependencies that can be containted within the micro/testing:micro_test +// target bleeding on to all the tests. +inline void InitializeTest() { InitializeTarget(); } +} // namespace tflite_micro + +#define TF_LITE_MICRO_TESTS_BEGIN \ + namespace micro_test { \ + int tests_passed; \ + int tests_failed; \ + bool is_test_complete; \ + bool did_test_fail; \ + } \ + \ + int main(int argc, char** argv) { \ + micro_test::tests_passed = 0; \ + micro_test::tests_failed = 0; \ + tflite_micro::InitializeTest(); + +#define TF_LITE_MICRO_TESTS_END \ + MicroPrintf("%d/%d tests passed", micro_test::tests_passed, \ + (micro_test::tests_failed + micro_test::tests_passed)); \ + if (micro_test::tests_failed == 0) { \ + MicroPrintf("~~~ALL TESTS PASSED~~~\n"); \ + return kTfLiteOk; \ + } else { \ + MicroPrintf("~~~SOME TESTS FAILED~~~\n"); \ + return kTfLiteError; \ + } \ + } + +// TODO(petewarden): I'm going to hell for what I'm doing to this poor for loop. +#define TF_LITE_MICRO_TEST(name) \ + MicroPrintf("Testing " #name); \ + for (micro_test::is_test_complete = false, \ + micro_test::did_test_fail = false; \ + !micro_test::is_test_complete; micro_test::is_test_complete = true, \ + micro_test::tests_passed += (micro_test::did_test_fail) ? 0 : 1, \ + micro_test::tests_failed += (micro_test::did_test_fail) ? 1 : 0) + +#define TF_LITE_MICRO_EXPECT(x) \ + do { \ + if (!(x)) { \ + MicroPrintf(#x " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_EQ(x, y) \ + do { \ + auto vx = x; \ + auto vy = y; \ + bool isFloatingX = (std::is_floating_point::value); \ + bool isFloatingY = (std::is_floating_point::value); \ + if (isFloatingX && isFloatingY) { \ + auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ + if (delta > std::numeric_limits::epsilon()) { \ + MicroPrintf(#x " == " #y " failed at %s:%d (%f vs %f)", __FILE__, \ + __LINE__, static_cast(vx), \ + static_cast(vy)); \ + micro_test::did_test_fail = true; \ + } \ + } else if ((vx) != (vy)) { \ + MicroPrintf(#x " == " #y " failed at %s:%d (%d vs %d)", __FILE__, \ + __LINE__, static_cast(vx), static_cast(vy)); \ + if (isFloatingX || isFloatingY) { \ + MicroPrintf("-----------WARNING-----------"); \ + MicroPrintf("Only one of the values is floating point value."); \ + } \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_NE(x, y) \ + do { \ + auto vx = x; \ + auto vy = y; \ + bool isFloatingX = (std::is_floating_point::value); \ + bool isFloatingY = (std::is_floating_point::value); \ + if (isFloatingX && isFloatingY) { \ + auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ + if (delta <= std::numeric_limits::epsilon()) { \ + MicroPrintf(#x " != " #y " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } else if ((vx) == (vy)) { \ + MicroPrintf(#x " != " #y " failed at %s:%d", __FILE__, __LINE__); \ + if (isFloatingX || isFloatingY) { \ + MicroPrintf("-----------WARNING-----------"); \ + MicroPrintf("Only one of the values is floating point value."); \ + } \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +// TODO(wangtz): Making it more generic once needed. +#define TF_LITE_MICRO_ARRAY_ELEMENT_EXPECT_NEAR(arr1, idx1, arr2, idx2, \ + epsilon) \ + do { \ + auto delta = ((arr1)[(idx1)] > (arr2)[(idx2)]) \ + ? ((arr1)[(idx1)] - (arr2)[(idx2)]) \ + : ((arr2)[(idx2)] - (arr1)[(idx1)]); \ + if (delta > epsilon) { \ + MicroPrintf(#arr1 "[%d] (%f) near " #arr2 "[%d] (%f) failed at %s:%d", \ + static_cast(idx1), static_cast((arr1)[(idx1)]), \ + static_cast(idx2), static_cast((arr2)[(idx2)]), \ + __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +// The check vx != vy is needed to properly handle the case where both +// x and y evaluate to infinity. See #46960 for more details. +#define TF_LITE_MICRO_EXPECT_NEAR(x, y, epsilon) \ + do { \ + auto vx = (x); \ + auto vy = (y); \ + auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ + if (vx != vy && delta > epsilon) { \ + MicroPrintf(#x " (%f) near " #y " (%f) failed at %s:%d", \ + static_cast(vx), static_cast(vy), __FILE__, \ + __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_GT(x, y) \ + do { \ + if ((x) <= (y)) { \ + MicroPrintf(#x " > " #y " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_LT(x, y) \ + do { \ + if ((x) >= (y)) { \ + MicroPrintf(#x " < " #y " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_GE(x, y) \ + do { \ + if ((x) < (y)) { \ + MicroPrintf(#x " >= " #y " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_LE(x, y) \ + do { \ + if ((x) > (y)) { \ + MicroPrintf(#x " <= " #y " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_TRUE(x) \ + do { \ + if (!(x)) { \ + MicroPrintf(#x " was not true failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_FALSE(x) \ + do { \ + if (x) { \ + MicroPrintf(#x " was not false failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_FAIL(msg) \ + do { \ + MicroPrintf("FAIL: %s", msg, __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_STRING_EQ(string1, string2) \ + do { \ + for (int i = 0; string1[i] != '\0' && string2[i] != '\0'; i++) { \ + if (string1[i] != string2[i]) { \ + MicroPrintf("FAIL: %s did not match %s", string1, string2, __FILE__, \ + __LINE__); \ + micro_test::did_test_fail = true; \ + break; \ + } \ + } \ + } while (false) + +#endif // TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h b/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h new file mode 100644 index 00000000..2103196e --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h @@ -0,0 +1,23 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ +#define TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ + +// See generate_test_models.py for updating the contents of this model: +extern const unsigned char kTestConvModelData[]; +extern const unsigned int kTestConvModelDataSize; + +#endif // TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h new file mode 100644 index 00000000..6580ad16 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h @@ -0,0 +1,45 @@ +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ +#define TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ + +#include "tensorflow/lite/c/c_api_types.h" +#include "tensorflow/lite/core/api/flatbuffer_conversions.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// Forward declaration of the ErrorReporter class to hide it from the TFLM code. +class ErrorReporter; + +using TfLiteBridgeBuiltinDataAllocator = BuiltinDataAllocator; + +using TfLiteBridgeBuiltinParseFunction = + TfLiteStatus (*)(const Operator* op, ErrorReporter* error_reporter, + BuiltinDataAllocator* allocator, void** builtin_data); + +// Converts the tensor data type used in the flatbuffer to the representation +// used by the runtime. +TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type); + +// CallBuiltinParseFunction is a wrapper function to wrap the parser function +// calls to Call parser(op, allocator, builtin_data) +TfLiteStatus CallBuiltinParseFunction(TfLiteBridgeBuiltinParseFunction parser, + const Operator* op, + BuiltinDataAllocator* allocator, + void** builtin_data); +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h new file mode 100644 index 00000000..23793558 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h @@ -0,0 +1,36 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ +#define TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ + +#include + +#include "tensorflow/lite/core/api/error_reporter.h" +#include "tensorflow/lite/micro/compatibility.h" + +namespace tflite_micro { +// Get a pointer to a singleton global error reporter. +ErrorReporter* GetMicroErrorReporter(); +class MicroErrorReporter : public ErrorReporter { + public: + ~MicroErrorReporter() override {} + int Report(const char* format, va_list args) override; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h new file mode 100644 index 00000000..405d007f --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h @@ -0,0 +1,273 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ +#define TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ + +#include +#include +#include +#include + +#include "tensorflow/lite/micro/micro_log.h" + +namespace tflite_micro { + +// The maxmimum length of a string. +static constexpr int kMaxStringLength = 32; + +// The maximum length of a table row, applies to the header as well. +static constexpr int kMaxRowLength = 100; + +// The default padding between columns in a table. +static constexpr int kDefaultColumnPadding = 4; + +// Defines how formatted data is printed to stdout. +enum class PrettyPrintType { + // Prints as a CSV file. + kCsv, + // Prints as a formatted table. + kTable, +}; + +// Returns the length of the longest string in an array. +// Args: +// - strings: An array of strings. +// - count: The number of strings in the array. +int GetLongestStringLength(const char strings[][kMaxStringLength], int count); + +// Adds padding between two columns in a table. +// ex) "hello" is being inserted into a column. The largest value in that column +// is 10, and there's a global padding of 4 spaces. Therefore, 9 spaces (10 +// - 5 + 4) are added as padding. +// Args: +// - string: The input padding string. +// - size: The size of the string that's being inserted into a column. +// - max_size: The size of the largest string in the column. +// - padding: The amount of padding to add to each column regardless of its +// size. +void FillColumnPadding(char* string, int size, int max_size, + int padding = kDefaultColumnPadding); + +// Fills a string with a specified value. +// Args: +// - string: The input string. This is filled in with the specified value. +// - size: The size of the string after being filled in. This must be less than +// the allocated space for the string. +// - buffer_size: The size of the string's buffer. +// - value: The value to insert into the string. Defaults to a space. +void FillString(char* string, int size, int buffer_size, char value = ' '); + +// Concatenates the input string onto the first. +// Args: +// - output: The destination string for where to append input. +// - input: The input string to concatenate. +// - size: The number of characters to concatenate from the first string. If +// negative, the whole input string will be concatenated. +void MicroStrcat(char* output, const char* input, int size = -1); + +// Copies the input string into the output. +void MicroStrcpy(char* output, const char* input); + +// Formats a division operation to have a specified number of decimal places. +// Args: +// - output: The output string to be formatted. +// - numerator: The numerator in the division operation. +// - denominator: The denominator in the division operation. +// - decimal places: The number of decimal places to print to. +void FormatIntegerDivide(char* output, int64_t numerator, int64_t denominator, + int decimal_places); + +// Formats a division operation as a percentage. +// Args: +// - output: The output string to be formatted. +// - numerator: The numerator in the division operation. +// - denominator: The denominator in the division operation. +// - decimal places: The number of decimal places to print to. +void FormatAsPercentage(char* output, int64_t numerator, int64_t denominator, + int decimal_places); + +void PrettyPrintTableHeader(PrettyPrintType type, const char* table_name); + +// Formats a number as a string. +// Args: +// - output: The location of where to write the formatted number. +// - value: The value to write to a string. +template +void FormatNumber(char* output, T value); + +// Pretty prints a table to stdout. +// Note: kMaxRows and kColumns should describe the allocated size of the table, +// not the amount of data that is populated. It is required that all +// columns are filled out, but not all rows. +// +// ex) PrintTable<3, 25>(headers, data, 4); +// This will print a table with 3 columns and 4 rows. In this example, it +// is required that data is defined as char[3][25][kMaxStringLength] to +// properly print. +// +// op cycles cpu % +// ------------------------- +// foo | 1000 | 10 +// bar | 2500 | 25 +// baz | 1000 | 10 +// lorem | 2000 | 20 +// +// Args: +// - headers: A 1D array of strings containing the headers of the table. This +// must be equal in size to kColumns. +// - data: A 2D array of string data organized in [columns, rows]. As stated +// above, it is required that all columns are populated, but not all rows. +// - rows: The number of populated rows in `data`. +template +void PrintTable(const char headers[kColumns][kMaxStringLength], + const char data[kColumns][kMaxRows][kMaxStringLength], + const int rows) { + // Get the maximum width for each column in the table. + int max_column_width[kColumns]; + for (int i = 0; i < kColumns; ++i) { + max_column_width[i] = std::max(GetLongestStringLength(data[i], rows), + static_cast(strlen(headers[i]))); + } + + // Add padding between each item in the header so it can be printed on one + // line. + char header_spaces[kColumns][kMaxStringLength]; + for (int i = 0; i < kColumns; ++i) { + FillColumnPadding(header_spaces[i], strlen(headers[i]), max_column_width[i], + kDefaultColumnPadding + 2); + } + + // Print the header. + char header[kMaxRowLength]; + memset(header, 0, kMaxRowLength); + for (int i = 0; i < kColumns; ++i) { + MicroStrcat(header, headers[i]); + MicroStrcat(header, header_spaces[i]); + } + MicroPrintf("%s", header); + + // Print a separator to separate the header from the data. + char separator[kMaxRowLength]; + FillString(separator, strlen(header) - 1, kMaxRowLength, '-'); + MicroPrintf("%s", separator); + + for (int i = 0; i < rows; ++i) { + char spaces[kColumns][kMaxStringLength]; + for (int j = 0; j < kColumns; ++j) { + FillColumnPadding(spaces[j], strlen(data[j][i]), max_column_width[j]); + } + + char row[kMaxRowLength]; + memset(row, 0, kMaxRowLength); + + // Concatenate each column in a row with the format "[data][padding]| " + for (int j = 0; j < kColumns; ++j) { + MicroStrcat(row, data[j][i]); + MicroStrcat(row, spaces[j]); + MicroStrcat(row, "| "); + } + + MicroPrintf("%s", row); + } + + MicroPrintf(separator); + MicroPrintf(""); +} + +// Pretty prints a csv to stdout. +// Note: kMaxRows and kColumns should describe the allocated size of the table, +// not the amount of data that is populated. It is required that all +// columns are filled out, but not all rows. +// +// ex) +// op,cycles,%cpu +// foo,1000,10 +// bar,2500,25 +// baz,1000,10 +// +// Args: +// - headers: A 1D array of strings containing the headers of the table. This +// must be equal in size to kColumns. +// - data: A 2D array of string data organized in [columns, rows]. As stated +// above, it is required that all columns are populated, but not all rows. +// - rows: The number of populated rows in `data`. +template +void PrintCsv(const char headers[kColumns][kMaxStringLength], + const char data[kColumns][kMaxRows][kMaxStringLength], + const int rows) { + char header[kMaxRowLength]; + memset(header, 0, kMaxRowLength); + for (int i = 0; i < kColumns; ++i) { + MicroStrcat(header, headers[i]); + if (i < kColumns - 1) { + MicroStrcat(header, ","); + } + } + + MicroPrintf("%s", header); + + char row[kMaxRowLength]; + for (int i = 0; i < rows; ++i) { + memset(row, 0, kMaxRowLength); + for (int j = 0; j < kColumns; ++j) { + MicroStrcat(row, data[j][i]); + if (j < kColumns - 1) { + MicroStrcat(row, ","); + } + } + + MicroPrintf("%s", row); + } + + MicroPrintf(""); // Serves as a new line. +} + +// Prints a 2D array of strings in a formatted manner along with a table name +// that includes the table type. +// +// Note: kMaxRows and kColumns should describe the allocated size of the table, +// not the amount of data that is populated. It is required that all +// columns are filled out, but not all rows. +// +// ex) PrettyPrint::kCsv will print a csv with a [[ CSV ]]: table_name header. +// +// Args: +// - headers: A 1D array of strings containing the headers of the table. This +// must be equal in size to kColumns. +// - data: A 2D array of string data organized in [columns, rows]. As stated +// above, it is required that all columns are populated, but not all rows. +// - rows: The number of populated rows in `data`. +// - type: The format type that should be used to pretty print. +// - table_name: The name of the table to be printed alongside the format type. +template +void PrintFormattedData(const char headers[kColumns][kMaxStringLength], + const char data[kColumns][kMaxRows][kMaxStringLength], + const int rows, const PrettyPrintType type, + const char* table_name) { + PrettyPrintTableHeader(type, table_name); + switch (type) { + case PrettyPrintType::kCsv: + PrintCsv(headers, data, rows); + break; + case PrettyPrintType::kTable: + PrintTable(headers, data, rows); + break; + } +} + +} // namespace tflite_micro + +#endif // TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h new file mode 100644 index 00000000..103c1385 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h @@ -0,0 +1,41 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TFLM_BENCHMARK_INTERNAL_METRICS_H_ +#define TFLM_BENCHMARK_INTERNAL_METRICS_H_ + +#include + +#include +#include + +#include "tensorflow/lite/micro/micro_profiler.h" +#include "tensorflow/lite/micro/recording_micro_allocator.h" +#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h" + +namespace tflite_micro { + +// Logs the allocation events. Prints out two tables, one for the arena +// allocations, and one for each type of TFLM allocation type. +// Args: +// - allocator: The recording micro allocator used during the invocation +// process. +// - type: Which print format should be used to output the allocation data to +// stdout. +void LogAllocatorEvents(const tflite_micro::RecordingMicroAllocator& allocator, + PrettyPrintType type); +} // namespace tflite_micro + +#endif // TFLM_BENCHMARK_INTERNAL_METRICS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h new file mode 100644 index 00000000..4f8f6b3a --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h @@ -0,0 +1,127 @@ +/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TFLM_BENCHMARK_OP_RESOLVER_H_ +#define TFLM_BENCHMARK_OP_RESOLVER_H_ + +#include + +#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" +#include "tensorflow/lite/micro/micro_op_resolver.h" + +namespace tflite_micro { + +inline TfLiteStatus CreateOpResolver( + tflite_micro::MicroMutableOpResolver<96>& op_resolver) { + TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected()); + TF_LITE_ENSURE_STATUS(op_resolver.AddAdd()); + TF_LITE_ENSURE_STATUS(op_resolver.AddAbs()); + TF_LITE_ENSURE_STATUS(op_resolver.AddAddN()); + TF_LITE_ENSURE_STATUS(op_resolver.AddArgMax()); + TF_LITE_ENSURE_STATUS(op_resolver.AddArgMin()); + TF_LITE_ENSURE_STATUS(op_resolver.AddAssignVariable()); + TF_LITE_ENSURE_STATUS(op_resolver.AddBatchToSpaceNd()); + TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastArgs()); + TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastTo()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCallOnce()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCast()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCeil()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCircularBuffer()); + TF_LITE_ENSURE_STATUS(op_resolver.AddConcatenation()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCos()); + TF_LITE_ENSURE_STATUS(op_resolver.AddCumSum()); + TF_LITE_ENSURE_STATUS(op_resolver.AddDepthToSpace()); + TF_LITE_ENSURE_STATUS(op_resolver.AddDequantize()); + TF_LITE_ENSURE_STATUS(op_resolver.AddDiv()); + TF_LITE_ENSURE_STATUS(op_resolver.AddElu()); + TF_LITE_ENSURE_STATUS(op_resolver.AddEqual()); + TF_LITE_ENSURE_STATUS(op_resolver.AddEthosU()); + TF_LITE_ENSURE_STATUS(op_resolver.AddExp()); + TF_LITE_ENSURE_STATUS(op_resolver.AddExpandDims()); + TF_LITE_ENSURE_STATUS(op_resolver.AddFill()); + TF_LITE_ENSURE_STATUS(op_resolver.AddFloor()); + TF_LITE_ENSURE_STATUS(op_resolver.AddFloorDiv()); + TF_LITE_ENSURE_STATUS(op_resolver.AddFloorMod()); + TF_LITE_ENSURE_STATUS(op_resolver.AddGather()); + TF_LITE_ENSURE_STATUS(op_resolver.AddGatherNd()); + TF_LITE_ENSURE_STATUS(op_resolver.AddGreater()); + TF_LITE_ENSURE_STATUS(op_resolver.AddGreaterEqual()); + TF_LITE_ENSURE_STATUS(op_resolver.AddHardSwish()); + TF_LITE_ENSURE_STATUS(op_resolver.AddIf()); + TF_LITE_ENSURE_STATUS(op_resolver.AddL2Normalization()); + TF_LITE_ENSURE_STATUS(op_resolver.AddL2Pool2D()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLeakyRelu()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLess()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLessEqual()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLog()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalAnd()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalNot()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalOr()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLogistic()); + TF_LITE_ENSURE_STATUS(op_resolver.AddLogSoftmax()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMaximum()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMirrorPad()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMean()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMinimum()); + TF_LITE_ENSURE_STATUS(op_resolver.AddNeg()); + TF_LITE_ENSURE_STATUS(op_resolver.AddNotEqual()); + TF_LITE_ENSURE_STATUS(op_resolver.AddPack()); + TF_LITE_ENSURE_STATUS(op_resolver.AddPadV2()); + TF_LITE_ENSURE_STATUS(op_resolver.AddPrelu()); + TF_LITE_ENSURE_STATUS(op_resolver.AddQuantize()); + TF_LITE_ENSURE_STATUS(op_resolver.AddReadVariable()); + TF_LITE_ENSURE_STATUS(op_resolver.AddReduceMax()); + TF_LITE_ENSURE_STATUS(op_resolver.AddRelu()); + TF_LITE_ENSURE_STATUS(op_resolver.AddRelu6()); + TF_LITE_ENSURE_STATUS(op_resolver.AddReshape()); + TF_LITE_ENSURE_STATUS(op_resolver.AddResizeBilinear()); + TF_LITE_ENSURE_STATUS(op_resolver.AddResizeNearestNeighbor()); + TF_LITE_ENSURE_STATUS(op_resolver.AddRound()); + TF_LITE_ENSURE_STATUS(op_resolver.AddRsqrt()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSelectV2()); + TF_LITE_ENSURE_STATUS(op_resolver.AddShape()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSin()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSlice()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSoftmax()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSpaceToBatchNd()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSpaceToDepth()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSplit()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSplitV()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSqueeze()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSqrt()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSquare()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSquaredDifference()); + TF_LITE_ENSURE_STATUS(op_resolver.AddStridedSlice()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSub()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSum()); + TF_LITE_ENSURE_STATUS(op_resolver.AddSvdf()); + TF_LITE_ENSURE_STATUS(op_resolver.AddTanh()); + TF_LITE_ENSURE_STATUS(op_resolver.AddTransposeConv()); + TF_LITE_ENSURE_STATUS(op_resolver.AddTranspose()); + TF_LITE_ENSURE_STATUS(op_resolver.AddUnpack()); + TF_LITE_ENSURE_STATUS(op_resolver.AddUnidirectionalSequenceLSTM()); + TF_LITE_ENSURE_STATUS(op_resolver.AddVarHandle()); + TF_LITE_ENSURE_STATUS(op_resolver.AddWhile()); + TF_LITE_ENSURE_STATUS(op_resolver.AddZerosLike()); + TF_LITE_ENSURE_STATUS(op_resolver.AddDepthwiseConv2D()); + TF_LITE_ENSURE_STATUS(op_resolver.AddConv2D()); + TF_LITE_ENSURE_STATUS(op_resolver.AddAveragePool2D()); + TF_LITE_ENSURE_STATUS(op_resolver.AddPad()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMaxPool2D()); + TF_LITE_ENSURE_STATUS(op_resolver.AddMul()); + return kTfLiteOk; +} +} // namespace tflite_micro +#endif // TFLM_BENCHMARK_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h b/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h new file mode 100644 index 00000000..d0ca67b0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h @@ -0,0 +1,75 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ +#define TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ + +// Most of the definitions have been moved to this subheader so that Micro +// can include it without relying on and , which isn't +// available on all platforms. + +// Arduino build defines abs as a macro here. That is invalid C++, and breaks +// libc++'s header, undefine it. +#ifdef abs +#undef abs +#endif + +#include + +#include "tensorflow/lite/core/c/common.h" + +namespace tflite_micro { + +// Map statically from a C++ type to a TfLiteType. Used in interpreter for +// safe casts. +// Example: +// typeToTfLiteType() -> kTfLiteBool +template +constexpr TfLiteType typeToTfLiteType() { + return kTfLiteNoType; +} +// Map from TfLiteType to the corresponding C++ type. +// Example: +// TfLiteTypeToType::Type -> bool +template +struct TfLiteTypeToType {}; // Specializations below + +// Template specialization for both typeToTfLiteType and TfLiteTypeToType. +#define MATCH_TYPE_AND_TFLITE_TYPE(CPP_TYPE, TFLITE_TYPE_ENUM) \ + template <> \ + constexpr TfLiteType typeToTfLiteType() { \ + return TFLITE_TYPE_ENUM; \ + } \ + template <> \ + struct TfLiteTypeToType { \ + using Type = CPP_TYPE; \ + } + +// No string mapping is included here, since the TF Lite packed representation +// doesn't correspond to a C++ type well. +MATCH_TYPE_AND_TFLITE_TYPE(int32_t, kTfLiteInt32); +MATCH_TYPE_AND_TFLITE_TYPE(uint32_t, kTfLiteUInt32); +MATCH_TYPE_AND_TFLITE_TYPE(int16_t, kTfLiteInt16); +MATCH_TYPE_AND_TFLITE_TYPE(uint16_t, kTfLiteUInt16); +MATCH_TYPE_AND_TFLITE_TYPE(int64_t, kTfLiteInt64); +MATCH_TYPE_AND_TFLITE_TYPE(float, kTfLiteFloat32); +MATCH_TYPE_AND_TFLITE_TYPE(unsigned char, kTfLiteUInt8); +MATCH_TYPE_AND_TFLITE_TYPE(int8_t, kTfLiteInt8); +MATCH_TYPE_AND_TFLITE_TYPE(bool, kTfLiteBool); +MATCH_TYPE_AND_TFLITE_TYPE(TfLiteFloat16, kTfLiteFloat16); +MATCH_TYPE_AND_TFLITE_TYPE(double, kTfLiteFloat64); +MATCH_TYPE_AND_TFLITE_TYPE(uint64_t, kTfLiteUInt64); + +} // namespace tflite_micro +#endif // TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h b/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h new file mode 100644 index 00000000..2446f9e0 --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h @@ -0,0 +1,24644 @@ +// automatically generated by the FlatBuffers compiler, do not modify + + +#ifndef FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ +#define FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ + +#include "flatbuffers/flatbuffers.h" + +// Ensure the included flatbuffers.h is the same version as when this file was +// generated, otherwise it may not be compatible. +static_assert(FLATBUFFERS_VERSION_MAJOR == 2 && + FLATBUFFERS_VERSION_MINOR == 0 && + FLATBUFFERS_VERSION_REVISION == 6, + "Non-compatible flatbuffers version included"); + +namespace tflite_micro { + +struct CustomQuantization; +struct CustomQuantizationBuilder; +struct CustomQuantizationT; + +struct QuantizationParameters; +struct QuantizationParametersBuilder; +struct QuantizationParametersT; + +struct Int32Vector; +struct Int32VectorBuilder; +struct Int32VectorT; + +struct Uint16Vector; +struct Uint16VectorBuilder; +struct Uint16VectorT; + +struct Uint8Vector; +struct Uint8VectorBuilder; +struct Uint8VectorT; + +struct DimensionMetadata; +struct DimensionMetadataBuilder; +struct DimensionMetadataT; + +struct SparsityParameters; +struct SparsityParametersBuilder; +struct SparsityParametersT; + +struct VariantSubType; +struct VariantSubTypeBuilder; +struct VariantSubTypeT; + +struct Tensor; +struct TensorBuilder; +struct TensorT; + +struct StablehloGatherOptions; +struct StablehloGatherOptionsBuilder; +struct StablehloGatherOptionsT; + +struct StablehloTransposeOptions; +struct StablehloTransposeOptionsBuilder; +struct StablehloTransposeOptionsT; + +struct StablehloDotGeneralOptions; +struct StablehloDotGeneralOptionsBuilder; +struct StablehloDotGeneralOptionsT; + +struct StablehloReduceWindowOptions; +struct StablehloReduceWindowOptionsBuilder; +struct StablehloReduceWindowOptionsT; + +struct StablehloWhileOptions; +struct StablehloWhileOptionsBuilder; +struct StablehloWhileOptionsT; + +struct StablehloSortOptions; +struct StablehloSortOptionsBuilder; +struct StablehloSortOptionsT; + +struct StablehloConcatenateOptions; +struct StablehloConcatenateOptionsBuilder; +struct StablehloConcatenateOptionsT; + +struct StablehloBroadcastInDimOptions; +struct StablehloBroadcastInDimOptionsBuilder; +struct StablehloBroadcastInDimOptionsT; + +struct StablehloCompareOptions; +struct StablehloCompareOptionsBuilder; +struct StablehloCompareOptionsT; + +struct StablehloDynamicSliceOptions; +struct StablehloDynamicSliceOptionsBuilder; +struct StablehloDynamicSliceOptionsT; + +struct StablehloPadOptions; +struct StablehloPadOptionsBuilder; +struct StablehloPadOptionsT; + +struct StablehloIotaOptions; +struct StablehloIotaOptionsBuilder; +struct StablehloIotaOptionsT; + +struct StablehloCustomCallOptions; +struct StablehloCustomCallOptionsBuilder; +struct StablehloCustomCallOptionsT; + +struct StablehloReduceOptions; +struct StablehloReduceOptionsBuilder; +struct StablehloReduceOptionsT; + +struct StablehloSliceOptions; +struct StablehloSliceOptionsBuilder; +struct StablehloSliceOptionsT; + +struct StablehloConvolutionOptions; +struct StablehloConvolutionOptionsBuilder; +struct StablehloConvolutionOptionsT; + +struct StablehloScatterOptions; +struct StablehloScatterOptionsBuilder; +struct StablehloScatterOptionsT; + +struct StablehloRngBitGeneratorOptions; +struct StablehloRngBitGeneratorOptionsBuilder; +struct StablehloRngBitGeneratorOptionsT; + +struct Conv2DOptions; +struct Conv2DOptionsBuilder; +struct Conv2DOptionsT; + +struct Conv3DOptions; +struct Conv3DOptionsBuilder; +struct Conv3DOptionsT; + +struct Pool2DOptions; +struct Pool2DOptionsBuilder; +struct Pool2DOptionsT; + +struct DepthwiseConv2DOptions; +struct DepthwiseConv2DOptionsBuilder; +struct DepthwiseConv2DOptionsT; + +struct ConcatEmbeddingsOptions; +struct ConcatEmbeddingsOptionsBuilder; +struct ConcatEmbeddingsOptionsT; + +struct LSHProjectionOptions; +struct LSHProjectionOptionsBuilder; +struct LSHProjectionOptionsT; + +struct SVDFOptions; +struct SVDFOptionsBuilder; +struct SVDFOptionsT; + +struct RNNOptions; +struct RNNOptionsBuilder; +struct RNNOptionsT; + +struct SequenceRNNOptions; +struct SequenceRNNOptionsBuilder; +struct SequenceRNNOptionsT; + +struct BidirectionalSequenceRNNOptions; +struct BidirectionalSequenceRNNOptionsBuilder; +struct BidirectionalSequenceRNNOptionsT; + +struct FullyConnectedOptions; +struct FullyConnectedOptionsBuilder; +struct FullyConnectedOptionsT; + +struct SoftmaxOptions; +struct SoftmaxOptionsBuilder; +struct SoftmaxOptionsT; + +struct ConcatenationOptions; +struct ConcatenationOptionsBuilder; +struct ConcatenationOptionsT; + +struct AddOptions; +struct AddOptionsBuilder; +struct AddOptionsT; + +struct MulOptions; +struct MulOptionsBuilder; +struct MulOptionsT; + +struct L2NormOptions; +struct L2NormOptionsBuilder; +struct L2NormOptionsT; + +struct LocalResponseNormalizationOptions; +struct LocalResponseNormalizationOptionsBuilder; +struct LocalResponseNormalizationOptionsT; + +struct LSTMOptions; +struct LSTMOptionsBuilder; +struct LSTMOptionsT; + +struct UnidirectionalSequenceLSTMOptions; +struct UnidirectionalSequenceLSTMOptionsBuilder; +struct UnidirectionalSequenceLSTMOptionsT; + +struct BidirectionalSequenceLSTMOptions; +struct BidirectionalSequenceLSTMOptionsBuilder; +struct BidirectionalSequenceLSTMOptionsT; + +struct ResizeBilinearOptions; +struct ResizeBilinearOptionsBuilder; +struct ResizeBilinearOptionsT; + +struct ResizeNearestNeighborOptions; +struct ResizeNearestNeighborOptionsBuilder; +struct ResizeNearestNeighborOptionsT; + +struct CallOptions; +struct CallOptionsBuilder; +struct CallOptionsT; + +struct PadOptions; +struct PadOptionsBuilder; +struct PadOptionsT; + +struct PadV2Options; +struct PadV2OptionsBuilder; +struct PadV2OptionsT; + +struct ReshapeOptions; +struct ReshapeOptionsBuilder; +struct ReshapeOptionsT; + +struct SpaceToBatchNDOptions; +struct SpaceToBatchNDOptionsBuilder; +struct SpaceToBatchNDOptionsT; + +struct BatchToSpaceNDOptions; +struct BatchToSpaceNDOptionsBuilder; +struct BatchToSpaceNDOptionsT; + +struct SkipGramOptions; +struct SkipGramOptionsBuilder; +struct SkipGramOptionsT; + +struct SpaceToDepthOptions; +struct SpaceToDepthOptionsBuilder; +struct SpaceToDepthOptionsT; + +struct DepthToSpaceOptions; +struct DepthToSpaceOptionsBuilder; +struct DepthToSpaceOptionsT; + +struct SubOptions; +struct SubOptionsBuilder; +struct SubOptionsT; + +struct DivOptions; +struct DivOptionsBuilder; +struct DivOptionsT; + +struct TopKV2Options; +struct TopKV2OptionsBuilder; +struct TopKV2OptionsT; + +struct EmbeddingLookupSparseOptions; +struct EmbeddingLookupSparseOptionsBuilder; +struct EmbeddingLookupSparseOptionsT; + +struct GatherOptions; +struct GatherOptionsBuilder; +struct GatherOptionsT; + +struct TransposeOptions; +struct TransposeOptionsBuilder; +struct TransposeOptionsT; + +struct ExpOptions; +struct ExpOptionsBuilder; +struct ExpOptionsT; + +struct CosOptions; +struct CosOptionsBuilder; +struct CosOptionsT; + +struct ReducerOptions; +struct ReducerOptionsBuilder; +struct ReducerOptionsT; + +struct SqueezeOptions; +struct SqueezeOptionsBuilder; +struct SqueezeOptionsT; + +struct SplitOptions; +struct SplitOptionsBuilder; +struct SplitOptionsT; + +struct SplitVOptions; +struct SplitVOptionsBuilder; +struct SplitVOptionsT; + +struct StridedSliceOptions; +struct StridedSliceOptionsBuilder; +struct StridedSliceOptionsT; + +struct LogSoftmaxOptions; +struct LogSoftmaxOptionsBuilder; +struct LogSoftmaxOptionsT; + +struct CastOptions; +struct CastOptionsBuilder; +struct CastOptionsT; + +struct DequantizeOptions; +struct DequantizeOptionsBuilder; +struct DequantizeOptionsT; + +struct MaximumMinimumOptions; +struct MaximumMinimumOptionsBuilder; +struct MaximumMinimumOptionsT; + +struct TileOptions; +struct TileOptionsBuilder; +struct TileOptionsT; + +struct ArgMaxOptions; +struct ArgMaxOptionsBuilder; +struct ArgMaxOptionsT; + +struct ArgMinOptions; +struct ArgMinOptionsBuilder; +struct ArgMinOptionsT; + +struct GreaterOptions; +struct GreaterOptionsBuilder; +struct GreaterOptionsT; + +struct GreaterEqualOptions; +struct GreaterEqualOptionsBuilder; +struct GreaterEqualOptionsT; + +struct LessOptions; +struct LessOptionsBuilder; +struct LessOptionsT; + +struct LessEqualOptions; +struct LessEqualOptionsBuilder; +struct LessEqualOptionsT; + +struct NegOptions; +struct NegOptionsBuilder; +struct NegOptionsT; + +struct SelectOptions; +struct SelectOptionsBuilder; +struct SelectOptionsT; + +struct SliceOptions; +struct SliceOptionsBuilder; +struct SliceOptionsT; + +struct TransposeConvOptions; +struct TransposeConvOptionsBuilder; +struct TransposeConvOptionsT; + +struct ExpandDimsOptions; +struct ExpandDimsOptionsBuilder; +struct ExpandDimsOptionsT; + +struct SparseToDenseOptions; +struct SparseToDenseOptionsBuilder; +struct SparseToDenseOptionsT; + +struct EqualOptions; +struct EqualOptionsBuilder; +struct EqualOptionsT; + +struct NotEqualOptions; +struct NotEqualOptionsBuilder; +struct NotEqualOptionsT; + +struct ShapeOptions; +struct ShapeOptionsBuilder; +struct ShapeOptionsT; + +struct RankOptions; +struct RankOptionsBuilder; +struct RankOptionsT; + +struct PowOptions; +struct PowOptionsBuilder; +struct PowOptionsT; + +struct FakeQuantOptions; +struct FakeQuantOptionsBuilder; +struct FakeQuantOptionsT; + +struct PackOptions; +struct PackOptionsBuilder; +struct PackOptionsT; + +struct LogicalOrOptions; +struct LogicalOrOptionsBuilder; +struct LogicalOrOptionsT; + +struct OneHotOptions; +struct OneHotOptionsBuilder; +struct OneHotOptionsT; + +struct AbsOptions; +struct AbsOptionsBuilder; +struct AbsOptionsT; + +struct HardSwishOptions; +struct HardSwishOptionsBuilder; +struct HardSwishOptionsT; + +struct LogicalAndOptions; +struct LogicalAndOptionsBuilder; +struct LogicalAndOptionsT; + +struct LogicalNotOptions; +struct LogicalNotOptionsBuilder; +struct LogicalNotOptionsT; + +struct UnpackOptions; +struct UnpackOptionsBuilder; +struct UnpackOptionsT; + +struct FloorDivOptions; +struct FloorDivOptionsBuilder; +struct FloorDivOptionsT; + +struct SquareOptions; +struct SquareOptionsBuilder; +struct SquareOptionsT; + +struct ZerosLikeOptions; +struct ZerosLikeOptionsBuilder; +struct ZerosLikeOptionsT; + +struct FillOptions; +struct FillOptionsBuilder; +struct FillOptionsT; + +struct FloorModOptions; +struct FloorModOptionsBuilder; +struct FloorModOptionsT; + +struct RangeOptions; +struct RangeOptionsBuilder; +struct RangeOptionsT; + +struct LeakyReluOptions; +struct LeakyReluOptionsBuilder; +struct LeakyReluOptionsT; + +struct SquaredDifferenceOptions; +struct SquaredDifferenceOptionsBuilder; +struct SquaredDifferenceOptionsT; + +struct MirrorPadOptions; +struct MirrorPadOptionsBuilder; +struct MirrorPadOptionsT; + +struct UniqueOptions; +struct UniqueOptionsBuilder; +struct UniqueOptionsT; + +struct ReverseV2Options; +struct ReverseV2OptionsBuilder; +struct ReverseV2OptionsT; + +struct AddNOptions; +struct AddNOptionsBuilder; +struct AddNOptionsT; + +struct GatherNdOptions; +struct GatherNdOptionsBuilder; +struct GatherNdOptionsT; + +struct WhereOptions; +struct WhereOptionsBuilder; +struct WhereOptionsT; + +struct ReverseSequenceOptions; +struct ReverseSequenceOptionsBuilder; +struct ReverseSequenceOptionsT; + +struct MatrixDiagOptions; +struct MatrixDiagOptionsBuilder; +struct MatrixDiagOptionsT; + +struct QuantizeOptions; +struct QuantizeOptionsBuilder; +struct QuantizeOptionsT; + +struct MatrixSetDiagOptions; +struct MatrixSetDiagOptionsBuilder; +struct MatrixSetDiagOptionsT; + +struct IfOptions; +struct IfOptionsBuilder; +struct IfOptionsT; + +struct CallOnceOptions; +struct CallOnceOptionsBuilder; +struct CallOnceOptionsT; + +struct WhileOptions; +struct WhileOptionsBuilder; +struct WhileOptionsT; + +struct NonMaxSuppressionV4Options; +struct NonMaxSuppressionV4OptionsBuilder; +struct NonMaxSuppressionV4OptionsT; + +struct NonMaxSuppressionV5Options; +struct NonMaxSuppressionV5OptionsBuilder; +struct NonMaxSuppressionV5OptionsT; + +struct ScatterNdOptions; +struct ScatterNdOptionsBuilder; +struct ScatterNdOptionsT; + +struct SelectV2Options; +struct SelectV2OptionsBuilder; +struct SelectV2OptionsT; + +struct DensifyOptions; +struct DensifyOptionsBuilder; +struct DensifyOptionsT; + +struct SegmentSumOptions; +struct SegmentSumOptionsBuilder; +struct SegmentSumOptionsT; + +struct BatchMatMulOptions; +struct BatchMatMulOptionsBuilder; +struct BatchMatMulOptionsT; + +struct CumsumOptions; +struct CumsumOptionsBuilder; +struct CumsumOptionsT; + +struct BroadcastToOptions; +struct BroadcastToOptionsBuilder; +struct BroadcastToOptionsT; + +struct Rfft2dOptions; +struct Rfft2dOptionsBuilder; +struct Rfft2dOptionsT; + +struct HashtableOptions; +struct HashtableOptionsBuilder; +struct HashtableOptionsT; + +struct HashtableFindOptions; +struct HashtableFindOptionsBuilder; +struct HashtableFindOptionsT; + +struct HashtableImportOptions; +struct HashtableImportOptionsBuilder; +struct HashtableImportOptionsT; + +struct HashtableSizeOptions; +struct HashtableSizeOptionsBuilder; +struct HashtableSizeOptionsT; + +struct VarHandleOptions; +struct VarHandleOptionsBuilder; +struct VarHandleOptionsT; + +struct ReadVariableOptions; +struct ReadVariableOptionsBuilder; +struct ReadVariableOptionsT; + +struct AssignVariableOptions; +struct AssignVariableOptionsBuilder; +struct AssignVariableOptionsT; + +struct RandomOptions; +struct RandomOptionsBuilder; +struct RandomOptionsT; + +struct BucketizeOptions; +struct BucketizeOptionsBuilder; +struct BucketizeOptionsT; + +struct GeluOptions; +struct GeluOptionsBuilder; +struct GeluOptionsT; + +struct DynamicUpdateSliceOptions; +struct DynamicUpdateSliceOptionsBuilder; +struct DynamicUpdateSliceOptionsT; + +struct UnsortedSegmentProdOptions; +struct UnsortedSegmentProdOptionsBuilder; +struct UnsortedSegmentProdOptionsT; + +struct UnsortedSegmentMaxOptions; +struct UnsortedSegmentMaxOptionsBuilder; +struct UnsortedSegmentMaxOptionsT; + +struct UnsortedSegmentSumOptions; +struct UnsortedSegmentSumOptionsBuilder; +struct UnsortedSegmentSumOptionsT; + +struct ATan2Options; +struct ATan2OptionsBuilder; +struct ATan2OptionsT; + +struct UnsortedSegmentMinOptions; +struct UnsortedSegmentMinOptionsBuilder; +struct UnsortedSegmentMinOptionsT; + +struct SignOptions; +struct SignOptionsBuilder; +struct SignOptionsT; + +struct BitcastOptions; +struct BitcastOptionsBuilder; +struct BitcastOptionsT; + +struct BitwiseXorOptions; +struct BitwiseXorOptionsBuilder; +struct BitwiseXorOptionsT; + +struct RightShiftOptions; +struct RightShiftOptionsBuilder; +struct RightShiftOptionsT; + +struct DilateOptions; +struct DilateOptionsBuilder; +struct DilateOptionsT; + +struct ReduceWindowOptions; +struct ReduceWindowOptionsBuilder; +struct ReduceWindowOptionsT; + +struct OperatorCode; +struct OperatorCodeBuilder; +struct OperatorCodeT; + +struct Operator; +struct OperatorBuilder; +struct OperatorT; + +struct SubGraph; +struct SubGraphBuilder; +struct SubGraphT; + +struct Buffer; +struct BufferBuilder; +struct BufferT; + +struct Metadata; +struct MetadataBuilder; +struct MetadataT; + +struct TensorMap; +struct TensorMapBuilder; +struct TensorMapT; + +struct SignatureDef; +struct SignatureDefBuilder; +struct SignatureDefT; + +struct Model; +struct ModelBuilder; +struct ModelT; + +enum TensorType : int8_t { + TensorType_FLOAT32 = 0, + TensorType_FLOAT16 = 1, + TensorType_INT32 = 2, + TensorType_UINT8 = 3, + TensorType_INT64 = 4, + TensorType_STRING = 5, + TensorType_BOOL = 6, + TensorType_INT16 = 7, + TensorType_COMPLEX64 = 8, + TensorType_INT8 = 9, + TensorType_FLOAT64 = 10, + TensorType_COMPLEX128 = 11, + TensorType_UINT64 = 12, + TensorType_RESOURCE = 13, + TensorType_VARIANT = 14, + TensorType_UINT32 = 15, + TensorType_UINT16 = 16, + TensorType_INT4 = 17, + TensorType_MIN = TensorType_FLOAT32, + TensorType_MAX = TensorType_INT4 +}; + +inline const TensorType (&EnumValuesTensorType())[18] { + static const TensorType values[] = { + TensorType_FLOAT32, + TensorType_FLOAT16, + TensorType_INT32, + TensorType_UINT8, + TensorType_INT64, + TensorType_STRING, + TensorType_BOOL, + TensorType_INT16, + TensorType_COMPLEX64, + TensorType_INT8, + TensorType_FLOAT64, + TensorType_COMPLEX128, + TensorType_UINT64, + TensorType_RESOURCE, + TensorType_VARIANT, + TensorType_UINT32, + TensorType_UINT16, + TensorType_INT4 + }; + return values; +} + +inline const char * const *EnumNamesTensorType() { + static const char * const names[19] = { + "FLOAT32", + "FLOAT16", + "INT32", + "UINT8", + "INT64", + "STRING", + "BOOL", + "INT16", + "COMPLEX64", + "INT8", + "FLOAT64", + "COMPLEX128", + "UINT64", + "RESOURCE", + "VARIANT", + "UINT32", + "UINT16", + "INT4", + nullptr + }; + return names; +} + +inline const char *EnumNameTensorType(TensorType e) { + if (flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_INT4)) return ""; + const size_t index = static_cast(e); + return EnumNamesTensorType()[index]; +} + +enum QuantizationDetails : uint8_t { + QuantizationDetails_NONE = 0, + QuantizationDetails_CustomQuantization = 1, + QuantizationDetails_MIN = QuantizationDetails_NONE, + QuantizationDetails_MAX = QuantizationDetails_CustomQuantization +}; + +inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] { + static const QuantizationDetails values[] = { + QuantizationDetails_NONE, + QuantizationDetails_CustomQuantization + }; + return values; +} + +inline const char * const *EnumNamesQuantizationDetails() { + static const char * const names[3] = { + "NONE", + "CustomQuantization", + nullptr + }; + return names; +} + +inline const char *EnumNameQuantizationDetails(QuantizationDetails e) { + if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return ""; + const size_t index = static_cast(e); + return EnumNamesQuantizationDetails()[index]; +} + +template struct QuantizationDetailsTraits { + static const QuantizationDetails enum_value = QuantizationDetails_NONE; +}; + +template<> struct QuantizationDetailsTraits { + static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; +}; + +template struct QuantizationDetailsUnionTraits { + static const QuantizationDetails enum_value = QuantizationDetails_NONE; +}; + +template<> struct QuantizationDetailsUnionTraits { + static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; +}; + +struct QuantizationDetailsUnion { + QuantizationDetails type; + void *value; + + QuantizationDetailsUnion() : type(QuantizationDetails_NONE), value(nullptr) {} + QuantizationDetailsUnion(QuantizationDetailsUnion&& u) FLATBUFFERS_NOEXCEPT : + type(QuantizationDetails_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + QuantizationDetailsUnion(const QuantizationDetailsUnion &); + QuantizationDetailsUnion &operator=(const QuantizationDetailsUnion &u) + { QuantizationDetailsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + QuantizationDetailsUnion &operator=(QuantizationDetailsUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~QuantizationDetailsUnion() { Reset(); } + + void Reset(); + + template + void Set(T&& val) { + typedef typename std::remove_reference::type RT; + Reset(); + type = QuantizationDetailsUnionTraits::enum_value; + if (type != QuantizationDetails_NONE) { + value = new RT(std::forward(val)); + } + } + + static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite_micro::CustomQuantizationT *AsCustomQuantization() { + return type == QuantizationDetails_CustomQuantization ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CustomQuantizationT *AsCustomQuantization() const { + return type == QuantizationDetails_CustomQuantization ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type); +bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum DimensionType : int8_t { + DimensionType_DENSE = 0, + DimensionType_SPARSE_CSR = 1, + DimensionType_MIN = DimensionType_DENSE, + DimensionType_MAX = DimensionType_SPARSE_CSR +}; + +inline const DimensionType (&EnumValuesDimensionType())[2] { + static const DimensionType values[] = { + DimensionType_DENSE, + DimensionType_SPARSE_CSR + }; + return values; +} + +inline const char * const *EnumNamesDimensionType() { + static const char * const names[3] = { + "DENSE", + "SPARSE_CSR", + nullptr + }; + return names; +} + +inline const char *EnumNameDimensionType(DimensionType e) { + if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return ""; + const size_t index = static_cast(e); + return EnumNamesDimensionType()[index]; +} + +enum SparseIndexVector : uint8_t { + SparseIndexVector_NONE = 0, + SparseIndexVector_Int32Vector = 1, + SparseIndexVector_Uint16Vector = 2, + SparseIndexVector_Uint8Vector = 3, + SparseIndexVector_MIN = SparseIndexVector_NONE, + SparseIndexVector_MAX = SparseIndexVector_Uint8Vector +}; + +inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] { + static const SparseIndexVector values[] = { + SparseIndexVector_NONE, + SparseIndexVector_Int32Vector, + SparseIndexVector_Uint16Vector, + SparseIndexVector_Uint8Vector + }; + return values; +} + +inline const char * const *EnumNamesSparseIndexVector() { + static const char * const names[5] = { + "NONE", + "Int32Vector", + "Uint16Vector", + "Uint8Vector", + nullptr + }; + return names; +} + +inline const char *EnumNameSparseIndexVector(SparseIndexVector e) { + if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return ""; + const size_t index = static_cast(e); + return EnumNamesSparseIndexVector()[index]; +} + +template struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_NONE; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; +}; + +template<> struct SparseIndexVectorTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; +}; + +template struct SparseIndexVectorUnionTraits { + static const SparseIndexVector enum_value = SparseIndexVector_NONE; +}; + +template<> struct SparseIndexVectorUnionTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; +}; + +template<> struct SparseIndexVectorUnionTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; +}; + +template<> struct SparseIndexVectorUnionTraits { + static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; +}; + +struct SparseIndexVectorUnion { + SparseIndexVector type; + void *value; + + SparseIndexVectorUnion() : type(SparseIndexVector_NONE), value(nullptr) {} + SparseIndexVectorUnion(SparseIndexVectorUnion&& u) FLATBUFFERS_NOEXCEPT : + type(SparseIndexVector_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + SparseIndexVectorUnion(const SparseIndexVectorUnion &); + SparseIndexVectorUnion &operator=(const SparseIndexVectorUnion &u) + { SparseIndexVectorUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + SparseIndexVectorUnion &operator=(SparseIndexVectorUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~SparseIndexVectorUnion() { Reset(); } + + void Reset(); + + template + void Set(T&& val) { + typedef typename std::remove_reference::type RT; + Reset(); + type = SparseIndexVectorUnionTraits::enum_value; + if (type != SparseIndexVector_NONE) { + value = new RT(std::forward(val)); + } + } + + static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite_micro::Int32VectorT *AsInt32Vector() { + return type == SparseIndexVector_Int32Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Int32VectorT *AsInt32Vector() const { + return type == SparseIndexVector_Int32Vector ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::Uint16VectorT *AsUint16Vector() { + return type == SparseIndexVector_Uint16Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Uint16VectorT *AsUint16Vector() const { + return type == SparseIndexVector_Uint16Vector ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::Uint8VectorT *AsUint8Vector() { + return type == SparseIndexVector_Uint8Vector ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Uint8VectorT *AsUint8Vector() const { + return type == SparseIndexVector_Uint8Vector ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type); +bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum BuiltinOperator : int32_t { + BuiltinOperator_ADD = 0, + BuiltinOperator_AVERAGE_POOL_2D = 1, + BuiltinOperator_CONCATENATION = 2, + BuiltinOperator_CONV_2D = 3, + BuiltinOperator_DEPTHWISE_CONV_2D = 4, + BuiltinOperator_DEPTH_TO_SPACE = 5, + BuiltinOperator_DEQUANTIZE = 6, + BuiltinOperator_EMBEDDING_LOOKUP = 7, + BuiltinOperator_FLOOR = 8, + BuiltinOperator_FULLY_CONNECTED = 9, + BuiltinOperator_HASHTABLE_LOOKUP = 10, + BuiltinOperator_L2_NORMALIZATION = 11, + BuiltinOperator_L2_POOL_2D = 12, + BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, + BuiltinOperator_LOGISTIC = 14, + BuiltinOperator_LSH_PROJECTION = 15, + BuiltinOperator_LSTM = 16, + BuiltinOperator_MAX_POOL_2D = 17, + BuiltinOperator_MUL = 18, + BuiltinOperator_RELU = 19, + BuiltinOperator_RELU_N1_TO_1 = 20, + BuiltinOperator_RELU6 = 21, + BuiltinOperator_RESHAPE = 22, + BuiltinOperator_RESIZE_BILINEAR = 23, + BuiltinOperator_RNN = 24, + BuiltinOperator_SOFTMAX = 25, + BuiltinOperator_SPACE_TO_DEPTH = 26, + BuiltinOperator_SVDF = 27, + BuiltinOperator_TANH = 28, + BuiltinOperator_CONCAT_EMBEDDINGS = 29, + BuiltinOperator_SKIP_GRAM = 30, + BuiltinOperator_CALL = 31, + BuiltinOperator_CUSTOM = 32, + BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, + BuiltinOperator_PAD = 34, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, + BuiltinOperator_GATHER = 36, + BuiltinOperator_BATCH_TO_SPACE_ND = 37, + BuiltinOperator_SPACE_TO_BATCH_ND = 38, + BuiltinOperator_TRANSPOSE = 39, + BuiltinOperator_MEAN = 40, + BuiltinOperator_SUB = 41, + BuiltinOperator_DIV = 42, + BuiltinOperator_SQUEEZE = 43, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, + BuiltinOperator_STRIDED_SLICE = 45, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, + BuiltinOperator_EXP = 47, + BuiltinOperator_TOPK_V2 = 48, + BuiltinOperator_SPLIT = 49, + BuiltinOperator_LOG_SOFTMAX = 50, + BuiltinOperator_DELEGATE = 51, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, + BuiltinOperator_CAST = 53, + BuiltinOperator_PRELU = 54, + BuiltinOperator_MAXIMUM = 55, + BuiltinOperator_ARG_MAX = 56, + BuiltinOperator_MINIMUM = 57, + BuiltinOperator_LESS = 58, + BuiltinOperator_NEG = 59, + BuiltinOperator_PADV2 = 60, + BuiltinOperator_GREATER = 61, + BuiltinOperator_GREATER_EQUAL = 62, + BuiltinOperator_LESS_EQUAL = 63, + BuiltinOperator_SELECT = 64, + BuiltinOperator_SLICE = 65, + BuiltinOperator_SIN = 66, + BuiltinOperator_TRANSPOSE_CONV = 67, + BuiltinOperator_SPARSE_TO_DENSE = 68, + BuiltinOperator_TILE = 69, + BuiltinOperator_EXPAND_DIMS = 70, + BuiltinOperator_EQUAL = 71, + BuiltinOperator_NOT_EQUAL = 72, + BuiltinOperator_LOG = 73, + BuiltinOperator_SUM = 74, + BuiltinOperator_SQRT = 75, + BuiltinOperator_RSQRT = 76, + BuiltinOperator_SHAPE = 77, + BuiltinOperator_POW = 78, + BuiltinOperator_ARG_MIN = 79, + BuiltinOperator_FAKE_QUANT = 80, + BuiltinOperator_REDUCE_PROD = 81, + BuiltinOperator_REDUCE_MAX = 82, + BuiltinOperator_PACK = 83, + BuiltinOperator_LOGICAL_OR = 84, + BuiltinOperator_ONE_HOT = 85, + BuiltinOperator_LOGICAL_AND = 86, + BuiltinOperator_LOGICAL_NOT = 87, + BuiltinOperator_UNPACK = 88, + BuiltinOperator_REDUCE_MIN = 89, + BuiltinOperator_FLOOR_DIV = 90, + BuiltinOperator_REDUCE_ANY = 91, + BuiltinOperator_SQUARE = 92, + BuiltinOperator_ZEROS_LIKE = 93, + BuiltinOperator_FILL = 94, + BuiltinOperator_FLOOR_MOD = 95, + BuiltinOperator_RANGE = 96, + BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, + BuiltinOperator_LEAKY_RELU = 98, + BuiltinOperator_SQUARED_DIFFERENCE = 99, + BuiltinOperator_MIRROR_PAD = 100, + BuiltinOperator_ABS = 101, + BuiltinOperator_SPLIT_V = 102, + BuiltinOperator_UNIQUE = 103, + BuiltinOperator_CEIL = 104, + BuiltinOperator_REVERSE_V2 = 105, + BuiltinOperator_ADD_N = 106, + BuiltinOperator_GATHER_ND = 107, + BuiltinOperator_COS = 108, + BuiltinOperator_WHERE = 109, + BuiltinOperator_RANK = 110, + BuiltinOperator_ELU = 111, + BuiltinOperator_REVERSE_SEQUENCE = 112, + BuiltinOperator_MATRIX_DIAG = 113, + BuiltinOperator_QUANTIZE = 114, + BuiltinOperator_MATRIX_SET_DIAG = 115, + BuiltinOperator_ROUND = 116, + BuiltinOperator_HARD_SWISH = 117, + BuiltinOperator_IF = 118, + BuiltinOperator_WHILE = 119, + BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, + BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, + BuiltinOperator_SCATTER_ND = 122, + BuiltinOperator_SELECT_V2 = 123, + BuiltinOperator_DENSIFY = 124, + BuiltinOperator_SEGMENT_SUM = 125, + BuiltinOperator_BATCH_MATMUL = 126, + BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES = 127, + BuiltinOperator_CUMSUM = 128, + BuiltinOperator_CALL_ONCE = 129, + BuiltinOperator_BROADCAST_TO = 130, + BuiltinOperator_RFFT2D = 131, + BuiltinOperator_CONV_3D = 132, + BuiltinOperator_IMAG = 133, + BuiltinOperator_REAL = 134, + BuiltinOperator_COMPLEX_ABS = 135, + BuiltinOperator_HASHTABLE = 136, + BuiltinOperator_HASHTABLE_FIND = 137, + BuiltinOperator_HASHTABLE_IMPORT = 138, + BuiltinOperator_HASHTABLE_SIZE = 139, + BuiltinOperator_REDUCE_ALL = 140, + BuiltinOperator_CONV_3D_TRANSPOSE = 141, + BuiltinOperator_VAR_HANDLE = 142, + BuiltinOperator_READ_VARIABLE = 143, + BuiltinOperator_ASSIGN_VARIABLE = 144, + BuiltinOperator_BROADCAST_ARGS = 145, + BuiltinOperator_RANDOM_STANDARD_NORMAL = 146, + BuiltinOperator_BUCKETIZE = 147, + BuiltinOperator_RANDOM_UNIFORM = 148, + BuiltinOperator_MULTINOMIAL = 149, + BuiltinOperator_GELU = 150, + BuiltinOperator_DYNAMIC_UPDATE_SLICE = 151, + BuiltinOperator_RELU_0_TO_1 = 152, + BuiltinOperator_UNSORTED_SEGMENT_PROD = 153, + BuiltinOperator_UNSORTED_SEGMENT_MAX = 154, + BuiltinOperator_UNSORTED_SEGMENT_SUM = 155, + BuiltinOperator_ATAN2 = 156, + BuiltinOperator_UNSORTED_SEGMENT_MIN = 157, + BuiltinOperator_SIGN = 158, + BuiltinOperator_BITCAST = 159, + BuiltinOperator_BITWISE_XOR = 160, + BuiltinOperator_RIGHT_SHIFT = 161, + BuiltinOperator_STABLEHLO_LOGISTIC = 162, + BuiltinOperator_STABLEHLO_ADD = 163, + BuiltinOperator_STABLEHLO_DIVIDE = 164, + BuiltinOperator_STABLEHLO_MULTIPLY = 165, + BuiltinOperator_STABLEHLO_MAXIMUM = 166, + BuiltinOperator_STABLEHLO_RESHAPE = 167, + BuiltinOperator_STABLEHLO_CLAMP = 168, + BuiltinOperator_STABLEHLO_CONCATENATE = 169, + BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM = 170, + BuiltinOperator_STABLEHLO_CONVOLUTION = 171, + BuiltinOperator_STABLEHLO_SLICE = 172, + BuiltinOperator_STABLEHLO_CUSTOM_CALL = 173, + BuiltinOperator_STABLEHLO_REDUCE = 174, + BuiltinOperator_STABLEHLO_ABS = 175, + BuiltinOperator_STABLEHLO_AND = 176, + BuiltinOperator_STABLEHLO_COSINE = 177, + BuiltinOperator_STABLEHLO_EXPONENTIAL = 178, + BuiltinOperator_STABLEHLO_FLOOR = 179, + BuiltinOperator_STABLEHLO_LOG = 180, + BuiltinOperator_STABLEHLO_MINIMUM = 181, + BuiltinOperator_STABLEHLO_NEGATE = 182, + BuiltinOperator_STABLEHLO_OR = 183, + BuiltinOperator_STABLEHLO_POWER = 184, + BuiltinOperator_STABLEHLO_REMAINDER = 185, + BuiltinOperator_STABLEHLO_RSQRT = 186, + BuiltinOperator_STABLEHLO_SELECT = 187, + BuiltinOperator_STABLEHLO_SUBTRACT = 188, + BuiltinOperator_STABLEHLO_TANH = 189, + BuiltinOperator_STABLEHLO_SCATTER = 190, + BuiltinOperator_STABLEHLO_COMPARE = 191, + BuiltinOperator_STABLEHLO_CONVERT = 192, + BuiltinOperator_STABLEHLO_DYNAMIC_SLICE = 193, + BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE = 194, + BuiltinOperator_STABLEHLO_PAD = 195, + BuiltinOperator_STABLEHLO_IOTA = 196, + BuiltinOperator_STABLEHLO_DOT_GENERAL = 197, + BuiltinOperator_STABLEHLO_REDUCE_WINDOW = 198, + BuiltinOperator_STABLEHLO_SORT = 199, + BuiltinOperator_STABLEHLO_WHILE = 200, + BuiltinOperator_STABLEHLO_GATHER = 201, + BuiltinOperator_STABLEHLO_TRANSPOSE = 202, + BuiltinOperator_DILATE = 203, + BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR = 204, + BuiltinOperator_REDUCE_WINDOW = 205, + BuiltinOperator_MIN = BuiltinOperator_ADD, + BuiltinOperator_MAX = BuiltinOperator_REDUCE_WINDOW +}; + +inline const BuiltinOperator (&EnumValuesBuiltinOperator())[206] { + static const BuiltinOperator values[] = { + BuiltinOperator_ADD, + BuiltinOperator_AVERAGE_POOL_2D, + BuiltinOperator_CONCATENATION, + BuiltinOperator_CONV_2D, + BuiltinOperator_DEPTHWISE_CONV_2D, + BuiltinOperator_DEPTH_TO_SPACE, + BuiltinOperator_DEQUANTIZE, + BuiltinOperator_EMBEDDING_LOOKUP, + BuiltinOperator_FLOOR, + BuiltinOperator_FULLY_CONNECTED, + BuiltinOperator_HASHTABLE_LOOKUP, + BuiltinOperator_L2_NORMALIZATION, + BuiltinOperator_L2_POOL_2D, + BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + BuiltinOperator_LOGISTIC, + BuiltinOperator_LSH_PROJECTION, + BuiltinOperator_LSTM, + BuiltinOperator_MAX_POOL_2D, + BuiltinOperator_MUL, + BuiltinOperator_RELU, + BuiltinOperator_RELU_N1_TO_1, + BuiltinOperator_RELU6, + BuiltinOperator_RESHAPE, + BuiltinOperator_RESIZE_BILINEAR, + BuiltinOperator_RNN, + BuiltinOperator_SOFTMAX, + BuiltinOperator_SPACE_TO_DEPTH, + BuiltinOperator_SVDF, + BuiltinOperator_TANH, + BuiltinOperator_CONCAT_EMBEDDINGS, + BuiltinOperator_SKIP_GRAM, + BuiltinOperator_CALL, + BuiltinOperator_CUSTOM, + BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, + BuiltinOperator_PAD, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_GATHER, + BuiltinOperator_BATCH_TO_SPACE_ND, + BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOperator_TRANSPOSE, + BuiltinOperator_MEAN, + BuiltinOperator_SUB, + BuiltinOperator_DIV, + BuiltinOperator_SQUEEZE, + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_STRIDED_SLICE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, + BuiltinOperator_EXP, + BuiltinOperator_TOPK_V2, + BuiltinOperator_SPLIT, + BuiltinOperator_LOG_SOFTMAX, + BuiltinOperator_DELEGATE, + BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, + BuiltinOperator_CAST, + BuiltinOperator_PRELU, + BuiltinOperator_MAXIMUM, + BuiltinOperator_ARG_MAX, + BuiltinOperator_MINIMUM, + BuiltinOperator_LESS, + BuiltinOperator_NEG, + BuiltinOperator_PADV2, + BuiltinOperator_GREATER, + BuiltinOperator_GREATER_EQUAL, + BuiltinOperator_LESS_EQUAL, + BuiltinOperator_SELECT, + BuiltinOperator_SLICE, + BuiltinOperator_SIN, + BuiltinOperator_TRANSPOSE_CONV, + BuiltinOperator_SPARSE_TO_DENSE, + BuiltinOperator_TILE, + BuiltinOperator_EXPAND_DIMS, + BuiltinOperator_EQUAL, + BuiltinOperator_NOT_EQUAL, + BuiltinOperator_LOG, + BuiltinOperator_SUM, + BuiltinOperator_SQRT, + BuiltinOperator_RSQRT, + BuiltinOperator_SHAPE, + BuiltinOperator_POW, + BuiltinOperator_ARG_MIN, + BuiltinOperator_FAKE_QUANT, + BuiltinOperator_REDUCE_PROD, + BuiltinOperator_REDUCE_MAX, + BuiltinOperator_PACK, + BuiltinOperator_LOGICAL_OR, + BuiltinOperator_ONE_HOT, + BuiltinOperator_LOGICAL_AND, + BuiltinOperator_LOGICAL_NOT, + BuiltinOperator_UNPACK, + BuiltinOperator_REDUCE_MIN, + BuiltinOperator_FLOOR_DIV, + BuiltinOperator_REDUCE_ANY, + BuiltinOperator_SQUARE, + BuiltinOperator_ZEROS_LIKE, + BuiltinOperator_FILL, + BuiltinOperator_FLOOR_MOD, + BuiltinOperator_RANGE, + BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + BuiltinOperator_LEAKY_RELU, + BuiltinOperator_SQUARED_DIFFERENCE, + BuiltinOperator_MIRROR_PAD, + BuiltinOperator_ABS, + BuiltinOperator_SPLIT_V, + BuiltinOperator_UNIQUE, + BuiltinOperator_CEIL, + BuiltinOperator_REVERSE_V2, + BuiltinOperator_ADD_N, + BuiltinOperator_GATHER_ND, + BuiltinOperator_COS, + BuiltinOperator_WHERE, + BuiltinOperator_RANK, + BuiltinOperator_ELU, + BuiltinOperator_REVERSE_SEQUENCE, + BuiltinOperator_MATRIX_DIAG, + BuiltinOperator_QUANTIZE, + BuiltinOperator_MATRIX_SET_DIAG, + BuiltinOperator_ROUND, + BuiltinOperator_HARD_SWISH, + BuiltinOperator_IF, + BuiltinOperator_WHILE, + BuiltinOperator_NON_MAX_SUPPRESSION_V4, + BuiltinOperator_NON_MAX_SUPPRESSION_V5, + BuiltinOperator_SCATTER_ND, + BuiltinOperator_SELECT_V2, + BuiltinOperator_DENSIFY, + BuiltinOperator_SEGMENT_SUM, + BuiltinOperator_BATCH_MATMUL, + BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES, + BuiltinOperator_CUMSUM, + BuiltinOperator_CALL_ONCE, + BuiltinOperator_BROADCAST_TO, + BuiltinOperator_RFFT2D, + BuiltinOperator_CONV_3D, + BuiltinOperator_IMAG, + BuiltinOperator_REAL, + BuiltinOperator_COMPLEX_ABS, + BuiltinOperator_HASHTABLE, + BuiltinOperator_HASHTABLE_FIND, + BuiltinOperator_HASHTABLE_IMPORT, + BuiltinOperator_HASHTABLE_SIZE, + BuiltinOperator_REDUCE_ALL, + BuiltinOperator_CONV_3D_TRANSPOSE, + BuiltinOperator_VAR_HANDLE, + BuiltinOperator_READ_VARIABLE, + BuiltinOperator_ASSIGN_VARIABLE, + BuiltinOperator_BROADCAST_ARGS, + BuiltinOperator_RANDOM_STANDARD_NORMAL, + BuiltinOperator_BUCKETIZE, + BuiltinOperator_RANDOM_UNIFORM, + BuiltinOperator_MULTINOMIAL, + BuiltinOperator_GELU, + BuiltinOperator_DYNAMIC_UPDATE_SLICE, + BuiltinOperator_RELU_0_TO_1, + BuiltinOperator_UNSORTED_SEGMENT_PROD, + BuiltinOperator_UNSORTED_SEGMENT_MAX, + BuiltinOperator_UNSORTED_SEGMENT_SUM, + BuiltinOperator_ATAN2, + BuiltinOperator_UNSORTED_SEGMENT_MIN, + BuiltinOperator_SIGN, + BuiltinOperator_BITCAST, + BuiltinOperator_BITWISE_XOR, + BuiltinOperator_RIGHT_SHIFT, + BuiltinOperator_STABLEHLO_LOGISTIC, + BuiltinOperator_STABLEHLO_ADD, + BuiltinOperator_STABLEHLO_DIVIDE, + BuiltinOperator_STABLEHLO_MULTIPLY, + BuiltinOperator_STABLEHLO_MAXIMUM, + BuiltinOperator_STABLEHLO_RESHAPE, + BuiltinOperator_STABLEHLO_CLAMP, + BuiltinOperator_STABLEHLO_CONCATENATE, + BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM, + BuiltinOperator_STABLEHLO_CONVOLUTION, + BuiltinOperator_STABLEHLO_SLICE, + BuiltinOperator_STABLEHLO_CUSTOM_CALL, + BuiltinOperator_STABLEHLO_REDUCE, + BuiltinOperator_STABLEHLO_ABS, + BuiltinOperator_STABLEHLO_AND, + BuiltinOperator_STABLEHLO_COSINE, + BuiltinOperator_STABLEHLO_EXPONENTIAL, + BuiltinOperator_STABLEHLO_FLOOR, + BuiltinOperator_STABLEHLO_LOG, + BuiltinOperator_STABLEHLO_MINIMUM, + BuiltinOperator_STABLEHLO_NEGATE, + BuiltinOperator_STABLEHLO_OR, + BuiltinOperator_STABLEHLO_POWER, + BuiltinOperator_STABLEHLO_REMAINDER, + BuiltinOperator_STABLEHLO_RSQRT, + BuiltinOperator_STABLEHLO_SELECT, + BuiltinOperator_STABLEHLO_SUBTRACT, + BuiltinOperator_STABLEHLO_TANH, + BuiltinOperator_STABLEHLO_SCATTER, + BuiltinOperator_STABLEHLO_COMPARE, + BuiltinOperator_STABLEHLO_CONVERT, + BuiltinOperator_STABLEHLO_DYNAMIC_SLICE, + BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE, + BuiltinOperator_STABLEHLO_PAD, + BuiltinOperator_STABLEHLO_IOTA, + BuiltinOperator_STABLEHLO_DOT_GENERAL, + BuiltinOperator_STABLEHLO_REDUCE_WINDOW, + BuiltinOperator_STABLEHLO_SORT, + BuiltinOperator_STABLEHLO_WHILE, + BuiltinOperator_STABLEHLO_GATHER, + BuiltinOperator_STABLEHLO_TRANSPOSE, + BuiltinOperator_DILATE, + BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR, + BuiltinOperator_REDUCE_WINDOW + }; + return values; +} + +inline const char * const *EnumNamesBuiltinOperator() { + static const char * const names[207] = { + "ADD", + "AVERAGE_POOL_2D", + "CONCATENATION", + "CONV_2D", + "DEPTHWISE_CONV_2D", + "DEPTH_TO_SPACE", + "DEQUANTIZE", + "EMBEDDING_LOOKUP", + "FLOOR", + "FULLY_CONNECTED", + "HASHTABLE_LOOKUP", + "L2_NORMALIZATION", + "L2_POOL_2D", + "LOCAL_RESPONSE_NORMALIZATION", + "LOGISTIC", + "LSH_PROJECTION", + "LSTM", + "MAX_POOL_2D", + "MUL", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "RESHAPE", + "RESIZE_BILINEAR", + "RNN", + "SOFTMAX", + "SPACE_TO_DEPTH", + "SVDF", + "TANH", + "CONCAT_EMBEDDINGS", + "SKIP_GRAM", + "CALL", + "CUSTOM", + "EMBEDDING_LOOKUP_SPARSE", + "PAD", + "UNIDIRECTIONAL_SEQUENCE_RNN", + "GATHER", + "BATCH_TO_SPACE_ND", + "SPACE_TO_BATCH_ND", + "TRANSPOSE", + "MEAN", + "SUB", + "DIV", + "SQUEEZE", + "UNIDIRECTIONAL_SEQUENCE_LSTM", + "STRIDED_SLICE", + "BIDIRECTIONAL_SEQUENCE_RNN", + "EXP", + "TOPK_V2", + "SPLIT", + "LOG_SOFTMAX", + "DELEGATE", + "BIDIRECTIONAL_SEQUENCE_LSTM", + "CAST", + "PRELU", + "MAXIMUM", + "ARG_MAX", + "MINIMUM", + "LESS", + "NEG", + "PADV2", + "GREATER", + "GREATER_EQUAL", + "LESS_EQUAL", + "SELECT", + "SLICE", + "SIN", + "TRANSPOSE_CONV", + "SPARSE_TO_DENSE", + "TILE", + "EXPAND_DIMS", + "EQUAL", + "NOT_EQUAL", + "LOG", + "SUM", + "SQRT", + "RSQRT", + "SHAPE", + "POW", + "ARG_MIN", + "FAKE_QUANT", + "REDUCE_PROD", + "REDUCE_MAX", + "PACK", + "LOGICAL_OR", + "ONE_HOT", + "LOGICAL_AND", + "LOGICAL_NOT", + "UNPACK", + "REDUCE_MIN", + "FLOOR_DIV", + "REDUCE_ANY", + "SQUARE", + "ZEROS_LIKE", + "FILL", + "FLOOR_MOD", + "RANGE", + "RESIZE_NEAREST_NEIGHBOR", + "LEAKY_RELU", + "SQUARED_DIFFERENCE", + "MIRROR_PAD", + "ABS", + "SPLIT_V", + "UNIQUE", + "CEIL", + "REVERSE_V2", + "ADD_N", + "GATHER_ND", + "COS", + "WHERE", + "RANK", + "ELU", + "REVERSE_SEQUENCE", + "MATRIX_DIAG", + "QUANTIZE", + "MATRIX_SET_DIAG", + "ROUND", + "HARD_SWISH", + "IF", + "WHILE", + "NON_MAX_SUPPRESSION_V4", + "NON_MAX_SUPPRESSION_V5", + "SCATTER_ND", + "SELECT_V2", + "DENSIFY", + "SEGMENT_SUM", + "BATCH_MATMUL", + "PLACEHOLDER_FOR_GREATER_OP_CODES", + "CUMSUM", + "CALL_ONCE", + "BROADCAST_TO", + "RFFT2D", + "CONV_3D", + "IMAG", + "REAL", + "COMPLEX_ABS", + "HASHTABLE", + "HASHTABLE_FIND", + "HASHTABLE_IMPORT", + "HASHTABLE_SIZE", + "REDUCE_ALL", + "CONV_3D_TRANSPOSE", + "VAR_HANDLE", + "READ_VARIABLE", + "ASSIGN_VARIABLE", + "BROADCAST_ARGS", + "RANDOM_STANDARD_NORMAL", + "BUCKETIZE", + "RANDOM_UNIFORM", + "MULTINOMIAL", + "GELU", + "DYNAMIC_UPDATE_SLICE", + "RELU_0_TO_1", + "UNSORTED_SEGMENT_PROD", + "UNSORTED_SEGMENT_MAX", + "UNSORTED_SEGMENT_SUM", + "ATAN2", + "UNSORTED_SEGMENT_MIN", + "SIGN", + "BITCAST", + "BITWISE_XOR", + "RIGHT_SHIFT", + "STABLEHLO_LOGISTIC", + "STABLEHLO_ADD", + "STABLEHLO_DIVIDE", + "STABLEHLO_MULTIPLY", + "STABLEHLO_MAXIMUM", + "STABLEHLO_RESHAPE", + "STABLEHLO_CLAMP", + "STABLEHLO_CONCATENATE", + "STABLEHLO_BROADCAST_IN_DIM", + "STABLEHLO_CONVOLUTION", + "STABLEHLO_SLICE", + "STABLEHLO_CUSTOM_CALL", + "STABLEHLO_REDUCE", + "STABLEHLO_ABS", + "STABLEHLO_AND", + "STABLEHLO_COSINE", + "STABLEHLO_EXPONENTIAL", + "STABLEHLO_FLOOR", + "STABLEHLO_LOG", + "STABLEHLO_MINIMUM", + "STABLEHLO_NEGATE", + "STABLEHLO_OR", + "STABLEHLO_POWER", + "STABLEHLO_REMAINDER", + "STABLEHLO_RSQRT", + "STABLEHLO_SELECT", + "STABLEHLO_SUBTRACT", + "STABLEHLO_TANH", + "STABLEHLO_SCATTER", + "STABLEHLO_COMPARE", + "STABLEHLO_CONVERT", + "STABLEHLO_DYNAMIC_SLICE", + "STABLEHLO_DYNAMIC_UPDATE_SLICE", + "STABLEHLO_PAD", + "STABLEHLO_IOTA", + "STABLEHLO_DOT_GENERAL", + "STABLEHLO_REDUCE_WINDOW", + "STABLEHLO_SORT", + "STABLEHLO_WHILE", + "STABLEHLO_GATHER", + "STABLEHLO_TRANSPOSE", + "DILATE", + "STABLEHLO_RNG_BIT_GENERATOR", + "REDUCE_WINDOW", + nullptr + }; + return names; +} + +inline const char *EnumNameBuiltinOperator(BuiltinOperator e) { + if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_REDUCE_WINDOW)) return ""; + const size_t index = static_cast(e); + return EnumNamesBuiltinOperator()[index]; +} + +enum BuiltinOptions : uint8_t { + BuiltinOptions_NONE = 0, + BuiltinOptions_Conv2DOptions = 1, + BuiltinOptions_DepthwiseConv2DOptions = 2, + BuiltinOptions_ConcatEmbeddingsOptions = 3, + BuiltinOptions_LSHProjectionOptions = 4, + BuiltinOptions_Pool2DOptions = 5, + BuiltinOptions_SVDFOptions = 6, + BuiltinOptions_RNNOptions = 7, + BuiltinOptions_FullyConnectedOptions = 8, + BuiltinOptions_SoftmaxOptions = 9, + BuiltinOptions_ConcatenationOptions = 10, + BuiltinOptions_AddOptions = 11, + BuiltinOptions_L2NormOptions = 12, + BuiltinOptions_LocalResponseNormalizationOptions = 13, + BuiltinOptions_LSTMOptions = 14, + BuiltinOptions_ResizeBilinearOptions = 15, + BuiltinOptions_CallOptions = 16, + BuiltinOptions_ReshapeOptions = 17, + BuiltinOptions_SkipGramOptions = 18, + BuiltinOptions_SpaceToDepthOptions = 19, + BuiltinOptions_EmbeddingLookupSparseOptions = 20, + BuiltinOptions_MulOptions = 21, + BuiltinOptions_PadOptions = 22, + BuiltinOptions_GatherOptions = 23, + BuiltinOptions_BatchToSpaceNDOptions = 24, + BuiltinOptions_SpaceToBatchNDOptions = 25, + BuiltinOptions_TransposeOptions = 26, + BuiltinOptions_ReducerOptions = 27, + BuiltinOptions_SubOptions = 28, + BuiltinOptions_DivOptions = 29, + BuiltinOptions_SqueezeOptions = 30, + BuiltinOptions_SequenceRNNOptions = 31, + BuiltinOptions_StridedSliceOptions = 32, + BuiltinOptions_ExpOptions = 33, + BuiltinOptions_TopKV2Options = 34, + BuiltinOptions_SplitOptions = 35, + BuiltinOptions_LogSoftmaxOptions = 36, + BuiltinOptions_CastOptions = 37, + BuiltinOptions_DequantizeOptions = 38, + BuiltinOptions_MaximumMinimumOptions = 39, + BuiltinOptions_ArgMaxOptions = 40, + BuiltinOptions_LessOptions = 41, + BuiltinOptions_NegOptions = 42, + BuiltinOptions_PadV2Options = 43, + BuiltinOptions_GreaterOptions = 44, + BuiltinOptions_GreaterEqualOptions = 45, + BuiltinOptions_LessEqualOptions = 46, + BuiltinOptions_SelectOptions = 47, + BuiltinOptions_SliceOptions = 48, + BuiltinOptions_TransposeConvOptions = 49, + BuiltinOptions_SparseToDenseOptions = 50, + BuiltinOptions_TileOptions = 51, + BuiltinOptions_ExpandDimsOptions = 52, + BuiltinOptions_EqualOptions = 53, + BuiltinOptions_NotEqualOptions = 54, + BuiltinOptions_ShapeOptions = 55, + BuiltinOptions_PowOptions = 56, + BuiltinOptions_ArgMinOptions = 57, + BuiltinOptions_FakeQuantOptions = 58, + BuiltinOptions_PackOptions = 59, + BuiltinOptions_LogicalOrOptions = 60, + BuiltinOptions_OneHotOptions = 61, + BuiltinOptions_LogicalAndOptions = 62, + BuiltinOptions_LogicalNotOptions = 63, + BuiltinOptions_UnpackOptions = 64, + BuiltinOptions_FloorDivOptions = 65, + BuiltinOptions_SquareOptions = 66, + BuiltinOptions_ZerosLikeOptions = 67, + BuiltinOptions_FillOptions = 68, + BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, + BuiltinOptions_BidirectionalSequenceRNNOptions = 70, + BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, + BuiltinOptions_FloorModOptions = 72, + BuiltinOptions_RangeOptions = 73, + BuiltinOptions_ResizeNearestNeighborOptions = 74, + BuiltinOptions_LeakyReluOptions = 75, + BuiltinOptions_SquaredDifferenceOptions = 76, + BuiltinOptions_MirrorPadOptions = 77, + BuiltinOptions_AbsOptions = 78, + BuiltinOptions_SplitVOptions = 79, + BuiltinOptions_UniqueOptions = 80, + BuiltinOptions_ReverseV2Options = 81, + BuiltinOptions_AddNOptions = 82, + BuiltinOptions_GatherNdOptions = 83, + BuiltinOptions_CosOptions = 84, + BuiltinOptions_WhereOptions = 85, + BuiltinOptions_RankOptions = 86, + BuiltinOptions_ReverseSequenceOptions = 87, + BuiltinOptions_MatrixDiagOptions = 88, + BuiltinOptions_QuantizeOptions = 89, + BuiltinOptions_MatrixSetDiagOptions = 90, + BuiltinOptions_HardSwishOptions = 91, + BuiltinOptions_IfOptions = 92, + BuiltinOptions_WhileOptions = 93, + BuiltinOptions_DepthToSpaceOptions = 94, + BuiltinOptions_NonMaxSuppressionV4Options = 95, + BuiltinOptions_NonMaxSuppressionV5Options = 96, + BuiltinOptions_ScatterNdOptions = 97, + BuiltinOptions_SelectV2Options = 98, + BuiltinOptions_DensifyOptions = 99, + BuiltinOptions_SegmentSumOptions = 100, + BuiltinOptions_BatchMatMulOptions = 101, + BuiltinOptions_CumsumOptions = 102, + BuiltinOptions_CallOnceOptions = 103, + BuiltinOptions_BroadcastToOptions = 104, + BuiltinOptions_Rfft2dOptions = 105, + BuiltinOptions_Conv3DOptions = 106, + BuiltinOptions_HashtableOptions = 107, + BuiltinOptions_HashtableFindOptions = 108, + BuiltinOptions_HashtableImportOptions = 109, + BuiltinOptions_HashtableSizeOptions = 110, + BuiltinOptions_VarHandleOptions = 111, + BuiltinOptions_ReadVariableOptions = 112, + BuiltinOptions_AssignVariableOptions = 113, + BuiltinOptions_RandomOptions = 114, + BuiltinOptions_BucketizeOptions = 115, + BuiltinOptions_GeluOptions = 116, + BuiltinOptions_DynamicUpdateSliceOptions = 117, + BuiltinOptions_UnsortedSegmentProdOptions = 118, + BuiltinOptions_UnsortedSegmentMaxOptions = 119, + BuiltinOptions_UnsortedSegmentMinOptions = 120, + BuiltinOptions_UnsortedSegmentSumOptions = 121, + BuiltinOptions_ATan2Options = 122, + BuiltinOptions_SignOptions = 123, + BuiltinOptions_BitcastOptions = 124, + BuiltinOptions_BitwiseXorOptions = 125, + BuiltinOptions_RightShiftOptions = 126, + BuiltinOptions_MIN = BuiltinOptions_NONE, + BuiltinOptions_MAX = BuiltinOptions_RightShiftOptions +}; + +inline const BuiltinOptions (&EnumValuesBuiltinOptions())[127] { + static const BuiltinOptions values[] = { + BuiltinOptions_NONE, + BuiltinOptions_Conv2DOptions, + BuiltinOptions_DepthwiseConv2DOptions, + BuiltinOptions_ConcatEmbeddingsOptions, + BuiltinOptions_LSHProjectionOptions, + BuiltinOptions_Pool2DOptions, + BuiltinOptions_SVDFOptions, + BuiltinOptions_RNNOptions, + BuiltinOptions_FullyConnectedOptions, + BuiltinOptions_SoftmaxOptions, + BuiltinOptions_ConcatenationOptions, + BuiltinOptions_AddOptions, + BuiltinOptions_L2NormOptions, + BuiltinOptions_LocalResponseNormalizationOptions, + BuiltinOptions_LSTMOptions, + BuiltinOptions_ResizeBilinearOptions, + BuiltinOptions_CallOptions, + BuiltinOptions_ReshapeOptions, + BuiltinOptions_SkipGramOptions, + BuiltinOptions_SpaceToDepthOptions, + BuiltinOptions_EmbeddingLookupSparseOptions, + BuiltinOptions_MulOptions, + BuiltinOptions_PadOptions, + BuiltinOptions_GatherOptions, + BuiltinOptions_BatchToSpaceNDOptions, + BuiltinOptions_SpaceToBatchNDOptions, + BuiltinOptions_TransposeOptions, + BuiltinOptions_ReducerOptions, + BuiltinOptions_SubOptions, + BuiltinOptions_DivOptions, + BuiltinOptions_SqueezeOptions, + BuiltinOptions_SequenceRNNOptions, + BuiltinOptions_StridedSliceOptions, + BuiltinOptions_ExpOptions, + BuiltinOptions_TopKV2Options, + BuiltinOptions_SplitOptions, + BuiltinOptions_LogSoftmaxOptions, + BuiltinOptions_CastOptions, + BuiltinOptions_DequantizeOptions, + BuiltinOptions_MaximumMinimumOptions, + BuiltinOptions_ArgMaxOptions, + BuiltinOptions_LessOptions, + BuiltinOptions_NegOptions, + BuiltinOptions_PadV2Options, + BuiltinOptions_GreaterOptions, + BuiltinOptions_GreaterEqualOptions, + BuiltinOptions_LessEqualOptions, + BuiltinOptions_SelectOptions, + BuiltinOptions_SliceOptions, + BuiltinOptions_TransposeConvOptions, + BuiltinOptions_SparseToDenseOptions, + BuiltinOptions_TileOptions, + BuiltinOptions_ExpandDimsOptions, + BuiltinOptions_EqualOptions, + BuiltinOptions_NotEqualOptions, + BuiltinOptions_ShapeOptions, + BuiltinOptions_PowOptions, + BuiltinOptions_ArgMinOptions, + BuiltinOptions_FakeQuantOptions, + BuiltinOptions_PackOptions, + BuiltinOptions_LogicalOrOptions, + BuiltinOptions_OneHotOptions, + BuiltinOptions_LogicalAndOptions, + BuiltinOptions_LogicalNotOptions, + BuiltinOptions_UnpackOptions, + BuiltinOptions_FloorDivOptions, + BuiltinOptions_SquareOptions, + BuiltinOptions_ZerosLikeOptions, + BuiltinOptions_FillOptions, + BuiltinOptions_BidirectionalSequenceLSTMOptions, + BuiltinOptions_BidirectionalSequenceRNNOptions, + BuiltinOptions_UnidirectionalSequenceLSTMOptions, + BuiltinOptions_FloorModOptions, + BuiltinOptions_RangeOptions, + BuiltinOptions_ResizeNearestNeighborOptions, + BuiltinOptions_LeakyReluOptions, + BuiltinOptions_SquaredDifferenceOptions, + BuiltinOptions_MirrorPadOptions, + BuiltinOptions_AbsOptions, + BuiltinOptions_SplitVOptions, + BuiltinOptions_UniqueOptions, + BuiltinOptions_ReverseV2Options, + BuiltinOptions_AddNOptions, + BuiltinOptions_GatherNdOptions, + BuiltinOptions_CosOptions, + BuiltinOptions_WhereOptions, + BuiltinOptions_RankOptions, + BuiltinOptions_ReverseSequenceOptions, + BuiltinOptions_MatrixDiagOptions, + BuiltinOptions_QuantizeOptions, + BuiltinOptions_MatrixSetDiagOptions, + BuiltinOptions_HardSwishOptions, + BuiltinOptions_IfOptions, + BuiltinOptions_WhileOptions, + BuiltinOptions_DepthToSpaceOptions, + BuiltinOptions_NonMaxSuppressionV4Options, + BuiltinOptions_NonMaxSuppressionV5Options, + BuiltinOptions_ScatterNdOptions, + BuiltinOptions_SelectV2Options, + BuiltinOptions_DensifyOptions, + BuiltinOptions_SegmentSumOptions, + BuiltinOptions_BatchMatMulOptions, + BuiltinOptions_CumsumOptions, + BuiltinOptions_CallOnceOptions, + BuiltinOptions_BroadcastToOptions, + BuiltinOptions_Rfft2dOptions, + BuiltinOptions_Conv3DOptions, + BuiltinOptions_HashtableOptions, + BuiltinOptions_HashtableFindOptions, + BuiltinOptions_HashtableImportOptions, + BuiltinOptions_HashtableSizeOptions, + BuiltinOptions_VarHandleOptions, + BuiltinOptions_ReadVariableOptions, + BuiltinOptions_AssignVariableOptions, + BuiltinOptions_RandomOptions, + BuiltinOptions_BucketizeOptions, + BuiltinOptions_GeluOptions, + BuiltinOptions_DynamicUpdateSliceOptions, + BuiltinOptions_UnsortedSegmentProdOptions, + BuiltinOptions_UnsortedSegmentMaxOptions, + BuiltinOptions_UnsortedSegmentMinOptions, + BuiltinOptions_UnsortedSegmentSumOptions, + BuiltinOptions_ATan2Options, + BuiltinOptions_SignOptions, + BuiltinOptions_BitcastOptions, + BuiltinOptions_BitwiseXorOptions, + BuiltinOptions_RightShiftOptions + }; + return values; +} + +inline const char * const *EnumNamesBuiltinOptions() { + static const char * const names[128] = { + "NONE", + "Conv2DOptions", + "DepthwiseConv2DOptions", + "ConcatEmbeddingsOptions", + "LSHProjectionOptions", + "Pool2DOptions", + "SVDFOptions", + "RNNOptions", + "FullyConnectedOptions", + "SoftmaxOptions", + "ConcatenationOptions", + "AddOptions", + "L2NormOptions", + "LocalResponseNormalizationOptions", + "LSTMOptions", + "ResizeBilinearOptions", + "CallOptions", + "ReshapeOptions", + "SkipGramOptions", + "SpaceToDepthOptions", + "EmbeddingLookupSparseOptions", + "MulOptions", + "PadOptions", + "GatherOptions", + "BatchToSpaceNDOptions", + "SpaceToBatchNDOptions", + "TransposeOptions", + "ReducerOptions", + "SubOptions", + "DivOptions", + "SqueezeOptions", + "SequenceRNNOptions", + "StridedSliceOptions", + "ExpOptions", + "TopKV2Options", + "SplitOptions", + "LogSoftmaxOptions", + "CastOptions", + "DequantizeOptions", + "MaximumMinimumOptions", + "ArgMaxOptions", + "LessOptions", + "NegOptions", + "PadV2Options", + "GreaterOptions", + "GreaterEqualOptions", + "LessEqualOptions", + "SelectOptions", + "SliceOptions", + "TransposeConvOptions", + "SparseToDenseOptions", + "TileOptions", + "ExpandDimsOptions", + "EqualOptions", + "NotEqualOptions", + "ShapeOptions", + "PowOptions", + "ArgMinOptions", + "FakeQuantOptions", + "PackOptions", + "LogicalOrOptions", + "OneHotOptions", + "LogicalAndOptions", + "LogicalNotOptions", + "UnpackOptions", + "FloorDivOptions", + "SquareOptions", + "ZerosLikeOptions", + "FillOptions", + "BidirectionalSequenceLSTMOptions", + "BidirectionalSequenceRNNOptions", + "UnidirectionalSequenceLSTMOptions", + "FloorModOptions", + "RangeOptions", + "ResizeNearestNeighborOptions", + "LeakyReluOptions", + "SquaredDifferenceOptions", + "MirrorPadOptions", + "AbsOptions", + "SplitVOptions", + "UniqueOptions", + "ReverseV2Options", + "AddNOptions", + "GatherNdOptions", + "CosOptions", + "WhereOptions", + "RankOptions", + "ReverseSequenceOptions", + "MatrixDiagOptions", + "QuantizeOptions", + "MatrixSetDiagOptions", + "HardSwishOptions", + "IfOptions", + "WhileOptions", + "DepthToSpaceOptions", + "NonMaxSuppressionV4Options", + "NonMaxSuppressionV5Options", + "ScatterNdOptions", + "SelectV2Options", + "DensifyOptions", + "SegmentSumOptions", + "BatchMatMulOptions", + "CumsumOptions", + "CallOnceOptions", + "BroadcastToOptions", + "Rfft2dOptions", + "Conv3DOptions", + "HashtableOptions", + "HashtableFindOptions", + "HashtableImportOptions", + "HashtableSizeOptions", + "VarHandleOptions", + "ReadVariableOptions", + "AssignVariableOptions", + "RandomOptions", + "BucketizeOptions", + "GeluOptions", + "DynamicUpdateSliceOptions", + "UnsortedSegmentProdOptions", + "UnsortedSegmentMaxOptions", + "UnsortedSegmentMinOptions", + "UnsortedSegmentSumOptions", + "ATan2Options", + "SignOptions", + "BitcastOptions", + "BitwiseXorOptions", + "RightShiftOptions", + nullptr + }; + return names; +} + +inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { + if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_RightShiftOptions)) return ""; + const size_t index = static_cast(e); + return EnumNamesBuiltinOptions()[index]; +} + +template struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NONE; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SignOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BitcastOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BitwiseXorOptions; +}; + +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RightShiftOptions; +}; + +template struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NONE; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_SignOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BitcastOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_BitwiseXorOptions; +}; + +template<> struct BuiltinOptionsUnionTraits { + static const BuiltinOptions enum_value = BuiltinOptions_RightShiftOptions; +}; + +struct BuiltinOptionsUnion { + BuiltinOptions type; + void *value; + + BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {} + BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT : + type(BuiltinOptions_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + BuiltinOptionsUnion(const BuiltinOptionsUnion &); + BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) + { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~BuiltinOptionsUnion() { Reset(); } + + void Reset(); + + template + void Set(T&& val) { + typedef typename std::remove_reference::type RT; + Reset(); + type = BuiltinOptionsUnionTraits::enum_value; + if (type != BuiltinOptions_NONE) { + value = new RT(std::forward(val)); + } + } + + static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite_micro::Conv2DOptionsT *AsConv2DOptions() { + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Conv2DOptionsT *AsConv2DOptions() const { + return type == BuiltinOptions_Conv2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() { + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const { + return type == BuiltinOptions_DepthwiseConv2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() { + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const { + return type == BuiltinOptions_ConcatEmbeddingsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LSHProjectionOptionsT *AsLSHProjectionOptions() { + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LSHProjectionOptionsT *AsLSHProjectionOptions() const { + return type == BuiltinOptions_LSHProjectionOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::Pool2DOptionsT *AsPool2DOptions() { + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Pool2DOptionsT *AsPool2DOptions() const { + return type == BuiltinOptions_Pool2DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SVDFOptionsT *AsSVDFOptions() { + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SVDFOptionsT *AsSVDFOptions() const { + return type == BuiltinOptions_SVDFOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::RNNOptionsT *AsRNNOptions() { + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::RNNOptionsT *AsRNNOptions() const { + return type == BuiltinOptions_RNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::FullyConnectedOptionsT *AsFullyConnectedOptions() { + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::FullyConnectedOptionsT *AsFullyConnectedOptions() const { + return type == BuiltinOptions_FullyConnectedOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SoftmaxOptionsT *AsSoftmaxOptions() { + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SoftmaxOptionsT *AsSoftmaxOptions() const { + return type == BuiltinOptions_SoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ConcatenationOptionsT *AsConcatenationOptions() { + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ConcatenationOptionsT *AsConcatenationOptions() const { + return type == BuiltinOptions_ConcatenationOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::AddOptionsT *AsAddOptions() { + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::AddOptionsT *AsAddOptions() const { + return type == BuiltinOptions_AddOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::L2NormOptionsT *AsL2NormOptions() { + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::L2NormOptionsT *AsL2NormOptions() const { + return type == BuiltinOptions_L2NormOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() { + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const { + return type == BuiltinOptions_LocalResponseNormalizationOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LSTMOptionsT *AsLSTMOptions() { + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LSTMOptionsT *AsLSTMOptions() const { + return type == BuiltinOptions_LSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ResizeBilinearOptionsT *AsResizeBilinearOptions() { + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ResizeBilinearOptionsT *AsResizeBilinearOptions() const { + return type == BuiltinOptions_ResizeBilinearOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::CallOptionsT *AsCallOptions() { + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CallOptionsT *AsCallOptions() const { + return type == BuiltinOptions_CallOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReshapeOptionsT *AsReshapeOptions() { + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReshapeOptionsT *AsReshapeOptions() const { + return type == BuiltinOptions_ReshapeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SkipGramOptionsT *AsSkipGramOptions() { + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SkipGramOptionsT *AsSkipGramOptions() const { + return type == BuiltinOptions_SkipGramOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SpaceToDepthOptionsT *AsSpaceToDepthOptions() { + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SpaceToDepthOptionsT *AsSpaceToDepthOptions() const { + return type == BuiltinOptions_SpaceToDepthOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() { + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const { + return type == BuiltinOptions_EmbeddingLookupSparseOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::MulOptionsT *AsMulOptions() { + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::MulOptionsT *AsMulOptions() const { + return type == BuiltinOptions_MulOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::PadOptionsT *AsPadOptions() { + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::PadOptionsT *AsPadOptions() const { + return type == BuiltinOptions_PadOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::GatherOptionsT *AsGatherOptions() { + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::GatherOptionsT *AsGatherOptions() const { + return type == BuiltinOptions_GatherOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() { + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const { + return type == BuiltinOptions_BatchToSpaceNDOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() { + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const { + return type == BuiltinOptions_SpaceToBatchNDOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::TransposeOptionsT *AsTransposeOptions() { + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::TransposeOptionsT *AsTransposeOptions() const { + return type == BuiltinOptions_TransposeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReducerOptionsT *AsReducerOptions() { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReducerOptionsT *AsReducerOptions() const { + return type == BuiltinOptions_ReducerOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SubOptionsT *AsSubOptions() { + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SubOptionsT *AsSubOptions() const { + return type == BuiltinOptions_SubOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DivOptionsT *AsDivOptions() { + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DivOptionsT *AsDivOptions() const { + return type == BuiltinOptions_DivOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SqueezeOptionsT *AsSqueezeOptions() { + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SqueezeOptionsT *AsSqueezeOptions() const { + return type == BuiltinOptions_SqueezeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SequenceRNNOptionsT *AsSequenceRNNOptions() { + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SequenceRNNOptionsT *AsSequenceRNNOptions() const { + return type == BuiltinOptions_SequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StridedSliceOptionsT *AsStridedSliceOptions() { + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StridedSliceOptionsT *AsStridedSliceOptions() const { + return type == BuiltinOptions_StridedSliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ExpOptionsT *AsExpOptions() { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ExpOptionsT *AsExpOptions() const { + return type == BuiltinOptions_ExpOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::TopKV2OptionsT *AsTopKV2Options() { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::TopKV2OptionsT *AsTopKV2Options() const { + return type == BuiltinOptions_TopKV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SplitOptionsT *AsSplitOptions() { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SplitOptionsT *AsSplitOptions() const { + return type == BuiltinOptions_SplitOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LogSoftmaxOptionsT *AsLogSoftmaxOptions() { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LogSoftmaxOptionsT *AsLogSoftmaxOptions() const { + return type == BuiltinOptions_LogSoftmaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::CastOptionsT *AsCastOptions() { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CastOptionsT *AsCastOptions() const { + return type == BuiltinOptions_CastOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DequantizeOptionsT *AsDequantizeOptions() { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DequantizeOptionsT *AsDequantizeOptions() const { + return type == BuiltinOptions_DequantizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::MaximumMinimumOptionsT *AsMaximumMinimumOptions() { + return type == BuiltinOptions_MaximumMinimumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::MaximumMinimumOptionsT *AsMaximumMinimumOptions() const { + return type == BuiltinOptions_MaximumMinimumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ArgMaxOptionsT *AsArgMaxOptions() { + return type == BuiltinOptions_ArgMaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ArgMaxOptionsT *AsArgMaxOptions() const { + return type == BuiltinOptions_ArgMaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LessOptionsT *AsLessOptions() { + return type == BuiltinOptions_LessOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LessOptionsT *AsLessOptions() const { + return type == BuiltinOptions_LessOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::NegOptionsT *AsNegOptions() { + return type == BuiltinOptions_NegOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::NegOptionsT *AsNegOptions() const { + return type == BuiltinOptions_NegOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::PadV2OptionsT *AsPadV2Options() { + return type == BuiltinOptions_PadV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::PadV2OptionsT *AsPadV2Options() const { + return type == BuiltinOptions_PadV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::GreaterOptionsT *AsGreaterOptions() { + return type == BuiltinOptions_GreaterOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::GreaterOptionsT *AsGreaterOptions() const { + return type == BuiltinOptions_GreaterOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::GreaterEqualOptionsT *AsGreaterEqualOptions() { + return type == BuiltinOptions_GreaterEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::GreaterEqualOptionsT *AsGreaterEqualOptions() const { + return type == BuiltinOptions_GreaterEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LessEqualOptionsT *AsLessEqualOptions() { + return type == BuiltinOptions_LessEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LessEqualOptionsT *AsLessEqualOptions() const { + return type == BuiltinOptions_LessEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SelectOptionsT *AsSelectOptions() { + return type == BuiltinOptions_SelectOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SelectOptionsT *AsSelectOptions() const { + return type == BuiltinOptions_SelectOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SliceOptionsT *AsSliceOptions() { + return type == BuiltinOptions_SliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SliceOptionsT *AsSliceOptions() const { + return type == BuiltinOptions_SliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::TransposeConvOptionsT *AsTransposeConvOptions() { + return type == BuiltinOptions_TransposeConvOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::TransposeConvOptionsT *AsTransposeConvOptions() const { + return type == BuiltinOptions_TransposeConvOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SparseToDenseOptionsT *AsSparseToDenseOptions() { + return type == BuiltinOptions_SparseToDenseOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SparseToDenseOptionsT *AsSparseToDenseOptions() const { + return type == BuiltinOptions_SparseToDenseOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::TileOptionsT *AsTileOptions() { + return type == BuiltinOptions_TileOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::TileOptionsT *AsTileOptions() const { + return type == BuiltinOptions_TileOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ExpandDimsOptionsT *AsExpandDimsOptions() { + return type == BuiltinOptions_ExpandDimsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ExpandDimsOptionsT *AsExpandDimsOptions() const { + return type == BuiltinOptions_ExpandDimsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::EqualOptionsT *AsEqualOptions() { + return type == BuiltinOptions_EqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::EqualOptionsT *AsEqualOptions() const { + return type == BuiltinOptions_EqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::NotEqualOptionsT *AsNotEqualOptions() { + return type == BuiltinOptions_NotEqualOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::NotEqualOptionsT *AsNotEqualOptions() const { + return type == BuiltinOptions_NotEqualOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ShapeOptionsT *AsShapeOptions() { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ShapeOptionsT *AsShapeOptions() const { + return type == BuiltinOptions_ShapeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::PowOptionsT *AsPowOptions() { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::PowOptionsT *AsPowOptions() const { + return type == BuiltinOptions_PowOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ArgMinOptionsT *AsArgMinOptions() { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ArgMinOptionsT *AsArgMinOptions() const { + return type == BuiltinOptions_ArgMinOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::FakeQuantOptionsT *AsFakeQuantOptions() { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::FakeQuantOptionsT *AsFakeQuantOptions() const { + return type == BuiltinOptions_FakeQuantOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::PackOptionsT *AsPackOptions() { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::PackOptionsT *AsPackOptions() const { + return type == BuiltinOptions_PackOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LogicalOrOptionsT *AsLogicalOrOptions() { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LogicalOrOptionsT *AsLogicalOrOptions() const { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::OneHotOptionsT *AsOneHotOptions() { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::OneHotOptionsT *AsOneHotOptions() const { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LogicalAndOptionsT *AsLogicalAndOptions() { + return type == BuiltinOptions_LogicalAndOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LogicalAndOptionsT *AsLogicalAndOptions() const { + return type == BuiltinOptions_LogicalAndOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LogicalNotOptionsT *AsLogicalNotOptions() { + return type == BuiltinOptions_LogicalNotOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LogicalNotOptionsT *AsLogicalNotOptions() const { + return type == BuiltinOptions_LogicalNotOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnpackOptionsT *AsUnpackOptions() { + return type == BuiltinOptions_UnpackOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnpackOptionsT *AsUnpackOptions() const { + return type == BuiltinOptions_UnpackOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::FloorDivOptionsT *AsFloorDivOptions() { + return type == BuiltinOptions_FloorDivOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::FloorDivOptionsT *AsFloorDivOptions() const { + return type == BuiltinOptions_FloorDivOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SquareOptionsT *AsSquareOptions() { + return type == BuiltinOptions_SquareOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SquareOptionsT *AsSquareOptions() const { + return type == BuiltinOptions_SquareOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ZerosLikeOptionsT *AsZerosLikeOptions() { + return type == BuiltinOptions_ZerosLikeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ZerosLikeOptionsT *AsZerosLikeOptions() const { + return type == BuiltinOptions_ZerosLikeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::FillOptionsT *AsFillOptions() { + return type == BuiltinOptions_FillOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::FillOptionsT *AsFillOptions() const { + return type == BuiltinOptions_FillOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() { + return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() const { + return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() { + return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() const { + return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() { + return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() const { + return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::FloorModOptionsT *AsFloorModOptions() { + return type == BuiltinOptions_FloorModOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::FloorModOptionsT *AsFloorModOptions() const { + return type == BuiltinOptions_FloorModOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::RangeOptionsT *AsRangeOptions() { + return type == BuiltinOptions_RangeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::RangeOptionsT *AsRangeOptions() const { + return type == BuiltinOptions_RangeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() { + return type == BuiltinOptions_ResizeNearestNeighborOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() const { + return type == BuiltinOptions_ResizeNearestNeighborOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::LeakyReluOptionsT *AsLeakyReluOptions() { + return type == BuiltinOptions_LeakyReluOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::LeakyReluOptionsT *AsLeakyReluOptions() const { + return type == BuiltinOptions_LeakyReluOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() { + return type == BuiltinOptions_SquaredDifferenceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() const { + return type == BuiltinOptions_SquaredDifferenceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::MirrorPadOptionsT *AsMirrorPadOptions() { + return type == BuiltinOptions_MirrorPadOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::MirrorPadOptionsT *AsMirrorPadOptions() const { + return type == BuiltinOptions_MirrorPadOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::AbsOptionsT *AsAbsOptions() { + return type == BuiltinOptions_AbsOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::AbsOptionsT *AsAbsOptions() const { + return type == BuiltinOptions_AbsOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SplitVOptionsT *AsSplitVOptions() { + return type == BuiltinOptions_SplitVOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SplitVOptionsT *AsSplitVOptions() const { + return type == BuiltinOptions_SplitVOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UniqueOptionsT *AsUniqueOptions() { + return type == BuiltinOptions_UniqueOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UniqueOptionsT *AsUniqueOptions() const { + return type == BuiltinOptions_UniqueOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReverseV2OptionsT *AsReverseV2Options() { + return type == BuiltinOptions_ReverseV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReverseV2OptionsT *AsReverseV2Options() const { + return type == BuiltinOptions_ReverseV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::AddNOptionsT *AsAddNOptions() { + return type == BuiltinOptions_AddNOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::AddNOptionsT *AsAddNOptions() const { + return type == BuiltinOptions_AddNOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::GatherNdOptionsT *AsGatherNdOptions() { + return type == BuiltinOptions_GatherNdOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::GatherNdOptionsT *AsGatherNdOptions() const { + return type == BuiltinOptions_GatherNdOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::CosOptionsT *AsCosOptions() { + return type == BuiltinOptions_CosOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CosOptionsT *AsCosOptions() const { + return type == BuiltinOptions_CosOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::WhereOptionsT *AsWhereOptions() { + return type == BuiltinOptions_WhereOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::WhereOptionsT *AsWhereOptions() const { + return type == BuiltinOptions_WhereOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::RankOptionsT *AsRankOptions() { + return type == BuiltinOptions_RankOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::RankOptionsT *AsRankOptions() const { + return type == BuiltinOptions_RankOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReverseSequenceOptionsT *AsReverseSequenceOptions() { + return type == BuiltinOptions_ReverseSequenceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReverseSequenceOptionsT *AsReverseSequenceOptions() const { + return type == BuiltinOptions_ReverseSequenceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::MatrixDiagOptionsT *AsMatrixDiagOptions() { + return type == BuiltinOptions_MatrixDiagOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::MatrixDiagOptionsT *AsMatrixDiagOptions() const { + return type == BuiltinOptions_MatrixDiagOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::QuantizeOptionsT *AsQuantizeOptions() { + return type == BuiltinOptions_QuantizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::QuantizeOptionsT *AsQuantizeOptions() const { + return type == BuiltinOptions_QuantizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() { + return type == BuiltinOptions_MatrixSetDiagOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() const { + return type == BuiltinOptions_MatrixSetDiagOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::HardSwishOptionsT *AsHardSwishOptions() { + return type == BuiltinOptions_HardSwishOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::HardSwishOptionsT *AsHardSwishOptions() const { + return type == BuiltinOptions_HardSwishOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::IfOptionsT *AsIfOptions() { + return type == BuiltinOptions_IfOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::IfOptionsT *AsIfOptions() const { + return type == BuiltinOptions_IfOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::WhileOptionsT *AsWhileOptions() { + return type == BuiltinOptions_WhileOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::WhileOptionsT *AsWhileOptions() const { + return type == BuiltinOptions_WhileOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DepthToSpaceOptionsT *AsDepthToSpaceOptions() { + return type == BuiltinOptions_DepthToSpaceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DepthToSpaceOptionsT *AsDepthToSpaceOptions() const { + return type == BuiltinOptions_DepthToSpaceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() { + return type == BuiltinOptions_NonMaxSuppressionV4Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() const { + return type == BuiltinOptions_NonMaxSuppressionV4Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() { + return type == BuiltinOptions_NonMaxSuppressionV5Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() const { + return type == BuiltinOptions_NonMaxSuppressionV5Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ScatterNdOptionsT *AsScatterNdOptions() { + return type == BuiltinOptions_ScatterNdOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ScatterNdOptionsT *AsScatterNdOptions() const { + return type == BuiltinOptions_ScatterNdOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SelectV2OptionsT *AsSelectV2Options() { + return type == BuiltinOptions_SelectV2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SelectV2OptionsT *AsSelectV2Options() const { + return type == BuiltinOptions_SelectV2Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DensifyOptionsT *AsDensifyOptions() { + return type == BuiltinOptions_DensifyOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DensifyOptionsT *AsDensifyOptions() const { + return type == BuiltinOptions_DensifyOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SegmentSumOptionsT *AsSegmentSumOptions() { + return type == BuiltinOptions_SegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SegmentSumOptionsT *AsSegmentSumOptions() const { + return type == BuiltinOptions_SegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BatchMatMulOptionsT *AsBatchMatMulOptions() { + return type == BuiltinOptions_BatchMatMulOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BatchMatMulOptionsT *AsBatchMatMulOptions() const { + return type == BuiltinOptions_BatchMatMulOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::CumsumOptionsT *AsCumsumOptions() { + return type == BuiltinOptions_CumsumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CumsumOptionsT *AsCumsumOptions() const { + return type == BuiltinOptions_CumsumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::CallOnceOptionsT *AsCallOnceOptions() { + return type == BuiltinOptions_CallOnceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::CallOnceOptionsT *AsCallOnceOptions() const { + return type == BuiltinOptions_CallOnceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BroadcastToOptionsT *AsBroadcastToOptions() { + return type == BuiltinOptions_BroadcastToOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BroadcastToOptionsT *AsBroadcastToOptions() const { + return type == BuiltinOptions_BroadcastToOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::Rfft2dOptionsT *AsRfft2dOptions() { + return type == BuiltinOptions_Rfft2dOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Rfft2dOptionsT *AsRfft2dOptions() const { + return type == BuiltinOptions_Rfft2dOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::Conv3DOptionsT *AsConv3DOptions() { + return type == BuiltinOptions_Conv3DOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::Conv3DOptionsT *AsConv3DOptions() const { + return type == BuiltinOptions_Conv3DOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::HashtableOptionsT *AsHashtableOptions() { + return type == BuiltinOptions_HashtableOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::HashtableOptionsT *AsHashtableOptions() const { + return type == BuiltinOptions_HashtableOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::HashtableFindOptionsT *AsHashtableFindOptions() { + return type == BuiltinOptions_HashtableFindOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::HashtableFindOptionsT *AsHashtableFindOptions() const { + return type == BuiltinOptions_HashtableFindOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::HashtableImportOptionsT *AsHashtableImportOptions() { + return type == BuiltinOptions_HashtableImportOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::HashtableImportOptionsT *AsHashtableImportOptions() const { + return type == BuiltinOptions_HashtableImportOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::HashtableSizeOptionsT *AsHashtableSizeOptions() { + return type == BuiltinOptions_HashtableSizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::HashtableSizeOptionsT *AsHashtableSizeOptions() const { + return type == BuiltinOptions_HashtableSizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::VarHandleOptionsT *AsVarHandleOptions() { + return type == BuiltinOptions_VarHandleOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::VarHandleOptionsT *AsVarHandleOptions() const { + return type == BuiltinOptions_VarHandleOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReadVariableOptionsT *AsReadVariableOptions() { + return type == BuiltinOptions_ReadVariableOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReadVariableOptionsT *AsReadVariableOptions() const { + return type == BuiltinOptions_ReadVariableOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::AssignVariableOptionsT *AsAssignVariableOptions() { + return type == BuiltinOptions_AssignVariableOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::AssignVariableOptionsT *AsAssignVariableOptions() const { + return type == BuiltinOptions_AssignVariableOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::RandomOptionsT *AsRandomOptions() { + return type == BuiltinOptions_RandomOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::RandomOptionsT *AsRandomOptions() const { + return type == BuiltinOptions_RandomOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BucketizeOptionsT *AsBucketizeOptions() { + return type == BuiltinOptions_BucketizeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BucketizeOptionsT *AsBucketizeOptions() const { + return type == BuiltinOptions_BucketizeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::GeluOptionsT *AsGeluOptions() { + return type == BuiltinOptions_GeluOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::GeluOptionsT *AsGeluOptions() const { + return type == BuiltinOptions_GeluOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() { + return type == BuiltinOptions_DynamicUpdateSliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() const { + return type == BuiltinOptions_DynamicUpdateSliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() { + return type == BuiltinOptions_UnsortedSegmentProdOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() const { + return type == BuiltinOptions_UnsortedSegmentProdOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() { + return type == BuiltinOptions_UnsortedSegmentMaxOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() const { + return type == BuiltinOptions_UnsortedSegmentMaxOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() { + return type == BuiltinOptions_UnsortedSegmentMinOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() const { + return type == BuiltinOptions_UnsortedSegmentMinOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() { + return type == BuiltinOptions_UnsortedSegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() const { + return type == BuiltinOptions_UnsortedSegmentSumOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ATan2OptionsT *AsATan2Options() { + return type == BuiltinOptions_ATan2Options ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ATan2OptionsT *AsATan2Options() const { + return type == BuiltinOptions_ATan2Options ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::SignOptionsT *AsSignOptions() { + return type == BuiltinOptions_SignOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::SignOptionsT *AsSignOptions() const { + return type == BuiltinOptions_SignOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BitcastOptionsT *AsBitcastOptions() { + return type == BuiltinOptions_BitcastOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BitcastOptionsT *AsBitcastOptions() const { + return type == BuiltinOptions_BitcastOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::BitwiseXorOptionsT *AsBitwiseXorOptions() { + return type == BuiltinOptions_BitwiseXorOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::BitwiseXorOptionsT *AsBitwiseXorOptions() const { + return type == BuiltinOptions_BitwiseXorOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::RightShiftOptionsT *AsRightShiftOptions() { + return type == BuiltinOptions_RightShiftOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::RightShiftOptionsT *AsRightShiftOptions() const { + return type == BuiltinOptions_RightShiftOptions ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); +bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum BuiltinOptions2 : uint8_t { + BuiltinOptions2_NONE = 0, + BuiltinOptions2_StablehloConcatenateOptions = 1, + BuiltinOptions2_StablehloBroadcastInDimOptions = 2, + BuiltinOptions2_StablehloSliceOptions = 3, + BuiltinOptions2_StablehloConvolutionOptions = 4, + BuiltinOptions2_StablehloCustomCallOptions = 5, + BuiltinOptions2_StablehloReduceOptions = 6, + BuiltinOptions2_StablehloScatterOptions = 7, + BuiltinOptions2_StablehloCompareOptions = 8, + BuiltinOptions2_StablehloDynamicSliceOptions = 9, + BuiltinOptions2_StablehloPadOptions = 10, + BuiltinOptions2_StablehloIotaOptions = 11, + BuiltinOptions2_StablehloDotGeneralOptions = 12, + BuiltinOptions2_StablehloReduceWindowOptions = 13, + BuiltinOptions2_StablehloSortOptions = 14, + BuiltinOptions2_StablehloWhileOptions = 15, + BuiltinOptions2_StablehloGatherOptions = 16, + BuiltinOptions2_StablehloTransposeOptions = 17, + BuiltinOptions2_DilateOptions = 18, + BuiltinOptions2_StablehloRngBitGeneratorOptions = 19, + BuiltinOptions2_ReduceWindowOptions = 20, + BuiltinOptions2_MIN = BuiltinOptions2_NONE, + BuiltinOptions2_MAX = BuiltinOptions2_ReduceWindowOptions +}; + +inline const BuiltinOptions2 (&EnumValuesBuiltinOptions2())[21] { + static const BuiltinOptions2 values[] = { + BuiltinOptions2_NONE, + BuiltinOptions2_StablehloConcatenateOptions, + BuiltinOptions2_StablehloBroadcastInDimOptions, + BuiltinOptions2_StablehloSliceOptions, + BuiltinOptions2_StablehloConvolutionOptions, + BuiltinOptions2_StablehloCustomCallOptions, + BuiltinOptions2_StablehloReduceOptions, + BuiltinOptions2_StablehloScatterOptions, + BuiltinOptions2_StablehloCompareOptions, + BuiltinOptions2_StablehloDynamicSliceOptions, + BuiltinOptions2_StablehloPadOptions, + BuiltinOptions2_StablehloIotaOptions, + BuiltinOptions2_StablehloDotGeneralOptions, + BuiltinOptions2_StablehloReduceWindowOptions, + BuiltinOptions2_StablehloSortOptions, + BuiltinOptions2_StablehloWhileOptions, + BuiltinOptions2_StablehloGatherOptions, + BuiltinOptions2_StablehloTransposeOptions, + BuiltinOptions2_DilateOptions, + BuiltinOptions2_StablehloRngBitGeneratorOptions, + BuiltinOptions2_ReduceWindowOptions + }; + return values; +} + +inline const char * const *EnumNamesBuiltinOptions2() { + static const char * const names[22] = { + "NONE", + "StablehloConcatenateOptions", + "StablehloBroadcastInDimOptions", + "StablehloSliceOptions", + "StablehloConvolutionOptions", + "StablehloCustomCallOptions", + "StablehloReduceOptions", + "StablehloScatterOptions", + "StablehloCompareOptions", + "StablehloDynamicSliceOptions", + "StablehloPadOptions", + "StablehloIotaOptions", + "StablehloDotGeneralOptions", + "StablehloReduceWindowOptions", + "StablehloSortOptions", + "StablehloWhileOptions", + "StablehloGatherOptions", + "StablehloTransposeOptions", + "DilateOptions", + "StablehloRngBitGeneratorOptions", + "ReduceWindowOptions", + nullptr + }; + return names; +} + +inline const char *EnumNameBuiltinOptions2(BuiltinOptions2 e) { + if (flatbuffers::IsOutRange(e, BuiltinOptions2_NONE, BuiltinOptions2_ReduceWindowOptions)) return ""; + const size_t index = static_cast(e); + return EnumNamesBuiltinOptions2()[index]; +} + +template struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions; +}; + +template<> struct BuiltinOptions2Traits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions; +}; + +template struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions; +}; + +template<> struct BuiltinOptions2UnionTraits { + static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions; +}; + +struct BuiltinOptions2Union { + BuiltinOptions2 type; + void *value; + + BuiltinOptions2Union() : type(BuiltinOptions2_NONE), value(nullptr) {} + BuiltinOptions2Union(BuiltinOptions2Union&& u) FLATBUFFERS_NOEXCEPT : + type(BuiltinOptions2_NONE), value(nullptr) + { std::swap(type, u.type); std::swap(value, u.value); } + BuiltinOptions2Union(const BuiltinOptions2Union &); + BuiltinOptions2Union &operator=(const BuiltinOptions2Union &u) + { BuiltinOptions2Union t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } + BuiltinOptions2Union &operator=(BuiltinOptions2Union &&u) FLATBUFFERS_NOEXCEPT + { std::swap(type, u.type); std::swap(value, u.value); return *this; } + ~BuiltinOptions2Union() { Reset(); } + + void Reset(); + + template + void Set(T&& val) { + typedef typename std::remove_reference::type RT; + Reset(); + type = BuiltinOptions2UnionTraits::enum_value; + if (type != BuiltinOptions2_NONE) { + value = new RT(std::forward(val)); + } + } + + static void *UnPack(const void *obj, BuiltinOptions2 type, const flatbuffers::resolver_function_t *resolver); + flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; + + tflite_micro::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() { + return type == BuiltinOptions2_StablehloConcatenateOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() const { + return type == BuiltinOptions2_StablehloConcatenateOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() { + return type == BuiltinOptions2_StablehloBroadcastInDimOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() const { + return type == BuiltinOptions2_StablehloBroadcastInDimOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloSliceOptionsT *AsStablehloSliceOptions() { + return type == BuiltinOptions2_StablehloSliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloSliceOptionsT *AsStablehloSliceOptions() const { + return type == BuiltinOptions2_StablehloSliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() { + return type == BuiltinOptions2_StablehloConvolutionOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() const { + return type == BuiltinOptions2_StablehloConvolutionOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() { + return type == BuiltinOptions2_StablehloCustomCallOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() const { + return type == BuiltinOptions2_StablehloCustomCallOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloReduceOptionsT *AsStablehloReduceOptions() { + return type == BuiltinOptions2_StablehloReduceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloReduceOptionsT *AsStablehloReduceOptions() const { + return type == BuiltinOptions2_StablehloReduceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloScatterOptionsT *AsStablehloScatterOptions() { + return type == BuiltinOptions2_StablehloScatterOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloScatterOptionsT *AsStablehloScatterOptions() const { + return type == BuiltinOptions2_StablehloScatterOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloCompareOptionsT *AsStablehloCompareOptions() { + return type == BuiltinOptions2_StablehloCompareOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloCompareOptionsT *AsStablehloCompareOptions() const { + return type == BuiltinOptions2_StablehloCompareOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() { + return type == BuiltinOptions2_StablehloDynamicSliceOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() const { + return type == BuiltinOptions2_StablehloDynamicSliceOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloPadOptionsT *AsStablehloPadOptions() { + return type == BuiltinOptions2_StablehloPadOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloPadOptionsT *AsStablehloPadOptions() const { + return type == BuiltinOptions2_StablehloPadOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloIotaOptionsT *AsStablehloIotaOptions() { + return type == BuiltinOptions2_StablehloIotaOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloIotaOptionsT *AsStablehloIotaOptions() const { + return type == BuiltinOptions2_StablehloIotaOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() { + return type == BuiltinOptions2_StablehloDotGeneralOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() const { + return type == BuiltinOptions2_StablehloDotGeneralOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() { + return type == BuiltinOptions2_StablehloReduceWindowOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() const { + return type == BuiltinOptions2_StablehloReduceWindowOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloSortOptionsT *AsStablehloSortOptions() { + return type == BuiltinOptions2_StablehloSortOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloSortOptionsT *AsStablehloSortOptions() const { + return type == BuiltinOptions2_StablehloSortOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloWhileOptionsT *AsStablehloWhileOptions() { + return type == BuiltinOptions2_StablehloWhileOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloWhileOptionsT *AsStablehloWhileOptions() const { + return type == BuiltinOptions2_StablehloWhileOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloGatherOptionsT *AsStablehloGatherOptions() { + return type == BuiltinOptions2_StablehloGatherOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloGatherOptionsT *AsStablehloGatherOptions() const { + return type == BuiltinOptions2_StablehloGatherOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloTransposeOptionsT *AsStablehloTransposeOptions() { + return type == BuiltinOptions2_StablehloTransposeOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloTransposeOptionsT *AsStablehloTransposeOptions() const { + return type == BuiltinOptions2_StablehloTransposeOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::DilateOptionsT *AsDilateOptions() { + return type == BuiltinOptions2_DilateOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::DilateOptionsT *AsDilateOptions() const { + return type == BuiltinOptions2_DilateOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() { + return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() const { + return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ? + reinterpret_cast(value) : nullptr; + } + tflite_micro::ReduceWindowOptionsT *AsReduceWindowOptions() { + return type == BuiltinOptions2_ReduceWindowOptions ? + reinterpret_cast(value) : nullptr; + } + const tflite_micro::ReduceWindowOptionsT *AsReduceWindowOptions() const { + return type == BuiltinOptions2_ReduceWindowOptions ? + reinterpret_cast(value) : nullptr; + } +}; + +bool VerifyBuiltinOptions2(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type); +bool VerifyBuiltinOptions2Vector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); + +enum StablehloPrecisionConfig : uint32_t { + StablehloPrecisionConfig_DEFAULT = 0, + StablehloPrecisionConfig_HIGH = 1, + StablehloPrecisionConfig_HIGHEST = 2, + StablehloPrecisionConfig_MIN = StablehloPrecisionConfig_DEFAULT, + StablehloPrecisionConfig_MAX = StablehloPrecisionConfig_HIGHEST +}; + +inline const StablehloPrecisionConfig (&EnumValuesStablehloPrecisionConfig())[3] { + static const StablehloPrecisionConfig values[] = { + StablehloPrecisionConfig_DEFAULT, + StablehloPrecisionConfig_HIGH, + StablehloPrecisionConfig_HIGHEST + }; + return values; +} + +inline const char * const *EnumNamesStablehloPrecisionConfig() { + static const char * const names[4] = { + "DEFAULT", + "HIGH", + "HIGHEST", + nullptr + }; + return names; +} + +inline const char *EnumNameStablehloPrecisionConfig(StablehloPrecisionConfig e) { + if (flatbuffers::IsOutRange(e, StablehloPrecisionConfig_DEFAULT, StablehloPrecisionConfig_HIGHEST)) return ""; + const size_t index = static_cast(e); + return EnumNamesStablehloPrecisionConfig()[index]; +} + +enum StablehloComparisonDirection : uint32_t { + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ = 0, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE = 1, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE = 2, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT = 3, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE = 4, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT = 5, + StablehloComparisonDirection_MIN = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, + StablehloComparisonDirection_MAX = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT +}; + +inline const StablehloComparisonDirection (&EnumValuesStablehloComparisonDirection())[6] { + static const StablehloComparisonDirection values[] = { + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE, + StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT + }; + return values; +} + +inline const char * const *EnumNamesStablehloComparisonDirection() { + static const char * const names[7] = { + "STABLEHLO_COMPARISON_DIRECTION_EQ", + "STABLEHLO_COMPARISON_DIRECTION_NE", + "STABLEHLO_COMPARISON_DIRECTION_GE", + "STABLEHLO_COMPARISON_DIRECTION_GT", + "STABLEHLO_COMPARISON_DIRECTION_LE", + "STABLEHLO_COMPARISON_DIRECTION_LT", + nullptr + }; + return names; +} + +inline const char *EnumNameStablehloComparisonDirection(StablehloComparisonDirection e) { + if (flatbuffers::IsOutRange(e, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT)) return ""; + const size_t index = static_cast(e); + return EnumNamesStablehloComparisonDirection()[index]; +} + +enum StablehloComparisonType : uint32_t { + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE = 0, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT = 1, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER = 2, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED = 3, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED = 4, + StablehloComparisonType_MIN = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, + StablehloComparisonType_MAX = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED +}; + +inline const StablehloComparisonType (&EnumValuesStablehloComparisonType())[5] { + static const StablehloComparisonType values[] = { + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED, + StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED + }; + return values; +} + +inline const char * const *EnumNamesStablehloComparisonType() { + static const char * const names[6] = { + "STABLEHLO_COMPARISON_TYPE_NOTYPE", + "STABLEHLO_COMPARISON_TYPE_FLOAT", + "STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER", + "STABLEHLO_COMPARISON_TYPE_SIGNED", + "STABLEHLO_COMPARISON_TYPE_UNSIGNED", + nullptr + }; + return names; +} + +inline const char *EnumNameStablehloComparisonType(StablehloComparisonType e) { + if (flatbuffers::IsOutRange(e, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED)) return ""; + const size_t index = static_cast(e); + return EnumNamesStablehloComparisonType()[index]; +} + +enum RngAlgorithm : int8_t { + RngAlgorithm_DEFAULT = 0, + RngAlgorithm_PHILOX = 1, + RngAlgorithm_THREEFRY = 2, + RngAlgorithm_MIN = RngAlgorithm_DEFAULT, + RngAlgorithm_MAX = RngAlgorithm_THREEFRY +}; + +inline const RngAlgorithm (&EnumValuesRngAlgorithm())[3] { + static const RngAlgorithm values[] = { + RngAlgorithm_DEFAULT, + RngAlgorithm_PHILOX, + RngAlgorithm_THREEFRY + }; + return values; +} + +inline const char * const *EnumNamesRngAlgorithm() { + static const char * const names[4] = { + "DEFAULT", + "PHILOX", + "THREEFRY", + nullptr + }; + return names; +} + +inline const char *EnumNameRngAlgorithm(RngAlgorithm e) { + if (flatbuffers::IsOutRange(e, RngAlgorithm_DEFAULT, RngAlgorithm_THREEFRY)) return ""; + const size_t index = static_cast(e); + return EnumNamesRngAlgorithm()[index]; +} + +enum Padding : int8_t { + Padding_SAME = 0, + Padding_VALID = 1, + Padding_MIN = Padding_SAME, + Padding_MAX = Padding_VALID +}; + +inline const Padding (&EnumValuesPadding())[2] { + static const Padding values[] = { + Padding_SAME, + Padding_VALID + }; + return values; +} + +inline const char * const *EnumNamesPadding() { + static const char * const names[3] = { + "SAME", + "VALID", + nullptr + }; + return names; +} + +inline const char *EnumNamePadding(Padding e) { + if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return ""; + const size_t index = static_cast(e); + return EnumNamesPadding()[index]; +} + +enum ActivationFunctionType : int8_t { + ActivationFunctionType_NONE = 0, + ActivationFunctionType_RELU = 1, + ActivationFunctionType_RELU_N1_TO_1 = 2, + ActivationFunctionType_RELU6 = 3, + ActivationFunctionType_TANH = 4, + ActivationFunctionType_SIGN_BIT = 5, + ActivationFunctionType_MIN = ActivationFunctionType_NONE, + ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT +}; + +inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] { + static const ActivationFunctionType values[] = { + ActivationFunctionType_NONE, + ActivationFunctionType_RELU, + ActivationFunctionType_RELU_N1_TO_1, + ActivationFunctionType_RELU6, + ActivationFunctionType_TANH, + ActivationFunctionType_SIGN_BIT + }; + return values; +} + +inline const char * const *EnumNamesActivationFunctionType() { + static const char * const names[7] = { + "NONE", + "RELU", + "RELU_N1_TO_1", + "RELU6", + "TANH", + "SIGN_BIT", + nullptr + }; + return names; +} + +inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) { + if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return ""; + const size_t index = static_cast(e); + return EnumNamesActivationFunctionType()[index]; +} + +enum LSHProjectionType : int8_t { + LSHProjectionType_UNKNOWN = 0, + LSHProjectionType_SPARSE = 1, + LSHProjectionType_DENSE = 2, + LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, + LSHProjectionType_MAX = LSHProjectionType_DENSE +}; + +inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] { + static const LSHProjectionType values[] = { + LSHProjectionType_UNKNOWN, + LSHProjectionType_SPARSE, + LSHProjectionType_DENSE + }; + return values; +} + +inline const char * const *EnumNamesLSHProjectionType() { + static const char * const names[4] = { + "UNKNOWN", + "SPARSE", + "DENSE", + nullptr + }; + return names; +} + +inline const char *EnumNameLSHProjectionType(LSHProjectionType e) { + if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return ""; + const size_t index = static_cast(e); + return EnumNamesLSHProjectionType()[index]; +} + +enum FullyConnectedOptionsWeightsFormat : int8_t { + FullyConnectedOptionsWeightsFormat_DEFAULT = 0, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, + FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 +}; + +inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] { + static const FullyConnectedOptionsWeightsFormat values[] = { + FullyConnectedOptionsWeightsFormat_DEFAULT, + FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 + }; + return values; +} + +inline const char * const *EnumNamesFullyConnectedOptionsWeightsFormat() { + static const char * const names[3] = { + "DEFAULT", + "SHUFFLED4x16INT8", + nullptr + }; + return names; +} + +inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) { + if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return ""; + const size_t index = static_cast(e); + return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; +} + +enum LSTMKernelType : int8_t { + LSTMKernelType_FULL = 0, + LSTMKernelType_BASIC = 1, + LSTMKernelType_MIN = LSTMKernelType_FULL, + LSTMKernelType_MAX = LSTMKernelType_BASIC +}; + +inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] { + static const LSTMKernelType values[] = { + LSTMKernelType_FULL, + LSTMKernelType_BASIC + }; + return values; +} + +inline const char * const *EnumNamesLSTMKernelType() { + static const char * const names[3] = { + "FULL", + "BASIC", + nullptr + }; + return names; +} + +inline const char *EnumNameLSTMKernelType(LSTMKernelType e) { + if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return ""; + const size_t index = static_cast(e); + return EnumNamesLSTMKernelType()[index]; +} + +enum CombinerType : int8_t { + CombinerType_SUM = 0, + CombinerType_MEAN = 1, + CombinerType_SQRTN = 2, + CombinerType_MIN = CombinerType_SUM, + CombinerType_MAX = CombinerType_SQRTN +}; + +inline const CombinerType (&EnumValuesCombinerType())[3] { + static const CombinerType values[] = { + CombinerType_SUM, + CombinerType_MEAN, + CombinerType_SQRTN + }; + return values; +} + +inline const char * const *EnumNamesCombinerType() { + static const char * const names[4] = { + "SUM", + "MEAN", + "SQRTN", + nullptr + }; + return names; +} + +inline const char *EnumNameCombinerType(CombinerType e) { + if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return ""; + const size_t index = static_cast(e); + return EnumNamesCombinerType()[index]; +} + +enum MirrorPadMode : int8_t { + MirrorPadMode_REFLECT = 0, + MirrorPadMode_SYMMETRIC = 1, + MirrorPadMode_MIN = MirrorPadMode_REFLECT, + MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC +}; + +inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] { + static const MirrorPadMode values[] = { + MirrorPadMode_REFLECT, + MirrorPadMode_SYMMETRIC + }; + return values; +} + +inline const char * const *EnumNamesMirrorPadMode() { + static const char * const names[3] = { + "REFLECT", + "SYMMETRIC", + nullptr + }; + return names; +} + +inline const char *EnumNameMirrorPadMode(MirrorPadMode e) { + if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return ""; + const size_t index = static_cast(e); + return EnumNamesMirrorPadMode()[index]; +} + +enum ReduceWindowFunction : int32_t { + ReduceWindowFunction_UNSUPPORTED = 0, + ReduceWindowFunction_ADD = 1, + ReduceWindowFunction_MUL = 2, + ReduceWindowFunction_MINIMUM = 3, + ReduceWindowFunction_MAXIMUM = 4, + ReduceWindowFunction_ALL = 5, + ReduceWindowFunction_ANY = 6, + ReduceWindowFunction_MIN = ReduceWindowFunction_UNSUPPORTED, + ReduceWindowFunction_MAX = ReduceWindowFunction_ANY +}; + +inline const ReduceWindowFunction (&EnumValuesReduceWindowFunction())[7] { + static const ReduceWindowFunction values[] = { + ReduceWindowFunction_UNSUPPORTED, + ReduceWindowFunction_ADD, + ReduceWindowFunction_MUL, + ReduceWindowFunction_MINIMUM, + ReduceWindowFunction_MAXIMUM, + ReduceWindowFunction_ALL, + ReduceWindowFunction_ANY + }; + return values; +} + +inline const char * const *EnumNamesReduceWindowFunction() { + static const char * const names[8] = { + "UNSUPPORTED", + "ADD", + "MUL", + "MINIMUM", + "MAXIMUM", + "ALL", + "ANY", + nullptr + }; + return names; +} + +inline const char *EnumNameReduceWindowFunction(ReduceWindowFunction e) { + if (flatbuffers::IsOutRange(e, ReduceWindowFunction_UNSUPPORTED, ReduceWindowFunction_ANY)) return ""; + const size_t index = static_cast(e); + return EnumNamesReduceWindowFunction()[index]; +} + +enum CustomOptionsFormat : int8_t { + CustomOptionsFormat_FLEXBUFFERS = 0, + CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, + CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS +}; + +inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] { + static const CustomOptionsFormat values[] = { + CustomOptionsFormat_FLEXBUFFERS + }; + return values; +} + +inline const char * const *EnumNamesCustomOptionsFormat() { + static const char * const names[2] = { + "FLEXBUFFERS", + nullptr + }; + return names; +} + +inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) { + if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return ""; + const size_t index = static_cast(e); + return EnumNamesCustomOptionsFormat()[index]; +} + +struct CustomQuantizationT : public flatbuffers::NativeTable { + typedef CustomQuantization TableType; + std::vector custom{}; +}; + +struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CustomQuantizationT NativeTableType; + typedef CustomQuantizationBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CUSTOM = 4 + }; + const flatbuffers::Vector *custom() const { + return GetPointer *>(VT_CUSTOM); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CUSTOM) && + verifier.VerifyVector(custom()) && + verifier.EndTable(); + } + CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CustomQuantizationBuilder { + typedef CustomQuantization Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_custom(flatbuffers::Offset> custom) { + fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); + } + explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCustomQuantization( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> custom = 0) { + CustomQuantizationBuilder builder_(_fbb); + builder_.add_custom(custom); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateCustomQuantizationDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *custom = nullptr) { + if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); } + auto custom__ = custom ? _fbb.CreateVector(*custom) : 0; + return tflite_micro::CreateCustomQuantization( + _fbb, + custom__); +} + +flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct QuantizationParametersT : public flatbuffers::NativeTable { + typedef QuantizationParameters TableType; + std::vector min{}; + std::vector max{}; + std::vector scale{}; + std::vector zero_point{}; + tflite_micro::QuantizationDetailsUnion details{}; + int32_t quantized_dimension = 0; +}; + +struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantizationParametersT NativeTableType; + typedef QuantizationParametersBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MIN = 4, + VT_MAX = 6, + VT_SCALE = 8, + VT_ZERO_POINT = 10, + VT_DETAILS_TYPE = 12, + VT_DETAILS = 14, + VT_QUANTIZED_DIMENSION = 16 + }; + const flatbuffers::Vector *min() const { + return GetPointer *>(VT_MIN); + } + const flatbuffers::Vector *max() const { + return GetPointer *>(VT_MAX); + } + const flatbuffers::Vector *scale() const { + return GetPointer *>(VT_SCALE); + } + const flatbuffers::Vector *zero_point() const { + return GetPointer *>(VT_ZERO_POINT); + } + tflite_micro::QuantizationDetails details_type() const { + return static_cast(GetField(VT_DETAILS_TYPE, 0)); + } + const void *details() const { + return GetPointer(VT_DETAILS); + } + template const T *details_as() const; + const tflite_micro::CustomQuantization *details_as_CustomQuantization() const { + return details_type() == tflite_micro::QuantizationDetails_CustomQuantization ? static_cast(details()) : nullptr; + } + int32_t quantized_dimension() const { + return GetField(VT_QUANTIZED_DIMENSION, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_MIN) && + verifier.VerifyVector(min()) && + VerifyOffset(verifier, VT_MAX) && + verifier.VerifyVector(max()) && + VerifyOffset(verifier, VT_SCALE) && + verifier.VerifyVector(scale()) && + VerifyOffset(verifier, VT_ZERO_POINT) && + verifier.VerifyVector(zero_point()) && + VerifyField(verifier, VT_DETAILS_TYPE, 1) && + VerifyOffset(verifier, VT_DETAILS) && + VerifyQuantizationDetails(verifier, details(), details_type()) && + VerifyField(verifier, VT_QUANTIZED_DIMENSION, 4) && + verifier.EndTable(); + } + QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite_micro::CustomQuantization *QuantizationParameters::details_as() const { + return details_as_CustomQuantization(); +} + +struct QuantizationParametersBuilder { + typedef QuantizationParameters Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min(flatbuffers::Offset> min) { + fbb_.AddOffset(QuantizationParameters::VT_MIN, min); + } + void add_max(flatbuffers::Offset> max) { + fbb_.AddOffset(QuantizationParameters::VT_MAX, max); + } + void add_scale(flatbuffers::Offset> scale) { + fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); + } + void add_zero_point(flatbuffers::Offset> zero_point) { + fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); + } + void add_details_type(tflite_micro::QuantizationDetails details_type) { + fbb_.AddElement(QuantizationParameters::VT_DETAILS_TYPE, static_cast(details_type), 0); + } + void add_details(flatbuffers::Offset details) { + fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); + } + void add_quantized_dimension(int32_t quantized_dimension) { + fbb_.AddElement(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0); + } + explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantizationParameters( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> min = 0, + flatbuffers::Offset> max = 0, + flatbuffers::Offset> scale = 0, + flatbuffers::Offset> zero_point = 0, + tflite_micro::QuantizationDetails details_type = tflite_micro::QuantizationDetails_NONE, + flatbuffers::Offset details = 0, + int32_t quantized_dimension = 0) { + QuantizationParametersBuilder builder_(_fbb); + builder_.add_quantized_dimension(quantized_dimension); + builder_.add_details(details); + builder_.add_zero_point(zero_point); + builder_.add_scale(scale); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_details_type(details_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateQuantizationParametersDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *min = nullptr, + const std::vector *max = nullptr, + const std::vector *scale = nullptr, + const std::vector *zero_point = nullptr, + tflite_micro::QuantizationDetails details_type = tflite_micro::QuantizationDetails_NONE, + flatbuffers::Offset details = 0, + int32_t quantized_dimension = 0) { + auto min__ = min ? _fbb.CreateVector(*min) : 0; + auto max__ = max ? _fbb.CreateVector(*max) : 0; + auto scale__ = scale ? _fbb.CreateVector(*scale) : 0; + auto zero_point__ = zero_point ? _fbb.CreateVector(*zero_point) : 0; + return tflite_micro::CreateQuantizationParameters( + _fbb, + min__, + max__, + scale__, + zero_point__, + details_type, + details, + quantized_dimension); +} + +flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Int32VectorT : public flatbuffers::NativeTable { + typedef Int32Vector TableType; + std::vector values{}; +}; + +struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Int32VectorT NativeTableType; + typedef Int32VectorBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Int32VectorBuilder { + typedef Int32Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Int32Vector::VT_VALUES, values); + } + explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateInt32Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Int32VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateInt32VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite_micro::CreateInt32Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Uint16VectorT : public flatbuffers::NativeTable { + typedef Uint16Vector TableType; + std::vector values{}; +}; + +struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Uint16VectorT NativeTableType; + typedef Uint16VectorBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Uint16VectorBuilder { + typedef Uint16Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Uint16Vector::VT_VALUES, values); + } + explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUint16Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Uint16VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateUint16VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); } + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite_micro::CreateUint16Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Uint8VectorT : public flatbuffers::NativeTable { + typedef Uint8Vector TableType; + std::vector values{}; +}; + +struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Uint8VectorT NativeTableType; + typedef Uint8VectorBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES = 4 + }; + const flatbuffers::Vector *values() const { + return GetPointer *>(VT_VALUES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_VALUES) && + verifier.VerifyVector(values()) && + verifier.EndTable(); + } + Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Uint8VectorBuilder { + typedef Uint8Vector Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values(flatbuffers::Offset> values) { + fbb_.AddOffset(Uint8Vector::VT_VALUES, values); + } + explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUint8Vector( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> values = 0) { + Uint8VectorBuilder builder_(_fbb); + builder_.add_values(values); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateUint8VectorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *values = nullptr) { + if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); } + auto values__ = values ? _fbb.CreateVector(*values) : 0; + return tflite_micro::CreateUint8Vector( + _fbb, + values__); +} + +flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DimensionMetadataT : public flatbuffers::NativeTable { + typedef DimensionMetadata TableType; + tflite_micro::DimensionType format = tflite_micro::DimensionType_DENSE; + int32_t dense_size = 0; + tflite_micro::SparseIndexVectorUnion array_segments{}; + tflite_micro::SparseIndexVectorUnion array_indices{}; +}; + +struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DimensionMetadataT NativeTableType; + typedef DimensionMetadataBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FORMAT = 4, + VT_DENSE_SIZE = 6, + VT_ARRAY_SEGMENTS_TYPE = 8, + VT_ARRAY_SEGMENTS = 10, + VT_ARRAY_INDICES_TYPE = 12, + VT_ARRAY_INDICES = 14 + }; + tflite_micro::DimensionType format() const { + return static_cast(GetField(VT_FORMAT, 0)); + } + int32_t dense_size() const { + return GetField(VT_DENSE_SIZE, 0); + } + tflite_micro::SparseIndexVector array_segments_type() const { + return static_cast(GetField(VT_ARRAY_SEGMENTS_TYPE, 0)); + } + const void *array_segments() const { + return GetPointer(VT_ARRAY_SEGMENTS); + } + template const T *array_segments_as() const; + const tflite_micro::Int32Vector *array_segments_as_Int32Vector() const { + return array_segments_type() == tflite_micro::SparseIndexVector_Int32Vector ? static_cast(array_segments()) : nullptr; + } + const tflite_micro::Uint16Vector *array_segments_as_Uint16Vector() const { + return array_segments_type() == tflite_micro::SparseIndexVector_Uint16Vector ? static_cast(array_segments()) : nullptr; + } + const tflite_micro::Uint8Vector *array_segments_as_Uint8Vector() const { + return array_segments_type() == tflite_micro::SparseIndexVector_Uint8Vector ? static_cast(array_segments()) : nullptr; + } + tflite_micro::SparseIndexVector array_indices_type() const { + return static_cast(GetField(VT_ARRAY_INDICES_TYPE, 0)); + } + const void *array_indices() const { + return GetPointer(VT_ARRAY_INDICES); + } + template const T *array_indices_as() const; + const tflite_micro::Int32Vector *array_indices_as_Int32Vector() const { + return array_indices_type() == tflite_micro::SparseIndexVector_Int32Vector ? static_cast(array_indices()) : nullptr; + } + const tflite_micro::Uint16Vector *array_indices_as_Uint16Vector() const { + return array_indices_type() == tflite_micro::SparseIndexVector_Uint16Vector ? static_cast(array_indices()) : nullptr; + } + const tflite_micro::Uint8Vector *array_indices_as_Uint8Vector() const { + return array_indices_type() == tflite_micro::SparseIndexVector_Uint8Vector ? static_cast(array_indices()) : nullptr; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FORMAT, 1) && + VerifyField(verifier, VT_DENSE_SIZE, 4) && + VerifyField(verifier, VT_ARRAY_SEGMENTS_TYPE, 1) && + VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && + VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && + VerifyField(verifier, VT_ARRAY_INDICES_TYPE, 1) && + VerifyOffset(verifier, VT_ARRAY_INDICES) && + VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && + verifier.EndTable(); + } + DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite_micro::Int32Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Int32Vector(); +} + +template<> inline const tflite_micro::Uint16Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Uint16Vector(); +} + +template<> inline const tflite_micro::Uint8Vector *DimensionMetadata::array_segments_as() const { + return array_segments_as_Uint8Vector(); +} + +template<> inline const tflite_micro::Int32Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Int32Vector(); +} + +template<> inline const tflite_micro::Uint16Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Uint16Vector(); +} + +template<> inline const tflite_micro::Uint8Vector *DimensionMetadata::array_indices_as() const { + return array_indices_as_Uint8Vector(); +} + +struct DimensionMetadataBuilder { + typedef DimensionMetadata Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_format(tflite_micro::DimensionType format) { + fbb_.AddElement(DimensionMetadata::VT_FORMAT, static_cast(format), 0); + } + void add_dense_size(int32_t dense_size) { + fbb_.AddElement(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); + } + void add_array_segments_type(tflite_micro::SparseIndexVector array_segments_type) { + fbb_.AddElement(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast(array_segments_type), 0); + } + void add_array_segments(flatbuffers::Offset array_segments) { + fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); + } + void add_array_indices_type(tflite_micro::SparseIndexVector array_indices_type) { + fbb_.AddElement(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast(array_indices_type), 0); + } + void add_array_indices(flatbuffers::Offset array_indices) { + fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); + } + explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDimensionMetadata( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::DimensionType format = tflite_micro::DimensionType_DENSE, + int32_t dense_size = 0, + tflite_micro::SparseIndexVector array_segments_type = tflite_micro::SparseIndexVector_NONE, + flatbuffers::Offset array_segments = 0, + tflite_micro::SparseIndexVector array_indices_type = tflite_micro::SparseIndexVector_NONE, + flatbuffers::Offset array_indices = 0) { + DimensionMetadataBuilder builder_(_fbb); + builder_.add_array_indices(array_indices); + builder_.add_array_segments(array_segments); + builder_.add_dense_size(dense_size); + builder_.add_array_indices_type(array_indices_type); + builder_.add_array_segments_type(array_segments_type); + builder_.add_format(format); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparsityParametersT : public flatbuffers::NativeTable { + typedef SparsityParameters TableType; + std::vector traversal_order{}; + std::vector block_map{}; + std::vector> dim_metadata{}; + SparsityParametersT() = default; + SparsityParametersT(const SparsityParametersT &o); + SparsityParametersT(SparsityParametersT&&) FLATBUFFERS_NOEXCEPT = default; + SparsityParametersT &operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT; +}; + +struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparsityParametersT NativeTableType; + typedef SparsityParametersBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TRAVERSAL_ORDER = 4, + VT_BLOCK_MAP = 6, + VT_DIM_METADATA = 8 + }; + const flatbuffers::Vector *traversal_order() const { + return GetPointer *>(VT_TRAVERSAL_ORDER); + } + const flatbuffers::Vector *block_map() const { + return GetPointer *>(VT_BLOCK_MAP); + } + const flatbuffers::Vector> *dim_metadata() const { + return GetPointer> *>(VT_DIM_METADATA); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && + verifier.VerifyVector(traversal_order()) && + VerifyOffset(verifier, VT_BLOCK_MAP) && + verifier.VerifyVector(block_map()) && + VerifyOffset(verifier, VT_DIM_METADATA) && + verifier.VerifyVector(dim_metadata()) && + verifier.VerifyVectorOfTables(dim_metadata()) && + verifier.EndTable(); + } + SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparsityParametersBuilder { + typedef SparsityParameters Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_traversal_order(flatbuffers::Offset> traversal_order) { + fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); + } + void add_block_map(flatbuffers::Offset> block_map) { + fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); + } + void add_dim_metadata(flatbuffers::Offset>> dim_metadata) { + fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); + } + explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparsityParameters( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> traversal_order = 0, + flatbuffers::Offset> block_map = 0, + flatbuffers::Offset>> dim_metadata = 0) { + SparsityParametersBuilder builder_(_fbb); + builder_.add_dim_metadata(dim_metadata); + builder_.add_block_map(block_map); + builder_.add_traversal_order(traversal_order); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSparsityParametersDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *traversal_order = nullptr, + const std::vector *block_map = nullptr, + const std::vector> *dim_metadata = nullptr) { + auto traversal_order__ = traversal_order ? _fbb.CreateVector(*traversal_order) : 0; + auto block_map__ = block_map ? _fbb.CreateVector(*block_map) : 0; + auto dim_metadata__ = dim_metadata ? _fbb.CreateVector>(*dim_metadata) : 0; + return tflite_micro::CreateSparsityParameters( + _fbb, + traversal_order__, + block_map__, + dim_metadata__); +} + +flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct VariantSubTypeT : public flatbuffers::NativeTable { + typedef VariantSubType TableType; + std::vector shape{}; + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32; + bool has_rank = false; +}; + +struct VariantSubType FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef VariantSubTypeT NativeTableType; + typedef VariantSubTypeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SHAPE = 4, + VT_TYPE = 6, + VT_HAS_RANK = 8 + }; + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + tflite_micro::TensorType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + bool has_rank() const { + return GetField(VT_HAS_RANK, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + VerifyField(verifier, VT_TYPE, 1) && + VerifyField(verifier, VT_HAS_RANK, 1) && + verifier.EndTable(); + } + VariantSubTypeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct VariantSubTypeBuilder { + typedef VariantSubType Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(VariantSubType::VT_SHAPE, shape); + } + void add_type(tflite_micro::TensorType type) { + fbb_.AddElement(VariantSubType::VT_TYPE, static_cast(type), 0); + } + void add_has_rank(bool has_rank) { + fbb_.AddElement(VariantSubType::VT_HAS_RANK, static_cast(has_rank), 0); + } + explicit VariantSubTypeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateVariantSubType( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> shape = 0, + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, + bool has_rank = false) { + VariantSubTypeBuilder builder_(_fbb); + builder_.add_shape(shape); + builder_.add_has_rank(has_rank); + builder_.add_type(type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateVariantSubTypeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *shape = nullptr, + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, + bool has_rank = false) { + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + return tflite_micro::CreateVariantSubType( + _fbb, + shape__, + type, + has_rank); +} + +flatbuffers::Offset CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorT : public flatbuffers::NativeTable { + typedef Tensor TableType; + std::vector shape{}; + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32; + uint32_t buffer = 0; + std::string name{}; + std::unique_ptr quantization{}; + bool is_variable = false; + std::unique_ptr sparsity{}; + std::vector shape_signature{}; + bool has_rank = false; + std::vector> variant_tensors{}; + TensorT() = default; + TensorT(const TensorT &o); + TensorT(TensorT&&) FLATBUFFERS_NOEXCEPT = default; + TensorT &operator=(TensorT o) FLATBUFFERS_NOEXCEPT; +}; + +struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorT NativeTableType; + typedef TensorBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SHAPE = 4, + VT_TYPE = 6, + VT_BUFFER = 8, + VT_NAME = 10, + VT_QUANTIZATION = 12, + VT_IS_VARIABLE = 14, + VT_SPARSITY = 16, + VT_SHAPE_SIGNATURE = 18, + VT_HAS_RANK = 20, + VT_VARIANT_TENSORS = 22 + }; + const flatbuffers::Vector *shape() const { + return GetPointer *>(VT_SHAPE); + } + tflite_micro::TensorType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + uint32_t buffer() const { + return GetField(VT_BUFFER, 0); + } + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + const tflite_micro::QuantizationParameters *quantization() const { + return GetPointer(VT_QUANTIZATION); + } + bool is_variable() const { + return GetField(VT_IS_VARIABLE, 0) != 0; + } + const tflite_micro::SparsityParameters *sparsity() const { + return GetPointer(VT_SPARSITY); + } + const flatbuffers::Vector *shape_signature() const { + return GetPointer *>(VT_SHAPE_SIGNATURE); + } + bool has_rank() const { + return GetField(VT_HAS_RANK, 0) != 0; + } + const flatbuffers::Vector> *variant_tensors() const { + return GetPointer> *>(VT_VARIANT_TENSORS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SHAPE) && + verifier.VerifyVector(shape()) && + VerifyField(verifier, VT_TYPE, 1) && + VerifyField(verifier, VT_BUFFER, 4) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyOffset(verifier, VT_QUANTIZATION) && + verifier.VerifyTable(quantization()) && + VerifyField(verifier, VT_IS_VARIABLE, 1) && + VerifyOffset(verifier, VT_SPARSITY) && + verifier.VerifyTable(sparsity()) && + VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && + verifier.VerifyVector(shape_signature()) && + VerifyField(verifier, VT_HAS_RANK, 1) && + VerifyOffset(verifier, VT_VARIANT_TENSORS) && + verifier.VerifyVector(variant_tensors()) && + verifier.VerifyVectorOfTables(variant_tensors()) && + verifier.EndTable(); + } + TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorBuilder { + typedef Tensor Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_shape(flatbuffers::Offset> shape) { + fbb_.AddOffset(Tensor::VT_SHAPE, shape); + } + void add_type(tflite_micro::TensorType type) { + fbb_.AddElement(Tensor::VT_TYPE, static_cast(type), 0); + } + void add_buffer(uint32_t buffer) { + fbb_.AddElement(Tensor::VT_BUFFER, buffer, 0); + } + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Tensor::VT_NAME, name); + } + void add_quantization(flatbuffers::Offset quantization) { + fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); + } + void add_is_variable(bool is_variable) { + fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); + } + void add_sparsity(flatbuffers::Offset sparsity) { + fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); + } + void add_shape_signature(flatbuffers::Offset> shape_signature) { + fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); + } + void add_has_rank(bool has_rank) { + fbb_.AddElement(Tensor::VT_HAS_RANK, static_cast(has_rank), 0); + } + void add_variant_tensors(flatbuffers::Offset>> variant_tensors) { + fbb_.AddOffset(Tensor::VT_VARIANT_TENSORS, variant_tensors); + } + explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensor( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> shape = 0, + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, + uint32_t buffer = 0, + flatbuffers::Offset name = 0, + flatbuffers::Offset quantization = 0, + bool is_variable = false, + flatbuffers::Offset sparsity = 0, + flatbuffers::Offset> shape_signature = 0, + bool has_rank = false, + flatbuffers::Offset>> variant_tensors = 0) { + TensorBuilder builder_(_fbb); + builder_.add_variant_tensors(variant_tensors); + builder_.add_shape_signature(shape_signature); + builder_.add_sparsity(sparsity); + builder_.add_quantization(quantization); + builder_.add_name(name); + builder_.add_buffer(buffer); + builder_.add_shape(shape); + builder_.add_has_rank(has_rank); + builder_.add_is_variable(is_variable); + builder_.add_type(type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTensorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *shape = nullptr, + tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, + uint32_t buffer = 0, + const char *name = nullptr, + flatbuffers::Offset quantization = 0, + bool is_variable = false, + flatbuffers::Offset sparsity = 0, + const std::vector *shape_signature = nullptr, + bool has_rank = false, + const std::vector> *variant_tensors = nullptr) { + auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; + auto name__ = name ? _fbb.CreateString(name) : 0; + auto shape_signature__ = shape_signature ? _fbb.CreateVector(*shape_signature) : 0; + auto variant_tensors__ = variant_tensors ? _fbb.CreateVector>(*variant_tensors) : 0; + return tflite_micro::CreateTensor( + _fbb, + shape__, + type, + buffer, + name__, + quantization, + is_variable, + sparsity, + shape_signature__, + has_rank, + variant_tensors__); +} + +flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloGatherOptionsT : public flatbuffers::NativeTable { + typedef StablehloGatherOptions TableType; + std::vector offset_dims{}; + std::vector collapsed_slice_dims{}; + std::vector start_index_map{}; + int64_t index_vector_dim = 0; + std::vector slice_sizes{}; + bool indices_are_sorted = false; +}; + +struct StablehloGatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloGatherOptionsT NativeTableType; + typedef StablehloGatherOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OFFSET_DIMS = 4, + VT_COLLAPSED_SLICE_DIMS = 6, + VT_START_INDEX_MAP = 8, + VT_INDEX_VECTOR_DIM = 10, + VT_SLICE_SIZES = 12, + VT_INDICES_ARE_SORTED = 14 + }; + const flatbuffers::Vector *offset_dims() const { + return GetPointer *>(VT_OFFSET_DIMS); + } + const flatbuffers::Vector *collapsed_slice_dims() const { + return GetPointer *>(VT_COLLAPSED_SLICE_DIMS); + } + const flatbuffers::Vector *start_index_map() const { + return GetPointer *>(VT_START_INDEX_MAP); + } + int64_t index_vector_dim() const { + return GetField(VT_INDEX_VECTOR_DIM, 0); + } + const flatbuffers::Vector *slice_sizes() const { + return GetPointer *>(VT_SLICE_SIZES); + } + bool indices_are_sorted() const { + return GetField(VT_INDICES_ARE_SORTED, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_OFFSET_DIMS) && + verifier.VerifyVector(offset_dims()) && + VerifyOffset(verifier, VT_COLLAPSED_SLICE_DIMS) && + verifier.VerifyVector(collapsed_slice_dims()) && + VerifyOffset(verifier, VT_START_INDEX_MAP) && + verifier.VerifyVector(start_index_map()) && + VerifyField(verifier, VT_INDEX_VECTOR_DIM, 8) && + VerifyOffset(verifier, VT_SLICE_SIZES) && + verifier.VerifyVector(slice_sizes()) && + VerifyField(verifier, VT_INDICES_ARE_SORTED, 1) && + verifier.EndTable(); + } + StablehloGatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloGatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloGatherOptionsBuilder { + typedef StablehloGatherOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_offset_dims(flatbuffers::Offset> offset_dims) { + fbb_.AddOffset(StablehloGatherOptions::VT_OFFSET_DIMS, offset_dims); + } + void add_collapsed_slice_dims(flatbuffers::Offset> collapsed_slice_dims) { + fbb_.AddOffset(StablehloGatherOptions::VT_COLLAPSED_SLICE_DIMS, collapsed_slice_dims); + } + void add_start_index_map(flatbuffers::Offset> start_index_map) { + fbb_.AddOffset(StablehloGatherOptions::VT_START_INDEX_MAP, start_index_map); + } + void add_index_vector_dim(int64_t index_vector_dim) { + fbb_.AddElement(StablehloGatherOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0); + } + void add_slice_sizes(flatbuffers::Offset> slice_sizes) { + fbb_.AddOffset(StablehloGatherOptions::VT_SLICE_SIZES, slice_sizes); + } + void add_indices_are_sorted(bool indices_are_sorted) { + fbb_.AddElement(StablehloGatherOptions::VT_INDICES_ARE_SORTED, static_cast(indices_are_sorted), 0); + } + explicit StablehloGatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloGatherOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> offset_dims = 0, + flatbuffers::Offset> collapsed_slice_dims = 0, + flatbuffers::Offset> start_index_map = 0, + int64_t index_vector_dim = 0, + flatbuffers::Offset> slice_sizes = 0, + bool indices_are_sorted = false) { + StablehloGatherOptionsBuilder builder_(_fbb); + builder_.add_index_vector_dim(index_vector_dim); + builder_.add_slice_sizes(slice_sizes); + builder_.add_start_index_map(start_index_map); + builder_.add_collapsed_slice_dims(collapsed_slice_dims); + builder_.add_offset_dims(offset_dims); + builder_.add_indices_are_sorted(indices_are_sorted); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloGatherOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *offset_dims = nullptr, + const std::vector *collapsed_slice_dims = nullptr, + const std::vector *start_index_map = nullptr, + int64_t index_vector_dim = 0, + const std::vector *slice_sizes = nullptr, + bool indices_are_sorted = false) { + auto offset_dims__ = offset_dims ? _fbb.CreateVector(*offset_dims) : 0; + auto collapsed_slice_dims__ = collapsed_slice_dims ? _fbb.CreateVector(*collapsed_slice_dims) : 0; + auto start_index_map__ = start_index_map ? _fbb.CreateVector(*start_index_map) : 0; + auto slice_sizes__ = slice_sizes ? _fbb.CreateVector(*slice_sizes) : 0; + return tflite_micro::CreateStablehloGatherOptions( + _fbb, + offset_dims__, + collapsed_slice_dims__, + start_index_map__, + index_vector_dim, + slice_sizes__, + indices_are_sorted); +} + +flatbuffers::Offset CreateStablehloGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloTransposeOptionsT : public flatbuffers::NativeTable { + typedef StablehloTransposeOptions TableType; + std::vector permutation{}; +}; + +struct StablehloTransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloTransposeOptionsT NativeTableType; + typedef StablehloTransposeOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PERMUTATION = 4 + }; + const flatbuffers::Vector *permutation() const { + return GetPointer *>(VT_PERMUTATION); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_PERMUTATION) && + verifier.VerifyVector(permutation()) && + verifier.EndTable(); + } + StablehloTransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloTransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloTransposeOptionsBuilder { + typedef StablehloTransposeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_permutation(flatbuffers::Offset> permutation) { + fbb_.AddOffset(StablehloTransposeOptions::VT_PERMUTATION, permutation); + } + explicit StablehloTransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloTransposeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> permutation = 0) { + StablehloTransposeOptionsBuilder builder_(_fbb); + builder_.add_permutation(permutation); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloTransposeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *permutation = nullptr) { + auto permutation__ = permutation ? _fbb.CreateVector(*permutation) : 0; + return tflite_micro::CreateStablehloTransposeOptions( + _fbb, + permutation__); +} + +flatbuffers::Offset CreateStablehloTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloDotGeneralOptionsT : public flatbuffers::NativeTable { + typedef StablehloDotGeneralOptions TableType; + std::vector lhs_batching_dimensions{}; + std::vector rhs_batching_dimensions{}; + std::vector lhs_contracting_dimensions{}; + std::vector rhs_contracting_dimensions{}; + std::vector precision_config{}; +}; + +struct StablehloDotGeneralOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloDotGeneralOptionsT NativeTableType; + typedef StablehloDotGeneralOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_LHS_BATCHING_DIMENSIONS = 4, + VT_RHS_BATCHING_DIMENSIONS = 6, + VT_LHS_CONTRACTING_DIMENSIONS = 8, + VT_RHS_CONTRACTING_DIMENSIONS = 10, + VT_PRECISION_CONFIG = 12 + }; + const flatbuffers::Vector *lhs_batching_dimensions() const { + return GetPointer *>(VT_LHS_BATCHING_DIMENSIONS); + } + const flatbuffers::Vector *rhs_batching_dimensions() const { + return GetPointer *>(VT_RHS_BATCHING_DIMENSIONS); + } + const flatbuffers::Vector *lhs_contracting_dimensions() const { + return GetPointer *>(VT_LHS_CONTRACTING_DIMENSIONS); + } + const flatbuffers::Vector *rhs_contracting_dimensions() const { + return GetPointer *>(VT_RHS_CONTRACTING_DIMENSIONS); + } + const flatbuffers::Vector *precision_config() const { + return GetPointer *>(VT_PRECISION_CONFIG); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_LHS_BATCHING_DIMENSIONS) && + verifier.VerifyVector(lhs_batching_dimensions()) && + VerifyOffset(verifier, VT_RHS_BATCHING_DIMENSIONS) && + verifier.VerifyVector(rhs_batching_dimensions()) && + VerifyOffset(verifier, VT_LHS_CONTRACTING_DIMENSIONS) && + verifier.VerifyVector(lhs_contracting_dimensions()) && + VerifyOffset(verifier, VT_RHS_CONTRACTING_DIMENSIONS) && + verifier.VerifyVector(rhs_contracting_dimensions()) && + VerifyOffset(verifier, VT_PRECISION_CONFIG) && + verifier.VerifyVector(precision_config()) && + verifier.EndTable(); + } + StablehloDotGeneralOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloDotGeneralOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloDotGeneralOptionsBuilder { + typedef StablehloDotGeneralOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_lhs_batching_dimensions(flatbuffers::Offset> lhs_batching_dimensions) { + fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_BATCHING_DIMENSIONS, lhs_batching_dimensions); + } + void add_rhs_batching_dimensions(flatbuffers::Offset> rhs_batching_dimensions) { + fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_BATCHING_DIMENSIONS, rhs_batching_dimensions); + } + void add_lhs_contracting_dimensions(flatbuffers::Offset> lhs_contracting_dimensions) { + fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_CONTRACTING_DIMENSIONS, lhs_contracting_dimensions); + } + void add_rhs_contracting_dimensions(flatbuffers::Offset> rhs_contracting_dimensions) { + fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_CONTRACTING_DIMENSIONS, rhs_contracting_dimensions); + } + void add_precision_config(flatbuffers::Offset> precision_config) { + fbb_.AddOffset(StablehloDotGeneralOptions::VT_PRECISION_CONFIG, precision_config); + } + explicit StablehloDotGeneralOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloDotGeneralOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> lhs_batching_dimensions = 0, + flatbuffers::Offset> rhs_batching_dimensions = 0, + flatbuffers::Offset> lhs_contracting_dimensions = 0, + flatbuffers::Offset> rhs_contracting_dimensions = 0, + flatbuffers::Offset> precision_config = 0) { + StablehloDotGeneralOptionsBuilder builder_(_fbb); + builder_.add_precision_config(precision_config); + builder_.add_rhs_contracting_dimensions(rhs_contracting_dimensions); + builder_.add_lhs_contracting_dimensions(lhs_contracting_dimensions); + builder_.add_rhs_batching_dimensions(rhs_batching_dimensions); + builder_.add_lhs_batching_dimensions(lhs_batching_dimensions); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloDotGeneralOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *lhs_batching_dimensions = nullptr, + const std::vector *rhs_batching_dimensions = nullptr, + const std::vector *lhs_contracting_dimensions = nullptr, + const std::vector *rhs_contracting_dimensions = nullptr, + const std::vector *precision_config = nullptr) { + auto lhs_batching_dimensions__ = lhs_batching_dimensions ? _fbb.CreateVector(*lhs_batching_dimensions) : 0; + auto rhs_batching_dimensions__ = rhs_batching_dimensions ? _fbb.CreateVector(*rhs_batching_dimensions) : 0; + auto lhs_contracting_dimensions__ = lhs_contracting_dimensions ? _fbb.CreateVector(*lhs_contracting_dimensions) : 0; + auto rhs_contracting_dimensions__ = rhs_contracting_dimensions ? _fbb.CreateVector(*rhs_contracting_dimensions) : 0; + auto precision_config__ = precision_config ? _fbb.CreateVector(*precision_config) : 0; + return tflite_micro::CreateStablehloDotGeneralOptions( + _fbb, + lhs_batching_dimensions__, + rhs_batching_dimensions__, + lhs_contracting_dimensions__, + rhs_contracting_dimensions__, + precision_config__); +} + +flatbuffers::Offset CreateStablehloDotGeneralOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloReduceWindowOptionsT : public flatbuffers::NativeTable { + typedef StablehloReduceWindowOptions TableType; + std::vector window_dimensions{}; + std::vector window_strides{}; + std::vector base_dilations{}; + std::vector window_dilations{}; + std::vector padding{}; + int32_t body_subgraph_index = 0; +}; + +struct StablehloReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloReduceWindowOptionsT NativeTableType; + typedef StablehloReduceWindowOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_WINDOW_DIMENSIONS = 4, + VT_WINDOW_STRIDES = 6, + VT_BASE_DILATIONS = 8, + VT_WINDOW_DILATIONS = 10, + VT_PADDING = 12, + VT_BODY_SUBGRAPH_INDEX = 14 + }; + const flatbuffers::Vector *window_dimensions() const { + return GetPointer *>(VT_WINDOW_DIMENSIONS); + } + const flatbuffers::Vector *window_strides() const { + return GetPointer *>(VT_WINDOW_STRIDES); + } + const flatbuffers::Vector *base_dilations() const { + return GetPointer *>(VT_BASE_DILATIONS); + } + const flatbuffers::Vector *window_dilations() const { + return GetPointer *>(VT_WINDOW_DILATIONS); + } + const flatbuffers::Vector *padding() const { + return GetPointer *>(VT_PADDING); + } + int32_t body_subgraph_index() const { + return GetField(VT_BODY_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_WINDOW_DIMENSIONS) && + verifier.VerifyVector(window_dimensions()) && + VerifyOffset(verifier, VT_WINDOW_STRIDES) && + verifier.VerifyVector(window_strides()) && + VerifyOffset(verifier, VT_BASE_DILATIONS) && + verifier.VerifyVector(base_dilations()) && + VerifyOffset(verifier, VT_WINDOW_DILATIONS) && + verifier.VerifyVector(window_dilations()) && + VerifyOffset(verifier, VT_PADDING) && + verifier.VerifyVector(padding()) && + VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + StablehloReduceWindowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloReduceWindowOptionsBuilder { + typedef StablehloReduceWindowOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_window_dimensions(flatbuffers::Offset> window_dimensions) { + fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DIMENSIONS, window_dimensions); + } + void add_window_strides(flatbuffers::Offset> window_strides) { + fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_STRIDES, window_strides); + } + void add_base_dilations(flatbuffers::Offset> base_dilations) { + fbb_.AddOffset(StablehloReduceWindowOptions::VT_BASE_DILATIONS, base_dilations); + } + void add_window_dilations(flatbuffers::Offset> window_dilations) { + fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DILATIONS, window_dilations); + } + void add_padding(flatbuffers::Offset> padding) { + fbb_.AddOffset(StablehloReduceWindowOptions::VT_PADDING, padding); + } + void add_body_subgraph_index(int32_t body_subgraph_index) { + fbb_.AddElement(StablehloReduceWindowOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); + } + explicit StablehloReduceWindowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloReduceWindowOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> window_dimensions = 0, + flatbuffers::Offset> window_strides = 0, + flatbuffers::Offset> base_dilations = 0, + flatbuffers::Offset> window_dilations = 0, + flatbuffers::Offset> padding = 0, + int32_t body_subgraph_index = 0) { + StablehloReduceWindowOptionsBuilder builder_(_fbb); + builder_.add_body_subgraph_index(body_subgraph_index); + builder_.add_padding(padding); + builder_.add_window_dilations(window_dilations); + builder_.add_base_dilations(base_dilations); + builder_.add_window_strides(window_strides); + builder_.add_window_dimensions(window_dimensions); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloReduceWindowOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *window_dimensions = nullptr, + const std::vector *window_strides = nullptr, + const std::vector *base_dilations = nullptr, + const std::vector *window_dilations = nullptr, + const std::vector *padding = nullptr, + int32_t body_subgraph_index = 0) { + auto window_dimensions__ = window_dimensions ? _fbb.CreateVector(*window_dimensions) : 0; + auto window_strides__ = window_strides ? _fbb.CreateVector(*window_strides) : 0; + auto base_dilations__ = base_dilations ? _fbb.CreateVector(*base_dilations) : 0; + auto window_dilations__ = window_dilations ? _fbb.CreateVector(*window_dilations) : 0; + auto padding__ = padding ? _fbb.CreateVector(*padding) : 0; + return tflite_micro::CreateStablehloReduceWindowOptions( + _fbb, + window_dimensions__, + window_strides__, + base_dilations__, + window_dilations__, + padding__, + body_subgraph_index); +} + +flatbuffers::Offset CreateStablehloReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloWhileOptionsT : public flatbuffers::NativeTable { + typedef StablehloWhileOptions TableType; + int32_t cond_subgraph_index = 0; + int32_t body_subgraph_index = 0; +}; + +struct StablehloWhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloWhileOptionsT NativeTableType; + typedef StablehloWhileOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COND_SUBGRAPH_INDEX = 4, + VT_BODY_SUBGRAPH_INDEX = 6 + }; + int32_t cond_subgraph_index() const { + return GetField(VT_COND_SUBGRAPH_INDEX, 0); + } + int32_t body_subgraph_index() const { + return GetField(VT_BODY_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COND_SUBGRAPH_INDEX, 4) && + VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + StablehloWhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloWhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloWhileOptionsBuilder { + typedef StablehloWhileOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_cond_subgraph_index(int32_t cond_subgraph_index) { + fbb_.AddElement(StablehloWhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); + } + void add_body_subgraph_index(int32_t body_subgraph_index) { + fbb_.AddElement(StablehloWhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); + } + explicit StablehloWhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloWhileOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t cond_subgraph_index = 0, + int32_t body_subgraph_index = 0) { + StablehloWhileOptionsBuilder builder_(_fbb); + builder_.add_body_subgraph_index(body_subgraph_index); + builder_.add_cond_subgraph_index(cond_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloSortOptionsT : public flatbuffers::NativeTable { + typedef StablehloSortOptions TableType; + int64_t dimension = 0; + bool is_stable = false; + int32_t comparator_subgraph_index = 0; +}; + +struct StablehloSortOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloSortOptionsT NativeTableType; + typedef StablehloSortOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DIMENSION = 4, + VT_IS_STABLE = 6, + VT_COMPARATOR_SUBGRAPH_INDEX = 8 + }; + int64_t dimension() const { + return GetField(VT_DIMENSION, 0); + } + bool is_stable() const { + return GetField(VT_IS_STABLE, 0) != 0; + } + int32_t comparator_subgraph_index() const { + return GetField(VT_COMPARATOR_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DIMENSION, 8) && + VerifyField(verifier, VT_IS_STABLE, 1) && + VerifyField(verifier, VT_COMPARATOR_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + StablehloSortOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloSortOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloSortOptionsBuilder { + typedef StablehloSortOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_dimension(int64_t dimension) { + fbb_.AddElement(StablehloSortOptions::VT_DIMENSION, dimension, 0); + } + void add_is_stable(bool is_stable) { + fbb_.AddElement(StablehloSortOptions::VT_IS_STABLE, static_cast(is_stable), 0); + } + void add_comparator_subgraph_index(int32_t comparator_subgraph_index) { + fbb_.AddElement(StablehloSortOptions::VT_COMPARATOR_SUBGRAPH_INDEX, comparator_subgraph_index, 0); + } + explicit StablehloSortOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloSortOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t dimension = 0, + bool is_stable = false, + int32_t comparator_subgraph_index = 0) { + StablehloSortOptionsBuilder builder_(_fbb); + builder_.add_dimension(dimension); + builder_.add_comparator_subgraph_index(comparator_subgraph_index); + builder_.add_is_stable(is_stable); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloSortOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloConcatenateOptionsT : public flatbuffers::NativeTable { + typedef StablehloConcatenateOptions TableType; + int64_t dimension = 0; +}; + +struct StablehloConcatenateOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloConcatenateOptionsT NativeTableType; + typedef StablehloConcatenateOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DIMENSION = 4 + }; + int64_t dimension() const { + return GetField(VT_DIMENSION, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DIMENSION, 8) && + verifier.EndTable(); + } + StablehloConcatenateOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloConcatenateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloConcatenateOptionsBuilder { + typedef StablehloConcatenateOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_dimension(int64_t dimension) { + fbb_.AddElement(StablehloConcatenateOptions::VT_DIMENSION, dimension, 0); + } + explicit StablehloConcatenateOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloConcatenateOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t dimension = 0) { + StablehloConcatenateOptionsBuilder builder_(_fbb); + builder_.add_dimension(dimension); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloConcatenateOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloBroadcastInDimOptionsT : public flatbuffers::NativeTable { + typedef StablehloBroadcastInDimOptions TableType; + std::vector broadcast_dimensions{}; +}; + +struct StablehloBroadcastInDimOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloBroadcastInDimOptionsT NativeTableType; + typedef StablehloBroadcastInDimOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BROADCAST_DIMENSIONS = 4 + }; + const flatbuffers::Vector *broadcast_dimensions() const { + return GetPointer *>(VT_BROADCAST_DIMENSIONS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BROADCAST_DIMENSIONS) && + verifier.VerifyVector(broadcast_dimensions()) && + verifier.EndTable(); + } + StablehloBroadcastInDimOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloBroadcastInDimOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloBroadcastInDimOptionsBuilder { + typedef StablehloBroadcastInDimOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_broadcast_dimensions(flatbuffers::Offset> broadcast_dimensions) { + fbb_.AddOffset(StablehloBroadcastInDimOptions::VT_BROADCAST_DIMENSIONS, broadcast_dimensions); + } + explicit StablehloBroadcastInDimOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloBroadcastInDimOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> broadcast_dimensions = 0) { + StablehloBroadcastInDimOptionsBuilder builder_(_fbb); + builder_.add_broadcast_dimensions(broadcast_dimensions); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloBroadcastInDimOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *broadcast_dimensions = nullptr) { + auto broadcast_dimensions__ = broadcast_dimensions ? _fbb.CreateVector(*broadcast_dimensions) : 0; + return tflite_micro::CreateStablehloBroadcastInDimOptions( + _fbb, + broadcast_dimensions__); +} + +flatbuffers::Offset CreateStablehloBroadcastInDimOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloCompareOptionsT : public flatbuffers::NativeTable { + typedef StablehloCompareOptions TableType; + tflite_micro::StablehloComparisonDirection comparison_direction = tflite_micro::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ; + tflite_micro::StablehloComparisonType compare_type = tflite_micro::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE; +}; + +struct StablehloCompareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloCompareOptionsT NativeTableType; + typedef StablehloCompareOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COMPARISON_DIRECTION = 4, + VT_COMPARE_TYPE = 6 + }; + tflite_micro::StablehloComparisonDirection comparison_direction() const { + return static_cast(GetField(VT_COMPARISON_DIRECTION, 0)); + } + tflite_micro::StablehloComparisonType compare_type() const { + return static_cast(GetField(VT_COMPARE_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COMPARISON_DIRECTION, 4) && + VerifyField(verifier, VT_COMPARE_TYPE, 4) && + verifier.EndTable(); + } + StablehloCompareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloCompareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloCompareOptionsBuilder { + typedef StablehloCompareOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_comparison_direction(tflite_micro::StablehloComparisonDirection comparison_direction) { + fbb_.AddElement(StablehloCompareOptions::VT_COMPARISON_DIRECTION, static_cast(comparison_direction), 0); + } + void add_compare_type(tflite_micro::StablehloComparisonType compare_type) { + fbb_.AddElement(StablehloCompareOptions::VT_COMPARE_TYPE, static_cast(compare_type), 0); + } + explicit StablehloCompareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloCompareOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::StablehloComparisonDirection comparison_direction = tflite_micro::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, + tflite_micro::StablehloComparisonType compare_type = tflite_micro::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE) { + StablehloCompareOptionsBuilder builder_(_fbb); + builder_.add_compare_type(compare_type); + builder_.add_comparison_direction(comparison_direction); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloCompareOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloDynamicSliceOptionsT : public flatbuffers::NativeTable { + typedef StablehloDynamicSliceOptions TableType; + std::vector slice_sizes{}; +}; + +struct StablehloDynamicSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloDynamicSliceOptionsT NativeTableType; + typedef StablehloDynamicSliceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SLICE_SIZES = 4 + }; + const flatbuffers::Vector *slice_sizes() const { + return GetPointer *>(VT_SLICE_SIZES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SLICE_SIZES) && + verifier.VerifyVector(slice_sizes()) && + verifier.EndTable(); + } + StablehloDynamicSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloDynamicSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloDynamicSliceOptionsBuilder { + typedef StablehloDynamicSliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_slice_sizes(flatbuffers::Offset> slice_sizes) { + fbb_.AddOffset(StablehloDynamicSliceOptions::VT_SLICE_SIZES, slice_sizes); + } + explicit StablehloDynamicSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloDynamicSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> slice_sizes = 0) { + StablehloDynamicSliceOptionsBuilder builder_(_fbb); + builder_.add_slice_sizes(slice_sizes); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloDynamicSliceOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *slice_sizes = nullptr) { + auto slice_sizes__ = slice_sizes ? _fbb.CreateVector(*slice_sizes) : 0; + return tflite_micro::CreateStablehloDynamicSliceOptions( + _fbb, + slice_sizes__); +} + +flatbuffers::Offset CreateStablehloDynamicSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloPadOptionsT : public flatbuffers::NativeTable { + typedef StablehloPadOptions TableType; + std::vector edge_padding_low{}; + std::vector edge_padding_high{}; + std::vector interior_padding{}; +}; + +struct StablehloPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloPadOptionsT NativeTableType; + typedef StablehloPadOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EDGE_PADDING_LOW = 4, + VT_EDGE_PADDING_HIGH = 6, + VT_INTERIOR_PADDING = 8 + }; + const flatbuffers::Vector *edge_padding_low() const { + return GetPointer *>(VT_EDGE_PADDING_LOW); + } + const flatbuffers::Vector *edge_padding_high() const { + return GetPointer *>(VT_EDGE_PADDING_HIGH); + } + const flatbuffers::Vector *interior_padding() const { + return GetPointer *>(VT_INTERIOR_PADDING); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_EDGE_PADDING_LOW) && + verifier.VerifyVector(edge_padding_low()) && + VerifyOffset(verifier, VT_EDGE_PADDING_HIGH) && + verifier.VerifyVector(edge_padding_high()) && + VerifyOffset(verifier, VT_INTERIOR_PADDING) && + verifier.VerifyVector(interior_padding()) && + verifier.EndTable(); + } + StablehloPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloPadOptionsBuilder { + typedef StablehloPadOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_edge_padding_low(flatbuffers::Offset> edge_padding_low) { + fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_LOW, edge_padding_low); + } + void add_edge_padding_high(flatbuffers::Offset> edge_padding_high) { + fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_HIGH, edge_padding_high); + } + void add_interior_padding(flatbuffers::Offset> interior_padding) { + fbb_.AddOffset(StablehloPadOptions::VT_INTERIOR_PADDING, interior_padding); + } + explicit StablehloPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloPadOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> edge_padding_low = 0, + flatbuffers::Offset> edge_padding_high = 0, + flatbuffers::Offset> interior_padding = 0) { + StablehloPadOptionsBuilder builder_(_fbb); + builder_.add_interior_padding(interior_padding); + builder_.add_edge_padding_high(edge_padding_high); + builder_.add_edge_padding_low(edge_padding_low); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloPadOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *edge_padding_low = nullptr, + const std::vector *edge_padding_high = nullptr, + const std::vector *interior_padding = nullptr) { + auto edge_padding_low__ = edge_padding_low ? _fbb.CreateVector(*edge_padding_low) : 0; + auto edge_padding_high__ = edge_padding_high ? _fbb.CreateVector(*edge_padding_high) : 0; + auto interior_padding__ = interior_padding ? _fbb.CreateVector(*interior_padding) : 0; + return tflite_micro::CreateStablehloPadOptions( + _fbb, + edge_padding_low__, + edge_padding_high__, + interior_padding__); +} + +flatbuffers::Offset CreateStablehloPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloIotaOptionsT : public flatbuffers::NativeTable { + typedef StablehloIotaOptions TableType; + int64_t iota_dimension = 0; +}; + +struct StablehloIotaOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloIotaOptionsT NativeTableType; + typedef StablehloIotaOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IOTA_DIMENSION = 4 + }; + int64_t iota_dimension() const { + return GetField(VT_IOTA_DIMENSION, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IOTA_DIMENSION, 8) && + verifier.EndTable(); + } + StablehloIotaOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloIotaOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloIotaOptionsBuilder { + typedef StablehloIotaOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_iota_dimension(int64_t iota_dimension) { + fbb_.AddElement(StablehloIotaOptions::VT_IOTA_DIMENSION, iota_dimension, 0); + } + explicit StablehloIotaOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloIotaOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t iota_dimension = 0) { + StablehloIotaOptionsBuilder builder_(_fbb); + builder_.add_iota_dimension(iota_dimension); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloIotaOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloCustomCallOptionsT : public flatbuffers::NativeTable { + typedef StablehloCustomCallOptions TableType; + std::string call_target_name{}; + bool has_side_effect = false; + std::string backend_config{}; + int32_t api_version = 0; + std::vector called_computations{}; + std::vector custom_attributes{}; +}; + +struct StablehloCustomCallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloCustomCallOptionsT NativeTableType; + typedef StablehloCustomCallOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CALL_TARGET_NAME = 4, + VT_HAS_SIDE_EFFECT = 6, + VT_BACKEND_CONFIG = 8, + VT_API_VERSION = 10, + VT_CALLED_COMPUTATIONS = 12, + VT_CUSTOM_ATTRIBUTES = 14 + }; + const flatbuffers::String *call_target_name() const { + return GetPointer(VT_CALL_TARGET_NAME); + } + bool has_side_effect() const { + return GetField(VT_HAS_SIDE_EFFECT, 0) != 0; + } + const flatbuffers::String *backend_config() const { + return GetPointer(VT_BACKEND_CONFIG); + } + int32_t api_version() const { + return GetField(VT_API_VERSION, 0); + } + const flatbuffers::Vector *called_computations() const { + return GetPointer *>(VT_CALLED_COMPUTATIONS); + } + const flatbuffers::Vector *custom_attributes() const { + return GetPointer *>(VT_CUSTOM_ATTRIBUTES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CALL_TARGET_NAME) && + verifier.VerifyString(call_target_name()) && + VerifyField(verifier, VT_HAS_SIDE_EFFECT, 1) && + VerifyOffset(verifier, VT_BACKEND_CONFIG) && + verifier.VerifyString(backend_config()) && + VerifyField(verifier, VT_API_VERSION, 4) && + VerifyOffset(verifier, VT_CALLED_COMPUTATIONS) && + verifier.VerifyVector(called_computations()) && + VerifyOffset(verifier, VT_CUSTOM_ATTRIBUTES) && + verifier.VerifyVector(custom_attributes()) && + verifier.EndTable(); + } + StablehloCustomCallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloCustomCallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloCustomCallOptionsBuilder { + typedef StablehloCustomCallOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_call_target_name(flatbuffers::Offset call_target_name) { + fbb_.AddOffset(StablehloCustomCallOptions::VT_CALL_TARGET_NAME, call_target_name); + } + void add_has_side_effect(bool has_side_effect) { + fbb_.AddElement(StablehloCustomCallOptions::VT_HAS_SIDE_EFFECT, static_cast(has_side_effect), 0); + } + void add_backend_config(flatbuffers::Offset backend_config) { + fbb_.AddOffset(StablehloCustomCallOptions::VT_BACKEND_CONFIG, backend_config); + } + void add_api_version(int32_t api_version) { + fbb_.AddElement(StablehloCustomCallOptions::VT_API_VERSION, api_version, 0); + } + void add_called_computations(flatbuffers::Offset> called_computations) { + fbb_.AddOffset(StablehloCustomCallOptions::VT_CALLED_COMPUTATIONS, called_computations); + } + void add_custom_attributes(flatbuffers::Offset> custom_attributes) { + fbb_.AddOffset(StablehloCustomCallOptions::VT_CUSTOM_ATTRIBUTES, custom_attributes); + } + explicit StablehloCustomCallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloCustomCallOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset call_target_name = 0, + bool has_side_effect = false, + flatbuffers::Offset backend_config = 0, + int32_t api_version = 0, + flatbuffers::Offset> called_computations = 0, + flatbuffers::Offset> custom_attributes = 0) { + StablehloCustomCallOptionsBuilder builder_(_fbb); + builder_.add_custom_attributes(custom_attributes); + builder_.add_called_computations(called_computations); + builder_.add_api_version(api_version); + builder_.add_backend_config(backend_config); + builder_.add_call_target_name(call_target_name); + builder_.add_has_side_effect(has_side_effect); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloCustomCallOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *call_target_name = nullptr, + bool has_side_effect = false, + const char *backend_config = nullptr, + int32_t api_version = 0, + const std::vector *called_computations = nullptr, + const std::vector *custom_attributes = nullptr) { + auto call_target_name__ = call_target_name ? _fbb.CreateString(call_target_name) : 0; + auto backend_config__ = backend_config ? _fbb.CreateString(backend_config) : 0; + auto called_computations__ = called_computations ? _fbb.CreateVector(*called_computations) : 0; + auto custom_attributes__ = custom_attributes ? _fbb.CreateVector(*custom_attributes) : 0; + return tflite_micro::CreateStablehloCustomCallOptions( + _fbb, + call_target_name__, + has_side_effect, + backend_config__, + api_version, + called_computations__, + custom_attributes__); +} + +flatbuffers::Offset CreateStablehloCustomCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloReduceOptionsT : public flatbuffers::NativeTable { + typedef StablehloReduceOptions TableType; + std::vector dimensions{}; + int32_t body_subgraph_index = 0; +}; + +struct StablehloReduceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloReduceOptionsT NativeTableType; + typedef StablehloReduceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DIMENSIONS = 4, + VT_BODY_SUBGRAPH_INDEX = 6 + }; + const flatbuffers::Vector *dimensions() const { + return GetPointer *>(VT_DIMENSIONS); + } + int32_t body_subgraph_index() const { + return GetField(VT_BODY_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DIMENSIONS) && + verifier.VerifyVector(dimensions()) && + VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + StablehloReduceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloReduceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloReduceOptionsBuilder { + typedef StablehloReduceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_dimensions(flatbuffers::Offset> dimensions) { + fbb_.AddOffset(StablehloReduceOptions::VT_DIMENSIONS, dimensions); + } + void add_body_subgraph_index(int32_t body_subgraph_index) { + fbb_.AddElement(StablehloReduceOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); + } + explicit StablehloReduceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloReduceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> dimensions = 0, + int32_t body_subgraph_index = 0) { + StablehloReduceOptionsBuilder builder_(_fbb); + builder_.add_body_subgraph_index(body_subgraph_index); + builder_.add_dimensions(dimensions); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloReduceOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *dimensions = nullptr, + int32_t body_subgraph_index = 0) { + auto dimensions__ = dimensions ? _fbb.CreateVector(*dimensions) : 0; + return tflite_micro::CreateStablehloReduceOptions( + _fbb, + dimensions__, + body_subgraph_index); +} + +flatbuffers::Offset CreateStablehloReduceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloSliceOptionsT : public flatbuffers::NativeTable { + typedef StablehloSliceOptions TableType; + std::vector start_indices{}; + std::vector limit_indices{}; + std::vector strides{}; +}; + +struct StablehloSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloSliceOptionsT NativeTableType; + typedef StablehloSliceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_START_INDICES = 4, + VT_LIMIT_INDICES = 6, + VT_STRIDES = 8 + }; + const flatbuffers::Vector *start_indices() const { + return GetPointer *>(VT_START_INDICES); + } + const flatbuffers::Vector *limit_indices() const { + return GetPointer *>(VT_LIMIT_INDICES); + } + const flatbuffers::Vector *strides() const { + return GetPointer *>(VT_STRIDES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_START_INDICES) && + verifier.VerifyVector(start_indices()) && + VerifyOffset(verifier, VT_LIMIT_INDICES) && + verifier.VerifyVector(limit_indices()) && + VerifyOffset(verifier, VT_STRIDES) && + verifier.VerifyVector(strides()) && + verifier.EndTable(); + } + StablehloSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloSliceOptionsBuilder { + typedef StablehloSliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_start_indices(flatbuffers::Offset> start_indices) { + fbb_.AddOffset(StablehloSliceOptions::VT_START_INDICES, start_indices); + } + void add_limit_indices(flatbuffers::Offset> limit_indices) { + fbb_.AddOffset(StablehloSliceOptions::VT_LIMIT_INDICES, limit_indices); + } + void add_strides(flatbuffers::Offset> strides) { + fbb_.AddOffset(StablehloSliceOptions::VT_STRIDES, strides); + } + explicit StablehloSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> start_indices = 0, + flatbuffers::Offset> limit_indices = 0, + flatbuffers::Offset> strides = 0) { + StablehloSliceOptionsBuilder builder_(_fbb); + builder_.add_strides(strides); + builder_.add_limit_indices(limit_indices); + builder_.add_start_indices(start_indices); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloSliceOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *start_indices = nullptr, + const std::vector *limit_indices = nullptr, + const std::vector *strides = nullptr) { + auto start_indices__ = start_indices ? _fbb.CreateVector(*start_indices) : 0; + auto limit_indices__ = limit_indices ? _fbb.CreateVector(*limit_indices) : 0; + auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; + return tflite_micro::CreateStablehloSliceOptions( + _fbb, + start_indices__, + limit_indices__, + strides__); +} + +flatbuffers::Offset CreateStablehloSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloConvolutionOptionsT : public flatbuffers::NativeTable { + typedef StablehloConvolutionOptions TableType; + std::vector window_strides{}; + std::vector padding{}; + std::vector lhs_dilation{}; + std::vector rhs_dilation{}; + std::vector window_reversal{}; + int64_t input_batch_dimension = 0; + int64_t input_feature_dimension = 0; + std::vector input_spatial_dimensions{}; + int64_t kernel_input_feature_dimension = 0; + int64_t kernel_output_feature_dimension = 0; + std::vector kernel_spatial_dimensions{}; + int64_t output_batch_dimension = 0; + int64_t output_feature_dimension = 0; + std::vector output_spatial_dimensions{}; + int64_t feature_group_count = 0; + int64_t batch_group_count = 0; + std::vector precision_config{}; +}; + +struct StablehloConvolutionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloConvolutionOptionsT NativeTableType; + typedef StablehloConvolutionOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_WINDOW_STRIDES = 4, + VT_PADDING = 6, + VT_LHS_DILATION = 8, + VT_RHS_DILATION = 10, + VT_WINDOW_REVERSAL = 12, + VT_INPUT_BATCH_DIMENSION = 14, + VT_INPUT_FEATURE_DIMENSION = 16, + VT_INPUT_SPATIAL_DIMENSIONS = 18, + VT_KERNEL_INPUT_FEATURE_DIMENSION = 20, + VT_KERNEL_OUTPUT_FEATURE_DIMENSION = 22, + VT_KERNEL_SPATIAL_DIMENSIONS = 24, + VT_OUTPUT_BATCH_DIMENSION = 26, + VT_OUTPUT_FEATURE_DIMENSION = 28, + VT_OUTPUT_SPATIAL_DIMENSIONS = 30, + VT_FEATURE_GROUP_COUNT = 32, + VT_BATCH_GROUP_COUNT = 34, + VT_PRECISION_CONFIG = 36 + }; + const flatbuffers::Vector *window_strides() const { + return GetPointer *>(VT_WINDOW_STRIDES); + } + const flatbuffers::Vector *padding() const { + return GetPointer *>(VT_PADDING); + } + const flatbuffers::Vector *lhs_dilation() const { + return GetPointer *>(VT_LHS_DILATION); + } + const flatbuffers::Vector *rhs_dilation() const { + return GetPointer *>(VT_RHS_DILATION); + } + const flatbuffers::Vector *window_reversal() const { + return GetPointer *>(VT_WINDOW_REVERSAL); + } + int64_t input_batch_dimension() const { + return GetField(VT_INPUT_BATCH_DIMENSION, 0); + } + int64_t input_feature_dimension() const { + return GetField(VT_INPUT_FEATURE_DIMENSION, 0); + } + const flatbuffers::Vector *input_spatial_dimensions() const { + return GetPointer *>(VT_INPUT_SPATIAL_DIMENSIONS); + } + int64_t kernel_input_feature_dimension() const { + return GetField(VT_KERNEL_INPUT_FEATURE_DIMENSION, 0); + } + int64_t kernel_output_feature_dimension() const { + return GetField(VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 0); + } + const flatbuffers::Vector *kernel_spatial_dimensions() const { + return GetPointer *>(VT_KERNEL_SPATIAL_DIMENSIONS); + } + int64_t output_batch_dimension() const { + return GetField(VT_OUTPUT_BATCH_DIMENSION, 0); + } + int64_t output_feature_dimension() const { + return GetField(VT_OUTPUT_FEATURE_DIMENSION, 0); + } + const flatbuffers::Vector *output_spatial_dimensions() const { + return GetPointer *>(VT_OUTPUT_SPATIAL_DIMENSIONS); + } + int64_t feature_group_count() const { + return GetField(VT_FEATURE_GROUP_COUNT, 0); + } + int64_t batch_group_count() const { + return GetField(VT_BATCH_GROUP_COUNT, 0); + } + const flatbuffers::Vector *precision_config() const { + return GetPointer *>(VT_PRECISION_CONFIG); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_WINDOW_STRIDES) && + verifier.VerifyVector(window_strides()) && + VerifyOffset(verifier, VT_PADDING) && + verifier.VerifyVector(padding()) && + VerifyOffset(verifier, VT_LHS_DILATION) && + verifier.VerifyVector(lhs_dilation()) && + VerifyOffset(verifier, VT_RHS_DILATION) && + verifier.VerifyVector(rhs_dilation()) && + VerifyOffset(verifier, VT_WINDOW_REVERSAL) && + verifier.VerifyVector(window_reversal()) && + VerifyField(verifier, VT_INPUT_BATCH_DIMENSION, 8) && + VerifyField(verifier, VT_INPUT_FEATURE_DIMENSION, 8) && + VerifyOffset(verifier, VT_INPUT_SPATIAL_DIMENSIONS) && + verifier.VerifyVector(input_spatial_dimensions()) && + VerifyField(verifier, VT_KERNEL_INPUT_FEATURE_DIMENSION, 8) && + VerifyField(verifier, VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 8) && + VerifyOffset(verifier, VT_KERNEL_SPATIAL_DIMENSIONS) && + verifier.VerifyVector(kernel_spatial_dimensions()) && + VerifyField(verifier, VT_OUTPUT_BATCH_DIMENSION, 8) && + VerifyField(verifier, VT_OUTPUT_FEATURE_DIMENSION, 8) && + VerifyOffset(verifier, VT_OUTPUT_SPATIAL_DIMENSIONS) && + verifier.VerifyVector(output_spatial_dimensions()) && + VerifyField(verifier, VT_FEATURE_GROUP_COUNT, 8) && + VerifyField(verifier, VT_BATCH_GROUP_COUNT, 8) && + VerifyOffset(verifier, VT_PRECISION_CONFIG) && + verifier.VerifyVector(precision_config()) && + verifier.EndTable(); + } + StablehloConvolutionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloConvolutionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloConvolutionOptionsBuilder { + typedef StablehloConvolutionOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_window_strides(flatbuffers::Offset> window_strides) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_STRIDES, window_strides); + } + void add_padding(flatbuffers::Offset> padding) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_PADDING, padding); + } + void add_lhs_dilation(flatbuffers::Offset> lhs_dilation) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_LHS_DILATION, lhs_dilation); + } + void add_rhs_dilation(flatbuffers::Offset> rhs_dilation) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_RHS_DILATION, rhs_dilation); + } + void add_window_reversal(flatbuffers::Offset> window_reversal) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_REVERSAL, window_reversal); + } + void add_input_batch_dimension(int64_t input_batch_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_INPUT_BATCH_DIMENSION, input_batch_dimension, 0); + } + void add_input_feature_dimension(int64_t input_feature_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_INPUT_FEATURE_DIMENSION, input_feature_dimension, 0); + } + void add_input_spatial_dimensions(flatbuffers::Offset> input_spatial_dimensions) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_INPUT_SPATIAL_DIMENSIONS, input_spatial_dimensions); + } + void add_kernel_input_feature_dimension(int64_t kernel_input_feature_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_KERNEL_INPUT_FEATURE_DIMENSION, kernel_input_feature_dimension, 0); + } + void add_kernel_output_feature_dimension(int64_t kernel_output_feature_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_KERNEL_OUTPUT_FEATURE_DIMENSION, kernel_output_feature_dimension, 0); + } + void add_kernel_spatial_dimensions(flatbuffers::Offset> kernel_spatial_dimensions) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_KERNEL_SPATIAL_DIMENSIONS, kernel_spatial_dimensions); + } + void add_output_batch_dimension(int64_t output_batch_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_OUTPUT_BATCH_DIMENSION, output_batch_dimension, 0); + } + void add_output_feature_dimension(int64_t output_feature_dimension) { + fbb_.AddElement(StablehloConvolutionOptions::VT_OUTPUT_FEATURE_DIMENSION, output_feature_dimension, 0); + } + void add_output_spatial_dimensions(flatbuffers::Offset> output_spatial_dimensions) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_OUTPUT_SPATIAL_DIMENSIONS, output_spatial_dimensions); + } + void add_feature_group_count(int64_t feature_group_count) { + fbb_.AddElement(StablehloConvolutionOptions::VT_FEATURE_GROUP_COUNT, feature_group_count, 0); + } + void add_batch_group_count(int64_t batch_group_count) { + fbb_.AddElement(StablehloConvolutionOptions::VT_BATCH_GROUP_COUNT, batch_group_count, 0); + } + void add_precision_config(flatbuffers::Offset> precision_config) { + fbb_.AddOffset(StablehloConvolutionOptions::VT_PRECISION_CONFIG, precision_config); + } + explicit StablehloConvolutionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloConvolutionOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> window_strides = 0, + flatbuffers::Offset> padding = 0, + flatbuffers::Offset> lhs_dilation = 0, + flatbuffers::Offset> rhs_dilation = 0, + flatbuffers::Offset> window_reversal = 0, + int64_t input_batch_dimension = 0, + int64_t input_feature_dimension = 0, + flatbuffers::Offset> input_spatial_dimensions = 0, + int64_t kernel_input_feature_dimension = 0, + int64_t kernel_output_feature_dimension = 0, + flatbuffers::Offset> kernel_spatial_dimensions = 0, + int64_t output_batch_dimension = 0, + int64_t output_feature_dimension = 0, + flatbuffers::Offset> output_spatial_dimensions = 0, + int64_t feature_group_count = 0, + int64_t batch_group_count = 0, + flatbuffers::Offset> precision_config = 0) { + StablehloConvolutionOptionsBuilder builder_(_fbb); + builder_.add_batch_group_count(batch_group_count); + builder_.add_feature_group_count(feature_group_count); + builder_.add_output_feature_dimension(output_feature_dimension); + builder_.add_output_batch_dimension(output_batch_dimension); + builder_.add_kernel_output_feature_dimension(kernel_output_feature_dimension); + builder_.add_kernel_input_feature_dimension(kernel_input_feature_dimension); + builder_.add_input_feature_dimension(input_feature_dimension); + builder_.add_input_batch_dimension(input_batch_dimension); + builder_.add_precision_config(precision_config); + builder_.add_output_spatial_dimensions(output_spatial_dimensions); + builder_.add_kernel_spatial_dimensions(kernel_spatial_dimensions); + builder_.add_input_spatial_dimensions(input_spatial_dimensions); + builder_.add_window_reversal(window_reversal); + builder_.add_rhs_dilation(rhs_dilation); + builder_.add_lhs_dilation(lhs_dilation); + builder_.add_padding(padding); + builder_.add_window_strides(window_strides); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloConvolutionOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *window_strides = nullptr, + const std::vector *padding = nullptr, + const std::vector *lhs_dilation = nullptr, + const std::vector *rhs_dilation = nullptr, + const std::vector *window_reversal = nullptr, + int64_t input_batch_dimension = 0, + int64_t input_feature_dimension = 0, + const std::vector *input_spatial_dimensions = nullptr, + int64_t kernel_input_feature_dimension = 0, + int64_t kernel_output_feature_dimension = 0, + const std::vector *kernel_spatial_dimensions = nullptr, + int64_t output_batch_dimension = 0, + int64_t output_feature_dimension = 0, + const std::vector *output_spatial_dimensions = nullptr, + int64_t feature_group_count = 0, + int64_t batch_group_count = 0, + const std::vector *precision_config = nullptr) { + auto window_strides__ = window_strides ? _fbb.CreateVector(*window_strides) : 0; + auto padding__ = padding ? _fbb.CreateVector(*padding) : 0; + auto lhs_dilation__ = lhs_dilation ? _fbb.CreateVector(*lhs_dilation) : 0; + auto rhs_dilation__ = rhs_dilation ? _fbb.CreateVector(*rhs_dilation) : 0; + auto window_reversal__ = window_reversal ? _fbb.CreateVector(*window_reversal) : 0; + auto input_spatial_dimensions__ = input_spatial_dimensions ? _fbb.CreateVector(*input_spatial_dimensions) : 0; + auto kernel_spatial_dimensions__ = kernel_spatial_dimensions ? _fbb.CreateVector(*kernel_spatial_dimensions) : 0; + auto output_spatial_dimensions__ = output_spatial_dimensions ? _fbb.CreateVector(*output_spatial_dimensions) : 0; + auto precision_config__ = precision_config ? _fbb.CreateVector(*precision_config) : 0; + return tflite_micro::CreateStablehloConvolutionOptions( + _fbb, + window_strides__, + padding__, + lhs_dilation__, + rhs_dilation__, + window_reversal__, + input_batch_dimension, + input_feature_dimension, + input_spatial_dimensions__, + kernel_input_feature_dimension, + kernel_output_feature_dimension, + kernel_spatial_dimensions__, + output_batch_dimension, + output_feature_dimension, + output_spatial_dimensions__, + feature_group_count, + batch_group_count, + precision_config__); +} + +flatbuffers::Offset CreateStablehloConvolutionOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloScatterOptionsT : public flatbuffers::NativeTable { + typedef StablehloScatterOptions TableType; + bool indices_are_sorted = false; + std::vector update_window_dims{}; + std::vector inserted_window_dims{}; + std::vector scatter_dims_to_operand_dims{}; + int64_t index_vector_dim = 0; + bool unique_indices = false; + int32_t update_computation_subgraph_index = 0; +}; + +struct StablehloScatterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloScatterOptionsT NativeTableType; + typedef StablehloScatterOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_INDICES_ARE_SORTED = 4, + VT_UPDATE_WINDOW_DIMS = 6, + VT_INSERTED_WINDOW_DIMS = 8, + VT_SCATTER_DIMS_TO_OPERAND_DIMS = 10, + VT_INDEX_VECTOR_DIM = 12, + VT_UNIQUE_INDICES = 14, + VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX = 16 + }; + bool indices_are_sorted() const { + return GetField(VT_INDICES_ARE_SORTED, 0) != 0; + } + const flatbuffers::Vector *update_window_dims() const { + return GetPointer *>(VT_UPDATE_WINDOW_DIMS); + } + const flatbuffers::Vector *inserted_window_dims() const { + return GetPointer *>(VT_INSERTED_WINDOW_DIMS); + } + const flatbuffers::Vector *scatter_dims_to_operand_dims() const { + return GetPointer *>(VT_SCATTER_DIMS_TO_OPERAND_DIMS); + } + int64_t index_vector_dim() const { + return GetField(VT_INDEX_VECTOR_DIM, 0); + } + bool unique_indices() const { + return GetField(VT_UNIQUE_INDICES, 0) != 0; + } + int32_t update_computation_subgraph_index() const { + return GetField(VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_INDICES_ARE_SORTED, 1) && + VerifyOffset(verifier, VT_UPDATE_WINDOW_DIMS) && + verifier.VerifyVector(update_window_dims()) && + VerifyOffset(verifier, VT_INSERTED_WINDOW_DIMS) && + verifier.VerifyVector(inserted_window_dims()) && + VerifyOffset(verifier, VT_SCATTER_DIMS_TO_OPERAND_DIMS) && + verifier.VerifyVector(scatter_dims_to_operand_dims()) && + VerifyField(verifier, VT_INDEX_VECTOR_DIM, 8) && + VerifyField(verifier, VT_UNIQUE_INDICES, 1) && + VerifyField(verifier, VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + StablehloScatterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloScatterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloScatterOptionsBuilder { + typedef StablehloScatterOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_indices_are_sorted(bool indices_are_sorted) { + fbb_.AddElement(StablehloScatterOptions::VT_INDICES_ARE_SORTED, static_cast(indices_are_sorted), 0); + } + void add_update_window_dims(flatbuffers::Offset> update_window_dims) { + fbb_.AddOffset(StablehloScatterOptions::VT_UPDATE_WINDOW_DIMS, update_window_dims); + } + void add_inserted_window_dims(flatbuffers::Offset> inserted_window_dims) { + fbb_.AddOffset(StablehloScatterOptions::VT_INSERTED_WINDOW_DIMS, inserted_window_dims); + } + void add_scatter_dims_to_operand_dims(flatbuffers::Offset> scatter_dims_to_operand_dims) { + fbb_.AddOffset(StablehloScatterOptions::VT_SCATTER_DIMS_TO_OPERAND_DIMS, scatter_dims_to_operand_dims); + } + void add_index_vector_dim(int64_t index_vector_dim) { + fbb_.AddElement(StablehloScatterOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0); + } + void add_unique_indices(bool unique_indices) { + fbb_.AddElement(StablehloScatterOptions::VT_UNIQUE_INDICES, static_cast(unique_indices), 0); + } + void add_update_computation_subgraph_index(int32_t update_computation_subgraph_index) { + fbb_.AddElement(StablehloScatterOptions::VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, update_computation_subgraph_index, 0); + } + explicit StablehloScatterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloScatterOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool indices_are_sorted = false, + flatbuffers::Offset> update_window_dims = 0, + flatbuffers::Offset> inserted_window_dims = 0, + flatbuffers::Offset> scatter_dims_to_operand_dims = 0, + int64_t index_vector_dim = 0, + bool unique_indices = false, + int32_t update_computation_subgraph_index = 0) { + StablehloScatterOptionsBuilder builder_(_fbb); + builder_.add_index_vector_dim(index_vector_dim); + builder_.add_update_computation_subgraph_index(update_computation_subgraph_index); + builder_.add_scatter_dims_to_operand_dims(scatter_dims_to_operand_dims); + builder_.add_inserted_window_dims(inserted_window_dims); + builder_.add_update_window_dims(update_window_dims); + builder_.add_unique_indices(unique_indices); + builder_.add_indices_are_sorted(indices_are_sorted); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateStablehloScatterOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + bool indices_are_sorted = false, + const std::vector *update_window_dims = nullptr, + const std::vector *inserted_window_dims = nullptr, + const std::vector *scatter_dims_to_operand_dims = nullptr, + int64_t index_vector_dim = 0, + bool unique_indices = false, + int32_t update_computation_subgraph_index = 0) { + auto update_window_dims__ = update_window_dims ? _fbb.CreateVector(*update_window_dims) : 0; + auto inserted_window_dims__ = inserted_window_dims ? _fbb.CreateVector(*inserted_window_dims) : 0; + auto scatter_dims_to_operand_dims__ = scatter_dims_to_operand_dims ? _fbb.CreateVector(*scatter_dims_to_operand_dims) : 0; + return tflite_micro::CreateStablehloScatterOptions( + _fbb, + indices_are_sorted, + update_window_dims__, + inserted_window_dims__, + scatter_dims_to_operand_dims__, + index_vector_dim, + unique_indices, + update_computation_subgraph_index); +} + +flatbuffers::Offset CreateStablehloScatterOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StablehloRngBitGeneratorOptionsT : public flatbuffers::NativeTable { + typedef StablehloRngBitGeneratorOptions TableType; + tflite_micro::RngAlgorithm algorithm = tflite_micro::RngAlgorithm_DEFAULT; +}; + +struct StablehloRngBitGeneratorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StablehloRngBitGeneratorOptionsT NativeTableType; + typedef StablehloRngBitGeneratorOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALGORITHM = 4 + }; + tflite_micro::RngAlgorithm algorithm() const { + return static_cast(GetField(VT_ALGORITHM, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALGORITHM, 1) && + verifier.EndTable(); + } + StablehloRngBitGeneratorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StablehloRngBitGeneratorOptionsBuilder { + typedef StablehloRngBitGeneratorOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_algorithm(tflite_micro::RngAlgorithm algorithm) { + fbb_.AddElement(StablehloRngBitGeneratorOptions::VT_ALGORITHM, static_cast(algorithm), 0); + } + explicit StablehloRngBitGeneratorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStablehloRngBitGeneratorOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::RngAlgorithm algorithm = tflite_micro::RngAlgorithm_DEFAULT) { + StablehloRngBitGeneratorOptionsBuilder builder_(_fbb); + builder_.add_algorithm(algorithm); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStablehloRngBitGeneratorOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv2DOptionsT : public flatbuffers::NativeTable { + typedef Conv2DOptions TableType; + tflite_micro::Padding padding = tflite_micro::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + int32_t dilation_w_factor = 1; + int32_t dilation_h_factor = 1; + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_INT32; +}; + +struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv2DOptionsT NativeTableType; + typedef Conv2DOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_FUSED_ACTIVATION_FUNCTION = 10, + VT_DILATION_W_FACTOR = 12, + VT_DILATION_H_FACTOR = 14, + VT_QUANTIZED_BIAS_TYPE = 16 + }; + tflite_micro::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + int32_t dilation_w_factor() const { + return GetField(VT_DILATION_W_FACTOR, 1); + } + int32_t dilation_h_factor() const { + return GetField(VT_DILATION_H_FACTOR, 1); + } + tflite_micro::TensorType quantized_bias_type() const { + return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING, 1) && + VerifyField(verifier, VT_STRIDE_W, 4) && + VerifyField(verifier, VT_STRIDE_H, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && + VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && + VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && + verifier.EndTable(); + } + Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv2DOptionsBuilder { + typedef Conv2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite_micro::Padding padding) { + fbb_.AddElement(Conv2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(Conv2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(Conv2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_dilation_w_factor(int32_t dilation_w_factor) { + fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); + } + void add_dilation_h_factor(int32_t dilation_h_factor) { + fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); + } + void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { + fbb_.AddElement(Conv2DOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); + } + explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::Padding padding = tflite_micro::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1, + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { + Conv2DOptionsBuilder builder_(_fbb); + builder_.add_dilation_h_factor(dilation_h_factor); + builder_.add_dilation_w_factor(dilation_w_factor); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_quantized_bias_type(quantized_bias_type); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Conv3DOptionsT : public flatbuffers::NativeTable { + typedef Conv3DOptions TableType; + tflite_micro::Padding padding = tflite_micro::Padding_SAME; + int32_t stride_d = 0; + int32_t stride_w = 0; + int32_t stride_h = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + int32_t dilation_d_factor = 1; + int32_t dilation_w_factor = 1; + int32_t dilation_h_factor = 1; +}; + +struct Conv3DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Conv3DOptionsT NativeTableType; + typedef Conv3DOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_D = 6, + VT_STRIDE_W = 8, + VT_STRIDE_H = 10, + VT_FUSED_ACTIVATION_FUNCTION = 12, + VT_DILATION_D_FACTOR = 14, + VT_DILATION_W_FACTOR = 16, + VT_DILATION_H_FACTOR = 18 + }; + tflite_micro::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + int32_t stride_d() const { + return GetField(VT_STRIDE_D, 0); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + int32_t dilation_d_factor() const { + return GetField(VT_DILATION_D_FACTOR, 1); + } + int32_t dilation_w_factor() const { + return GetField(VT_DILATION_W_FACTOR, 1); + } + int32_t dilation_h_factor() const { + return GetField(VT_DILATION_H_FACTOR, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING, 1) && + VerifyField(verifier, VT_STRIDE_D, 4) && + VerifyField(verifier, VT_STRIDE_W, 4) && + VerifyField(verifier, VT_STRIDE_H, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_DILATION_D_FACTOR, 4) && + VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && + VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && + verifier.EndTable(); + } + Conv3DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Conv3DOptionsBuilder { + typedef Conv3DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite_micro::Padding padding) { + fbb_.AddElement(Conv3DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_d(int32_t stride_d) { + fbb_.AddElement(Conv3DOptions::VT_STRIDE_D, stride_d, 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(Conv3DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(Conv3DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Conv3DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_dilation_d_factor(int32_t dilation_d_factor) { + fbb_.AddElement(Conv3DOptions::VT_DILATION_D_FACTOR, dilation_d_factor, 1); + } + void add_dilation_w_factor(int32_t dilation_w_factor) { + fbb_.AddElement(Conv3DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); + } + void add_dilation_h_factor(int32_t dilation_h_factor) { + fbb_.AddElement(Conv3DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); + } + explicit Conv3DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConv3DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::Padding padding = tflite_micro::Padding_SAME, + int32_t stride_d = 0, + int32_t stride_w = 0, + int32_t stride_h = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + int32_t dilation_d_factor = 1, + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1) { + Conv3DOptionsBuilder builder_(_fbb); + builder_.add_dilation_h_factor(dilation_h_factor); + builder_.add_dilation_w_factor(dilation_w_factor); + builder_.add_dilation_d_factor(dilation_d_factor); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_stride_d(stride_d); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Pool2DOptionsT : public flatbuffers::NativeTable { + typedef Pool2DOptions TableType; + tflite_micro::Padding padding = tflite_micro::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + int32_t filter_width = 0; + int32_t filter_height = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; +}; + +struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Pool2DOptionsT NativeTableType; + typedef Pool2DOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_FILTER_WIDTH = 10, + VT_FILTER_HEIGHT = 12, + VT_FUSED_ACTIVATION_FUNCTION = 14 + }; + tflite_micro::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + int32_t filter_width() const { + return GetField(VT_FILTER_WIDTH, 0); + } + int32_t filter_height() const { + return GetField(VT_FILTER_HEIGHT, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING, 1) && + VerifyField(verifier, VT_STRIDE_W, 4) && + VerifyField(verifier, VT_STRIDE_H, 4) && + VerifyField(verifier, VT_FILTER_WIDTH, 4) && + VerifyField(verifier, VT_FILTER_HEIGHT, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + verifier.EndTable(); + } + Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Pool2DOptionsBuilder { + typedef Pool2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite_micro::Padding padding) { + fbb_.AddElement(Pool2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(Pool2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(Pool2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_filter_width(int32_t filter_width) { + fbb_.AddElement(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); + } + void add_filter_height(int32_t filter_height) { + fbb_.AddElement(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePool2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::Padding padding = tflite_micro::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t filter_width = 0, + int32_t filter_height = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { + Pool2DOptionsBuilder builder_(_fbb); + builder_.add_filter_height(filter_height); + builder_.add_filter_width(filter_width); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { + typedef DepthwiseConv2DOptions TableType; + tflite_micro::Padding padding = tflite_micro::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + int32_t depth_multiplier = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + int32_t dilation_w_factor = 1; + int32_t dilation_h_factor = 1; +}; + +struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DepthwiseConv2DOptionsT NativeTableType; + typedef DepthwiseConv2DOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_DEPTH_MULTIPLIER = 10, + VT_FUSED_ACTIVATION_FUNCTION = 12, + VT_DILATION_W_FACTOR = 14, + VT_DILATION_H_FACTOR = 16 + }; + tflite_micro::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + int32_t depth_multiplier() const { + return GetField(VT_DEPTH_MULTIPLIER, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + int32_t dilation_w_factor() const { + return GetField(VT_DILATION_W_FACTOR, 1); + } + int32_t dilation_h_factor() const { + return GetField(VT_DILATION_H_FACTOR, 1); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING, 1) && + VerifyField(verifier, VT_STRIDE_W, 4) && + VerifyField(verifier, VT_STRIDE_H, 4) && + VerifyField(verifier, VT_DEPTH_MULTIPLIER, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && + VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && + verifier.EndTable(); + } + DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DepthwiseConv2DOptionsBuilder { + typedef DepthwiseConv2DOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite_micro::Padding padding) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); + } + void add_depth_multiplier(int32_t depth_multiplier) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_dilation_w_factor(int32_t dilation_w_factor) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); + } + void add_dilation_h_factor(int32_t dilation_h_factor) { + fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); + } + explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepthwiseConv2DOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::Padding padding = tflite_micro::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + int32_t depth_multiplier = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + int32_t dilation_w_factor = 1, + int32_t dilation_h_factor = 1) { + DepthwiseConv2DOptionsBuilder builder_(_fbb); + builder_.add_dilation_h_factor(dilation_h_factor); + builder_.add_dilation_w_factor(dilation_w_factor); + builder_.add_depth_multiplier(depth_multiplier); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable { + typedef ConcatEmbeddingsOptions TableType; + int32_t num_channels = 0; + std::vector num_columns_per_channel{}; + std::vector embedding_dim_per_channel{}; +}; + +struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConcatEmbeddingsOptionsT NativeTableType; + typedef ConcatEmbeddingsOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_CHANNELS = 4, + VT_NUM_COLUMNS_PER_CHANNEL = 6, + VT_EMBEDDING_DIM_PER_CHANNEL = 8 + }; + int32_t num_channels() const { + return GetField(VT_NUM_CHANNELS, 0); + } + const flatbuffers::Vector *num_columns_per_channel() const { + return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); + } + const flatbuffers::Vector *embedding_dim_per_channel() const { + return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_CHANNELS, 4) && + VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && + verifier.VerifyVector(num_columns_per_channel()) && + VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && + verifier.VerifyVector(embedding_dim_per_channel()) && + verifier.EndTable(); + } + ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConcatEmbeddingsOptionsBuilder { + typedef ConcatEmbeddingsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_channels(int32_t num_channels) { + fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); + } + void add_num_columns_per_channel(flatbuffers::Offset> num_columns_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); + } + void add_embedding_dim_per_channel(flatbuffers::Offset> embedding_dim_per_channel) { + fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel); + } + explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConcatEmbeddingsOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, + flatbuffers::Offset> num_columns_per_channel = 0, + flatbuffers::Offset> embedding_dim_per_channel = 0) { + ConcatEmbeddingsOptionsBuilder builder_(_fbb); + builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); + builder_.add_num_columns_per_channel(num_columns_per_channel); + builder_.add_num_channels(num_channels); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateConcatEmbeddingsOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_channels = 0, + const std::vector *num_columns_per_channel = nullptr, + const std::vector *embedding_dim_per_channel = nullptr) { + auto num_columns_per_channel__ = num_columns_per_channel ? _fbb.CreateVector(*num_columns_per_channel) : 0; + auto embedding_dim_per_channel__ = embedding_dim_per_channel ? _fbb.CreateVector(*embedding_dim_per_channel) : 0; + return tflite_micro::CreateConcatEmbeddingsOptions( + _fbb, + num_channels, + num_columns_per_channel__, + embedding_dim_per_channel__); +} + +flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSHProjectionOptionsT : public flatbuffers::NativeTable { + typedef LSHProjectionOptions TableType; + tflite_micro::LSHProjectionType type = tflite_micro::LSHProjectionType_UNKNOWN; +}; + +struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSHProjectionOptionsT NativeTableType; + typedef LSHProjectionOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TYPE = 4 + }; + tflite_micro::LSHProjectionType type() const { + return static_cast(GetField(VT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TYPE, 1) && + verifier.EndTable(); + } + LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSHProjectionOptionsBuilder { + typedef LSHProjectionOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_type(tflite_micro::LSHProjectionType type) { + fbb_.AddElement(LSHProjectionOptions::VT_TYPE, static_cast(type), 0); + } + explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSHProjectionOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::LSHProjectionType type = tflite_micro::LSHProjectionType_UNKNOWN) { + LSHProjectionOptionsBuilder builder_(_fbb); + builder_.add_type(type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SVDFOptionsT : public flatbuffers::NativeTable { + typedef SVDFOptions TableType; + int32_t rank = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SVDFOptionsT NativeTableType; + typedef SVDFOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_RANK = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 + }; + int32_t rank() const { + return GetField(VT_RANK, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_RANK, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SVDFOptionsBuilder { + typedef SVDFOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_rank(int32_t rank) { + fbb_.AddElement(SVDFOptions::VT_RANK, rank, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSVDFOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t rank = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + SVDFOptionsBuilder builder_(_fbb); + builder_.add_rank(rank); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RNNOptionsT : public flatbuffers::NativeTable { + typedef RNNOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RNNOptionsT NativeTableType; + typedef RNNOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RNNOptionsBuilder { + typedef RNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + RNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SequenceRNNOptionsT : public flatbuffers::NativeTable { + typedef SequenceRNNOptions TableType; + bool time_major = false; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool asymmetric_quantize_inputs = false; +}; + +struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SequenceRNNOptionsT NativeTableType; + typedef SequenceRNNOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TIME_MAJOR, 1) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SequenceRNNOptionsBuilder { + typedef SequenceRNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_time_major(bool time_major) { + fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool asymmetric_quantize_inputs = false) { + SequenceRNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_time_major(time_major); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { + typedef BidirectionalSequenceRNNOptions TableType; + bool time_major = false; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool merge_outputs = false; + bool asymmetric_quantize_inputs = false; +}; + +struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BidirectionalSequenceRNNOptionsT NativeTableType; + typedef BidirectionalSequenceRNNOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TIME_MAJOR = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6, + VT_MERGE_OUTPUTS = 8, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 + }; + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool merge_outputs() const { + return GetField(VT_MERGE_OUTPUTS, 0) != 0; + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TIME_MAJOR, 1) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_MERGE_OUTPUTS, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BidirectionalSequenceRNNOptionsBuilder { + typedef BidirectionalSequenceRNNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_time_major(bool time_major) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_merge_outputs(bool merge_outputs) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool time_major = false, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool merge_outputs = false, + bool asymmetric_quantize_inputs = false) { + BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_merge_outputs(merge_outputs); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_time_major(time_major); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FullyConnectedOptionsT : public flatbuffers::NativeTable { + typedef FullyConnectedOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + tflite_micro::FullyConnectedOptionsWeightsFormat weights_format = tflite_micro::FullyConnectedOptionsWeightsFormat_DEFAULT; + bool keep_num_dims = false; + bool asymmetric_quantize_inputs = false; + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32; +}; + +struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FullyConnectedOptionsT NativeTableType; + typedef FullyConnectedOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_WEIGHTS_FORMAT = 6, + VT_KEEP_NUM_DIMS = 8, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 10, + VT_QUANTIZED_BIAS_TYPE = 12 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + tflite_micro::FullyConnectedOptionsWeightsFormat weights_format() const { + return static_cast(GetField(VT_WEIGHTS_FORMAT, 0)); + } + bool keep_num_dims() const { + return GetField(VT_KEEP_NUM_DIMS, 0) != 0; + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + tflite_micro::TensorType quantized_bias_type() const { + return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_WEIGHTS_FORMAT, 1) && + VerifyField(verifier, VT_KEEP_NUM_DIMS, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && + verifier.EndTable(); + } + FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FullyConnectedOptionsBuilder { + typedef FullyConnectedOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_weights_format(tflite_micro::FullyConnectedOptionsWeightsFormat weights_format) { + fbb_.AddElement(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast(weights_format), 0); + } + void add_keep_num_dims(bool keep_num_dims) { + fbb_.AddElement(FullyConnectedOptions::VT_KEEP_NUM_DIMS, static_cast(keep_num_dims), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { + fbb_.AddElement(FullyConnectedOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); + } + explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFullyConnectedOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + tflite_micro::FullyConnectedOptionsWeightsFormat weights_format = tflite_micro::FullyConnectedOptionsWeightsFormat_DEFAULT, + bool keep_num_dims = false, + bool asymmetric_quantize_inputs = false, + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { + FullyConnectedOptionsBuilder builder_(_fbb); + builder_.add_quantized_bias_type(quantized_bias_type); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_keep_num_dims(keep_num_dims); + builder_.add_weights_format(weights_format); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SoftmaxOptionsT : public flatbuffers::NativeTable { + typedef SoftmaxOptions TableType; + float beta = 0.0f; +}; + +struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SoftmaxOptionsT NativeTableType; + typedef SoftmaxOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BETA = 4 + }; + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BETA, 4) && + verifier.EndTable(); + } + SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SoftmaxOptionsBuilder { + typedef SoftmaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_beta(float beta) { + fbb_.AddElement(SoftmaxOptions::VT_BETA, beta, 0.0f); + } + explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSoftmaxOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float beta = 0.0f) { + SoftmaxOptionsBuilder builder_(_fbb); + builder_.add_beta(beta); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ConcatenationOptionsT : public flatbuffers::NativeTable { + typedef ConcatenationOptions TableType; + int32_t axis = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; +}; + +struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ConcatenationOptionsT NativeTableType; + typedef ConcatenationOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_FUSED_ACTIVATION_FUNCTION = 6 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + verifier.EndTable(); + } + ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ConcatenationOptionsBuilder { + typedef ConcatenationOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(ConcatenationOptions::VT_AXIS, axis, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateConcatenationOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { + ConcatenationOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddOptionsT : public flatbuffers::NativeTable { + typedef AddOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool pot_scale_int16 = true; +}; + +struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddOptionsT NativeTableType; + typedef AddOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_POT_SCALE_INT16 = 6 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool pot_scale_int16() const { + return GetField(VT_POT_SCALE_INT16, 1) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_POT_SCALE_INT16, 1) && + verifier.EndTable(); + } + AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddOptionsBuilder { + typedef AddOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_pot_scale_int16(bool pot_scale_int16) { + fbb_.AddElement(AddOptions::VT_POT_SCALE_INT16, static_cast(pot_scale_int16), 1); + } + explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool pot_scale_int16 = true) { + AddOptionsBuilder builder_(_fbb); + builder_.add_pot_scale_int16(pot_scale_int16); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MulOptionsT : public flatbuffers::NativeTable { + typedef MulOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; +}; + +struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MulOptionsT NativeTableType; + typedef MulOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + verifier.EndTable(); + } + MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MulOptionsBuilder { + typedef MulOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMulOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { + MulOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct L2NormOptionsT : public flatbuffers::NativeTable { + typedef L2NormOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; +}; + +struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef L2NormOptionsT NativeTableType; + typedef L2NormOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + verifier.EndTable(); + } + L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct L2NormOptionsBuilder { + typedef L2NormOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateL2NormOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { + L2NormOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { + typedef LocalResponseNormalizationOptions TableType; + int32_t radius = 0; + float bias = 0.0f; + float alpha = 0.0f; + float beta = 0.0f; +}; + +struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LocalResponseNormalizationOptionsT NativeTableType; + typedef LocalResponseNormalizationOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_RADIUS = 4, + VT_BIAS = 6, + VT_ALPHA = 8, + VT_BETA = 10 + }; + int32_t radius() const { + return GetField(VT_RADIUS, 0); + } + float bias() const { + return GetField(VT_BIAS, 0.0f); + } + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + float beta() const { + return GetField(VT_BETA, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_RADIUS, 4) && + VerifyField(verifier, VT_BIAS, 4) && + VerifyField(verifier, VT_ALPHA, 4) && + VerifyField(verifier, VT_BETA, 4) && + verifier.EndTable(); + } + LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LocalResponseNormalizationOptionsBuilder { + typedef LocalResponseNormalizationOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_radius(int32_t radius) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); + } + void add_bias(float bias) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); + } + void add_alpha(float alpha) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); + } + void add_beta(float beta) { + fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); + } + explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t radius = 0, + float bias = 0.0f, + float alpha = 0.0f, + float beta = 0.0f) { + LocalResponseNormalizationOptionsBuilder builder_(_fbb); + builder_.add_beta(beta); + builder_.add_alpha(alpha); + builder_.add_bias(bias); + builder_.add_radius(radius); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LSTMOptionsT : public flatbuffers::NativeTable { + typedef LSTMOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + tflite_micro::LSTMKernelType kernel_type = tflite_micro::LSTMKernelType_FULL; + bool asymmetric_quantize_inputs = false; +}; + +struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LSTMOptionsT NativeTableType; + typedef LSTMOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_KERNEL_TYPE = 10, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + tflite_micro::LSTMKernelType kernel_type() const { + return static_cast(GetField(VT_KERNEL_TYPE, 0)); + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_CELL_CLIP, 4) && + VerifyField(verifier, VT_PROJ_CLIP, 4) && + VerifyField(verifier, VT_KERNEL_TYPE, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LSTMOptionsBuilder { + typedef LSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_kernel_type(tflite_micro::LSTMKernelType kernel_type) { + fbb_.AddElement(LSTMOptions::VT_KERNEL_TYPE, static_cast(kernel_type), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + tflite_micro::LSTMKernelType kernel_type = tflite_micro::LSTMKernelType_FULL, + bool asymmetric_quantize_inputs = false) { + LSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_kernel_type(kernel_type); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { + typedef UnidirectionalSequenceLSTMOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + bool time_major = false; + bool asymmetric_quantize_inputs = false; + bool diagonal_recurrent_tensors = false; +}; + +struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnidirectionalSequenceLSTMOptionsT NativeTableType; + typedef UnidirectionalSequenceLSTMOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_TIME_MAJOR = 10, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 12, + VT_DIAGONAL_RECURRENT_TENSORS = 14 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + bool time_major() const { + return GetField(VT_TIME_MAJOR, 0) != 0; + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool diagonal_recurrent_tensors() const { + return GetField(VT_DIAGONAL_RECURRENT_TENSORS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_CELL_CLIP, 4) && + VerifyField(verifier, VT_PROJ_CLIP, 4) && + VerifyField(verifier, VT_TIME_MAJOR, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + VerifyField(verifier, VT_DIAGONAL_RECURRENT_TENSORS, 1) && + verifier.EndTable(); + } + UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnidirectionalSequenceLSTMOptionsBuilder { + typedef UnidirectionalSequenceLSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_time_major(bool time_major) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + void add_diagonal_recurrent_tensors(bool diagonal_recurrent_tensors) { + fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_DIAGONAL_RECURRENT_TENSORS, static_cast(diagonal_recurrent_tensors), 0); + } + explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + bool time_major = false, + bool asymmetric_quantize_inputs = false, + bool diagonal_recurrent_tensors = false) { + UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_diagonal_recurrent_tensors(diagonal_recurrent_tensors); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_time_major(time_major); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { + typedef BidirectionalSequenceLSTMOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + float cell_clip = 0.0f; + float proj_clip = 0.0f; + bool merge_outputs = false; + bool time_major = true; + bool asymmetric_quantize_inputs = false; +}; + +struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BidirectionalSequenceLSTMOptionsT NativeTableType; + typedef BidirectionalSequenceLSTMOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_CELL_CLIP = 6, + VT_PROJ_CLIP = 8, + VT_MERGE_OUTPUTS = 10, + VT_TIME_MAJOR = 12, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + float cell_clip() const { + return GetField(VT_CELL_CLIP, 0.0f); + } + float proj_clip() const { + return GetField(VT_PROJ_CLIP, 0.0f); + } + bool merge_outputs() const { + return GetField(VT_MERGE_OUTPUTS, 0) != 0; + } + bool time_major() const { + return GetField(VT_TIME_MAJOR, 1) != 0; + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_CELL_CLIP, 4) && + VerifyField(verifier, VT_PROJ_CLIP, 4) && + VerifyField(verifier, VT_MERGE_OUTPUTS, 1) && + VerifyField(verifier, VT_TIME_MAJOR, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BidirectionalSequenceLSTMOptionsBuilder { + typedef BidirectionalSequenceLSTMOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_cell_clip(float cell_clip) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); + } + void add_proj_clip(float proj_clip) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); + } + void add_merge_outputs(bool merge_outputs) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); + } + void add_time_major(bool time_major) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 1); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + float cell_clip = 0.0f, + float proj_clip = 0.0f, + bool merge_outputs = false, + bool time_major = true, + bool asymmetric_quantize_inputs = false) { + BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); + builder_.add_proj_clip(proj_clip); + builder_.add_cell_clip(cell_clip); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_time_major(time_major); + builder_.add_merge_outputs(merge_outputs); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { + typedef ResizeBilinearOptions TableType; + bool align_corners = false; + bool half_pixel_centers = false; +}; + +struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeBilinearOptionsT NativeTableType; + typedef ResizeBilinearOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALIGN_CORNERS = 8, + VT_HALF_PIXEL_CENTERS = 10 + }; + bool align_corners() const { + return GetField(VT_ALIGN_CORNERS, 0) != 0; + } + bool half_pixel_centers() const { + return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALIGN_CORNERS, 1) && + VerifyField(verifier, VT_HALF_PIXEL_CENTERS, 1) && + verifier.EndTable(); + } + ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ResizeBilinearOptionsBuilder { + typedef ResizeBilinearOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_align_corners(bool align_corners) { + fbb_.AddElement(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); + } + void add_half_pixel_centers(bool half_pixel_centers) { + fbb_.AddElement(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); + } + explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResizeBilinearOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool align_corners = false, + bool half_pixel_centers = false) { + ResizeBilinearOptionsBuilder builder_(_fbb); + builder_.add_half_pixel_centers(half_pixel_centers); + builder_.add_align_corners(align_corners); + return builder_.Finish(); +} + +flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable { + typedef ResizeNearestNeighborOptions TableType; + bool align_corners = false; + bool half_pixel_centers = false; +}; + +struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ResizeNearestNeighborOptionsT NativeTableType; + typedef ResizeNearestNeighborOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALIGN_CORNERS = 4, + VT_HALF_PIXEL_CENTERS = 6 + }; + bool align_corners() const { + return GetField(VT_ALIGN_CORNERS, 0) != 0; + } + bool half_pixel_centers() const { + return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALIGN_CORNERS, 1) && + VerifyField(verifier, VT_HALF_PIXEL_CENTERS, 1) && + verifier.EndTable(); + } + ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ResizeNearestNeighborOptionsBuilder { + typedef ResizeNearestNeighborOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_align_corners(bool align_corners) { + fbb_.AddElement(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); + } + void add_half_pixel_centers(bool half_pixel_centers) { + fbb_.AddElement(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); + } + explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateResizeNearestNeighborOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool align_corners = false, + bool half_pixel_centers = false) { + ResizeNearestNeighborOptionsBuilder builder_(_fbb); + builder_.add_half_pixel_centers(half_pixel_centers); + builder_.add_align_corners(align_corners); + return builder_.Finish(); +} + +flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CallOptionsT : public flatbuffers::NativeTable { + typedef CallOptions TableType; + uint32_t subgraph = 0; +}; + +struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CallOptionsT NativeTableType; + typedef CallOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SUBGRAPH = 4 + }; + uint32_t subgraph() const { + return GetField(VT_SUBGRAPH, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SUBGRAPH, 4) && + verifier.EndTable(); + } + CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CallOptionsBuilder { + typedef CallOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_subgraph(uint32_t subgraph) { + fbb_.AddElement(CallOptions::VT_SUBGRAPH, subgraph, 0); + } + explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCallOptions( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t subgraph = 0) { + CallOptionsBuilder builder_(_fbb); + builder_.add_subgraph(subgraph); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PadOptionsT : public flatbuffers::NativeTable { + typedef PadOptions TableType; +}; + +struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PadOptionsT NativeTableType; + typedef PadOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PadOptionsBuilder { + typedef PadOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePadOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + PadOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PadV2OptionsT : public flatbuffers::NativeTable { + typedef PadV2Options TableType; +}; + +struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PadV2OptionsT NativeTableType; + typedef PadV2OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PadV2OptionsBuilder { + typedef PadV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePadV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + PadV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReshapeOptionsT : public flatbuffers::NativeTable { + typedef ReshapeOptions TableType; + std::vector new_shape{}; +}; + +struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReshapeOptionsT NativeTableType; + typedef ReshapeOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NEW_SHAPE = 4 + }; + const flatbuffers::Vector *new_shape() const { + return GetPointer *>(VT_NEW_SHAPE); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NEW_SHAPE) && + verifier.VerifyVector(new_shape()) && + verifier.EndTable(); + } + ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReshapeOptionsBuilder { + typedef ReshapeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_new_shape(flatbuffers::Offset> new_shape) { + fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); + } + explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReshapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> new_shape = 0) { + ReshapeOptionsBuilder builder_(_fbb); + builder_.add_new_shape(new_shape); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateReshapeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *new_shape = nullptr) { + auto new_shape__ = new_shape ? _fbb.CreateVector(*new_shape) : 0; + return tflite_micro::CreateReshapeOptions( + _fbb, + new_shape__); +} + +flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { + typedef SpaceToBatchNDOptions TableType; +}; + +struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToBatchNDOptionsT NativeTableType; + typedef SpaceToBatchNDOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToBatchNDOptionsBuilder { + typedef SpaceToBatchNDOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToBatchNDOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SpaceToBatchNDOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { + typedef BatchToSpaceNDOptions TableType; +}; + +struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchToSpaceNDOptionsT NativeTableType; + typedef BatchToSpaceNDOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchToSpaceNDOptionsBuilder { + typedef BatchToSpaceNDOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchToSpaceNDOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + BatchToSpaceNDOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SkipGramOptionsT : public flatbuffers::NativeTable { + typedef SkipGramOptions TableType; + int32_t ngram_size = 0; + int32_t max_skip_size = 0; + bool include_all_ngrams = false; +}; + +struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SkipGramOptionsT NativeTableType; + typedef SkipGramOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NGRAM_SIZE = 4, + VT_MAX_SKIP_SIZE = 6, + VT_INCLUDE_ALL_NGRAMS = 8 + }; + int32_t ngram_size() const { + return GetField(VT_NGRAM_SIZE, 0); + } + int32_t max_skip_size() const { + return GetField(VT_MAX_SKIP_SIZE, 0); + } + bool include_all_ngrams() const { + return GetField(VT_INCLUDE_ALL_NGRAMS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NGRAM_SIZE, 4) && + VerifyField(verifier, VT_MAX_SKIP_SIZE, 4) && + VerifyField(verifier, VT_INCLUDE_ALL_NGRAMS, 1) && + verifier.EndTable(); + } + SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SkipGramOptionsBuilder { + typedef SkipGramOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_ngram_size(int32_t ngram_size) { + fbb_.AddElement(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); + } + void add_max_skip_size(int32_t max_skip_size) { + fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); + } + void add_include_all_ngrams(bool include_all_ngrams) { + fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast(include_all_ngrams), 0); + } + explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSkipGramOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t ngram_size = 0, + int32_t max_skip_size = 0, + bool include_all_ngrams = false) { + SkipGramOptionsBuilder builder_(_fbb); + builder_.add_max_skip_size(max_skip_size); + builder_.add_ngram_size(ngram_size); + builder_.add_include_all_ngrams(include_all_ngrams); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SpaceToDepthOptionsT : public flatbuffers::NativeTable { + typedef SpaceToDepthOptions TableType; + int32_t block_size = 0; +}; + +struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SpaceToDepthOptionsT NativeTableType; + typedef SpaceToDepthOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4 + }; + int32_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE, 4) && + verifier.EndTable(); + } + SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SpaceToDepthOptionsBuilder { + typedef SpaceToDepthOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int32_t block_size) { + fbb_.AddElement(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); + } + explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSpaceToDepthOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t block_size = 0) { + SpaceToDepthOptionsBuilder builder_(_fbb); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DepthToSpaceOptionsT : public flatbuffers::NativeTable { + typedef DepthToSpaceOptions TableType; + int32_t block_size = 0; +}; + +struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DepthToSpaceOptionsT NativeTableType; + typedef DepthToSpaceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BLOCK_SIZE = 4 + }; + int32_t block_size() const { + return GetField(VT_BLOCK_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BLOCK_SIZE, 4) && + verifier.EndTable(); + } + DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DepthToSpaceOptionsBuilder { + typedef DepthToSpaceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_block_size(int32_t block_size) { + fbb_.AddElement(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); + } + explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDepthToSpaceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t block_size = 0) { + DepthToSpaceOptionsBuilder builder_(_fbb); + builder_.add_block_size(block_size); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubOptionsT : public flatbuffers::NativeTable { + typedef SubOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + bool pot_scale_int16 = true; +}; + +struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubOptionsT NativeTableType; + typedef SubOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4, + VT_POT_SCALE_INT16 = 6 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool pot_scale_int16() const { + return GetField(VT_POT_SCALE_INT16, 1) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_POT_SCALE_INT16, 1) && + verifier.EndTable(); + } + SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubOptionsBuilder { + typedef SubOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_pot_scale_int16(bool pot_scale_int16) { + fbb_.AddElement(SubOptions::VT_POT_SCALE_INT16, static_cast(pot_scale_int16), 1); + } + explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + bool pot_scale_int16 = true) { + SubOptionsBuilder builder_(_fbb); + builder_.add_pot_scale_int16(pot_scale_int16); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DivOptionsT : public flatbuffers::NativeTable { + typedef DivOptions TableType; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; +}; + +struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DivOptionsT NativeTableType; + typedef DivOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_FUSED_ACTIVATION_FUNCTION = 4 + }; + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + verifier.EndTable(); + } + DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DivOptionsBuilder { + typedef DivOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDivOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { + DivOptionsBuilder builder_(_fbb); + builder_.add_fused_activation_function(fused_activation_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TopKV2OptionsT : public flatbuffers::NativeTable { + typedef TopKV2Options TableType; +}; + +struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TopKV2OptionsT NativeTableType; + typedef TopKV2OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TopKV2OptionsBuilder { + typedef TopKV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTopKV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + TopKV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable { + typedef EmbeddingLookupSparseOptions TableType; + tflite_micro::CombinerType combiner = tflite_micro::CombinerType_SUM; +}; + +struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EmbeddingLookupSparseOptionsT NativeTableType; + typedef EmbeddingLookupSparseOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COMBINER = 4 + }; + tflite_micro::CombinerType combiner() const { + return static_cast(GetField(VT_COMBINER, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COMBINER, 1) && + verifier.EndTable(); + } + EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EmbeddingLookupSparseOptionsBuilder { + typedef EmbeddingLookupSparseOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_combiner(tflite_micro::CombinerType combiner) { + fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast(combiner), 0); + } + explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::CombinerType combiner = tflite_micro::CombinerType_SUM) { + EmbeddingLookupSparseOptionsBuilder builder_(_fbb); + builder_.add_combiner(combiner); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherOptionsT : public flatbuffers::NativeTable { + typedef GatherOptions TableType; + int32_t axis = 0; + int32_t batch_dims = 0; +}; + +struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherOptionsT NativeTableType; + typedef GatherOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4, + VT_BATCH_DIMS = 6 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + int32_t batch_dims() const { + return GetField(VT_BATCH_DIMS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS, 4) && + VerifyField(verifier, VT_BATCH_DIMS, 4) && + verifier.EndTable(); + } + GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherOptionsBuilder { + typedef GatherOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(GatherOptions::VT_AXIS, axis, 0); + } + void add_batch_dims(int32_t batch_dims) { + fbb_.AddElement(GatherOptions::VT_BATCH_DIMS, batch_dims, 0); + } + explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0, + int32_t batch_dims = 0) { + GatherOptionsBuilder builder_(_fbb); + builder_.add_batch_dims(batch_dims); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TransposeOptionsT : public flatbuffers::NativeTable { + typedef TransposeOptions TableType; +}; + +struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TransposeOptionsT NativeTableType; + typedef TransposeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TransposeOptionsBuilder { + typedef TransposeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTransposeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + TransposeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpOptionsT : public flatbuffers::NativeTable { + typedef ExpOptions TableType; +}; + +struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpOptionsT NativeTableType; + typedef ExpOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpOptionsBuilder { + typedef ExpOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CosOptionsT : public flatbuffers::NativeTable { + typedef CosOptions TableType; +}; + +struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CosOptionsT NativeTableType; + typedef CosOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CosOptionsBuilder { + typedef CosOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCosOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + CosOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReducerOptionsT : public flatbuffers::NativeTable { + typedef ReducerOptions TableType; + bool keep_dims = false; +}; + +struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReducerOptionsT NativeTableType; + typedef ReducerOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_KEEP_DIMS = 4 + }; + bool keep_dims() const { + return GetField(VT_KEEP_DIMS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_KEEP_DIMS, 1) && + verifier.EndTable(); + } + ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReducerOptionsBuilder { + typedef ReducerOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_keep_dims(bool keep_dims) { + fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); + } + explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReducerOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool keep_dims = false) { + ReducerOptionsBuilder builder_(_fbb); + builder_.add_keep_dims(keep_dims); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SqueezeOptionsT : public flatbuffers::NativeTable { + typedef SqueezeOptions TableType; + std::vector squeeze_dims{}; +}; + +struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SqueezeOptionsT NativeTableType; + typedef SqueezeOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SQUEEZE_DIMS = 4 + }; + const flatbuffers::Vector *squeeze_dims() const { + return GetPointer *>(VT_SQUEEZE_DIMS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_SQUEEZE_DIMS) && + verifier.VerifyVector(squeeze_dims()) && + verifier.EndTable(); + } + SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SqueezeOptionsBuilder { + typedef SqueezeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_squeeze_dims(flatbuffers::Offset> squeeze_dims) { + fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); + } + explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSqueezeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> squeeze_dims = 0) { + SqueezeOptionsBuilder builder_(_fbb); + builder_.add_squeeze_dims(squeeze_dims); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSqueezeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *squeeze_dims = nullptr) { + auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0; + return tflite_micro::CreateSqueezeOptions( + _fbb, + squeeze_dims__); +} + +flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SplitOptionsT : public flatbuffers::NativeTable { + typedef SplitOptions TableType; + int32_t num_splits = 0; +}; + +struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitOptionsT NativeTableType; + typedef SplitOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_SPLITS = 4 + }; + int32_t num_splits() const { + return GetField(VT_NUM_SPLITS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_SPLITS, 4) && + verifier.EndTable(); + } + SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitOptionsBuilder { + typedef SplitOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_splits(int32_t num_splits) { + fbb_.AddElement(SplitOptions::VT_NUM_SPLITS, num_splits, 0); + } + explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_splits = 0) { + SplitOptionsBuilder builder_(_fbb); + builder_.add_num_splits(num_splits); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SplitVOptionsT : public flatbuffers::NativeTable { + typedef SplitVOptions TableType; + int32_t num_splits = 0; +}; + +struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SplitVOptionsT NativeTableType; + typedef SplitVOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM_SPLITS = 4 + }; + int32_t num_splits() const { + return GetField(VT_NUM_SPLITS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM_SPLITS, 4) && + verifier.EndTable(); + } + SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SplitVOptionsBuilder { + typedef SplitVOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num_splits(int32_t num_splits) { + fbb_.AddElement(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); + } + explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSplitVOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num_splits = 0) { + SplitVOptionsBuilder builder_(_fbb); + builder_.add_num_splits(num_splits); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct StridedSliceOptionsT : public flatbuffers::NativeTable { + typedef StridedSliceOptions TableType; + int32_t begin_mask = 0; + int32_t end_mask = 0; + int32_t ellipsis_mask = 0; + int32_t new_axis_mask = 0; + int32_t shrink_axis_mask = 0; + bool offset = false; +}; + +struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef StridedSliceOptionsT NativeTableType; + typedef StridedSliceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BEGIN_MASK = 4, + VT_END_MASK = 6, + VT_ELLIPSIS_MASK = 8, + VT_NEW_AXIS_MASK = 10, + VT_SHRINK_AXIS_MASK = 12, + VT_OFFSET = 14 + }; + int32_t begin_mask() const { + return GetField(VT_BEGIN_MASK, 0); + } + int32_t end_mask() const { + return GetField(VT_END_MASK, 0); + } + int32_t ellipsis_mask() const { + return GetField(VT_ELLIPSIS_MASK, 0); + } + int32_t new_axis_mask() const { + return GetField(VT_NEW_AXIS_MASK, 0); + } + int32_t shrink_axis_mask() const { + return GetField(VT_SHRINK_AXIS_MASK, 0); + } + bool offset() const { + return GetField(VT_OFFSET, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_BEGIN_MASK, 4) && + VerifyField(verifier, VT_END_MASK, 4) && + VerifyField(verifier, VT_ELLIPSIS_MASK, 4) && + VerifyField(verifier, VT_NEW_AXIS_MASK, 4) && + VerifyField(verifier, VT_SHRINK_AXIS_MASK, 4) && + VerifyField(verifier, VT_OFFSET, 1) && + verifier.EndTable(); + } + StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct StridedSliceOptionsBuilder { + typedef StridedSliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_begin_mask(int32_t begin_mask) { + fbb_.AddElement(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); + } + void add_end_mask(int32_t end_mask) { + fbb_.AddElement(StridedSliceOptions::VT_END_MASK, end_mask, 0); + } + void add_ellipsis_mask(int32_t ellipsis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); + } + void add_new_axis_mask(int32_t new_axis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); + } + void add_shrink_axis_mask(int32_t shrink_axis_mask) { + fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); + } + void add_offset(bool offset) { + fbb_.AddElement(StridedSliceOptions::VT_OFFSET, static_cast(offset), 0); + } + explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateStridedSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t begin_mask = 0, + int32_t end_mask = 0, + int32_t ellipsis_mask = 0, + int32_t new_axis_mask = 0, + int32_t shrink_axis_mask = 0, + bool offset = false) { + StridedSliceOptionsBuilder builder_(_fbb); + builder_.add_shrink_axis_mask(shrink_axis_mask); + builder_.add_new_axis_mask(new_axis_mask); + builder_.add_ellipsis_mask(ellipsis_mask); + builder_.add_end_mask(end_mask); + builder_.add_begin_mask(begin_mask); + builder_.add_offset(offset); + return builder_.Finish(); +} + +flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogSoftmaxOptionsT : public flatbuffers::NativeTable { + typedef LogSoftmaxOptions TableType; +}; + +struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogSoftmaxOptionsT NativeTableType; + typedef LogSoftmaxOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogSoftmaxOptionsBuilder { + typedef LogSoftmaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogSoftmaxOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogSoftmaxOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CastOptionsT : public flatbuffers::NativeTable { + typedef CastOptions TableType; + tflite_micro::TensorType in_data_type = tflite_micro::TensorType_FLOAT32; + tflite_micro::TensorType out_data_type = tflite_micro::TensorType_FLOAT32; +}; + +struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CastOptionsT NativeTableType; + typedef CastOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IN_DATA_TYPE = 4, + VT_OUT_DATA_TYPE = 6 + }; + tflite_micro::TensorType in_data_type() const { + return static_cast(GetField(VT_IN_DATA_TYPE, 0)); + } + tflite_micro::TensorType out_data_type() const { + return static_cast(GetField(VT_OUT_DATA_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IN_DATA_TYPE, 1) && + VerifyField(verifier, VT_OUT_DATA_TYPE, 1) && + verifier.EndTable(); + } + CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CastOptionsBuilder { + typedef CastOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_in_data_type(tflite_micro::TensorType in_data_type) { + fbb_.AddElement(CastOptions::VT_IN_DATA_TYPE, static_cast(in_data_type), 0); + } + void add_out_data_type(tflite_micro::TensorType out_data_type) { + fbb_.AddElement(CastOptions::VT_OUT_DATA_TYPE, static_cast(out_data_type), 0); + } + explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCastOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::TensorType in_data_type = tflite_micro::TensorType_FLOAT32, + tflite_micro::TensorType out_data_type = tflite_micro::TensorType_FLOAT32) { + CastOptionsBuilder builder_(_fbb); + builder_.add_out_data_type(out_data_type); + builder_.add_in_data_type(in_data_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DequantizeOptionsT : public flatbuffers::NativeTable { + typedef DequantizeOptions TableType; +}; + +struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DequantizeOptionsT NativeTableType; + typedef DequantizeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DequantizeOptionsBuilder { + typedef DequantizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDequantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DequantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MaximumMinimumOptionsT : public flatbuffers::NativeTable { + typedef MaximumMinimumOptions TableType; +}; + +struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MaximumMinimumOptionsT NativeTableType; + typedef MaximumMinimumOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MaximumMinimumOptionsBuilder { + typedef MaximumMinimumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMaximumMinimumOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MaximumMinimumOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TileOptionsT : public flatbuffers::NativeTable { + typedef TileOptions TableType; +}; + +struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TileOptionsT NativeTableType; + typedef TileOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TileOptionsBuilder { + typedef TileOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTileOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + TileOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMaxOptionsT : public flatbuffers::NativeTable { + typedef ArgMaxOptions TableType; + tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32; +}; + +struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMaxOptionsT NativeTableType; + typedef ArgMaxOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_TYPE = 4 + }; + tflite_micro::TensorType output_type() const { + return static_cast(GetField(VT_OUTPUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_TYPE, 1) && + verifier.EndTable(); + } + ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMaxOptionsBuilder { + typedef ArgMaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_type(tflite_micro::TensorType output_type) { + fbb_.AddElement(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); + } + explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMaxOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32) { + ArgMaxOptionsBuilder builder_(_fbb); + builder_.add_output_type(output_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ArgMinOptionsT : public flatbuffers::NativeTable { + typedef ArgMinOptions TableType; + tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32; +}; + +struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ArgMinOptionsT NativeTableType; + typedef ArgMinOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUTPUT_TYPE = 4 + }; + tflite_micro::TensorType output_type() const { + return static_cast(GetField(VT_OUTPUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUTPUT_TYPE, 1) && + verifier.EndTable(); + } + ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ArgMinOptionsBuilder { + typedef ArgMinOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_output_type(tflite_micro::TensorType output_type) { + fbb_.AddElement(ArgMinOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); + } + explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateArgMinOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32) { + ArgMinOptionsBuilder builder_(_fbb); + builder_.add_output_type(output_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterOptionsT : public flatbuffers::NativeTable { + typedef GreaterOptions TableType; +}; + +struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterOptionsT NativeTableType; + typedef GreaterOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterOptionsBuilder { + typedef GreaterOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreaterOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GreaterEqualOptionsT : public flatbuffers::NativeTable { + typedef GreaterEqualOptions TableType; +}; + +struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GreaterEqualOptionsT NativeTableType; + typedef GreaterEqualOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GreaterEqualOptionsBuilder { + typedef GreaterEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGreaterEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GreaterEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessOptionsT : public flatbuffers::NativeTable { + typedef LessOptions TableType; +}; + +struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessOptionsT NativeTableType; + typedef LessOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessOptionsBuilder { + typedef LessOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLessOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LessOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LessEqualOptionsT : public flatbuffers::NativeTable { + typedef LessEqualOptions TableType; +}; + +struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LessEqualOptionsT NativeTableType; + typedef LessEqualOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LessEqualOptionsBuilder { + typedef LessEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLessEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LessEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NegOptionsT : public flatbuffers::NativeTable { + typedef NegOptions TableType; +}; + +struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NegOptionsT NativeTableType; + typedef NegOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NegOptionsBuilder { + typedef NegOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNegOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + NegOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SelectOptionsT : public flatbuffers::NativeTable { + typedef SelectOptions TableType; +}; + +struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SelectOptionsT NativeTableType; + typedef SelectOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SelectOptionsBuilder { + typedef SelectOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSelectOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SliceOptionsT : public flatbuffers::NativeTable { + typedef SliceOptions TableType; +}; + +struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SliceOptionsT NativeTableType; + typedef SliceOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SliceOptionsBuilder { + typedef SliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SliceOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TransposeConvOptionsT : public flatbuffers::NativeTable { + typedef TransposeConvOptions TableType; + tflite_micro::Padding padding = tflite_micro::Padding_SAME; + int32_t stride_w = 0; + int32_t stride_h = 0; + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32; +}; + +struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TransposeConvOptionsT NativeTableType; + typedef TransposeConvOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_PADDING = 4, + VT_STRIDE_W = 6, + VT_STRIDE_H = 8, + VT_FUSED_ACTIVATION_FUNCTION = 10, + VT_QUANTIZED_BIAS_TYPE = 12 + }; + tflite_micro::Padding padding() const { + return static_cast(GetField(VT_PADDING, 0)); + } + int32_t stride_w() const { + return GetField(VT_STRIDE_W, 0); + } + int32_t stride_h() const { + return GetField(VT_STRIDE_H, 0); + } + tflite_micro::ActivationFunctionType fused_activation_function() const { + return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); + } + tflite_micro::TensorType quantized_bias_type() const { + return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_PADDING, 1) && + VerifyField(verifier, VT_STRIDE_W, 4) && + VerifyField(verifier, VT_STRIDE_H, 4) && + VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && + VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && + verifier.EndTable(); + } + TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TransposeConvOptionsBuilder { + typedef TransposeConvOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_padding(tflite_micro::Padding padding) { + fbb_.AddElement(TransposeConvOptions::VT_PADDING, static_cast(padding), 0); + } + void add_stride_w(int32_t stride_w) { + fbb_.AddElement(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); + } + void add_stride_h(int32_t stride_h) { + fbb_.AddElement(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); + } + void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { + fbb_.AddElement(TransposeConvOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); + } + void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { + fbb_.AddElement(TransposeConvOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); + } + explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTransposeConvOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::Padding padding = tflite_micro::Padding_SAME, + int32_t stride_w = 0, + int32_t stride_h = 0, + tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, + tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { + TransposeConvOptionsBuilder builder_(_fbb); + builder_.add_stride_h(stride_h); + builder_.add_stride_w(stride_w); + builder_.add_quantized_bias_type(quantized_bias_type); + builder_.add_fused_activation_function(fused_activation_function); + builder_.add_padding(padding); + return builder_.Finish(); +} + +flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ExpandDimsOptionsT : public flatbuffers::NativeTable { + typedef ExpandDimsOptions TableType; +}; + +struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ExpandDimsOptionsT NativeTableType; + typedef ExpandDimsOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ExpandDimsOptionsBuilder { + typedef ExpandDimsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateExpandDimsOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ExpandDimsOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SparseToDenseOptionsT : public flatbuffers::NativeTable { + typedef SparseToDenseOptions TableType; + bool validate_indices = false; +}; + +struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SparseToDenseOptionsT NativeTableType; + typedef SparseToDenseOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALIDATE_INDICES = 4 + }; + bool validate_indices() const { + return GetField(VT_VALIDATE_INDICES, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALIDATE_INDICES, 1) && + verifier.EndTable(); + } + SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SparseToDenseOptionsBuilder { + typedef SparseToDenseOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_validate_indices(bool validate_indices) { + fbb_.AddElement(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast(validate_indices), 0); + } + explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSparseToDenseOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool validate_indices = false) { + SparseToDenseOptionsBuilder builder_(_fbb); + builder_.add_validate_indices(validate_indices); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct EqualOptionsT : public flatbuffers::NativeTable { + typedef EqualOptions TableType; +}; + +struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef EqualOptionsT NativeTableType; + typedef EqualOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct EqualOptionsBuilder { + typedef EqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + EqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NotEqualOptionsT : public flatbuffers::NativeTable { + typedef NotEqualOptions TableType; +}; + +struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NotEqualOptionsT NativeTableType; + typedef NotEqualOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NotEqualOptionsBuilder { + typedef NotEqualOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNotEqualOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + NotEqualOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ShapeOptionsT : public flatbuffers::NativeTable { + typedef ShapeOptions TableType; + tflite_micro::TensorType out_type = tflite_micro::TensorType_FLOAT32; +}; + +struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ShapeOptionsT NativeTableType; + typedef ShapeOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OUT_TYPE = 4 + }; + tflite_micro::TensorType out_type() const { + return static_cast(GetField(VT_OUT_TYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OUT_TYPE, 1) && + verifier.EndTable(); + } + ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ShapeOptionsBuilder { + typedef ShapeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_out_type(tflite_micro::TensorType out_type) { + fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); + } + explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateShapeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::TensorType out_type = tflite_micro::TensorType_FLOAT32) { + ShapeOptionsBuilder builder_(_fbb); + builder_.add_out_type(out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RankOptionsT : public flatbuffers::NativeTable { + typedef RankOptions TableType; +}; + +struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RankOptionsT NativeTableType; + typedef RankOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RankOptionsBuilder { + typedef RankOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRankOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + RankOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PowOptionsT : public flatbuffers::NativeTable { + typedef PowOptions TableType; +}; + +struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PowOptionsT NativeTableType; + typedef PowOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PowOptionsBuilder { + typedef PowOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePowOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + PowOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FakeQuantOptionsT : public flatbuffers::NativeTable { + typedef FakeQuantOptions TableType; + float min = 0.0f; + float max = 0.0f; + int32_t num_bits = 0; + bool narrow_range = false; +}; + +struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FakeQuantOptionsT NativeTableType; + typedef FakeQuantOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MIN = 4, + VT_MAX = 6, + VT_NUM_BITS = 8, + VT_NARROW_RANGE = 10 + }; + float min() const { + return GetField(VT_MIN, 0.0f); + } + float max() const { + return GetField(VT_MAX, 0.0f); + } + int32_t num_bits() const { + return GetField(VT_NUM_BITS, 0); + } + bool narrow_range() const { + return GetField(VT_NARROW_RANGE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MIN, 4) && + VerifyField(verifier, VT_MAX, 4) && + VerifyField(verifier, VT_NUM_BITS, 4) && + VerifyField(verifier, VT_NARROW_RANGE, 1) && + verifier.EndTable(); + } + FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FakeQuantOptionsBuilder { + typedef FakeQuantOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_min(float min) { + fbb_.AddElement(FakeQuantOptions::VT_MIN, min, 0.0f); + } + void add_max(float max) { + fbb_.AddElement(FakeQuantOptions::VT_MAX, max, 0.0f); + } + void add_num_bits(int32_t num_bits) { + fbb_.AddElement(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); + } + void add_narrow_range(bool narrow_range) { + fbb_.AddElement(FakeQuantOptions::VT_NARROW_RANGE, static_cast(narrow_range), 0); + } + explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFakeQuantOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float min = 0.0f, + float max = 0.0f, + int32_t num_bits = 0, + bool narrow_range = false) { + FakeQuantOptionsBuilder builder_(_fbb); + builder_.add_num_bits(num_bits); + builder_.add_max(max); + builder_.add_min(min); + builder_.add_narrow_range(narrow_range); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct PackOptionsT : public flatbuffers::NativeTable { + typedef PackOptions TableType; + int32_t values_count = 0; + int32_t axis = 0; +}; + +struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef PackOptionsT NativeTableType; + typedef PackOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VALUES_COUNT = 4, + VT_AXIS = 6 + }; + int32_t values_count() const { + return GetField(VT_VALUES_COUNT, 0); + } + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VALUES_COUNT, 4) && + VerifyField(verifier, VT_AXIS, 4) && + verifier.EndTable(); + } + PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct PackOptionsBuilder { + typedef PackOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_values_count(int32_t values_count) { + fbb_.AddElement(PackOptions::VT_VALUES_COUNT, values_count, 0); + } + void add_axis(int32_t axis) { + fbb_.AddElement(PackOptions::VT_AXIS, axis, 0); + } + explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreatePackOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t values_count = 0, + int32_t axis = 0) { + PackOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_values_count(values_count); + return builder_.Finish(); +} + +flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalOrOptionsT : public flatbuffers::NativeTable { + typedef LogicalOrOptions TableType; +}; + +struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalOrOptionsT NativeTableType; + typedef LogicalOrOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalOrOptionsBuilder { + typedef LogicalOrOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalOrOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalOrOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OneHotOptionsT : public flatbuffers::NativeTable { + typedef OneHotOptions TableType; + int32_t axis = 0; +}; + +struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OneHotOptionsT NativeTableType; + typedef OneHotOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS, 4) && + verifier.EndTable(); + } + OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OneHotOptionsBuilder { + typedef OneHotOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); + } + explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOneHotOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { + OneHotOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AbsOptionsT : public flatbuffers::NativeTable { + typedef AbsOptions TableType; +}; + +struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AbsOptionsT NativeTableType; + typedef AbsOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AbsOptionsBuilder { + typedef AbsOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAbsOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + AbsOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HardSwishOptionsT : public flatbuffers::NativeTable { + typedef HardSwishOptions TableType; +}; + +struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HardSwishOptionsT NativeTableType; + typedef HardSwishOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HardSwishOptionsBuilder { + typedef HardSwishOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHardSwishOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + HardSwishOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalAndOptionsT : public flatbuffers::NativeTable { + typedef LogicalAndOptions TableType; +}; + +struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalAndOptionsT NativeTableType; + typedef LogicalAndOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalAndOptionsBuilder { + typedef LogicalAndOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalAndOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalAndOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LogicalNotOptionsT : public flatbuffers::NativeTable { + typedef LogicalNotOptions TableType; +}; + +struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalNotOptionsT NativeTableType; + typedef LogicalNotOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalNotOptionsBuilder { + typedef LogicalNotOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalNotOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalNotOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnpackOptionsT : public flatbuffers::NativeTable { + typedef UnpackOptions TableType; + int32_t num = 0; + int32_t axis = 0; +}; + +struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnpackOptionsT NativeTableType; + typedef UnpackOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NUM = 4, + VT_AXIS = 6 + }; + int32_t num() const { + return GetField(VT_NUM, 0); + } + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_NUM, 4) && + VerifyField(verifier, VT_AXIS, 4) && + verifier.EndTable(); + } + UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnpackOptionsBuilder { + typedef UnpackOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_num(int32_t num) { + fbb_.AddElement(UnpackOptions::VT_NUM, num, 0); + } + void add_axis(int32_t axis) { + fbb_.AddElement(UnpackOptions::VT_AXIS, axis, 0); + } + explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnpackOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t num = 0, + int32_t axis = 0) { + UnpackOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + builder_.add_num(num); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorDivOptionsT : public flatbuffers::NativeTable { + typedef FloorDivOptions TableType; +}; + +struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorDivOptionsT NativeTableType; + typedef FloorDivOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorDivOptionsBuilder { + typedef FloorDivOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorDivOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorDivOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquareOptionsT : public flatbuffers::NativeTable { + typedef SquareOptions TableType; +}; + +struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquareOptionsT NativeTableType; + typedef SquareOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquareOptionsBuilder { + typedef SquareOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquareOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SquareOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ZerosLikeOptionsT : public flatbuffers::NativeTable { + typedef ZerosLikeOptions TableType; +}; + +struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ZerosLikeOptionsT NativeTableType; + typedef ZerosLikeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ZerosLikeOptionsBuilder { + typedef ZerosLikeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateZerosLikeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ZerosLikeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FillOptionsT : public flatbuffers::NativeTable { + typedef FillOptions TableType; +}; + +struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FillOptionsT NativeTableType; + typedef FillOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FillOptionsBuilder { + typedef FillOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFillOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FillOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct FloorModOptionsT : public flatbuffers::NativeTable { + typedef FloorModOptions TableType; +}; + +struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef FloorModOptionsT NativeTableType; + typedef FloorModOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct FloorModOptionsBuilder { + typedef FloorModOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateFloorModOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + FloorModOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RangeOptionsT : public flatbuffers::NativeTable { + typedef RangeOptions TableType; +}; + +struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RangeOptionsT NativeTableType; + typedef RangeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RangeOptionsBuilder { + typedef RangeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRangeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + RangeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct LeakyReluOptionsT : public flatbuffers::NativeTable { + typedef LeakyReluOptions TableType; + float alpha = 0.0f; +}; + +struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LeakyReluOptionsT NativeTableType; + typedef LeakyReluOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ALPHA = 4 + }; + float alpha() const { + return GetField(VT_ALPHA, 0.0f); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ALPHA, 4) && + verifier.EndTable(); + } + LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LeakyReluOptionsBuilder { + typedef LeakyReluOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_alpha(float alpha) { + fbb_.AddElement(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); + } + explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLeakyReluOptions( + flatbuffers::FlatBufferBuilder &_fbb, + float alpha = 0.0f) { + LeakyReluOptionsBuilder builder_(_fbb); + builder_.add_alpha(alpha); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable { + typedef SquaredDifferenceOptions TableType; +}; + +struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SquaredDifferenceOptionsT NativeTableType; + typedef SquaredDifferenceOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SquaredDifferenceOptionsBuilder { + typedef SquaredDifferenceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSquaredDifferenceOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SquaredDifferenceOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MirrorPadOptionsT : public flatbuffers::NativeTable { + typedef MirrorPadOptions TableType; + tflite_micro::MirrorPadMode mode = tflite_micro::MirrorPadMode_REFLECT; +}; + +struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MirrorPadOptionsT NativeTableType; + typedef MirrorPadOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_MODE = 4 + }; + tflite_micro::MirrorPadMode mode() const { + return static_cast(GetField(VT_MODE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_MODE, 1) && + verifier.EndTable(); + } + MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MirrorPadOptionsBuilder { + typedef MirrorPadOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_mode(tflite_micro::MirrorPadMode mode) { + fbb_.AddElement(MirrorPadOptions::VT_MODE, static_cast(mode), 0); + } + explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMirrorPadOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::MirrorPadMode mode = tflite_micro::MirrorPadMode_REFLECT) { + MirrorPadOptionsBuilder builder_(_fbb); + builder_.add_mode(mode); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UniqueOptionsT : public flatbuffers::NativeTable { + typedef UniqueOptions TableType; + tflite_micro::TensorType idx_out_type = tflite_micro::TensorType_INT32; +}; + +struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UniqueOptionsT NativeTableType; + typedef UniqueOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_IDX_OUT_TYPE = 4 + }; + tflite_micro::TensorType idx_out_type() const { + return static_cast(GetField(VT_IDX_OUT_TYPE, 2)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_IDX_OUT_TYPE, 1) && + verifier.EndTable(); + } + UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UniqueOptionsBuilder { + typedef UniqueOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_idx_out_type(tflite_micro::TensorType idx_out_type) { + fbb_.AddElement(UniqueOptions::VT_IDX_OUT_TYPE, static_cast(idx_out_type), 2); + } + explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUniqueOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::TensorType idx_out_type = tflite_micro::TensorType_INT32) { + UniqueOptionsBuilder builder_(_fbb); + builder_.add_idx_out_type(idx_out_type); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseV2OptionsT : public flatbuffers::NativeTable { + typedef ReverseV2Options TableType; +}; + +struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseV2OptionsT NativeTableType; + typedef ReverseV2OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseV2OptionsBuilder { + typedef ReverseV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + ReverseV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AddNOptionsT : public flatbuffers::NativeTable { + typedef AddNOptions TableType; +}; + +struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AddNOptionsT NativeTableType; + typedef AddNOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AddNOptionsBuilder { + typedef AddNOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAddNOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + AddNOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GatherNdOptionsT : public flatbuffers::NativeTable { + typedef GatherNdOptions TableType; +}; + +struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GatherNdOptionsT NativeTableType; + typedef GatherNdOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GatherNdOptionsBuilder { + typedef GatherNdOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGatherNdOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + GatherNdOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct WhereOptionsT : public flatbuffers::NativeTable { + typedef WhereOptions TableType; +}; + +struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WhereOptionsT NativeTableType; + typedef WhereOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WhereOptionsBuilder { + typedef WhereOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWhereOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + WhereOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReverseSequenceOptionsT : public flatbuffers::NativeTable { + typedef ReverseSequenceOptions TableType; + int32_t seq_dim = 0; + int32_t batch_dim = 0; +}; + +struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReverseSequenceOptionsT NativeTableType; + typedef ReverseSequenceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEQ_DIM = 4, + VT_BATCH_DIM = 6 + }; + int32_t seq_dim() const { + return GetField(VT_SEQ_DIM, 0); + } + int32_t batch_dim() const { + return GetField(VT_BATCH_DIM, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEQ_DIM, 4) && + VerifyField(verifier, VT_BATCH_DIM, 4) && + verifier.EndTable(); + } + ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReverseSequenceOptionsBuilder { + typedef ReverseSequenceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seq_dim(int32_t seq_dim) { + fbb_.AddElement(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); + } + void add_batch_dim(int32_t batch_dim) { + fbb_.AddElement(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); + } + explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReverseSequenceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t seq_dim = 0, + int32_t batch_dim = 0) { + ReverseSequenceOptionsBuilder builder_(_fbb); + builder_.add_batch_dim(batch_dim); + builder_.add_seq_dim(seq_dim); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MatrixDiagOptionsT : public flatbuffers::NativeTable { + typedef MatrixDiagOptions TableType; +}; + +struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MatrixDiagOptionsT NativeTableType; + typedef MatrixDiagOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MatrixDiagOptionsBuilder { + typedef MatrixDiagOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMatrixDiagOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MatrixDiagOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct QuantizeOptionsT : public flatbuffers::NativeTable { + typedef QuantizeOptions TableType; +}; + +struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef QuantizeOptionsT NativeTableType; + typedef QuantizeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct QuantizeOptionsBuilder { + typedef QuantizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateQuantizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + QuantizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable { + typedef MatrixSetDiagOptions TableType; +}; + +struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MatrixSetDiagOptionsT NativeTableType; + typedef MatrixSetDiagOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MatrixSetDiagOptionsBuilder { + typedef MatrixSetDiagOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMatrixSetDiagOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + MatrixSetDiagOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct IfOptionsT : public flatbuffers::NativeTable { + typedef IfOptions TableType; + int32_t then_subgraph_index = 0; + int32_t else_subgraph_index = 0; +}; + +struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef IfOptionsT NativeTableType; + typedef IfOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_THEN_SUBGRAPH_INDEX = 4, + VT_ELSE_SUBGRAPH_INDEX = 6 + }; + int32_t then_subgraph_index() const { + return GetField(VT_THEN_SUBGRAPH_INDEX, 0); + } + int32_t else_subgraph_index() const { + return GetField(VT_ELSE_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_THEN_SUBGRAPH_INDEX, 4) && + VerifyField(verifier, VT_ELSE_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct IfOptionsBuilder { + typedef IfOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_then_subgraph_index(int32_t then_subgraph_index) { + fbb_.AddElement(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); + } + void add_else_subgraph_index(int32_t else_subgraph_index) { + fbb_.AddElement(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); + } + explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateIfOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t then_subgraph_index = 0, + int32_t else_subgraph_index = 0) { + IfOptionsBuilder builder_(_fbb); + builder_.add_else_subgraph_index(else_subgraph_index); + builder_.add_then_subgraph_index(then_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CallOnceOptionsT : public flatbuffers::NativeTable { + typedef CallOnceOptions TableType; + int32_t init_subgraph_index = 0; +}; + +struct CallOnceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CallOnceOptionsT NativeTableType; + typedef CallOnceOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_INIT_SUBGRAPH_INDEX = 4 + }; + int32_t init_subgraph_index() const { + return GetField(VT_INIT_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_INIT_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + CallOnceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CallOnceOptionsBuilder { + typedef CallOnceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_init_subgraph_index(int32_t init_subgraph_index) { + fbb_.AddElement(CallOnceOptions::VT_INIT_SUBGRAPH_INDEX, init_subgraph_index, 0); + } + explicit CallOnceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCallOnceOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t init_subgraph_index = 0) { + CallOnceOptionsBuilder builder_(_fbb); + builder_.add_init_subgraph_index(init_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct WhileOptionsT : public flatbuffers::NativeTable { + typedef WhileOptions TableType; + int32_t cond_subgraph_index = 0; + int32_t body_subgraph_index = 0; +}; + +struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef WhileOptionsT NativeTableType; + typedef WhileOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_COND_SUBGRAPH_INDEX = 4, + VT_BODY_SUBGRAPH_INDEX = 6 + }; + int32_t cond_subgraph_index() const { + return GetField(VT_COND_SUBGRAPH_INDEX, 0); + } + int32_t body_subgraph_index() const { + return GetField(VT_BODY_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_COND_SUBGRAPH_INDEX, 4) && + VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct WhileOptionsBuilder { + typedef WhileOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_cond_subgraph_index(int32_t cond_subgraph_index) { + fbb_.AddElement(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); + } + void add_body_subgraph_index(int32_t body_subgraph_index) { + fbb_.AddElement(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); + } + explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateWhileOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t cond_subgraph_index = 0, + int32_t body_subgraph_index = 0) { + WhileOptionsBuilder builder_(_fbb); + builder_.add_body_subgraph_index(body_subgraph_index); + builder_.add_cond_subgraph_index(cond_subgraph_index); + return builder_.Finish(); +} + +flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable { + typedef NonMaxSuppressionV4Options TableType; +}; + +struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonMaxSuppressionV4OptionsT NativeTableType; + typedef NonMaxSuppressionV4OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NonMaxSuppressionV4OptionsBuilder { + typedef NonMaxSuppressionV4Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonMaxSuppressionV4Options( + flatbuffers::FlatBufferBuilder &_fbb) { + NonMaxSuppressionV4OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable { + typedef NonMaxSuppressionV5Options TableType; +}; + +struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef NonMaxSuppressionV5OptionsT NativeTableType; + typedef NonMaxSuppressionV5OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct NonMaxSuppressionV5OptionsBuilder { + typedef NonMaxSuppressionV5Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateNonMaxSuppressionV5Options( + flatbuffers::FlatBufferBuilder &_fbb) { + NonMaxSuppressionV5OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ScatterNdOptionsT : public flatbuffers::NativeTable { + typedef ScatterNdOptions TableType; +}; + +struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ScatterNdOptionsT NativeTableType; + typedef ScatterNdOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ScatterNdOptionsBuilder { + typedef ScatterNdOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateScatterNdOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ScatterNdOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SelectV2OptionsT : public flatbuffers::NativeTable { + typedef SelectV2Options TableType; +}; + +struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SelectV2OptionsT NativeTableType; + typedef SelectV2OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SelectV2OptionsBuilder { + typedef SelectV2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSelectV2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + SelectV2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DensifyOptionsT : public flatbuffers::NativeTable { + typedef DensifyOptions TableType; +}; + +struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DensifyOptionsT NativeTableType; + typedef DensifyOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DensifyOptionsBuilder { + typedef DensifyOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDensifyOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DensifyOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SegmentSumOptionsT : public flatbuffers::NativeTable { + typedef SegmentSumOptions TableType; +}; + +struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SegmentSumOptionsT NativeTableType; + typedef SegmentSumOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SegmentSumOptionsBuilder { + typedef SegmentSumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSegmentSumOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SegmentSumOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BatchMatMulOptionsT : public flatbuffers::NativeTable { + typedef BatchMatMulOptions TableType; + bool adj_x = false; + bool adj_y = false; + bool asymmetric_quantize_inputs = false; +}; + +struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BatchMatMulOptionsT NativeTableType; + typedef BatchMatMulOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_ADJ_X = 4, + VT_ADJ_Y = 6, + VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 + }; + bool adj_x() const { + return GetField(VT_ADJ_X, 0) != 0; + } + bool adj_y() const { + return GetField(VT_ADJ_Y, 0) != 0; + } + bool asymmetric_quantize_inputs() const { + return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_ADJ_X, 1) && + VerifyField(verifier, VT_ADJ_Y, 1) && + VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && + verifier.EndTable(); + } + BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BatchMatMulOptionsBuilder { + typedef BatchMatMulOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_adj_x(bool adj_x) { + fbb_.AddElement(BatchMatMulOptions::VT_ADJ_X, static_cast(adj_x), 0); + } + void add_adj_y(bool adj_y) { + fbb_.AddElement(BatchMatMulOptions::VT_ADJ_Y, static_cast(adj_y), 0); + } + void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { + fbb_.AddElement(BatchMatMulOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); + } + explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBatchMatMulOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool adj_x = false, + bool adj_y = false, + bool asymmetric_quantize_inputs = false) { + BatchMatMulOptionsBuilder builder_(_fbb); + builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); + builder_.add_adj_y(adj_y); + builder_.add_adj_x(adj_x); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct CumsumOptionsT : public flatbuffers::NativeTable { + typedef CumsumOptions TableType; + bool exclusive = false; + bool reverse = false; +}; + +struct CumsumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef CumsumOptionsT NativeTableType; + typedef CumsumOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_EXCLUSIVE = 4, + VT_REVERSE = 6 + }; + bool exclusive() const { + return GetField(VT_EXCLUSIVE, 0) != 0; + } + bool reverse() const { + return GetField(VT_REVERSE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_EXCLUSIVE, 1) && + VerifyField(verifier, VT_REVERSE, 1) && + verifier.EndTable(); + } + CumsumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct CumsumOptionsBuilder { + typedef CumsumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_exclusive(bool exclusive) { + fbb_.AddElement(CumsumOptions::VT_EXCLUSIVE, static_cast(exclusive), 0); + } + void add_reverse(bool reverse) { + fbb_.AddElement(CumsumOptions::VT_REVERSE, static_cast(reverse), 0); + } + explicit CumsumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateCumsumOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool exclusive = false, + bool reverse = false) { + CumsumOptionsBuilder builder_(_fbb); + builder_.add_reverse(reverse); + builder_.add_exclusive(exclusive); + return builder_.Finish(); +} + +flatbuffers::Offset CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BroadcastToOptionsT : public flatbuffers::NativeTable { + typedef BroadcastToOptions TableType; +}; + +struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BroadcastToOptionsT NativeTableType; + typedef BroadcastToOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BroadcastToOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BroadcastToOptionsBuilder { + typedef BroadcastToOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BroadcastToOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBroadcastToOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + BroadcastToOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct Rfft2dOptionsT : public flatbuffers::NativeTable { + typedef Rfft2dOptions TableType; +}; + +struct Rfft2dOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef Rfft2dOptionsT NativeTableType; + typedef Rfft2dOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + Rfft2dOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct Rfft2dOptionsBuilder { + typedef Rfft2dOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit Rfft2dOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRfft2dOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + Rfft2dOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HashtableOptionsT : public flatbuffers::NativeTable { + typedef HashtableOptions TableType; + int32_t table_id = 0; + tflite_micro::TensorType key_dtype = tflite_micro::TensorType_FLOAT32; + tflite_micro::TensorType value_dtype = tflite_micro::TensorType_FLOAT32; +}; + +struct HashtableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HashtableOptionsT NativeTableType; + typedef HashtableOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TABLE_ID = 4, + VT_KEY_DTYPE = 6, + VT_VALUE_DTYPE = 8 + }; + int32_t table_id() const { + return GetField(VT_TABLE_ID, 0); + } + tflite_micro::TensorType key_dtype() const { + return static_cast(GetField(VT_KEY_DTYPE, 0)); + } + tflite_micro::TensorType value_dtype() const { + return static_cast(GetField(VT_VALUE_DTYPE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_TABLE_ID, 4) && + VerifyField(verifier, VT_KEY_DTYPE, 1) && + VerifyField(verifier, VT_VALUE_DTYPE, 1) && + verifier.EndTable(); + } + HashtableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HashtableOptionsBuilder { + typedef HashtableOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_table_id(int32_t table_id) { + fbb_.AddElement(HashtableOptions::VT_TABLE_ID, table_id, 0); + } + void add_key_dtype(tflite_micro::TensorType key_dtype) { + fbb_.AddElement(HashtableOptions::VT_KEY_DTYPE, static_cast(key_dtype), 0); + } + void add_value_dtype(tflite_micro::TensorType value_dtype) { + fbb_.AddElement(HashtableOptions::VT_VALUE_DTYPE, static_cast(value_dtype), 0); + } + explicit HashtableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHashtableOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t table_id = 0, + tflite_micro::TensorType key_dtype = tflite_micro::TensorType_FLOAT32, + tflite_micro::TensorType value_dtype = tflite_micro::TensorType_FLOAT32) { + HashtableOptionsBuilder builder_(_fbb); + builder_.add_table_id(table_id); + builder_.add_value_dtype(value_dtype); + builder_.add_key_dtype(key_dtype); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HashtableFindOptionsT : public flatbuffers::NativeTable { + typedef HashtableFindOptions TableType; +}; + +struct HashtableFindOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HashtableFindOptionsT NativeTableType; + typedef HashtableFindOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HashtableFindOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HashtableFindOptionsBuilder { + typedef HashtableFindOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HashtableFindOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHashtableFindOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + HashtableFindOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HashtableImportOptionsT : public flatbuffers::NativeTable { + typedef HashtableImportOptions TableType; +}; + +struct HashtableImportOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HashtableImportOptionsT NativeTableType; + typedef HashtableImportOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HashtableImportOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HashtableImportOptionsBuilder { + typedef HashtableImportOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HashtableImportOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHashtableImportOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + HashtableImportOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct HashtableSizeOptionsT : public flatbuffers::NativeTable { + typedef HashtableSizeOptions TableType; +}; + +struct HashtableSizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef HashtableSizeOptionsT NativeTableType; + typedef HashtableSizeOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + HashtableSizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct HashtableSizeOptionsBuilder { + typedef HashtableSizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit HashtableSizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateHashtableSizeOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + HashtableSizeOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct VarHandleOptionsT : public flatbuffers::NativeTable { + typedef VarHandleOptions TableType; + std::string container{}; + std::string shared_name{}; +}; + +struct VarHandleOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef VarHandleOptionsT NativeTableType; + typedef VarHandleOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_CONTAINER = 4, + VT_SHARED_NAME = 6 + }; + const flatbuffers::String *container() const { + return GetPointer(VT_CONTAINER); + } + const flatbuffers::String *shared_name() const { + return GetPointer(VT_SHARED_NAME); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_CONTAINER) && + verifier.VerifyString(container()) && + VerifyOffset(verifier, VT_SHARED_NAME) && + verifier.VerifyString(shared_name()) && + verifier.EndTable(); + } + VarHandleOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct VarHandleOptionsBuilder { + typedef VarHandleOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_container(flatbuffers::Offset container) { + fbb_.AddOffset(VarHandleOptions::VT_CONTAINER, container); + } + void add_shared_name(flatbuffers::Offset shared_name) { + fbb_.AddOffset(VarHandleOptions::VT_SHARED_NAME, shared_name); + } + explicit VarHandleOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateVarHandleOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset container = 0, + flatbuffers::Offset shared_name = 0) { + VarHandleOptionsBuilder builder_(_fbb); + builder_.add_shared_name(shared_name); + builder_.add_container(container); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateVarHandleOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *container = nullptr, + const char *shared_name = nullptr) { + auto container__ = container ? _fbb.CreateString(container) : 0; + auto shared_name__ = shared_name ? _fbb.CreateString(shared_name) : 0; + return tflite_micro::CreateVarHandleOptions( + _fbb, + container__, + shared_name__); +} + +flatbuffers::Offset CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReadVariableOptionsT : public flatbuffers::NativeTable { + typedef ReadVariableOptions TableType; +}; + +struct ReadVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReadVariableOptionsT NativeTableType; + typedef ReadVariableOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ReadVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReadVariableOptionsBuilder { + typedef ReadVariableOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ReadVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReadVariableOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + ReadVariableOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct AssignVariableOptionsT : public flatbuffers::NativeTable { + typedef AssignVariableOptions TableType; +}; + +struct AssignVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef AssignVariableOptionsT NativeTableType; + typedef AssignVariableOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + AssignVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct AssignVariableOptionsBuilder { + typedef AssignVariableOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit AssignVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateAssignVariableOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + AssignVariableOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RandomOptionsT : public flatbuffers::NativeTable { + typedef RandomOptions TableType; + int64_t seed = 0; + int64_t seed2 = 0; +}; + +struct RandomOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RandomOptionsT NativeTableType; + typedef RandomOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_SEED = 4, + VT_SEED2 = 6 + }; + int64_t seed() const { + return GetField(VT_SEED, 0); + } + int64_t seed2() const { + return GetField(VT_SEED2, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_SEED, 8) && + VerifyField(verifier, VT_SEED2, 8) && + verifier.EndTable(); + } + RandomOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RandomOptionsBuilder { + typedef RandomOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_seed(int64_t seed) { + fbb_.AddElement(RandomOptions::VT_SEED, seed, 0); + } + void add_seed2(int64_t seed2) { + fbb_.AddElement(RandomOptions::VT_SEED2, seed2, 0); + } + explicit RandomOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRandomOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int64_t seed = 0, + int64_t seed2 = 0) { + RandomOptionsBuilder builder_(_fbb); + builder_.add_seed2(seed2); + builder_.add_seed(seed); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BucketizeOptionsT : public flatbuffers::NativeTable { + typedef BucketizeOptions TableType; + std::vector boundaries{}; +}; + +struct BucketizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BucketizeOptionsT NativeTableType; + typedef BucketizeOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_BOUNDARIES = 4 + }; + const flatbuffers::Vector *boundaries() const { + return GetPointer *>(VT_BOUNDARIES); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_BOUNDARIES) && + verifier.VerifyVector(boundaries()) && + verifier.EndTable(); + } + BucketizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BucketizeOptionsBuilder { + typedef BucketizeOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_boundaries(flatbuffers::Offset> boundaries) { + fbb_.AddOffset(BucketizeOptions::VT_BOUNDARIES, boundaries); + } + explicit BucketizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBucketizeOptions( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> boundaries = 0) { + BucketizeOptionsBuilder builder_(_fbb); + builder_.add_boundaries(boundaries); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBucketizeOptionsDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *boundaries = nullptr) { + auto boundaries__ = boundaries ? _fbb.CreateVector(*boundaries) : 0; + return tflite_micro::CreateBucketizeOptions( + _fbb, + boundaries__); +} + +flatbuffers::Offset CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct GeluOptionsT : public flatbuffers::NativeTable { + typedef GeluOptions TableType; + bool approximate = false; +}; + +struct GeluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef GeluOptionsT NativeTableType; + typedef GeluOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_APPROXIMATE = 4 + }; + bool approximate() const { + return GetField(VT_APPROXIMATE, 0) != 0; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_APPROXIMATE, 1) && + verifier.EndTable(); + } + GeluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct GeluOptionsBuilder { + typedef GeluOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_approximate(bool approximate) { + fbb_.AddElement(GeluOptions::VT_APPROXIMATE, static_cast(approximate), 0); + } + explicit GeluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateGeluOptions( + flatbuffers::FlatBufferBuilder &_fbb, + bool approximate = false) { + GeluOptionsBuilder builder_(_fbb); + builder_.add_approximate(approximate); + return builder_.Finish(); +} + +flatbuffers::Offset CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DynamicUpdateSliceOptionsT : public flatbuffers::NativeTable { + typedef DynamicUpdateSliceOptions TableType; +}; + +struct DynamicUpdateSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DynamicUpdateSliceOptionsT NativeTableType; + typedef DynamicUpdateSliceOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DynamicUpdateSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DynamicUpdateSliceOptionsBuilder { + typedef DynamicUpdateSliceOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DynamicUpdateSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDynamicUpdateSliceOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DynamicUpdateSliceOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsortedSegmentProdOptionsT : public flatbuffers::NativeTable { + typedef UnsortedSegmentProdOptions TableType; +}; + +struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsortedSegmentProdOptionsT NativeTableType; + typedef UnsortedSegmentProdOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UnsortedSegmentProdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsortedSegmentProdOptionsBuilder { + typedef UnsortedSegmentProdOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UnsortedSegmentProdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsortedSegmentProdOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + UnsortedSegmentProdOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsortedSegmentMaxOptionsT : public flatbuffers::NativeTable { + typedef UnsortedSegmentMaxOptions TableType; +}; + +struct UnsortedSegmentMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsortedSegmentMaxOptionsT NativeTableType; + typedef UnsortedSegmentMaxOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UnsortedSegmentMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsortedSegmentMaxOptionsBuilder { + typedef UnsortedSegmentMaxOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UnsortedSegmentMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsortedSegmentMaxOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + UnsortedSegmentMaxOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsortedSegmentSumOptionsT : public flatbuffers::NativeTable { + typedef UnsortedSegmentSumOptions TableType; +}; + +struct UnsortedSegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsortedSegmentSumOptionsT NativeTableType; + typedef UnsortedSegmentSumOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UnsortedSegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsortedSegmentSumOptionsBuilder { + typedef UnsortedSegmentSumOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UnsortedSegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsortedSegmentSumOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + UnsortedSegmentSumOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ATan2OptionsT : public flatbuffers::NativeTable { + typedef ATan2Options TableType; +}; + +struct ATan2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ATan2OptionsT NativeTableType; + typedef ATan2OptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + ATan2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ATan2OptionsBuilder { + typedef ATan2Options Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit ATan2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateATan2Options( + flatbuffers::FlatBufferBuilder &_fbb) { + ATan2OptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct UnsortedSegmentMinOptionsT : public flatbuffers::NativeTable { + typedef UnsortedSegmentMinOptions TableType; +}; + +struct UnsortedSegmentMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef UnsortedSegmentMinOptionsT NativeTableType; + typedef UnsortedSegmentMinOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + UnsortedSegmentMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct UnsortedSegmentMinOptionsBuilder { + typedef UnsortedSegmentMinOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit UnsortedSegmentMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateUnsortedSegmentMinOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + UnsortedSegmentMinOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SignOptionsT : public flatbuffers::NativeTable { + typedef SignOptions TableType; +}; + +struct SignOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SignOptionsT NativeTableType; + typedef SignOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + SignOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SignOptionsBuilder { + typedef SignOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit SignOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSignOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + SignOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BitcastOptionsT : public flatbuffers::NativeTable { + typedef BitcastOptions TableType; +}; + +struct BitcastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BitcastOptionsT NativeTableType; + typedef BitcastOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BitcastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BitcastOptionsBuilder { + typedef BitcastOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BitcastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBitcastOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + BitcastOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BitwiseXorOptionsT : public flatbuffers::NativeTable { + typedef BitwiseXorOptions TableType; +}; + +struct BitwiseXorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BitwiseXorOptionsT NativeTableType; + typedef BitwiseXorOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + BitwiseXorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BitwiseXorOptionsBuilder { + typedef BitwiseXorOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit BitwiseXorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBitwiseXorOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + BitwiseXorOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct RightShiftOptionsT : public flatbuffers::NativeTable { + typedef RightShiftOptions TableType; +}; + +struct RightShiftOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef RightShiftOptionsT NativeTableType; + typedef RightShiftOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + RightShiftOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct RightShiftOptionsBuilder { + typedef RightShiftOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit RightShiftOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateRightShiftOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + RightShiftOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct DilateOptionsT : public flatbuffers::NativeTable { + typedef DilateOptions TableType; +}; + +struct DilateOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef DilateOptionsT NativeTableType; + typedef DilateOptionsBuilder Builder; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + DilateOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(DilateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct DilateOptionsBuilder { + typedef DilateOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit DilateOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateDilateOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + DilateOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateDilateOptions(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ReduceWindowOptionsT : public flatbuffers::NativeTable { + typedef ReduceWindowOptions TableType; + tflite_micro::ReduceWindowFunction reduce_function = tflite_micro::ReduceWindowFunction_UNSUPPORTED; +}; + +struct ReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ReduceWindowOptionsT NativeTableType; + typedef ReduceWindowOptionsBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_REDUCE_FUNCTION = 4 + }; + tflite_micro::ReduceWindowFunction reduce_function() const { + return static_cast(GetField(VT_REDUCE_FUNCTION, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_REDUCE_FUNCTION, 4) && + verifier.EndTable(); + } + ReduceWindowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ReduceWindowOptionsBuilder { + typedef ReduceWindowOptions Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_reduce_function(tflite_micro::ReduceWindowFunction reduce_function) { + fbb_.AddElement(ReduceWindowOptions::VT_REDUCE_FUNCTION, static_cast(reduce_function), 0); + } + explicit ReduceWindowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateReduceWindowOptions( + flatbuffers::FlatBufferBuilder &_fbb, + tflite_micro::ReduceWindowFunction reduce_function = tflite_micro::ReduceWindowFunction_UNSUPPORTED) { + ReduceWindowOptionsBuilder builder_(_fbb); + builder_.add_reduce_function(reduce_function); + return builder_.Finish(); +} + +flatbuffers::Offset CreateReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OperatorCodeT : public flatbuffers::NativeTable { + typedef OperatorCode TableType; + int8_t deprecated_builtin_code = 0; + std::string custom_code{}; + int32_t version = 1; + tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD; +}; + +struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OperatorCodeT NativeTableType; + typedef OperatorCodeBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DEPRECATED_BUILTIN_CODE = 4, + VT_CUSTOM_CODE = 6, + VT_VERSION = 8, + VT_BUILTIN_CODE = 10 + }; + int8_t deprecated_builtin_code() const { + return GetField(VT_DEPRECATED_BUILTIN_CODE, 0); + } + const flatbuffers::String *custom_code() const { + return GetPointer(VT_CUSTOM_CODE); + } + int32_t version() const { + return GetField(VT_VERSION, 1); + } + tflite_micro::BuiltinOperator builtin_code() const { + return static_cast(GetField(VT_BUILTIN_CODE, 0)); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_DEPRECATED_BUILTIN_CODE, 1) && + VerifyOffset(verifier, VT_CUSTOM_CODE) && + verifier.VerifyString(custom_code()) && + VerifyField(verifier, VT_VERSION, 4) && + VerifyField(verifier, VT_BUILTIN_CODE, 4) && + verifier.EndTable(); + } + OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OperatorCodeBuilder { + typedef OperatorCode Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_deprecated_builtin_code(int8_t deprecated_builtin_code) { + fbb_.AddElement(OperatorCode::VT_DEPRECATED_BUILTIN_CODE, deprecated_builtin_code, 0); + } + void add_custom_code(flatbuffers::Offset custom_code) { + fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); + } + void add_version(int32_t version) { + fbb_.AddElement(OperatorCode::VT_VERSION, version, 1); + } + void add_builtin_code(tflite_micro::BuiltinOperator builtin_code) { + fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, static_cast(builtin_code), 0); + } + explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOperatorCode( + flatbuffers::FlatBufferBuilder &_fbb, + int8_t deprecated_builtin_code = 0, + flatbuffers::Offset custom_code = 0, + int32_t version = 1, + tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD) { + OperatorCodeBuilder builder_(_fbb); + builder_.add_builtin_code(builtin_code); + builder_.add_version(version); + builder_.add_custom_code(custom_code); + builder_.add_deprecated_builtin_code(deprecated_builtin_code); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateOperatorCodeDirect( + flatbuffers::FlatBufferBuilder &_fbb, + int8_t deprecated_builtin_code = 0, + const char *custom_code = nullptr, + int32_t version = 1, + tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD) { + auto custom_code__ = custom_code ? _fbb.CreateString(custom_code) : 0; + return tflite_micro::CreateOperatorCode( + _fbb, + deprecated_builtin_code, + custom_code__, + version, + builtin_code); +} + +flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct OperatorT : public flatbuffers::NativeTable { + typedef Operator TableType; + uint32_t opcode_index = 0; + std::vector inputs{}; + std::vector outputs{}; + tflite_micro::BuiltinOptionsUnion builtin_options{}; + std::vector custom_options{}; + tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS; + std::vector mutating_variable_inputs{}; + std::vector intermediates{}; + uint64_t large_custom_options_offset = 0; + uint64_t large_custom_options_size = 0; + tflite_micro::BuiltinOptions2Union builtin_options_2{}; +}; + +struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OperatorT NativeTableType; + typedef OperatorBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_OPCODE_INDEX = 4, + VT_INPUTS = 6, + VT_OUTPUTS = 8, + VT_BUILTIN_OPTIONS_TYPE = 10, + VT_BUILTIN_OPTIONS = 12, + VT_CUSTOM_OPTIONS = 14, + VT_CUSTOM_OPTIONS_FORMAT = 16, + VT_MUTATING_VARIABLE_INPUTS = 18, + VT_INTERMEDIATES = 20, + VT_LARGE_CUSTOM_OPTIONS_OFFSET = 22, + VT_LARGE_CUSTOM_OPTIONS_SIZE = 24, + VT_BUILTIN_OPTIONS_2_TYPE = 26, + VT_BUILTIN_OPTIONS_2 = 28 + }; + uint32_t opcode_index() const { + return GetField(VT_OPCODE_INDEX, 0); + } + const flatbuffers::Vector *inputs() const { + return GetPointer *>(VT_INPUTS); + } + const flatbuffers::Vector *outputs() const { + return GetPointer *>(VT_OUTPUTS); + } + tflite_micro::BuiltinOptions builtin_options_type() const { + return static_cast(GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); + } + const void *builtin_options() const { + return GetPointer(VT_BUILTIN_OPTIONS); + } + template const T *builtin_options_as() const; + const tflite_micro::Conv2DOptions *builtin_options_as_Conv2DOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_Conv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DepthwiseConv2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ConcatEmbeddingsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LSHProjectionOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::Pool2DOptions *builtin_options_as_Pool2DOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_Pool2DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SVDFOptions *builtin_options_as_SVDFOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SVDFOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::RNNOptions *builtin_options_as_RNNOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_RNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_FullyConnectedOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SoftmaxOptions *builtin_options_as_SoftmaxOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SoftmaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ConcatenationOptions *builtin_options_as_ConcatenationOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ConcatenationOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::AddOptions *builtin_options_as_AddOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_AddOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::L2NormOptions *builtin_options_as_L2NormOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_L2NormOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LocalResponseNormalizationOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LSTMOptions *builtin_options_as_LSTMOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ResizeBilinearOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::CallOptions *builtin_options_as_CallOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_CallOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ReshapeOptions *builtin_options_as_ReshapeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ReshapeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SkipGramOptions *builtin_options_as_SkipGramOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SkipGramOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SpaceToDepthOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::MulOptions *builtin_options_as_MulOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_MulOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::PadOptions *builtin_options_as_PadOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::GatherOptions *builtin_options_as_GatherOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BatchToSpaceNDOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SpaceToBatchNDOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::TransposeOptions *builtin_options_as_TransposeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ReducerOptions *builtin_options_as_ReducerOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SubOptions *builtin_options_as_SubOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DivOptions *builtin_options_as_DivOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DivOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SqueezeOptions *builtin_options_as_SqueezeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SqueezeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SequenceRNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::StridedSliceOptions *builtin_options_as_StridedSliceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_StridedSliceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ExpOptions *builtin_options_as_ExpOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ExpOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::TopKV2Options *builtin_options_as_TopKV2Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_TopKV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SplitOptions *builtin_options_as_SplitOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SplitOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::CastOptions *builtin_options_as_CastOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DequantizeOptions *builtin_options_as_DequantizeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_MaximumMinimumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ArgMaxOptions *builtin_options_as_ArgMaxOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LessOptions *builtin_options_as_LessOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::NegOptions *builtin_options_as_NegOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::PadV2Options *builtin_options_as_PadV2Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::GreaterOptions *builtin_options_as_GreaterOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LessEqualOptions *builtin_options_as_LessEqualOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SelectOptions *builtin_options_as_SelectOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SliceOptions *builtin_options_as_SliceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SliceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::TransposeConvOptions *builtin_options_as_TransposeConvOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_TransposeConvOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SparseToDenseOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::TileOptions *builtin_options_as_TileOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_TileOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ExpandDimsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::EqualOptions *builtin_options_as_EqualOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_EqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::NotEqualOptions *builtin_options_as_NotEqualOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ShapeOptions *builtin_options_as_ShapeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::PowOptions *builtin_options_as_PowOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ArgMinOptions *builtin_options_as_ArgMinOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ArgMinOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::FakeQuantOptions *builtin_options_as_FakeQuantOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_FakeQuantOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::PackOptions *builtin_options_as_PackOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::OneHotOptions *builtin_options_as_OneHotOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LogicalAndOptions *builtin_options_as_LogicalAndOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalAndOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LogicalNotOptions *builtin_options_as_LogicalNotOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalNotOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnpackOptions *builtin_options_as_UnpackOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnpackOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::FloorDivOptions *builtin_options_as_FloorDivOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_FloorDivOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SquareOptions *builtin_options_as_SquareOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SquareOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ZerosLikeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::FillOptions *builtin_options_as_FillOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_FillOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BidirectionalSequenceLSTMOptions *builtin_options_as_BidirectionalSequenceLSTMOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BidirectionalSequenceRNNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnidirectionalSequenceLSTMOptions *builtin_options_as_UnidirectionalSequenceLSTMOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::FloorModOptions *builtin_options_as_FloorModOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_FloorModOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::RangeOptions *builtin_options_as_RangeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_RangeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ResizeNearestNeighborOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::LeakyReluOptions *builtin_options_as_LeakyReluOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_LeakyReluOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SquaredDifferenceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::MirrorPadOptions *builtin_options_as_MirrorPadOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_MirrorPadOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::AbsOptions *builtin_options_as_AbsOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_AbsOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SplitVOptions *builtin_options_as_SplitVOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SplitVOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UniqueOptions *builtin_options_as_UniqueOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UniqueOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ReverseV2Options *builtin_options_as_ReverseV2Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ReverseV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::AddNOptions *builtin_options_as_AddNOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_AddNOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::GatherNdOptions *builtin_options_as_GatherNdOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_GatherNdOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::CosOptions *builtin_options_as_CosOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_CosOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::WhereOptions *builtin_options_as_WhereOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_WhereOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::RankOptions *builtin_options_as_RankOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_RankOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ReverseSequenceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_MatrixDiagOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::QuantizeOptions *builtin_options_as_QuantizeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_QuantizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_MatrixSetDiagOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::HardSwishOptions *builtin_options_as_HardSwishOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_HardSwishOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::IfOptions *builtin_options_as_IfOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_IfOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::WhileOptions *builtin_options_as_WhileOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_WhileOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DepthToSpaceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_NonMaxSuppressionV4Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_NonMaxSuppressionV5Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ScatterNdOptions *builtin_options_as_ScatterNdOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ScatterNdOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SelectV2Options *builtin_options_as_SelectV2Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SelectV2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DensifyOptions *builtin_options_as_DensifyOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DensifyOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SegmentSumOptions *builtin_options_as_SegmentSumOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SegmentSumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BatchMatMulOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::CumsumOptions *builtin_options_as_CumsumOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_CumsumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::CallOnceOptions *builtin_options_as_CallOnceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_CallOnceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BroadcastToOptions *builtin_options_as_BroadcastToOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BroadcastToOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::Rfft2dOptions *builtin_options_as_Rfft2dOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_Rfft2dOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::Conv3DOptions *builtin_options_as_Conv3DOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_Conv3DOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::HashtableOptions *builtin_options_as_HashtableOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::HashtableFindOptions *builtin_options_as_HashtableFindOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableFindOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::HashtableImportOptions *builtin_options_as_HashtableImportOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableImportOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::HashtableSizeOptions *builtin_options_as_HashtableSizeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableSizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::VarHandleOptions *builtin_options_as_VarHandleOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_VarHandleOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ReadVariableOptions *builtin_options_as_ReadVariableOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ReadVariableOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::AssignVariableOptions *builtin_options_as_AssignVariableOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_AssignVariableOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::RandomOptions *builtin_options_as_RandomOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_RandomOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BucketizeOptions *builtin_options_as_BucketizeOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BucketizeOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::GeluOptions *builtin_options_as_GeluOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_GeluOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::DynamicUpdateSliceOptions *builtin_options_as_DynamicUpdateSliceOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_DynamicUpdateSliceOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnsortedSegmentProdOptions *builtin_options_as_UnsortedSegmentProdOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentProdOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnsortedSegmentMaxOptions *builtin_options_as_UnsortedSegmentMaxOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentMaxOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnsortedSegmentMinOptions *builtin_options_as_UnsortedSegmentMinOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentMinOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::UnsortedSegmentSumOptions *builtin_options_as_UnsortedSegmentSumOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentSumOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::ATan2Options *builtin_options_as_ATan2Options() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_ATan2Options ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::SignOptions *builtin_options_as_SignOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_SignOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BitcastOptions *builtin_options_as_BitcastOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BitcastOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::BitwiseXorOptions *builtin_options_as_BitwiseXorOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_BitwiseXorOptions ? static_cast(builtin_options()) : nullptr; + } + const tflite_micro::RightShiftOptions *builtin_options_as_RightShiftOptions() const { + return builtin_options_type() == tflite_micro::BuiltinOptions_RightShiftOptions ? static_cast(builtin_options()) : nullptr; + } + const flatbuffers::Vector *custom_options() const { + return GetPointer *>(VT_CUSTOM_OPTIONS); + } + tflite_micro::CustomOptionsFormat custom_options_format() const { + return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); + } + const flatbuffers::Vector *mutating_variable_inputs() const { + return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); + } + const flatbuffers::Vector *intermediates() const { + return GetPointer *>(VT_INTERMEDIATES); + } + uint64_t large_custom_options_offset() const { + return GetField(VT_LARGE_CUSTOM_OPTIONS_OFFSET, 0); + } + uint64_t large_custom_options_size() const { + return GetField(VT_LARGE_CUSTOM_OPTIONS_SIZE, 0); + } + tflite_micro::BuiltinOptions2 builtin_options_2_type() const { + return static_cast(GetField(VT_BUILTIN_OPTIONS_2_TYPE, 0)); + } + const void *builtin_options_2() const { + return GetPointer(VT_BUILTIN_OPTIONS_2); + } + template const T *builtin_options_2_as() const; + const tflite_micro::StablehloConcatenateOptions *builtin_options_2_as_StablehloConcatenateOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloConcatenateOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloBroadcastInDimOptions *builtin_options_2_as_StablehloBroadcastInDimOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloBroadcastInDimOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloSliceOptions *builtin_options_2_as_StablehloSliceOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloSliceOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloConvolutionOptions *builtin_options_2_as_StablehloConvolutionOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloConvolutionOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloCustomCallOptions *builtin_options_2_as_StablehloCustomCallOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloCustomCallOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloReduceOptions *builtin_options_2_as_StablehloReduceOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloReduceOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloScatterOptions *builtin_options_2_as_StablehloScatterOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloScatterOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloCompareOptions *builtin_options_2_as_StablehloCompareOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloCompareOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloDynamicSliceOptions *builtin_options_2_as_StablehloDynamicSliceOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloDynamicSliceOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloPadOptions *builtin_options_2_as_StablehloPadOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloPadOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloIotaOptions *builtin_options_2_as_StablehloIotaOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloIotaOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloDotGeneralOptions *builtin_options_2_as_StablehloDotGeneralOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloDotGeneralOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloReduceWindowOptions *builtin_options_2_as_StablehloReduceWindowOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloReduceWindowOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloSortOptions *builtin_options_2_as_StablehloSortOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloSortOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloWhileOptions *builtin_options_2_as_StablehloWhileOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloWhileOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloGatherOptions *builtin_options_2_as_StablehloGatherOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloGatherOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloTransposeOptions *builtin_options_2_as_StablehloTransposeOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloTransposeOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::DilateOptions *builtin_options_2_as_DilateOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_DilateOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::StablehloRngBitGeneratorOptions *builtin_options_2_as_StablehloRngBitGeneratorOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloRngBitGeneratorOptions ? static_cast(builtin_options_2()) : nullptr; + } + const tflite_micro::ReduceWindowOptions *builtin_options_2_as_ReduceWindowOptions() const { + return builtin_options_2_type() == tflite_micro::BuiltinOptions2_ReduceWindowOptions ? static_cast(builtin_options_2()) : nullptr; + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_OPCODE_INDEX, 4) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.VerifyVector(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.VerifyVector(outputs()) && + VerifyField(verifier, VT_BUILTIN_OPTIONS_TYPE, 1) && + VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && + VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && + VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && + verifier.VerifyVector(custom_options()) && + VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT, 1) && + VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && + verifier.VerifyVector(mutating_variable_inputs()) && + VerifyOffset(verifier, VT_INTERMEDIATES) && + verifier.VerifyVector(intermediates()) && + VerifyField(verifier, VT_LARGE_CUSTOM_OPTIONS_OFFSET, 8) && + VerifyField(verifier, VT_LARGE_CUSTOM_OPTIONS_SIZE, 8) && + VerifyField(verifier, VT_BUILTIN_OPTIONS_2_TYPE, 1) && + VerifyOffset(verifier, VT_BUILTIN_OPTIONS_2) && + VerifyBuiltinOptions2(verifier, builtin_options_2(), builtin_options_2_type()) && + verifier.EndTable(); + } + OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +template<> inline const tflite_micro::Conv2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_Conv2DOptions(); +} + +template<> inline const tflite_micro::DepthwiseConv2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_DepthwiseConv2DOptions(); +} + +template<> inline const tflite_micro::ConcatEmbeddingsOptions *Operator::builtin_options_as() const { + return builtin_options_as_ConcatEmbeddingsOptions(); +} + +template<> inline const tflite_micro::LSHProjectionOptions *Operator::builtin_options_as() const { + return builtin_options_as_LSHProjectionOptions(); +} + +template<> inline const tflite_micro::Pool2DOptions *Operator::builtin_options_as() const { + return builtin_options_as_Pool2DOptions(); +} + +template<> inline const tflite_micro::SVDFOptions *Operator::builtin_options_as() const { + return builtin_options_as_SVDFOptions(); +} + +template<> inline const tflite_micro::RNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_RNNOptions(); +} + +template<> inline const tflite_micro::FullyConnectedOptions *Operator::builtin_options_as() const { + return builtin_options_as_FullyConnectedOptions(); +} + +template<> inline const tflite_micro::SoftmaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_SoftmaxOptions(); +} + +template<> inline const tflite_micro::ConcatenationOptions *Operator::builtin_options_as() const { + return builtin_options_as_ConcatenationOptions(); +} + +template<> inline const tflite_micro::AddOptions *Operator::builtin_options_as() const { + return builtin_options_as_AddOptions(); +} + +template<> inline const tflite_micro::L2NormOptions *Operator::builtin_options_as() const { + return builtin_options_as_L2NormOptions(); +} + +template<> inline const tflite_micro::LocalResponseNormalizationOptions *Operator::builtin_options_as() const { + return builtin_options_as_LocalResponseNormalizationOptions(); +} + +template<> inline const tflite_micro::LSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_LSTMOptions(); +} + +template<> inline const tflite_micro::ResizeBilinearOptions *Operator::builtin_options_as() const { + return builtin_options_as_ResizeBilinearOptions(); +} + +template<> inline const tflite_micro::CallOptions *Operator::builtin_options_as() const { + return builtin_options_as_CallOptions(); +} + +template<> inline const tflite_micro::ReshapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReshapeOptions(); +} + +template<> inline const tflite_micro::SkipGramOptions *Operator::builtin_options_as() const { + return builtin_options_as_SkipGramOptions(); +} + +template<> inline const tflite_micro::SpaceToDepthOptions *Operator::builtin_options_as() const { + return builtin_options_as_SpaceToDepthOptions(); +} + +template<> inline const tflite_micro::EmbeddingLookupSparseOptions *Operator::builtin_options_as() const { + return builtin_options_as_EmbeddingLookupSparseOptions(); +} + +template<> inline const tflite_micro::MulOptions *Operator::builtin_options_as() const { + return builtin_options_as_MulOptions(); +} + +template<> inline const tflite_micro::PadOptions *Operator::builtin_options_as() const { + return builtin_options_as_PadOptions(); +} + +template<> inline const tflite_micro::GatherOptions *Operator::builtin_options_as() const { + return builtin_options_as_GatherOptions(); +} + +template<> inline const tflite_micro::BatchToSpaceNDOptions *Operator::builtin_options_as() const { + return builtin_options_as_BatchToSpaceNDOptions(); +} + +template<> inline const tflite_micro::SpaceToBatchNDOptions *Operator::builtin_options_as() const { + return builtin_options_as_SpaceToBatchNDOptions(); +} + +template<> inline const tflite_micro::TransposeOptions *Operator::builtin_options_as() const { + return builtin_options_as_TransposeOptions(); +} + +template<> inline const tflite_micro::ReducerOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReducerOptions(); +} + +template<> inline const tflite_micro::SubOptions *Operator::builtin_options_as() const { + return builtin_options_as_SubOptions(); +} + +template<> inline const tflite_micro::DivOptions *Operator::builtin_options_as() const { + return builtin_options_as_DivOptions(); +} + +template<> inline const tflite_micro::SqueezeOptions *Operator::builtin_options_as() const { + return builtin_options_as_SqueezeOptions(); +} + +template<> inline const tflite_micro::SequenceRNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_SequenceRNNOptions(); +} + +template<> inline const tflite_micro::StridedSliceOptions *Operator::builtin_options_as() const { + return builtin_options_as_StridedSliceOptions(); +} + +template<> inline const tflite_micro::ExpOptions *Operator::builtin_options_as() const { + return builtin_options_as_ExpOptions(); +} + +template<> inline const tflite_micro::TopKV2Options *Operator::builtin_options_as() const { + return builtin_options_as_TopKV2Options(); +} + +template<> inline const tflite_micro::SplitOptions *Operator::builtin_options_as() const { + return builtin_options_as_SplitOptions(); +} + +template<> inline const tflite_micro::LogSoftmaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogSoftmaxOptions(); +} + +template<> inline const tflite_micro::CastOptions *Operator::builtin_options_as() const { + return builtin_options_as_CastOptions(); +} + +template<> inline const tflite_micro::DequantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_DequantizeOptions(); +} + +template<> inline const tflite_micro::MaximumMinimumOptions *Operator::builtin_options_as() const { + return builtin_options_as_MaximumMinimumOptions(); +} + +template<> inline const tflite_micro::ArgMaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_ArgMaxOptions(); +} + +template<> inline const tflite_micro::LessOptions *Operator::builtin_options_as() const { + return builtin_options_as_LessOptions(); +} + +template<> inline const tflite_micro::NegOptions *Operator::builtin_options_as() const { + return builtin_options_as_NegOptions(); +} + +template<> inline const tflite_micro::PadV2Options *Operator::builtin_options_as() const { + return builtin_options_as_PadV2Options(); +} + +template<> inline const tflite_micro::GreaterOptions *Operator::builtin_options_as() const { + return builtin_options_as_GreaterOptions(); +} + +template<> inline const tflite_micro::GreaterEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_GreaterEqualOptions(); +} + +template<> inline const tflite_micro::LessEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_LessEqualOptions(); +} + +template<> inline const tflite_micro::SelectOptions *Operator::builtin_options_as() const { + return builtin_options_as_SelectOptions(); +} + +template<> inline const tflite_micro::SliceOptions *Operator::builtin_options_as() const { + return builtin_options_as_SliceOptions(); +} + +template<> inline const tflite_micro::TransposeConvOptions *Operator::builtin_options_as() const { + return builtin_options_as_TransposeConvOptions(); +} + +template<> inline const tflite_micro::SparseToDenseOptions *Operator::builtin_options_as() const { + return builtin_options_as_SparseToDenseOptions(); +} + +template<> inline const tflite_micro::TileOptions *Operator::builtin_options_as() const { + return builtin_options_as_TileOptions(); +} + +template<> inline const tflite_micro::ExpandDimsOptions *Operator::builtin_options_as() const { + return builtin_options_as_ExpandDimsOptions(); +} + +template<> inline const tflite_micro::EqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_EqualOptions(); +} + +template<> inline const tflite_micro::NotEqualOptions *Operator::builtin_options_as() const { + return builtin_options_as_NotEqualOptions(); +} + +template<> inline const tflite_micro::ShapeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ShapeOptions(); +} + +template<> inline const tflite_micro::PowOptions *Operator::builtin_options_as() const { + return builtin_options_as_PowOptions(); +} + +template<> inline const tflite_micro::ArgMinOptions *Operator::builtin_options_as() const { + return builtin_options_as_ArgMinOptions(); +} + +template<> inline const tflite_micro::FakeQuantOptions *Operator::builtin_options_as() const { + return builtin_options_as_FakeQuantOptions(); +} + +template<> inline const tflite_micro::PackOptions *Operator::builtin_options_as() const { + return builtin_options_as_PackOptions(); +} + +template<> inline const tflite_micro::LogicalOrOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalOrOptions(); +} + +template<> inline const tflite_micro::OneHotOptions *Operator::builtin_options_as() const { + return builtin_options_as_OneHotOptions(); +} + +template<> inline const tflite_micro::LogicalAndOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalAndOptions(); +} + +template<> inline const tflite_micro::LogicalNotOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalNotOptions(); +} + +template<> inline const tflite_micro::UnpackOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnpackOptions(); +} + +template<> inline const tflite_micro::FloorDivOptions *Operator::builtin_options_as() const { + return builtin_options_as_FloorDivOptions(); +} + +template<> inline const tflite_micro::SquareOptions *Operator::builtin_options_as() const { + return builtin_options_as_SquareOptions(); +} + +template<> inline const tflite_micro::ZerosLikeOptions *Operator::builtin_options_as() const { + return builtin_options_as_ZerosLikeOptions(); +} + +template<> inline const tflite_micro::FillOptions *Operator::builtin_options_as() const { + return builtin_options_as_FillOptions(); +} + +template<> inline const tflite_micro::BidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_BidirectionalSequenceLSTMOptions(); +} + +template<> inline const tflite_micro::BidirectionalSequenceRNNOptions *Operator::builtin_options_as() const { + return builtin_options_as_BidirectionalSequenceRNNOptions(); +} + +template<> inline const tflite_micro::UnidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnidirectionalSequenceLSTMOptions(); +} + +template<> inline const tflite_micro::FloorModOptions *Operator::builtin_options_as() const { + return builtin_options_as_FloorModOptions(); +} + +template<> inline const tflite_micro::RangeOptions *Operator::builtin_options_as() const { + return builtin_options_as_RangeOptions(); +} + +template<> inline const tflite_micro::ResizeNearestNeighborOptions *Operator::builtin_options_as() const { + return builtin_options_as_ResizeNearestNeighborOptions(); +} + +template<> inline const tflite_micro::LeakyReluOptions *Operator::builtin_options_as() const { + return builtin_options_as_LeakyReluOptions(); +} + +template<> inline const tflite_micro::SquaredDifferenceOptions *Operator::builtin_options_as() const { + return builtin_options_as_SquaredDifferenceOptions(); +} + +template<> inline const tflite_micro::MirrorPadOptions *Operator::builtin_options_as() const { + return builtin_options_as_MirrorPadOptions(); +} + +template<> inline const tflite_micro::AbsOptions *Operator::builtin_options_as() const { + return builtin_options_as_AbsOptions(); +} + +template<> inline const tflite_micro::SplitVOptions *Operator::builtin_options_as() const { + return builtin_options_as_SplitVOptions(); +} + +template<> inline const tflite_micro::UniqueOptions *Operator::builtin_options_as() const { + return builtin_options_as_UniqueOptions(); +} + +template<> inline const tflite_micro::ReverseV2Options *Operator::builtin_options_as() const { + return builtin_options_as_ReverseV2Options(); +} + +template<> inline const tflite_micro::AddNOptions *Operator::builtin_options_as() const { + return builtin_options_as_AddNOptions(); +} + +template<> inline const tflite_micro::GatherNdOptions *Operator::builtin_options_as() const { + return builtin_options_as_GatherNdOptions(); +} + +template<> inline const tflite_micro::CosOptions *Operator::builtin_options_as() const { + return builtin_options_as_CosOptions(); +} + +template<> inline const tflite_micro::WhereOptions *Operator::builtin_options_as() const { + return builtin_options_as_WhereOptions(); +} + +template<> inline const tflite_micro::RankOptions *Operator::builtin_options_as() const { + return builtin_options_as_RankOptions(); +} + +template<> inline const tflite_micro::ReverseSequenceOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReverseSequenceOptions(); +} + +template<> inline const tflite_micro::MatrixDiagOptions *Operator::builtin_options_as() const { + return builtin_options_as_MatrixDiagOptions(); +} + +template<> inline const tflite_micro::QuantizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_QuantizeOptions(); +} + +template<> inline const tflite_micro::MatrixSetDiagOptions *Operator::builtin_options_as() const { + return builtin_options_as_MatrixSetDiagOptions(); +} + +template<> inline const tflite_micro::HardSwishOptions *Operator::builtin_options_as() const { + return builtin_options_as_HardSwishOptions(); +} + +template<> inline const tflite_micro::IfOptions *Operator::builtin_options_as() const { + return builtin_options_as_IfOptions(); +} + +template<> inline const tflite_micro::WhileOptions *Operator::builtin_options_as() const { + return builtin_options_as_WhileOptions(); +} + +template<> inline const tflite_micro::DepthToSpaceOptions *Operator::builtin_options_as() const { + return builtin_options_as_DepthToSpaceOptions(); +} + +template<> inline const tflite_micro::NonMaxSuppressionV4Options *Operator::builtin_options_as() const { + return builtin_options_as_NonMaxSuppressionV4Options(); +} + +template<> inline const tflite_micro::NonMaxSuppressionV5Options *Operator::builtin_options_as() const { + return builtin_options_as_NonMaxSuppressionV5Options(); +} + +template<> inline const tflite_micro::ScatterNdOptions *Operator::builtin_options_as() const { + return builtin_options_as_ScatterNdOptions(); +} + +template<> inline const tflite_micro::SelectV2Options *Operator::builtin_options_as() const { + return builtin_options_as_SelectV2Options(); +} + +template<> inline const tflite_micro::DensifyOptions *Operator::builtin_options_as() const { + return builtin_options_as_DensifyOptions(); +} + +template<> inline const tflite_micro::SegmentSumOptions *Operator::builtin_options_as() const { + return builtin_options_as_SegmentSumOptions(); +} + +template<> inline const tflite_micro::BatchMatMulOptions *Operator::builtin_options_as() const { + return builtin_options_as_BatchMatMulOptions(); +} + +template<> inline const tflite_micro::CumsumOptions *Operator::builtin_options_as() const { + return builtin_options_as_CumsumOptions(); +} + +template<> inline const tflite_micro::CallOnceOptions *Operator::builtin_options_as() const { + return builtin_options_as_CallOnceOptions(); +} + +template<> inline const tflite_micro::BroadcastToOptions *Operator::builtin_options_as() const { + return builtin_options_as_BroadcastToOptions(); +} + +template<> inline const tflite_micro::Rfft2dOptions *Operator::builtin_options_as() const { + return builtin_options_as_Rfft2dOptions(); +} + +template<> inline const tflite_micro::Conv3DOptions *Operator::builtin_options_as() const { + return builtin_options_as_Conv3DOptions(); +} + +template<> inline const tflite_micro::HashtableOptions *Operator::builtin_options_as() const { + return builtin_options_as_HashtableOptions(); +} + +template<> inline const tflite_micro::HashtableFindOptions *Operator::builtin_options_as() const { + return builtin_options_as_HashtableFindOptions(); +} + +template<> inline const tflite_micro::HashtableImportOptions *Operator::builtin_options_as() const { + return builtin_options_as_HashtableImportOptions(); +} + +template<> inline const tflite_micro::HashtableSizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_HashtableSizeOptions(); +} + +template<> inline const tflite_micro::VarHandleOptions *Operator::builtin_options_as() const { + return builtin_options_as_VarHandleOptions(); +} + +template<> inline const tflite_micro::ReadVariableOptions *Operator::builtin_options_as() const { + return builtin_options_as_ReadVariableOptions(); +} + +template<> inline const tflite_micro::AssignVariableOptions *Operator::builtin_options_as() const { + return builtin_options_as_AssignVariableOptions(); +} + +template<> inline const tflite_micro::RandomOptions *Operator::builtin_options_as() const { + return builtin_options_as_RandomOptions(); +} + +template<> inline const tflite_micro::BucketizeOptions *Operator::builtin_options_as() const { + return builtin_options_as_BucketizeOptions(); +} + +template<> inline const tflite_micro::GeluOptions *Operator::builtin_options_as() const { + return builtin_options_as_GeluOptions(); +} + +template<> inline const tflite_micro::DynamicUpdateSliceOptions *Operator::builtin_options_as() const { + return builtin_options_as_DynamicUpdateSliceOptions(); +} + +template<> inline const tflite_micro::UnsortedSegmentProdOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnsortedSegmentProdOptions(); +} + +template<> inline const tflite_micro::UnsortedSegmentMaxOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnsortedSegmentMaxOptions(); +} + +template<> inline const tflite_micro::UnsortedSegmentMinOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnsortedSegmentMinOptions(); +} + +template<> inline const tflite_micro::UnsortedSegmentSumOptions *Operator::builtin_options_as() const { + return builtin_options_as_UnsortedSegmentSumOptions(); +} + +template<> inline const tflite_micro::ATan2Options *Operator::builtin_options_as() const { + return builtin_options_as_ATan2Options(); +} + +template<> inline const tflite_micro::SignOptions *Operator::builtin_options_as() const { + return builtin_options_as_SignOptions(); +} + +template<> inline const tflite_micro::BitcastOptions *Operator::builtin_options_as() const { + return builtin_options_as_BitcastOptions(); +} + +template<> inline const tflite_micro::BitwiseXorOptions *Operator::builtin_options_as() const { + return builtin_options_as_BitwiseXorOptions(); +} + +template<> inline const tflite_micro::RightShiftOptions *Operator::builtin_options_as() const { + return builtin_options_as_RightShiftOptions(); +} + +template<> inline const tflite_micro::StablehloConcatenateOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloConcatenateOptions(); +} + +template<> inline const tflite_micro::StablehloBroadcastInDimOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloBroadcastInDimOptions(); +} + +template<> inline const tflite_micro::StablehloSliceOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloSliceOptions(); +} + +template<> inline const tflite_micro::StablehloConvolutionOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloConvolutionOptions(); +} + +template<> inline const tflite_micro::StablehloCustomCallOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloCustomCallOptions(); +} + +template<> inline const tflite_micro::StablehloReduceOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloReduceOptions(); +} + +template<> inline const tflite_micro::StablehloScatterOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloScatterOptions(); +} + +template<> inline const tflite_micro::StablehloCompareOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloCompareOptions(); +} + +template<> inline const tflite_micro::StablehloDynamicSliceOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloDynamicSliceOptions(); +} + +template<> inline const tflite_micro::StablehloPadOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloPadOptions(); +} + +template<> inline const tflite_micro::StablehloIotaOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloIotaOptions(); +} + +template<> inline const tflite_micro::StablehloDotGeneralOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloDotGeneralOptions(); +} + +template<> inline const tflite_micro::StablehloReduceWindowOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloReduceWindowOptions(); +} + +template<> inline const tflite_micro::StablehloSortOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloSortOptions(); +} + +template<> inline const tflite_micro::StablehloWhileOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloWhileOptions(); +} + +template<> inline const tflite_micro::StablehloGatherOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloGatherOptions(); +} + +template<> inline const tflite_micro::StablehloTransposeOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloTransposeOptions(); +} + +template<> inline const tflite_micro::DilateOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_DilateOptions(); +} + +template<> inline const tflite_micro::StablehloRngBitGeneratorOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_StablehloRngBitGeneratorOptions(); +} + +template<> inline const tflite_micro::ReduceWindowOptions *Operator::builtin_options_2_as() const { + return builtin_options_2_as_ReduceWindowOptions(); +} + +struct OperatorBuilder { + typedef Operator Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_opcode_index(uint32_t opcode_index) { + fbb_.AddElement(Operator::VT_OPCODE_INDEX, opcode_index, 0); + } + void add_inputs(flatbuffers::Offset> inputs) { + fbb_.AddOffset(Operator::VT_INPUTS, inputs); + } + void add_outputs(flatbuffers::Offset> outputs) { + fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); + } + void add_builtin_options_type(tflite_micro::BuiltinOptions builtin_options_type) { + fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast(builtin_options_type), 0); + } + void add_builtin_options(flatbuffers::Offset builtin_options) { + fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); + } + void add_custom_options(flatbuffers::Offset> custom_options) { + fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); + } + void add_custom_options_format(tflite_micro::CustomOptionsFormat custom_options_format) { + fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); + } + void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { + fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); + } + void add_intermediates(flatbuffers::Offset> intermediates) { + fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); + } + void add_large_custom_options_offset(uint64_t large_custom_options_offset) { + fbb_.AddElement(Operator::VT_LARGE_CUSTOM_OPTIONS_OFFSET, large_custom_options_offset, 0); + } + void add_large_custom_options_size(uint64_t large_custom_options_size) { + fbb_.AddElement(Operator::VT_LARGE_CUSTOM_OPTIONS_SIZE, large_custom_options_size, 0); + } + void add_builtin_options_2_type(tflite_micro::BuiltinOptions2 builtin_options_2_type) { + fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_2_TYPE, static_cast(builtin_options_2_type), 0); + } + void add_builtin_options_2(flatbuffers::Offset builtin_options_2) { + fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS_2, builtin_options_2); + } + explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOperator( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, + flatbuffers::Offset> inputs = 0, + flatbuffers::Offset> outputs = 0, + tflite_micro::BuiltinOptions builtin_options_type = tflite_micro::BuiltinOptions_NONE, + flatbuffers::Offset builtin_options = 0, + flatbuffers::Offset> custom_options = 0, + tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS, + flatbuffers::Offset> mutating_variable_inputs = 0, + flatbuffers::Offset> intermediates = 0, + uint64_t large_custom_options_offset = 0, + uint64_t large_custom_options_size = 0, + tflite_micro::BuiltinOptions2 builtin_options_2_type = tflite_micro::BuiltinOptions2_NONE, + flatbuffers::Offset builtin_options_2 = 0) { + OperatorBuilder builder_(_fbb); + builder_.add_large_custom_options_size(large_custom_options_size); + builder_.add_large_custom_options_offset(large_custom_options_offset); + builder_.add_builtin_options_2(builtin_options_2); + builder_.add_intermediates(intermediates); + builder_.add_mutating_variable_inputs(mutating_variable_inputs); + builder_.add_custom_options(custom_options); + builder_.add_builtin_options(builtin_options); + builder_.add_outputs(outputs); + builder_.add_inputs(inputs); + builder_.add_opcode_index(opcode_index); + builder_.add_builtin_options_2_type(builtin_options_2_type); + builder_.add_custom_options_format(custom_options_format); + builder_.add_builtin_options_type(builtin_options_type); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateOperatorDirect( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t opcode_index = 0, + const std::vector *inputs = nullptr, + const std::vector *outputs = nullptr, + tflite_micro::BuiltinOptions builtin_options_type = tflite_micro::BuiltinOptions_NONE, + flatbuffers::Offset builtin_options = 0, + const std::vector *custom_options = nullptr, + tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS, + const std::vector *mutating_variable_inputs = nullptr, + const std::vector *intermediates = nullptr, + uint64_t large_custom_options_offset = 0, + uint64_t large_custom_options_size = 0, + tflite_micro::BuiltinOptions2 builtin_options_2_type = tflite_micro::BuiltinOptions2_NONE, + flatbuffers::Offset builtin_options_2 = 0) { + auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; + auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; + auto custom_options__ = custom_options ? _fbb.CreateVector(*custom_options) : 0; + auto mutating_variable_inputs__ = mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0; + auto intermediates__ = intermediates ? _fbb.CreateVector(*intermediates) : 0; + return tflite_micro::CreateOperator( + _fbb, + opcode_index, + inputs__, + outputs__, + builtin_options_type, + builtin_options, + custom_options__, + custom_options_format, + mutating_variable_inputs__, + intermediates__, + large_custom_options_offset, + large_custom_options_size, + builtin_options_2_type, + builtin_options_2); +} + +flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SubGraphT : public flatbuffers::NativeTable { + typedef SubGraph TableType; + std::vector> tensors{}; + std::vector inputs{}; + std::vector outputs{}; + std::vector> operators{}; + std::string name{}; + SubGraphT() = default; + SubGraphT(const SubGraphT &o); + SubGraphT(SubGraphT&&) FLATBUFFERS_NOEXCEPT = default; + SubGraphT &operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT; +}; + +struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SubGraphT NativeTableType; + typedef SubGraphBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_TENSORS = 4, + VT_INPUTS = 6, + VT_OUTPUTS = 8, + VT_OPERATORS = 10, + VT_NAME = 12 + }; + const flatbuffers::Vector> *tensors() const { + return GetPointer> *>(VT_TENSORS); + } + const flatbuffers::Vector *inputs() const { + return GetPointer *>(VT_INPUTS); + } + const flatbuffers::Vector *outputs() const { + return GetPointer *>(VT_OUTPUTS); + } + const flatbuffers::Vector> *operators() const { + return GetPointer> *>(VT_OPERATORS); + } + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_TENSORS) && + verifier.VerifyVector(tensors()) && + verifier.VerifyVectorOfTables(tensors()) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.VerifyVector(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.VerifyVector(outputs()) && + VerifyOffset(verifier, VT_OPERATORS) && + verifier.VerifyVector(operators()) && + verifier.VerifyVectorOfTables(operators()) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + verifier.EndTable(); + } + SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SubGraphBuilder { + typedef SubGraph Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_tensors(flatbuffers::Offset>> tensors) { + fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); + } + void add_inputs(flatbuffers::Offset> inputs) { + fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); + } + void add_outputs(flatbuffers::Offset> outputs) { + fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); + } + void add_operators(flatbuffers::Offset>> operators) { + fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); + } + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(SubGraph::VT_NAME, name); + } + explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSubGraph( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> tensors = 0, + flatbuffers::Offset> inputs = 0, + flatbuffers::Offset> outputs = 0, + flatbuffers::Offset>> operators = 0, + flatbuffers::Offset name = 0) { + SubGraphBuilder builder_(_fbb); + builder_.add_name(name); + builder_.add_operators(operators); + builder_.add_outputs(outputs); + builder_.add_inputs(inputs); + builder_.add_tensors(tensors); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSubGraphDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector> *tensors = nullptr, + const std::vector *inputs = nullptr, + const std::vector *outputs = nullptr, + const std::vector> *operators = nullptr, + const char *name = nullptr) { + auto tensors__ = tensors ? _fbb.CreateVector>(*tensors) : 0; + auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; + auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; + auto operators__ = operators ? _fbb.CreateVector>(*operators) : 0; + auto name__ = name ? _fbb.CreateString(name) : 0; + return tflite_micro::CreateSubGraph( + _fbb, + tensors__, + inputs__, + outputs__, + operators__, + name__); +} + +flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct BufferT : public flatbuffers::NativeTable { + typedef Buffer TableType; + std::vector data{}; + uint64_t offset = 0; + uint64_t size = 0; +}; + +struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef BufferT NativeTableType; + typedef BufferBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_DATA = 4, + VT_OFFSET = 6, + VT_SIZE = 8 + }; + const flatbuffers::Vector *data() const { + return GetPointer *>(VT_DATA); + } + uint64_t offset() const { + return GetField(VT_OFFSET, 0); + } + uint64_t size() const { + return GetField(VT_SIZE, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_DATA) && + verifier.VerifyVector(data()) && + VerifyField(verifier, VT_OFFSET, 8) && + VerifyField(verifier, VT_SIZE, 8) && + verifier.EndTable(); + } + BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct BufferBuilder { + typedef Buffer Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_data(flatbuffers::Offset> data) { + fbb_.AddOffset(Buffer::VT_DATA, data); + } + void add_offset(uint64_t offset) { + fbb_.AddElement(Buffer::VT_OFFSET, offset, 0); + } + void add_size(uint64_t size) { + fbb_.AddElement(Buffer::VT_SIZE, size, 0); + } + explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateBuffer( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset> data = 0, + uint64_t offset = 0, + uint64_t size = 0) { + BufferBuilder builder_(_fbb); + builder_.add_size(size); + builder_.add_offset(offset); + builder_.add_data(data); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateBufferDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector *data = nullptr, + uint64_t offset = 0, + uint64_t size = 0) { + if (data) { _fbb.ForceVectorAlignment(data->size(), sizeof(uint8_t), 16); } + auto data__ = data ? _fbb.CreateVector(*data) : 0; + return tflite_micro::CreateBuffer( + _fbb, + data__, + offset, + size); +} + +flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct MetadataT : public flatbuffers::NativeTable { + typedef Metadata TableType; + std::string name{}; + uint32_t buffer = 0; +}; + +struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef MetadataT NativeTableType; + typedef MetadataBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_BUFFER = 6 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + uint32_t buffer() const { + return GetField(VT_BUFFER, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyField(verifier, VT_BUFFER, 4) && + verifier.EndTable(); + } + MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct MetadataBuilder { + typedef Metadata Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(Metadata::VT_NAME, name); + } + void add_buffer(uint32_t buffer) { + fbb_.AddElement(Metadata::VT_BUFFER, buffer, 0); + } + explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateMetadata( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + uint32_t buffer = 0) { + MetadataBuilder builder_(_fbb); + builder_.add_buffer(buffer); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateMetadataDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + uint32_t buffer = 0) { + auto name__ = name ? _fbb.CreateString(name) : 0; + return tflite_micro::CreateMetadata( + _fbb, + name__, + buffer); +} + +flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct TensorMapT : public flatbuffers::NativeTable { + typedef TensorMap TableType; + std::string name{}; + uint32_t tensor_index = 0; +}; + +struct TensorMap FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef TensorMapT NativeTableType; + typedef TensorMapBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_NAME = 4, + VT_TENSOR_INDEX = 6 + }; + const flatbuffers::String *name() const { + return GetPointer(VT_NAME); + } + uint32_t tensor_index() const { + return GetField(VT_TENSOR_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_NAME) && + verifier.VerifyString(name()) && + VerifyField(verifier, VT_TENSOR_INDEX, 4) && + verifier.EndTable(); + } + TensorMapT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct TensorMapBuilder { + typedef TensorMap Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_name(flatbuffers::Offset name) { + fbb_.AddOffset(TensorMap::VT_NAME, name); + } + void add_tensor_index(uint32_t tensor_index) { + fbb_.AddElement(TensorMap::VT_TENSOR_INDEX, tensor_index, 0); + } + explicit TensorMapBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateTensorMap( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset name = 0, + uint32_t tensor_index = 0) { + TensorMapBuilder builder_(_fbb); + builder_.add_tensor_index(tensor_index); + builder_.add_name(name); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateTensorMapDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const char *name = nullptr, + uint32_t tensor_index = 0) { + auto name__ = name ? _fbb.CreateString(name) : 0; + return tflite_micro::CreateTensorMap( + _fbb, + name__, + tensor_index); +} + +flatbuffers::Offset CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct SignatureDefT : public flatbuffers::NativeTable { + typedef SignatureDef TableType; + std::vector> inputs{}; + std::vector> outputs{}; + std::string signature_key{}; + uint32_t subgraph_index = 0; + SignatureDefT() = default; + SignatureDefT(const SignatureDefT &o); + SignatureDefT(SignatureDefT&&) FLATBUFFERS_NOEXCEPT = default; + SignatureDefT &operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT; +}; + +struct SignatureDef FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef SignatureDefT NativeTableType; + typedef SignatureDefBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_INPUTS = 4, + VT_OUTPUTS = 6, + VT_SIGNATURE_KEY = 8, + VT_SUBGRAPH_INDEX = 12 + }; + const flatbuffers::Vector> *inputs() const { + return GetPointer> *>(VT_INPUTS); + } + const flatbuffers::Vector> *outputs() const { + return GetPointer> *>(VT_OUTPUTS); + } + const flatbuffers::String *signature_key() const { + return GetPointer(VT_SIGNATURE_KEY); + } + uint32_t subgraph_index() const { + return GetField(VT_SUBGRAPH_INDEX, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyOffset(verifier, VT_INPUTS) && + verifier.VerifyVector(inputs()) && + verifier.VerifyVectorOfTables(inputs()) && + VerifyOffset(verifier, VT_OUTPUTS) && + verifier.VerifyVector(outputs()) && + verifier.VerifyVectorOfTables(outputs()) && + VerifyOffset(verifier, VT_SIGNATURE_KEY) && + verifier.VerifyString(signature_key()) && + VerifyField(verifier, VT_SUBGRAPH_INDEX, 4) && + verifier.EndTable(); + } + SignatureDefT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct SignatureDefBuilder { + typedef SignatureDef Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_inputs(flatbuffers::Offset>> inputs) { + fbb_.AddOffset(SignatureDef::VT_INPUTS, inputs); + } + void add_outputs(flatbuffers::Offset>> outputs) { + fbb_.AddOffset(SignatureDef::VT_OUTPUTS, outputs); + } + void add_signature_key(flatbuffers::Offset signature_key) { + fbb_.AddOffset(SignatureDef::VT_SIGNATURE_KEY, signature_key); + } + void add_subgraph_index(uint32_t subgraph_index) { + fbb_.AddElement(SignatureDef::VT_SUBGRAPH_INDEX, subgraph_index, 0); + } + explicit SignatureDefBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateSignatureDef( + flatbuffers::FlatBufferBuilder &_fbb, + flatbuffers::Offset>> inputs = 0, + flatbuffers::Offset>> outputs = 0, + flatbuffers::Offset signature_key = 0, + uint32_t subgraph_index = 0) { + SignatureDefBuilder builder_(_fbb); + builder_.add_subgraph_index(subgraph_index); + builder_.add_signature_key(signature_key); + builder_.add_outputs(outputs); + builder_.add_inputs(inputs); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateSignatureDefDirect( + flatbuffers::FlatBufferBuilder &_fbb, + const std::vector> *inputs = nullptr, + const std::vector> *outputs = nullptr, + const char *signature_key = nullptr, + uint32_t subgraph_index = 0) { + auto inputs__ = inputs ? _fbb.CreateVector>(*inputs) : 0; + auto outputs__ = outputs ? _fbb.CreateVector>(*outputs) : 0; + auto signature_key__ = signature_key ? _fbb.CreateString(signature_key) : 0; + return tflite_micro::CreateSignatureDef( + _fbb, + inputs__, + outputs__, + signature_key__, + subgraph_index); +} + +flatbuffers::Offset CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +struct ModelT : public flatbuffers::NativeTable { + typedef Model TableType; + uint32_t version = 0; + std::vector> operator_codes{}; + std::vector> subgraphs{}; + std::string description{}; + std::vector> buffers{}; + std::vector metadata_buffer{}; + std::vector> metadata{}; + std::vector> signature_defs{}; + ModelT() = default; + ModelT(const ModelT &o); + ModelT(ModelT&&) FLATBUFFERS_NOEXCEPT = default; + ModelT &operator=(ModelT o) FLATBUFFERS_NOEXCEPT; +}; + +struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef ModelT NativeTableType; + typedef ModelBuilder Builder; + enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { + VT_VERSION = 4, + VT_OPERATOR_CODES = 6, + VT_SUBGRAPHS = 8, + VT_DESCRIPTION = 10, + VT_BUFFERS = 12, + VT_METADATA_BUFFER = 14, + VT_METADATA = 16, + VT_SIGNATURE_DEFS = 18 + }; + uint32_t version() const { + return GetField(VT_VERSION, 0); + } + const flatbuffers::Vector> *operator_codes() const { + return GetPointer> *>(VT_OPERATOR_CODES); + } + const flatbuffers::Vector> *subgraphs() const { + return GetPointer> *>(VT_SUBGRAPHS); + } + const flatbuffers::String *description() const { + return GetPointer(VT_DESCRIPTION); + } + const flatbuffers::Vector> *buffers() const { + return GetPointer> *>(VT_BUFFERS); + } + const flatbuffers::Vector *metadata_buffer() const { + return GetPointer *>(VT_METADATA_BUFFER); + } + const flatbuffers::Vector> *metadata() const { + return GetPointer> *>(VT_METADATA); + } + const flatbuffers::Vector> *signature_defs() const { + return GetPointer> *>(VT_SIGNATURE_DEFS); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_VERSION, 4) && + VerifyOffset(verifier, VT_OPERATOR_CODES) && + verifier.VerifyVector(operator_codes()) && + verifier.VerifyVectorOfTables(operator_codes()) && + VerifyOffset(verifier, VT_SUBGRAPHS) && + verifier.VerifyVector(subgraphs()) && + verifier.VerifyVectorOfTables(subgraphs()) && + VerifyOffset(verifier, VT_DESCRIPTION) && + verifier.VerifyString(description()) && + VerifyOffset(verifier, VT_BUFFERS) && + verifier.VerifyVector(buffers()) && + verifier.VerifyVectorOfTables(buffers()) && + VerifyOffset(verifier, VT_METADATA_BUFFER) && + verifier.VerifyVector(metadata_buffer()) && + VerifyOffset(verifier, VT_METADATA) && + verifier.VerifyVector(metadata()) && + verifier.VerifyVectorOfTables(metadata()) && + VerifyOffset(verifier, VT_SIGNATURE_DEFS) && + verifier.VerifyVector(signature_defs()) && + verifier.VerifyVectorOfTables(signature_defs()) && + verifier.EndTable(); + } + ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct ModelBuilder { + typedef Model Table; + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_version(uint32_t version) { + fbb_.AddElement(Model::VT_VERSION, version, 0); + } + void add_operator_codes(flatbuffers::Offset>> operator_codes) { + fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); + } + void add_subgraphs(flatbuffers::Offset>> subgraphs) { + fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); + } + void add_description(flatbuffers::Offset description) { + fbb_.AddOffset(Model::VT_DESCRIPTION, description); + } + void add_buffers(flatbuffers::Offset>> buffers) { + fbb_.AddOffset(Model::VT_BUFFERS, buffers); + } + void add_metadata_buffer(flatbuffers::Offset> metadata_buffer) { + fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); + } + void add_metadata(flatbuffers::Offset>> metadata) { + fbb_.AddOffset(Model::VT_METADATA, metadata); + } + void add_signature_defs(flatbuffers::Offset>> signature_defs) { + fbb_.AddOffset(Model::VT_SIGNATURE_DEFS, signature_defs); + } + explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateModel( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + flatbuffers::Offset>> operator_codes = 0, + flatbuffers::Offset>> subgraphs = 0, + flatbuffers::Offset description = 0, + flatbuffers::Offset>> buffers = 0, + flatbuffers::Offset> metadata_buffer = 0, + flatbuffers::Offset>> metadata = 0, + flatbuffers::Offset>> signature_defs = 0) { + ModelBuilder builder_(_fbb); + builder_.add_signature_defs(signature_defs); + builder_.add_metadata(metadata); + builder_.add_metadata_buffer(metadata_buffer); + builder_.add_buffers(buffers); + builder_.add_description(description); + builder_.add_subgraphs(subgraphs); + builder_.add_operator_codes(operator_codes); + builder_.add_version(version); + return builder_.Finish(); +} + +inline flatbuffers::Offset CreateModelDirect( + flatbuffers::FlatBufferBuilder &_fbb, + uint32_t version = 0, + const std::vector> *operator_codes = nullptr, + const std::vector> *subgraphs = nullptr, + const char *description = nullptr, + const std::vector> *buffers = nullptr, + const std::vector *metadata_buffer = nullptr, + const std::vector> *metadata = nullptr, + const std::vector> *signature_defs = nullptr) { + auto operator_codes__ = operator_codes ? _fbb.CreateVector>(*operator_codes) : 0; + auto subgraphs__ = subgraphs ? _fbb.CreateVector>(*subgraphs) : 0; + auto description__ = description ? _fbb.CreateString(description) : 0; + auto buffers__ = buffers ? _fbb.CreateVector>(*buffers) : 0; + auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector(*metadata_buffer) : 0; + auto metadata__ = metadata ? _fbb.CreateVector>(*metadata) : 0; + auto signature_defs__ = signature_defs ? _fbb.CreateVector>(*signature_defs) : 0; + return tflite_micro::CreateModel( + _fbb, + version, + operator_codes__, + subgraphs__, + description__, + buffers__, + metadata_buffer__, + metadata__, + signature_defs__); +} + +flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + +inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CustomQuantizationT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom.begin()); } } +} + +inline flatbuffers::Offset CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCustomQuantization(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16); + auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0; + return tflite_micro::CreateCustomQuantization( + _fbb, + _custom); +} + +inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new QuantizationParametersT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } } + { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } } + { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } } + { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } } + { auto _e = details_type(); _o->details.type = _e; } + { auto _e = details(); if (_e) _o->details.value = tflite_micro::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); } + { auto _e = quantized_dimension(); _o->quantized_dimension = _e; } +} + +inline flatbuffers::Offset QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateQuantizationParameters(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0; + auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0; + auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0; + auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; + auto _details_type = _o->details.type; + auto _details = _o->details.Pack(_fbb); + auto _quantized_dimension = _o->quantized_dimension; + return tflite_micro::CreateQuantizationParameters( + _fbb, + _min, + _max, + _scale, + _zero_point, + _details_type, + _details, + _quantized_dimension); +} + +inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Int32VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateInt32Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite_micro::CreateInt32Vector( + _fbb, + _values); +} + +inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Uint16VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUint16Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4); + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite_micro::CreateUint16Vector( + _fbb, + _values); +} + +inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Uint8VectorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values(); if (_e) { _o->values.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->values.begin()); } } +} + +inline flatbuffers::Offset Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUint8Vector(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4); + auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; + return tflite_micro::CreateUint8Vector( + _fbb, + _values); +} + +inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DimensionMetadataT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = format(); _o->format = _e; } + { auto _e = dense_size(); _o->dense_size = _e; } + { auto _e = array_segments_type(); _o->array_segments.type = _e; } + { auto _e = array_segments(); if (_e) _o->array_segments.value = tflite_micro::SparseIndexVectorUnion::UnPack(_e, array_segments_type(), _resolver); } + { auto _e = array_indices_type(); _o->array_indices.type = _e; } + { auto _e = array_indices(); if (_e) _o->array_indices.value = tflite_micro::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); } +} + +inline flatbuffers::Offset DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDimensionMetadata(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _format = _o->format; + auto _dense_size = _o->dense_size; + auto _array_segments_type = _o->array_segments.type; + auto _array_segments = _o->array_segments.Pack(_fbb); + auto _array_indices_type = _o->array_indices.type; + auto _array_indices = _o->array_indices.Pack(_fbb); + return tflite_micro::CreateDimensionMetadata( + _fbb, + _format, + _dense_size, + _array_segments_type, + _array_segments, + _array_indices_type, + _array_indices); +} + +inline SparsityParametersT::SparsityParametersT(const SparsityParametersT &o) + : traversal_order(o.traversal_order), + block_map(o.block_map) { + dim_metadata.reserve(o.dim_metadata.size()); + for (const auto &dim_metadata_ : o.dim_metadata) { dim_metadata.emplace_back((dim_metadata_) ? new tflite_micro::DimensionMetadataT(*dim_metadata_) : nullptr); } +} + +inline SparsityParametersT &SparsityParametersT::operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT { + std::swap(traversal_order, o.traversal_order); + std::swap(block_map, o.block_map); + std::swap(dim_metadata, o.dim_metadata); + return *this; +} + +inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SparsityParametersT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } } + { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } } + { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->dim_metadata[_i]) { _e->Get(_i)->UnPackTo(_o->dim_metadata[_i].get(), _resolver); } else { _o->dim_metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } +} + +inline flatbuffers::Offset SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSparsityParameters(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0; + auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0; + auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite_micro::CreateSparsityParameters( + _fbb, + _traversal_order, + _block_map, + _dim_metadata); +} + +inline VariantSubTypeT *VariantSubType::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new VariantSubTypeT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void VariantSubType::UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } + { auto _e = type(); _o->type = _e; } + { auto _e = has_rank(); _o->has_rank = _e; } +} + +inline flatbuffers::Offset VariantSubType::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateVariantSubType(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VariantSubTypeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; + auto _type = _o->type; + auto _has_rank = _o->has_rank; + return tflite_micro::CreateVariantSubType( + _fbb, + _shape, + _type, + _has_rank); +} + +inline TensorT::TensorT(const TensorT &o) + : shape(o.shape), + type(o.type), + buffer(o.buffer), + name(o.name), + quantization((o.quantization) ? new tflite_micro::QuantizationParametersT(*o.quantization) : nullptr), + is_variable(o.is_variable), + sparsity((o.sparsity) ? new tflite_micro::SparsityParametersT(*o.sparsity) : nullptr), + shape_signature(o.shape_signature), + has_rank(o.has_rank) { + variant_tensors.reserve(o.variant_tensors.size()); + for (const auto &variant_tensors_ : o.variant_tensors) { variant_tensors.emplace_back((variant_tensors_) ? new tflite_micro::VariantSubTypeT(*variant_tensors_) : nullptr); } +} + +inline TensorT &TensorT::operator=(TensorT o) FLATBUFFERS_NOEXCEPT { + std::swap(shape, o.shape); + std::swap(type, o.type); + std::swap(buffer, o.buffer); + std::swap(name, o.name); + std::swap(quantization, o.quantization); + std::swap(is_variable, o.is_variable); + std::swap(sparsity, o.sparsity); + std::swap(shape_signature, o.shape_signature); + std::swap(has_rank, o.has_rank); + std::swap(variant_tensors, o.variant_tensors); + return *this; +} + +inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TensorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } + { auto _e = type(); _o->type = _e; } + { auto _e = buffer(); _o->buffer = _e; } + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = quantization(); if (_e) { if(_o->quantization) { _e->UnPackTo(_o->quantization.get(), _resolver); } else { _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); } } } + { auto _e = is_variable(); _o->is_variable = _e; } + { auto _e = sparsity(); if (_e) { if(_o->sparsity) { _e->UnPackTo(_o->sparsity.get(), _resolver); } else { _o->sparsity = std::unique_ptr(_e->UnPack(_resolver)); } } } + { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } } + { auto _e = has_rank(); _o->has_rank = _e; } + { auto _e = variant_tensors(); if (_e) { _o->variant_tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->variant_tensors[_i]) { _e->Get(_i)->UnPackTo(_o->variant_tensors[_i].get(), _resolver); } else { _o->variant_tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } +} + +inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTensor(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; + auto _type = _o->type; + auto _buffer = _o->buffer; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; + auto _is_variable = _o->is_variable; + auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0; + auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0; + auto _has_rank = _o->has_rank; + auto _variant_tensors = _o->variant_tensors.size() ? _fbb.CreateVector> (_o->variant_tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateVariantSubType(*__va->__fbb, __va->__o->variant_tensors[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite_micro::CreateTensor( + _fbb, + _shape, + _type, + _buffer, + _name, + _quantization, + _is_variable, + _sparsity, + _shape_signature, + _has_rank, + _variant_tensors); +} + +inline StablehloGatherOptionsT *StablehloGatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloGatherOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloGatherOptions::UnPackTo(StablehloGatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = offset_dims(); if (_e) { _o->offset_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->offset_dims[_i] = _e->Get(_i); } } } + { auto _e = collapsed_slice_dims(); if (_e) { _o->collapsed_slice_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->collapsed_slice_dims[_i] = _e->Get(_i); } } } + { auto _e = start_index_map(); if (_e) { _o->start_index_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_index_map[_i] = _e->Get(_i); } } } + { auto _e = index_vector_dim(); _o->index_vector_dim = _e; } + { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } } + { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; } +} + +inline flatbuffers::Offset StablehloGatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloGatherOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloGatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _offset_dims = _o->offset_dims.size() ? _fbb.CreateVector(_o->offset_dims) : 0; + auto _collapsed_slice_dims = _o->collapsed_slice_dims.size() ? _fbb.CreateVector(_o->collapsed_slice_dims) : 0; + auto _start_index_map = _o->start_index_map.size() ? _fbb.CreateVector(_o->start_index_map) : 0; + auto _index_vector_dim = _o->index_vector_dim; + auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0; + auto _indices_are_sorted = _o->indices_are_sorted; + return tflite_micro::CreateStablehloGatherOptions( + _fbb, + _offset_dims, + _collapsed_slice_dims, + _start_index_map, + _index_vector_dim, + _slice_sizes, + _indices_are_sorted); +} + +inline StablehloTransposeOptionsT *StablehloTransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloTransposeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloTransposeOptions::UnPackTo(StablehloTransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = permutation(); if (_e) { _o->permutation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->permutation[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset StablehloTransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloTransposeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloTransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _permutation = _o->permutation.size() ? _fbb.CreateVector(_o->permutation) : 0; + return tflite_micro::CreateStablehloTransposeOptions( + _fbb, + _permutation); +} + +inline StablehloDotGeneralOptionsT *StablehloDotGeneralOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloDotGeneralOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloDotGeneralOptions::UnPackTo(StablehloDotGeneralOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = lhs_batching_dimensions(); if (_e) { _o->lhs_batching_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_batching_dimensions[_i] = _e->Get(_i); } } } + { auto _e = rhs_batching_dimensions(); if (_e) { _o->rhs_batching_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_batching_dimensions[_i] = _e->Get(_i); } } } + { auto _e = lhs_contracting_dimensions(); if (_e) { _o->lhs_contracting_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_contracting_dimensions[_i] = _e->Get(_i); } } } + { auto _e = rhs_contracting_dimensions(); if (_e) { _o->rhs_contracting_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_contracting_dimensions[_i] = _e->Get(_i); } } } + { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast(_e->Get(_i)); } } } +} + +inline flatbuffers::Offset StablehloDotGeneralOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloDotGeneralOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloDotGeneralOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloDotGeneralOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _lhs_batching_dimensions = _o->lhs_batching_dimensions.size() ? _fbb.CreateVector(_o->lhs_batching_dimensions) : 0; + auto _rhs_batching_dimensions = _o->rhs_batching_dimensions.size() ? _fbb.CreateVector(_o->rhs_batching_dimensions) : 0; + auto _lhs_contracting_dimensions = _o->lhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->lhs_contracting_dimensions) : 0; + auto _rhs_contracting_dimensions = _o->rhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->rhs_contracting_dimensions) : 0; + auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast(flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0; + return tflite_micro::CreateStablehloDotGeneralOptions( + _fbb, + _lhs_batching_dimensions, + _rhs_batching_dimensions, + _lhs_contracting_dimensions, + _rhs_contracting_dimensions, + _precision_config); +} + +inline StablehloReduceWindowOptionsT *StablehloReduceWindowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloReduceWindowOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloReduceWindowOptions::UnPackTo(StablehloReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = window_dimensions(); if (_e) { _o->window_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dimensions[_i] = _e->Get(_i); } } } + { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } } + { auto _e = base_dilations(); if (_e) { _o->base_dilations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->base_dilations[_i] = _e->Get(_i); } } } + { auto _e = window_dilations(); if (_e) { _o->window_dilations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dilations[_i] = _e->Get(_i); } } } + { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } } + { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } +} + +inline flatbuffers::Offset StablehloReduceWindowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloReduceWindowOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceWindowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _window_dimensions = _o->window_dimensions.size() ? _fbb.CreateVector(_o->window_dimensions) : 0; + auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0; + auto _base_dilations = _o->base_dilations.size() ? _fbb.CreateVector(_o->base_dilations) : 0; + auto _window_dilations = _o->window_dilations.size() ? _fbb.CreateVector(_o->window_dilations) : 0; + auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0; + auto _body_subgraph_index = _o->body_subgraph_index; + return tflite_micro::CreateStablehloReduceWindowOptions( + _fbb, + _window_dimensions, + _window_strides, + _base_dilations, + _window_dilations, + _padding, + _body_subgraph_index); +} + +inline StablehloWhileOptionsT *StablehloWhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloWhileOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloWhileOptions::UnPackTo(StablehloWhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } + { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } +} + +inline flatbuffers::Offset StablehloWhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloWhileOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloWhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _cond_subgraph_index = _o->cond_subgraph_index; + auto _body_subgraph_index = _o->body_subgraph_index; + return tflite_micro::CreateStablehloWhileOptions( + _fbb, + _cond_subgraph_index, + _body_subgraph_index); +} + +inline StablehloSortOptionsT *StablehloSortOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloSortOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloSortOptions::UnPackTo(StablehloSortOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = dimension(); _o->dimension = _e; } + { auto _e = is_stable(); _o->is_stable = _e; } + { auto _e = comparator_subgraph_index(); _o->comparator_subgraph_index = _e; } +} + +inline flatbuffers::Offset StablehloSortOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloSortOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloSortOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloSortOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _dimension = _o->dimension; + auto _is_stable = _o->is_stable; + auto _comparator_subgraph_index = _o->comparator_subgraph_index; + return tflite_micro::CreateStablehloSortOptions( + _fbb, + _dimension, + _is_stable, + _comparator_subgraph_index); +} + +inline StablehloConcatenateOptionsT *StablehloConcatenateOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloConcatenateOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloConcatenateOptions::UnPackTo(StablehloConcatenateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = dimension(); _o->dimension = _e; } +} + +inline flatbuffers::Offset StablehloConcatenateOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloConcatenateOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloConcatenateOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloConcatenateOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _dimension = _o->dimension; + return tflite_micro::CreateStablehloConcatenateOptions( + _fbb, + _dimension); +} + +inline StablehloBroadcastInDimOptionsT *StablehloBroadcastInDimOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloBroadcastInDimOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloBroadcastInDimOptions::UnPackTo(StablehloBroadcastInDimOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = broadcast_dimensions(); if (_e) { _o->broadcast_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->broadcast_dimensions[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset StablehloBroadcastInDimOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloBroadcastInDimOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloBroadcastInDimOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloBroadcastInDimOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _broadcast_dimensions = _o->broadcast_dimensions.size() ? _fbb.CreateVector(_o->broadcast_dimensions) : 0; + return tflite_micro::CreateStablehloBroadcastInDimOptions( + _fbb, + _broadcast_dimensions); +} + +inline StablehloCompareOptionsT *StablehloCompareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloCompareOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloCompareOptions::UnPackTo(StablehloCompareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = comparison_direction(); _o->comparison_direction = _e; } + { auto _e = compare_type(); _o->compare_type = _e; } +} + +inline flatbuffers::Offset StablehloCompareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloCompareOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloCompareOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloCompareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _comparison_direction = _o->comparison_direction; + auto _compare_type = _o->compare_type; + return tflite_micro::CreateStablehloCompareOptions( + _fbb, + _comparison_direction, + _compare_type); +} + +inline StablehloDynamicSliceOptionsT *StablehloDynamicSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloDynamicSliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloDynamicSliceOptions::UnPackTo(StablehloDynamicSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset StablehloDynamicSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloDynamicSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloDynamicSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloDynamicSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0; + return tflite_micro::CreateStablehloDynamicSliceOptions( + _fbb, + _slice_sizes); +} + +inline StablehloPadOptionsT *StablehloPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloPadOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloPadOptions::UnPackTo(StablehloPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = edge_padding_low(); if (_e) { _o->edge_padding_low.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_low[_i] = _e->Get(_i); } } } + { auto _e = edge_padding_high(); if (_e) { _o->edge_padding_high.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_high[_i] = _e->Get(_i); } } } + { auto _e = interior_padding(); if (_e) { _o->interior_padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->interior_padding[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset StablehloPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloPadOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _edge_padding_low = _o->edge_padding_low.size() ? _fbb.CreateVector(_o->edge_padding_low) : 0; + auto _edge_padding_high = _o->edge_padding_high.size() ? _fbb.CreateVector(_o->edge_padding_high) : 0; + auto _interior_padding = _o->interior_padding.size() ? _fbb.CreateVector(_o->interior_padding) : 0; + return tflite_micro::CreateStablehloPadOptions( + _fbb, + _edge_padding_low, + _edge_padding_high, + _interior_padding); +} + +inline StablehloIotaOptionsT *StablehloIotaOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloIotaOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloIotaOptions::UnPackTo(StablehloIotaOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = iota_dimension(); _o->iota_dimension = _e; } +} + +inline flatbuffers::Offset StablehloIotaOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloIotaOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloIotaOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloIotaOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _iota_dimension = _o->iota_dimension; + return tflite_micro::CreateStablehloIotaOptions( + _fbb, + _iota_dimension); +} + +inline StablehloCustomCallOptionsT *StablehloCustomCallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloCustomCallOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloCustomCallOptions::UnPackTo(StablehloCustomCallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = call_target_name(); if (_e) _o->call_target_name = _e->str(); } + { auto _e = has_side_effect(); _o->has_side_effect = _e; } + { auto _e = backend_config(); if (_e) _o->backend_config = _e->str(); } + { auto _e = api_version(); _o->api_version = _e; } + { auto _e = called_computations(); if (_e) { _o->called_computations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->called_computations[_i] = _e->Get(_i); } } } + { auto _e = custom_attributes(); if (_e) { _o->custom_attributes.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_attributes.begin()); } } +} + +inline flatbuffers::Offset StablehloCustomCallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloCustomCallOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloCustomCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloCustomCallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _call_target_name = _o->call_target_name.empty() ? 0 : _fbb.CreateString(_o->call_target_name); + auto _has_side_effect = _o->has_side_effect; + auto _backend_config = _o->backend_config.empty() ? 0 : _fbb.CreateString(_o->backend_config); + auto _api_version = _o->api_version; + auto _called_computations = _o->called_computations.size() ? _fbb.CreateVector(_o->called_computations) : 0; + auto _custom_attributes = _o->custom_attributes.size() ? _fbb.CreateVector(_o->custom_attributes) : 0; + return tflite_micro::CreateStablehloCustomCallOptions( + _fbb, + _call_target_name, + _has_side_effect, + _backend_config, + _api_version, + _called_computations, + _custom_attributes); +} + +inline StablehloReduceOptionsT *StablehloReduceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloReduceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloReduceOptions::UnPackTo(StablehloReduceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = dimensions(); if (_e) { _o->dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dimensions[_i] = _e->Get(_i); } } } + { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } +} + +inline flatbuffers::Offset StablehloReduceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloReduceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloReduceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _dimensions = _o->dimensions.size() ? _fbb.CreateVector(_o->dimensions) : 0; + auto _body_subgraph_index = _o->body_subgraph_index; + return tflite_micro::CreateStablehloReduceOptions( + _fbb, + _dimensions, + _body_subgraph_index); +} + +inline StablehloSliceOptionsT *StablehloSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloSliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloSliceOptions::UnPackTo(StablehloSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = start_indices(); if (_e) { _o->start_indices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_indices[_i] = _e->Get(_i); } } } + { auto _e = limit_indices(); if (_e) { _o->limit_indices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->limit_indices[_i] = _e->Get(_i); } } } + { auto _e = strides(); if (_e) { _o->strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->strides[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset StablehloSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _start_indices = _o->start_indices.size() ? _fbb.CreateVector(_o->start_indices) : 0; + auto _limit_indices = _o->limit_indices.size() ? _fbb.CreateVector(_o->limit_indices) : 0; + auto _strides = _o->strides.size() ? _fbb.CreateVector(_o->strides) : 0; + return tflite_micro::CreateStablehloSliceOptions( + _fbb, + _start_indices, + _limit_indices, + _strides); +} + +inline StablehloConvolutionOptionsT *StablehloConvolutionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloConvolutionOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloConvolutionOptions::UnPackTo(StablehloConvolutionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } } + { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } } + { auto _e = lhs_dilation(); if (_e) { _o->lhs_dilation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_dilation[_i] = _e->Get(_i); } } } + { auto _e = rhs_dilation(); if (_e) { _o->rhs_dilation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_dilation[_i] = _e->Get(_i); } } } + { auto _e = window_reversal(); if (_e) { _o->window_reversal.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_reversal[_i] = _e->Get(_i) != 0; } } } + { auto _e = input_batch_dimension(); _o->input_batch_dimension = _e; } + { auto _e = input_feature_dimension(); _o->input_feature_dimension = _e; } + { auto _e = input_spatial_dimensions(); if (_e) { _o->input_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->input_spatial_dimensions[_i] = _e->Get(_i); } } } + { auto _e = kernel_input_feature_dimension(); _o->kernel_input_feature_dimension = _e; } + { auto _e = kernel_output_feature_dimension(); _o->kernel_output_feature_dimension = _e; } + { auto _e = kernel_spatial_dimensions(); if (_e) { _o->kernel_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->kernel_spatial_dimensions[_i] = _e->Get(_i); } } } + { auto _e = output_batch_dimension(); _o->output_batch_dimension = _e; } + { auto _e = output_feature_dimension(); _o->output_feature_dimension = _e; } + { auto _e = output_spatial_dimensions(); if (_e) { _o->output_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->output_spatial_dimensions[_i] = _e->Get(_i); } } } + { auto _e = feature_group_count(); _o->feature_group_count = _e; } + { auto _e = batch_group_count(); _o->batch_group_count = _e; } + { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast(_e->Get(_i)); } } } +} + +inline flatbuffers::Offset StablehloConvolutionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloConvolutionOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloConvolutionOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloConvolutionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0; + auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0; + auto _lhs_dilation = _o->lhs_dilation.size() ? _fbb.CreateVector(_o->lhs_dilation) : 0; + auto _rhs_dilation = _o->rhs_dilation.size() ? _fbb.CreateVector(_o->rhs_dilation) : 0; + auto _window_reversal = _o->window_reversal.size() ? _fbb.CreateVector(_o->window_reversal) : 0; + auto _input_batch_dimension = _o->input_batch_dimension; + auto _input_feature_dimension = _o->input_feature_dimension; + auto _input_spatial_dimensions = _o->input_spatial_dimensions.size() ? _fbb.CreateVector(_o->input_spatial_dimensions) : 0; + auto _kernel_input_feature_dimension = _o->kernel_input_feature_dimension; + auto _kernel_output_feature_dimension = _o->kernel_output_feature_dimension; + auto _kernel_spatial_dimensions = _o->kernel_spatial_dimensions.size() ? _fbb.CreateVector(_o->kernel_spatial_dimensions) : 0; + auto _output_batch_dimension = _o->output_batch_dimension; + auto _output_feature_dimension = _o->output_feature_dimension; + auto _output_spatial_dimensions = _o->output_spatial_dimensions.size() ? _fbb.CreateVector(_o->output_spatial_dimensions) : 0; + auto _feature_group_count = _o->feature_group_count; + auto _batch_group_count = _o->batch_group_count; + auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast(flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0; + return tflite_micro::CreateStablehloConvolutionOptions( + _fbb, + _window_strides, + _padding, + _lhs_dilation, + _rhs_dilation, + _window_reversal, + _input_batch_dimension, + _input_feature_dimension, + _input_spatial_dimensions, + _kernel_input_feature_dimension, + _kernel_output_feature_dimension, + _kernel_spatial_dimensions, + _output_batch_dimension, + _output_feature_dimension, + _output_spatial_dimensions, + _feature_group_count, + _batch_group_count, + _precision_config); +} + +inline StablehloScatterOptionsT *StablehloScatterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloScatterOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloScatterOptions::UnPackTo(StablehloScatterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; } + { auto _e = update_window_dims(); if (_e) { _o->update_window_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->update_window_dims[_i] = _e->Get(_i); } } } + { auto _e = inserted_window_dims(); if (_e) { _o->inserted_window_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inserted_window_dims[_i] = _e->Get(_i); } } } + { auto _e = scatter_dims_to_operand_dims(); if (_e) { _o->scatter_dims_to_operand_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scatter_dims_to_operand_dims[_i] = _e->Get(_i); } } } + { auto _e = index_vector_dim(); _o->index_vector_dim = _e; } + { auto _e = unique_indices(); _o->unique_indices = _e; } + { auto _e = update_computation_subgraph_index(); _o->update_computation_subgraph_index = _e; } +} + +inline flatbuffers::Offset StablehloScatterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloScatterOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloScatterOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloScatterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _indices_are_sorted = _o->indices_are_sorted; + auto _update_window_dims = _o->update_window_dims.size() ? _fbb.CreateVector(_o->update_window_dims) : 0; + auto _inserted_window_dims = _o->inserted_window_dims.size() ? _fbb.CreateVector(_o->inserted_window_dims) : 0; + auto _scatter_dims_to_operand_dims = _o->scatter_dims_to_operand_dims.size() ? _fbb.CreateVector(_o->scatter_dims_to_operand_dims) : 0; + auto _index_vector_dim = _o->index_vector_dim; + auto _unique_indices = _o->unique_indices; + auto _update_computation_subgraph_index = _o->update_computation_subgraph_index; + return tflite_micro::CreateStablehloScatterOptions( + _fbb, + _indices_are_sorted, + _update_window_dims, + _inserted_window_dims, + _scatter_dims_to_operand_dims, + _index_vector_dim, + _unique_indices, + _update_computation_subgraph_index); +} + +inline StablehloRngBitGeneratorOptionsT *StablehloRngBitGeneratorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StablehloRngBitGeneratorOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StablehloRngBitGeneratorOptions::UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = algorithm(); _o->algorithm = _e; } +} + +inline flatbuffers::Offset StablehloRngBitGeneratorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStablehloRngBitGeneratorOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStablehloRngBitGeneratorOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloRngBitGeneratorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _algorithm = _o->algorithm; + return tflite_micro::CreateStablehloRngBitGeneratorOptions( + _fbb, + _algorithm); +} + +inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Conv2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } + { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } + { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } +} + +inline flatbuffers::Offset Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConv2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _fused_activation_function = _o->fused_activation_function; + auto _dilation_w_factor = _o->dilation_w_factor; + auto _dilation_h_factor = _o->dilation_h_factor; + auto _quantized_bias_type = _o->quantized_bias_type; + return tflite_micro::CreateConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _fused_activation_function, + _dilation_w_factor, + _dilation_h_factor, + _quantized_bias_type); +} + +inline Conv3DOptionsT *Conv3DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Conv3DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Conv3DOptions::UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_d(); _o->stride_d = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = dilation_d_factor(); _o->dilation_d_factor = _e; } + { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } + { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } +} + +inline flatbuffers::Offset Conv3DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConv3DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv3DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_d = _o->stride_d; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _fused_activation_function = _o->fused_activation_function; + auto _dilation_d_factor = _o->dilation_d_factor; + auto _dilation_w_factor = _o->dilation_w_factor; + auto _dilation_h_factor = _o->dilation_h_factor; + return tflite_micro::CreateConv3DOptions( + _fbb, + _padding, + _stride_d, + _stride_w, + _stride_h, + _fused_activation_function, + _dilation_d_factor, + _dilation_w_factor, + _dilation_h_factor); +} + +inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Pool2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = filter_width(); _o->filter_width = _e; } + { auto _e = filter_height(); _o->filter_height = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePool2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _filter_width = _o->filter_width; + auto _filter_height = _o->filter_height; + auto _fused_activation_function = _o->fused_activation_function; + return tflite_micro::CreatePool2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _filter_width, + _filter_height, + _fused_activation_function); +} + +inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DepthwiseConv2DOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = depth_multiplier(); _o->depth_multiplier = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } + { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } +} + +inline flatbuffers::Offset DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _depth_multiplier = _o->depth_multiplier; + auto _fused_activation_function = _o->fused_activation_function; + auto _dilation_w_factor = _o->dilation_w_factor; + auto _dilation_h_factor = _o->dilation_h_factor; + return tflite_micro::CreateDepthwiseConv2DOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _depth_multiplier, + _fused_activation_function, + _dilation_w_factor, + _dilation_h_factor); +} + +inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ConcatEmbeddingsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_channels(); _o->num_channels = _e; } + { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } } + { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_channels = _o->num_channels; + auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0; + auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0; + return tflite_micro::CreateConcatEmbeddingsOptions( + _fbb, + _num_channels, + _num_columns_per_channel, + _embedding_dim_per_channel); +} + +inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LSHProjectionOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = type(); _o->type = _e; } +} + +inline flatbuffers::Offset LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLSHProjectionOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _type = _o->type; + return tflite_micro::CreateLSHProjectionOptions( + _fbb, + _type); +} + +inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SVDFOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = rank(); _o->rank = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSVDFOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _rank = _o->rank; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateSVDFOptions( + _fbb, + _rank, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateRNNOptions( + _fbb, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SequenceRNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSequenceRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _time_major = _o->time_major; + auto _fused_activation_function = _o->fused_activation_function; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateSequenceRNNOptions( + _fbb, + _time_major, + _fused_activation_function, + _asymmetric_quantize_inputs); +} + +inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BidirectionalSequenceRNNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = merge_outputs(); _o->merge_outputs = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _time_major = _o->time_major; + auto _fused_activation_function = _o->fused_activation_function; + auto _merge_outputs = _o->merge_outputs; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateBidirectionalSequenceRNNOptions( + _fbb, + _time_major, + _fused_activation_function, + _merge_outputs, + _asymmetric_quantize_inputs); +} + +inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FullyConnectedOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = weights_format(); _o->weights_format = _e; } + { auto _e = keep_num_dims(); _o->keep_num_dims = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } + { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } +} + +inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFullyConnectedOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _weights_format = _o->weights_format; + auto _keep_num_dims = _o->keep_num_dims; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + auto _quantized_bias_type = _o->quantized_bias_type; + return tflite_micro::CreateFullyConnectedOptions( + _fbb, + _fused_activation_function, + _weights_format, + _keep_num_dims, + _asymmetric_quantize_inputs, + _quantized_bias_type); +} + +inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SoftmaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = beta(); _o->beta = _e; } +} + +inline flatbuffers::Offset SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSoftmaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _beta = _o->beta; + return tflite_micro::CreateSoftmaxOptions( + _fbb, + _beta); +} + +inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ConcatenationOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateConcatenationOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + auto _fused_activation_function = _o->fused_activation_function; + return tflite_micro::CreateConcatenationOptions( + _fbb, + _axis, + _fused_activation_function); +} + +inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AddOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; } +} + +inline flatbuffers::Offset AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAddOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _pot_scale_int16 = _o->pot_scale_int16; + return tflite_micro::CreateAddOptions( + _fbb, + _fused_activation_function, + _pot_scale_int16); +} + +inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MulOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMulOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite_micro::CreateMulOptions( + _fbb, + _fused_activation_function); +} + +inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new L2NormOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateL2NormOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite_micro::CreateL2NormOptions( + _fbb, + _fused_activation_function); +} + +inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LocalResponseNormalizationOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = radius(); _o->radius = _e; } + { auto _e = bias(); _o->bias = _e; } + { auto _e = alpha(); _o->alpha = _e; } + { auto _e = beta(); _o->beta = _e; } +} + +inline flatbuffers::Offset LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _radius = _o->radius; + auto _bias = _o->bias; + auto _alpha = _o->alpha; + auto _beta = _o->beta; + return tflite_micro::CreateLocalResponseNormalizationOptions( + _fbb, + _radius, + _bias, + _alpha, + _beta); +} + +inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = kernel_type(); _o->kernel_type = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _kernel_type = _o->kernel_type; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _kernel_type, + _asymmetric_quantize_inputs); +} + +inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnidirectionalSequenceLSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } + { auto _e = diagonal_recurrent_tensors(); _o->diagonal_recurrent_tensors = _e; } +} + +inline flatbuffers::Offset UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _time_major = _o->time_major; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + auto _diagonal_recurrent_tensors = _o->diagonal_recurrent_tensors; + return tflite_micro::CreateUnidirectionalSequenceLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _time_major, + _asymmetric_quantize_inputs, + _diagonal_recurrent_tensors); +} + +inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BidirectionalSequenceLSTMOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = cell_clip(); _o->cell_clip = _e; } + { auto _e = proj_clip(); _o->proj_clip = _e; } + { auto _e = merge_outputs(); _o->merge_outputs = _e; } + { auto _e = time_major(); _o->time_major = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _cell_clip = _o->cell_clip; + auto _proj_clip = _o->proj_clip; + auto _merge_outputs = _o->merge_outputs; + auto _time_major = _o->time_major; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateBidirectionalSequenceLSTMOptions( + _fbb, + _fused_activation_function, + _cell_clip, + _proj_clip, + _merge_outputs, + _time_major, + _asymmetric_quantize_inputs); +} + +inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ResizeBilinearOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = align_corners(); _o->align_corners = _e; } + { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } +} + +inline flatbuffers::Offset ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateResizeBilinearOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _align_corners = _o->align_corners; + auto _half_pixel_centers = _o->half_pixel_centers; + return tflite_micro::CreateResizeBilinearOptions( + _fbb, + _align_corners, + _half_pixel_centers); +} + +inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ResizeNearestNeighborOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = align_corners(); _o->align_corners = _e; } + { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } +} + +inline flatbuffers::Offset ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _align_corners = _o->align_corners; + auto _half_pixel_centers = _o->half_pixel_centers; + return tflite_micro::CreateResizeNearestNeighborOptions( + _fbb, + _align_corners, + _half_pixel_centers); +} + +inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CallOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = subgraph(); _o->subgraph = _e; } +} + +inline flatbuffers::Offset CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCallOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _subgraph = _o->subgraph; + return tflite_micro::CreateCallOptions( + _fbb, + _subgraph); +} + +inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PadOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePadOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreatePadOptions( + _fbb); +} + +inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PadV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePadV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreatePadV2Options( + _fbb); +} + +inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReshapeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReshapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0; + return tflite_micro::CreateReshapeOptions( + _fbb, + _new_shape); +} + +inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SpaceToBatchNDOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSpaceToBatchNDOptions( + _fbb); +} + +inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BatchToSpaceNDOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateBatchToSpaceNDOptions( + _fbb); +} + +inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SkipGramOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = ngram_size(); _o->ngram_size = _e; } + { auto _e = max_skip_size(); _o->max_skip_size = _e; } + { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; } +} + +inline flatbuffers::Offset SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSkipGramOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _ngram_size = _o->ngram_size; + auto _max_skip_size = _o->max_skip_size; + auto _include_all_ngrams = _o->include_all_ngrams; + return tflite_micro::CreateSkipGramOptions( + _fbb, + _ngram_size, + _max_skip_size, + _include_all_ngrams); +} + +inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SpaceToDepthOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = block_size(); _o->block_size = _e; } +} + +inline flatbuffers::Offset SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSpaceToDepthOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _block_size = _o->block_size; + return tflite_micro::CreateSpaceToDepthOptions( + _fbb, + _block_size); +} + +inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DepthToSpaceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = block_size(); _o->block_size = _e; } +} + +inline flatbuffers::Offset DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDepthToSpaceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _block_size = _o->block_size; + return tflite_micro::CreateDepthToSpaceOptions( + _fbb, + _block_size); +} + +inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SubOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; } +} + +inline flatbuffers::Offset SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSubOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + auto _pot_scale_int16 = _o->pot_scale_int16; + return tflite_micro::CreateSubOptions( + _fbb, + _fused_activation_function, + _pot_scale_int16); +} + +inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DivOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } +} + +inline flatbuffers::Offset DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDivOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _fused_activation_function = _o->fused_activation_function; + return tflite_micro::CreateDivOptions( + _fbb, + _fused_activation_function); +} + +inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TopKV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTopKV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateTopKV2Options( + _fbb); +} + +inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new EmbeddingLookupSparseOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = combiner(); _o->combiner = _e; } +} + +inline flatbuffers::Offset EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _combiner = _o->combiner; + return tflite_micro::CreateEmbeddingLookupSparseOptions( + _fbb, + _combiner); +} + +inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GatherOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } + { auto _e = batch_dims(); _o->batch_dims = _e; } +} + +inline flatbuffers::Offset GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGatherOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + auto _batch_dims = _o->batch_dims; + return tflite_micro::CreateGatherOptions( + _fbb, + _axis, + _batch_dims); +} + +inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TransposeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTransposeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateTransposeOptions( + _fbb); +} + +inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ExpOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExpOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateExpOptions( + _fbb); +} + +inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CosOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCosOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateCosOptions( + _fbb); +} + +inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReducerOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = keep_dims(); _o->keep_dims = _e; } +} + +inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReducerOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _keep_dims = _o->keep_dims; + return tflite_micro::CreateReducerOptions( + _fbb, + _keep_dims); +} + +inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SqueezeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSqueezeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; + return tflite_micro::CreateSqueezeOptions( + _fbb, + _squeeze_dims); +} + +inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SplitOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_splits(); _o->num_splits = _e; } +} + +inline flatbuffers::Offset SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSplitOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_splits = _o->num_splits; + return tflite_micro::CreateSplitOptions( + _fbb, + _num_splits); +} + +inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SplitVOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num_splits(); _o->num_splits = _e; } +} + +inline flatbuffers::Offset SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSplitVOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num_splits = _o->num_splits; + return tflite_micro::CreateSplitVOptions( + _fbb, + _num_splits); +} + +inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new StridedSliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = begin_mask(); _o->begin_mask = _e; } + { auto _e = end_mask(); _o->end_mask = _e; } + { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; } + { auto _e = new_axis_mask(); _o->new_axis_mask = _e; } + { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; } + { auto _e = offset(); _o->offset = _e; } +} + +inline flatbuffers::Offset StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateStridedSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _begin_mask = _o->begin_mask; + auto _end_mask = _o->end_mask; + auto _ellipsis_mask = _o->ellipsis_mask; + auto _new_axis_mask = _o->new_axis_mask; + auto _shrink_axis_mask = _o->shrink_axis_mask; + auto _offset = _o->offset; + return tflite_micro::CreateStridedSliceOptions( + _fbb, + _begin_mask, + _end_mask, + _ellipsis_mask, + _new_axis_mask, + _shrink_axis_mask, + _offset); +} + +inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogSoftmaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogSoftmaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLogSoftmaxOptions( + _fbb); +} + +inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CastOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = in_data_type(); _o->in_data_type = _e; } + { auto _e = out_data_type(); _o->out_data_type = _e; } +} + +inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCastOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _in_data_type = _o->in_data_type; + auto _out_data_type = _o->out_data_type; + return tflite_micro::CreateCastOptions( + _fbb, + _in_data_type, + _out_data_type); +} + +inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DequantizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDequantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateDequantizeOptions( + _fbb); +} + +inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MaximumMinimumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMaximumMinimumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateMaximumMinimumOptions( + _fbb); +} + +inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TileOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTileOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateTileOptions( + _fbb); +} + +inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ArgMaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = output_type(); _o->output_type = _e; } +} + +inline flatbuffers::Offset ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateArgMaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _output_type = _o->output_type; + return tflite_micro::CreateArgMaxOptions( + _fbb, + _output_type); +} + +inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ArgMinOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = output_type(); _o->output_type = _e; } +} + +inline flatbuffers::Offset ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateArgMinOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _output_type = _o->output_type; + return tflite_micro::CreateArgMinOptions( + _fbb, + _output_type); +} + +inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GreaterOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGreaterOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateGreaterOptions( + _fbb); +} + +inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GreaterEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGreaterEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateGreaterEqualOptions( + _fbb); +} + +inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LessOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLessOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLessOptions( + _fbb); +} + +inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LessEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLessEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLessEqualOptions( + _fbb); +} + +inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NegOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNegOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateNegOptions( + _fbb); +} + +inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SelectOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSelectOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSelectOptions( + _fbb); +} + +inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSliceOptions( + _fbb); +} + +inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TransposeConvOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = padding(); _o->padding = _e; } + { auto _e = stride_w(); _o->stride_w = _e; } + { auto _e = stride_h(); _o->stride_h = _e; } + { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } + { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } +} + +inline flatbuffers::Offset TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTransposeConvOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _padding = _o->padding; + auto _stride_w = _o->stride_w; + auto _stride_h = _o->stride_h; + auto _fused_activation_function = _o->fused_activation_function; + auto _quantized_bias_type = _o->quantized_bias_type; + return tflite_micro::CreateTransposeConvOptions( + _fbb, + _padding, + _stride_w, + _stride_h, + _fused_activation_function, + _quantized_bias_type); +} + +inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ExpandDimsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateExpandDimsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateExpandDimsOptions( + _fbb); +} + +inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SparseToDenseOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = validate_indices(); _o->validate_indices = _e; } +} + +inline flatbuffers::Offset SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSparseToDenseOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _validate_indices = _o->validate_indices; + return tflite_micro::CreateSparseToDenseOptions( + _fbb, + _validate_indices); +} + +inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new EqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateEqualOptions( + _fbb); +} + +inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NotEqualOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNotEqualOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateNotEqualOptions( + _fbb); +} + +inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ShapeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = out_type(); _o->out_type = _e; } +} + +inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateShapeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _out_type = _o->out_type; + return tflite_micro::CreateShapeOptions( + _fbb, + _out_type); +} + +inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RankOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRankOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateRankOptions( + _fbb); +} + +inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PowOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePowOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreatePowOptions( + _fbb); +} + +inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FakeQuantOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = min(); _o->min = _e; } + { auto _e = max(); _o->max = _e; } + { auto _e = num_bits(); _o->num_bits = _e; } + { auto _e = narrow_range(); _o->narrow_range = _e; } +} + +inline flatbuffers::Offset FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFakeQuantOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _min = _o->min; + auto _max = _o->max; + auto _num_bits = _o->num_bits; + auto _narrow_range = _o->narrow_range; + return tflite_micro::CreateFakeQuantOptions( + _fbb, + _min, + _max, + _num_bits, + _narrow_range); +} + +inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new PackOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = values_count(); _o->values_count = _e; } + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreatePackOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _values_count = _o->values_count; + auto _axis = _o->axis; + return tflite_micro::CreatePackOptions( + _fbb, + _values_count, + _axis); +} + +inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalOrOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalOrOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLogicalOrOptions( + _fbb); +} + +inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OneHotOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOneHotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + return tflite_micro::CreateOneHotOptions( + _fbb, + _axis); +} + +inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AbsOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAbsOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateAbsOptions( + _fbb); +} + +inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HardSwishOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHardSwishOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateHardSwishOptions( + _fbb); +} + +inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalAndOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalAndOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLogicalAndOptions( + _fbb); +} + +inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LogicalNotOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalNotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateLogicalNotOptions( + _fbb); +} + +inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnpackOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = num(); _o->num = _e; } + { auto _e = axis(); _o->axis = _e; } +} + +inline flatbuffers::Offset UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnpackOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _num = _o->num; + auto _axis = _o->axis; + return tflite_micro::CreateUnpackOptions( + _fbb, + _num, + _axis); +} + +inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FloorDivOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFloorDivOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateFloorDivOptions( + _fbb); +} + +inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SquareOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSquareOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSquareOptions( + _fbb); +} + +inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ZerosLikeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateZerosLikeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateZerosLikeOptions( + _fbb); +} + +inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FillOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFillOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateFillOptions( + _fbb); +} + +inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new FloorModOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateFloorModOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateFloorModOptions( + _fbb); +} + +inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RangeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRangeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateRangeOptions( + _fbb); +} + +inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new LeakyReluOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = alpha(); _o->alpha = _e; } +} + +inline flatbuffers::Offset LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLeakyReluOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _alpha = _o->alpha; + return tflite_micro::CreateLeakyReluOptions( + _fbb, + _alpha); +} + +inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SquaredDifferenceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSquaredDifferenceOptions( + _fbb); +} + +inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MirrorPadOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = mode(); _o->mode = _e; } +} + +inline flatbuffers::Offset MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMirrorPadOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _mode = _o->mode; + return tflite_micro::CreateMirrorPadOptions( + _fbb, + _mode); +} + +inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UniqueOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = idx_out_type(); _o->idx_out_type = _e; } +} + +inline flatbuffers::Offset UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUniqueOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _idx_out_type = _o->idx_out_type; + return tflite_micro::CreateUniqueOptions( + _fbb, + _idx_out_type); +} + +inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReverseV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReverseV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateReverseV2Options( + _fbb); +} + +inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AddNOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAddNOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateAddNOptions( + _fbb); +} + +inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GatherNdOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGatherNdOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateGatherNdOptions( + _fbb); +} + +inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new WhereOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateWhereOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateWhereOptions( + _fbb); +} + +inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReverseSequenceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = seq_dim(); _o->seq_dim = _e; } + { auto _e = batch_dim(); _o->batch_dim = _e; } +} + +inline flatbuffers::Offset ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReverseSequenceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _seq_dim = _o->seq_dim; + auto _batch_dim = _o->batch_dim; + return tflite_micro::CreateReverseSequenceOptions( + _fbb, + _seq_dim, + _batch_dim); +} + +inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MatrixDiagOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMatrixDiagOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateMatrixDiagOptions( + _fbb); +} + +inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new QuantizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateQuantizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateQuantizeOptions( + _fbb); +} + +inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MatrixSetDiagOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateMatrixSetDiagOptions( + _fbb); +} + +inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new IfOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; } + { auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; } +} + +inline flatbuffers::Offset IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateIfOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _then_subgraph_index = _o->then_subgraph_index; + auto _else_subgraph_index = _o->else_subgraph_index; + return tflite_micro::CreateIfOptions( + _fbb, + _then_subgraph_index, + _else_subgraph_index); +} + +inline CallOnceOptionsT *CallOnceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CallOnceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CallOnceOptions::UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = init_subgraph_index(); _o->init_subgraph_index = _e; } +} + +inline flatbuffers::Offset CallOnceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCallOnceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOnceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _init_subgraph_index = _o->init_subgraph_index; + return tflite_micro::CreateCallOnceOptions( + _fbb, + _init_subgraph_index); +} + +inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new WhileOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } + { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } +} + +inline flatbuffers::Offset WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateWhileOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _cond_subgraph_index = _o->cond_subgraph_index; + auto _body_subgraph_index = _o->body_subgraph_index; + return tflite_micro::CreateWhileOptions( + _fbb, + _cond_subgraph_index, + _body_subgraph_index); +} + +inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NonMaxSuppressionV4OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateNonMaxSuppressionV4Options( + _fbb); +} + +inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new NonMaxSuppressionV5OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateNonMaxSuppressionV5Options( + _fbb); +} + +inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ScatterNdOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateScatterNdOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateScatterNdOptions( + _fbb); +} + +inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SelectV2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSelectV2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSelectV2Options( + _fbb); +} + +inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DensifyOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDensifyOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateDensifyOptions( + _fbb); +} + +inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SegmentSumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSegmentSumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSegmentSumOptions( + _fbb); +} + +inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BatchMatMulOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = adj_x(); _o->adj_x = _e; } + { auto _e = adj_y(); _o->adj_y = _e; } + { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } +} + +inline flatbuffers::Offset BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBatchMatMulOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _adj_x = _o->adj_x; + auto _adj_y = _o->adj_y; + auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; + return tflite_micro::CreateBatchMatMulOptions( + _fbb, + _adj_x, + _adj_y, + _asymmetric_quantize_inputs); +} + +inline CumsumOptionsT *CumsumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new CumsumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void CumsumOptions::UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = exclusive(); _o->exclusive = _e; } + { auto _e = reverse(); _o->reverse = _e; } +} + +inline flatbuffers::Offset CumsumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateCumsumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CumsumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _exclusive = _o->exclusive; + auto _reverse = _o->reverse; + return tflite_micro::CreateCumsumOptions( + _fbb, + _exclusive, + _reverse); +} + +inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BroadcastToOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset BroadcastToOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBroadcastToOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateBroadcastToOptions( + _fbb); +} + +inline Rfft2dOptionsT *Rfft2dOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new Rfft2dOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Rfft2dOptions::UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset Rfft2dOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRfft2dOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Rfft2dOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateRfft2dOptions( + _fbb); +} + +inline HashtableOptionsT *HashtableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HashtableOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HashtableOptions::UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = table_id(); _o->table_id = _e; } + { auto _e = key_dtype(); _o->key_dtype = _e; } + { auto _e = value_dtype(); _o->value_dtype = _e; } +} + +inline flatbuffers::Offset HashtableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHashtableOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _table_id = _o->table_id; + auto _key_dtype = _o->key_dtype; + auto _value_dtype = _o->value_dtype; + return tflite_micro::CreateHashtableOptions( + _fbb, + _table_id, + _key_dtype, + _value_dtype); +} + +inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HashtableFindOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HashtableFindOptions::UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset HashtableFindOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHashtableFindOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableFindOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateHashtableFindOptions( + _fbb); +} + +inline HashtableImportOptionsT *HashtableImportOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HashtableImportOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HashtableImportOptions::UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset HashtableImportOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHashtableImportOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableImportOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateHashtableImportOptions( + _fbb); +} + +inline HashtableSizeOptionsT *HashtableSizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new HashtableSizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void HashtableSizeOptions::UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset HashtableSizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateHashtableSizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableSizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateHashtableSizeOptions( + _fbb); +} + +inline VarHandleOptionsT *VarHandleOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new VarHandleOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void VarHandleOptions::UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = container(); if (_e) _o->container = _e->str(); } + { auto _e = shared_name(); if (_e) _o->shared_name = _e->str(); } +} + +inline flatbuffers::Offset VarHandleOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateVarHandleOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VarHandleOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _container = _o->container.empty() ? 0 : _fbb.CreateString(_o->container); + auto _shared_name = _o->shared_name.empty() ? 0 : _fbb.CreateString(_o->shared_name); + return tflite_micro::CreateVarHandleOptions( + _fbb, + _container, + _shared_name); +} + +inline ReadVariableOptionsT *ReadVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReadVariableOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReadVariableOptions::UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ReadVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReadVariableOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReadVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateReadVariableOptions( + _fbb); +} + +inline AssignVariableOptionsT *AssignVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new AssignVariableOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void AssignVariableOptions::UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset AssignVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateAssignVariableOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AssignVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateAssignVariableOptions( + _fbb); +} + +inline RandomOptionsT *RandomOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RandomOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RandomOptions::UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = seed(); _o->seed = _e; } + { auto _e = seed2(); _o->seed2 = _e; } +} + +inline flatbuffers::Offset RandomOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRandomOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RandomOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _seed = _o->seed; + auto _seed2 = _o->seed2; + return tflite_micro::CreateRandomOptions( + _fbb, + _seed, + _seed2); +} + +inline BucketizeOptionsT *BucketizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BucketizeOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BucketizeOptions::UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = boundaries(); if (_e) { _o->boundaries.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->boundaries[_i] = _e->Get(_i); } } } +} + +inline flatbuffers::Offset BucketizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBucketizeOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BucketizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _boundaries = _o->boundaries.size() ? _fbb.CreateVector(_o->boundaries) : 0; + return tflite_micro::CreateBucketizeOptions( + _fbb, + _boundaries); +} + +inline GeluOptionsT *GeluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new GeluOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void GeluOptions::UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = approximate(); _o->approximate = _e; } +} + +inline flatbuffers::Offset GeluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateGeluOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GeluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _approximate = _o->approximate; + return tflite_micro::CreateGeluOptions( + _fbb, + _approximate); +} + +inline DynamicUpdateSliceOptionsT *DynamicUpdateSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DynamicUpdateSliceOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DynamicUpdateSliceOptions::UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DynamicUpdateSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDynamicUpdateSliceOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DynamicUpdateSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateDynamicUpdateSliceOptions( + _fbb); +} + +inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnsortedSegmentProdOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset UnsortedSegmentProdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnsortedSegmentProdOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateUnsortedSegmentProdOptions( + _fbb); +} + +inline UnsortedSegmentMaxOptionsT *UnsortedSegmentMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnsortedSegmentMaxOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnsortedSegmentMaxOptions::UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset UnsortedSegmentMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnsortedSegmentMaxOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateUnsortedSegmentMaxOptions( + _fbb); +} + +inline UnsortedSegmentSumOptionsT *UnsortedSegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnsortedSegmentSumOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnsortedSegmentSumOptions::UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset UnsortedSegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnsortedSegmentSumOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateUnsortedSegmentSumOptions( + _fbb); +} + +inline ATan2OptionsT *ATan2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ATan2OptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ATan2Options::UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset ATan2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateATan2Options(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ATan2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateATan2Options( + _fbb); +} + +inline UnsortedSegmentMinOptionsT *UnsortedSegmentMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new UnsortedSegmentMinOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void UnsortedSegmentMinOptions::UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset UnsortedSegmentMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateUnsortedSegmentMinOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateUnsortedSegmentMinOptions( + _fbb); +} + +inline SignOptionsT *SignOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SignOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SignOptions::UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset SignOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSignOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateSignOptions( + _fbb); +} + +inline BitcastOptionsT *BitcastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BitcastOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BitcastOptions::UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset BitcastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBitcastOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitcastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateBitcastOptions( + _fbb); +} + +inline BitwiseXorOptionsT *BitwiseXorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BitwiseXorOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void BitwiseXorOptions::UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset BitwiseXorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBitwiseXorOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitwiseXorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateBitwiseXorOptions( + _fbb); +} + +inline RightShiftOptionsT *RightShiftOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new RightShiftOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void RightShiftOptions::UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset RightShiftOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateRightShiftOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RightShiftOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateRightShiftOptions( + _fbb); +} + +inline DilateOptionsT *DilateOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new DilateOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void DilateOptions::UnPackTo(DilateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset DilateOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateDilateOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateDilateOptions(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DilateOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite_micro::CreateDilateOptions( + _fbb); +} + +inline ReduceWindowOptionsT *ReduceWindowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ReduceWindowOptionsT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void ReduceWindowOptions::UnPackTo(ReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = reduce_function(); _o->reduce_function = _e; } +} + +inline flatbuffers::Offset ReduceWindowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateReduceWindowOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReduceWindowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _reduce_function = _o->reduce_function; + return tflite_micro::CreateReduceWindowOptions( + _fbb, + _reduce_function); +} + +inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OperatorCodeT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = deprecated_builtin_code(); _o->deprecated_builtin_code = _e; } + { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); } + { auto _e = version(); _o->version = _e; } + { auto _e = builtin_code(); _o->builtin_code = _e; } +} + +inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOperatorCode(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _deprecated_builtin_code = _o->deprecated_builtin_code; + auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); + auto _version = _o->version; + auto _builtin_code = _o->builtin_code; + return tflite_micro::CreateOperatorCode( + _fbb, + _deprecated_builtin_code, + _custom_code, + _version, + _builtin_code); +} + +inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new OperatorT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = opcode_index(); _o->opcode_index = _e; } + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } + { auto _e = builtin_options_type(); _o->builtin_options.type = _e; } + { auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite_micro::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); } + { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_options.begin()); } } + { auto _e = custom_options_format(); _o->custom_options_format = _e; } + { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } } + { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } } + { auto _e = large_custom_options_offset(); _o->large_custom_options_offset = _e; } + { auto _e = large_custom_options_size(); _o->large_custom_options_size = _e; } + { auto _e = builtin_options_2_type(); _o->builtin_options_2.type = _e; } + { auto _e = builtin_options_2(); if (_e) _o->builtin_options_2.value = tflite_micro::BuiltinOptions2Union::UnPack(_e, builtin_options_2_type(), _resolver); } +} + +inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOperator(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _opcode_index = _o->opcode_index; + auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; + auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; + auto _builtin_options_type = _o->builtin_options.type; + auto _builtin_options = _o->builtin_options.Pack(_fbb); + auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; + auto _custom_options_format = _o->custom_options_format; + auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; + auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0; + auto _large_custom_options_offset = _o->large_custom_options_offset; + auto _large_custom_options_size = _o->large_custom_options_size; + auto _builtin_options_2_type = _o->builtin_options_2.type; + auto _builtin_options_2 = _o->builtin_options_2.Pack(_fbb); + return tflite_micro::CreateOperator( + _fbb, + _opcode_index, + _inputs, + _outputs, + _builtin_options_type, + _builtin_options, + _custom_options, + _custom_options_format, + _mutating_variable_inputs, + _intermediates, + _large_custom_options_offset, + _large_custom_options_size, + _builtin_options_2_type, + _builtin_options_2); +} + +inline SubGraphT::SubGraphT(const SubGraphT &o) + : inputs(o.inputs), + outputs(o.outputs), + name(o.name) { + tensors.reserve(o.tensors.size()); + for (const auto &tensors_ : o.tensors) { tensors.emplace_back((tensors_) ? new tflite_micro::TensorT(*tensors_) : nullptr); } + operators.reserve(o.operators.size()); + for (const auto &operators_ : o.operators) { operators.emplace_back((operators_) ? new tflite_micro::OperatorT(*operators_) : nullptr); } +} + +inline SubGraphT &SubGraphT::operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT { + std::swap(tensors, o.tensors); + std::swap(inputs, o.inputs); + std::swap(outputs, o.outputs); + std::swap(operators, o.operators); + std::swap(name, o.name); + return *this; +} + +inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SubGraphT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->tensors[_i]) { _e->Get(_i)->UnPackTo(_o->tensors[_i].get(), _resolver); } else { _o->tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } + { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operators[_i]) { _e->Get(_i)->UnPackTo(_o->operators[_i].get(), _resolver); } else { _o->operators[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = name(); if (_e) _o->name = _e->str(); } +} + +inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSubGraph(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _tensors = _o->tensors.size() ? _fbb.CreateVector> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; + auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; + auto _operators = _o->operators.size() ? _fbb.CreateVector> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + return tflite_micro::CreateSubGraph( + _fbb, + _tensors, + _inputs, + _outputs, + _operators, + _name); +} + +inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new BufferT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } } + { auto _e = offset(); _o->offset = _e; } + { auto _e = size(); _o->size = _e; } +} + +inline flatbuffers::Offset Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateBuffer(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + _fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16); + auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; + auto _offset = _o->offset; + auto _size = _o->size; + return tflite_micro::CreateBuffer( + _fbb, + _data, + _offset, + _size); +} + +inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new MetadataT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = buffer(); _o->buffer = _e; } +} + +inline flatbuffers::Offset Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateMetadata(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _buffer = _o->buffer; + return tflite_micro::CreateMetadata( + _fbb, + _name, + _buffer); +} + +inline TensorMapT *TensorMap::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new TensorMapT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void TensorMap::UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = name(); if (_e) _o->name = _e->str(); } + { auto _e = tensor_index(); _o->tensor_index = _e; } +} + +inline flatbuffers::Offset TensorMap::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateTensorMap(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorMapT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _tensor_index = _o->tensor_index; + return tflite_micro::CreateTensorMap( + _fbb, + _name, + _tensor_index); +} + +inline SignatureDefT::SignatureDefT(const SignatureDefT &o) + : signature_key(o.signature_key), + subgraph_index(o.subgraph_index) { + inputs.reserve(o.inputs.size()); + for (const auto &inputs_ : o.inputs) { inputs.emplace_back((inputs_) ? new tflite_micro::TensorMapT(*inputs_) : nullptr); } + outputs.reserve(o.outputs.size()); + for (const auto &outputs_ : o.outputs) { outputs.emplace_back((outputs_) ? new tflite_micro::TensorMapT(*outputs_) : nullptr); } +} + +inline SignatureDefT &SignatureDefT::operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT { + std::swap(inputs, o.inputs); + std::swap(outputs, o.outputs); + std::swap(signature_key, o.signature_key); + std::swap(subgraph_index, o.subgraph_index); + return *this; +} + +inline SignatureDefT *SignatureDef::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new SignatureDefT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void SignatureDef::UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->inputs[_i]) { _e->Get(_i)->UnPackTo(_o->inputs[_i].get(), _resolver); } else { _o->inputs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = signature_key(); if (_e) _o->signature_key = _e->str(); } + { auto _e = subgraph_index(); _o->subgraph_index = _e; } +} + +inline flatbuffers::Offset SignatureDef::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateSignatureDef(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignatureDefT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _inputs = _o->inputs.size() ? _fbb.CreateVector> (_o->inputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->inputs[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _outputs = _o->outputs.size() ? _fbb.CreateVector> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _signature_key = _o->signature_key.empty() ? 0 : _fbb.CreateString(_o->signature_key); + auto _subgraph_index = _o->subgraph_index; + return tflite_micro::CreateSignatureDef( + _fbb, + _inputs, + _outputs, + _signature_key, + _subgraph_index); +} + +inline ModelT::ModelT(const ModelT &o) + : version(o.version), + description(o.description), + metadata_buffer(o.metadata_buffer) { + operator_codes.reserve(o.operator_codes.size()); + for (const auto &operator_codes_ : o.operator_codes) { operator_codes.emplace_back((operator_codes_) ? new tflite_micro::OperatorCodeT(*operator_codes_) : nullptr); } + subgraphs.reserve(o.subgraphs.size()); + for (const auto &subgraphs_ : o.subgraphs) { subgraphs.emplace_back((subgraphs_) ? new tflite_micro::SubGraphT(*subgraphs_) : nullptr); } + buffers.reserve(o.buffers.size()); + for (const auto &buffers_ : o.buffers) { buffers.emplace_back((buffers_) ? new tflite_micro::BufferT(*buffers_) : nullptr); } + metadata.reserve(o.metadata.size()); + for (const auto &metadata_ : o.metadata) { metadata.emplace_back((metadata_) ? new tflite_micro::MetadataT(*metadata_) : nullptr); } + signature_defs.reserve(o.signature_defs.size()); + for (const auto &signature_defs_ : o.signature_defs) { signature_defs.emplace_back((signature_defs_) ? new tflite_micro::SignatureDefT(*signature_defs_) : nullptr); } +} + +inline ModelT &ModelT::operator=(ModelT o) FLATBUFFERS_NOEXCEPT { + std::swap(version, o.version); + std::swap(operator_codes, o.operator_codes); + std::swap(subgraphs, o.subgraphs); + std::swap(description, o.description); + std::swap(buffers, o.buffers); + std::swap(metadata_buffer, o.metadata_buffer); + std::swap(metadata, o.metadata); + std::swap(signature_defs, o.signature_defs); + return *this; +} + +inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = std::unique_ptr(new ModelT()); + UnPackTo(_o.get(), _resolver); + return _o.release(); +} + +inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = version(); _o->version = _e; } + { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operator_codes[_i]) { _e->Get(_i)->UnPackTo(_o->operator_codes[_i].get(), _resolver); } else { _o->operator_codes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraphs[_i]) { _e->Get(_i)->UnPackTo(_o->subgraphs[_i].get(), _resolver); } else { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = description(); if (_e) _o->description = _e->str(); } + { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->buffers[_i]) { _e->Get(_i)->UnPackTo(_o->buffers[_i].get(), _resolver); } else { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } } + { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->metadata[_i]) { _e->Get(_i)->UnPackTo(_o->metadata[_i].get(), _resolver); } else { _o->metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } + { auto _e = signature_defs(); if (_e) { _o->signature_defs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->signature_defs[_i]) { _e->Get(_i)->UnPackTo(_o->signature_defs[_i].get(), _resolver); } else { _o->signature_defs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } +} + +inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateModel(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _version = _o->version; + auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); + auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0; + auto _metadata = _o->metadata.size() ? _fbb.CreateVector> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0; + auto _signature_defs = _o->signature_defs.size() ? _fbb.CreateVector> (_o->signature_defs.size(), [](size_t i, _VectorArgs *__va) { return CreateSignatureDef(*__va->__fbb, __va->__o->signature_defs[i].get(), __va->__rehasher); }, &_va ) : 0; + return tflite_micro::CreateModel( + _fbb, + _version, + _operator_codes, + _subgraphs, + _description, + _buffers, + _metadata_buffer, + _metadata, + _signature_defs); +} + +inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) { + switch (type) { + case QuantizationDetails_NONE: { + return true; + } + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyQuantizationDetails( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) { + (void)resolver; + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + (void)_rehasher; + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(value); + return CreateCustomQuantization(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline QuantizationDetailsUnion::QuantizationDetailsUnion(const QuantizationDetailsUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case QuantizationDetails_CustomQuantization: { + value = new tflite_micro::CustomQuantizationT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void QuantizationDetailsUnion::Reset() { + switch (type) { + case QuantizationDetails_CustomQuantization: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = QuantizationDetails_NONE; +} + +inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) { + switch (type) { + case SparseIndexVector_NONE: { + return true; + } + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifySparseIndexVector( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) { + (void)resolver; + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + (void)_rehasher; + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(value); + return CreateInt32Vector(_fbb, ptr, _rehasher).Union(); + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(value); + return CreateUint16Vector(_fbb, ptr, _rehasher).Union(); + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(value); + return CreateUint8Vector(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline SparseIndexVectorUnion::SparseIndexVectorUnion(const SparseIndexVectorUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case SparseIndexVector_Int32Vector: { + value = new tflite_micro::Int32VectorT(*reinterpret_cast(u.value)); + break; + } + case SparseIndexVector_Uint16Vector: { + value = new tflite_micro::Uint16VectorT(*reinterpret_cast(u.value)); + break; + } + case SparseIndexVector_Uint8Vector: { + value = new tflite_micro::Uint8VectorT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void SparseIndexVectorUnion::Reset() { + switch (type) { + case SparseIndexVector_Int32Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case SparseIndexVector_Uint16Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case SparseIndexVector_Uint8Vector: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = SparseIndexVector_NONE; +} + +inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { + switch (type) { + case BuiltinOptions_NONE: { + return true; + } + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CumsumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_CallOnceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BroadcastToOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_Rfft2dOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_Conv3DOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HashtableOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HashtableFindOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HashtableImportOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_HashtableSizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_VarHandleOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ReadVariableOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_AssignVariableOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RandomOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BucketizeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_GeluOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_DynamicUpdateSliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnsortedSegmentProdOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnsortedSegmentMaxOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnsortedSegmentMinOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_UnsortedSegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_ATan2Options: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_SignOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BitcastOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_BitwiseXorOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions_RightShiftOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyBuiltinOptions( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) { + (void)resolver; + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CumsumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_CallOnceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BroadcastToOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_Rfft2dOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_Conv3DOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HashtableOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HashtableFindOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HashtableImportOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_HashtableSizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_VarHandleOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ReadVariableOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_AssignVariableOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RandomOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BucketizeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_GeluOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_DynamicUpdateSliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnsortedSegmentProdOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnsortedSegmentMaxOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnsortedSegmentMinOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_UnsortedSegmentSumOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_ATan2Options: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_SignOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BitcastOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_BitwiseXorOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions_RightShiftOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + (void)_rehasher; + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(value); + return CreateConv2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(value); + return CreateDepthwiseConv2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(value); + return CreateConcatEmbeddingsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(value); + return CreateLSHProjectionOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(value); + return CreatePool2DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(value); + return CreateSVDFOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(value); + return CreateFullyConnectedOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateSoftmaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(value); + return CreateConcatenationOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(value); + return CreateAddOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(value); + return CreateL2NormOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(value); + return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(value); + return CreateResizeBilinearOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(value); + return CreateCallOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateReshapeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(value); + return CreateSkipGramOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(value); + return CreateSpaceToDepthOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(value); + return CreateEmbeddingLookupSparseOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(value); + return CreateMulOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(value); + return CreatePadOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(value); + return CreateGatherOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(value); + return CreateBatchToSpaceNDOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(value); + return CreateSpaceToBatchNDOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(value); + return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(value); + return CreateSubOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(value); + return CreateDivOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(value); + return CreateSqueezeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + return CreateExpOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + return CreateTopKV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + return CreateSplitOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + return CreateCastOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(value); + return CreateMaximumMinimumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(value); + return CreateLessOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(value); + return CreateNegOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(value); + return CreatePadV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(value); + return CreateGreaterOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(value); + return CreateSelectOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(value); + return CreateTransposeConvOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(value); + return CreateSparseToDenseOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(value); + return CreateTileOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(value); + return CreateExpandDimsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(value); + return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + return CreatePowOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + return CreateArgMinOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + return CreatePackOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalAndOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalNotOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnpackOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(value); + return CreateFloorDivOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(value); + return CreateSquareOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(value); + return CreateZerosLikeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(value); + return CreateFillOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateBidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + return CreateBidirectionalSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(value); + return CreateFloorModOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(value); + return CreateRangeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(value); + return CreateResizeNearestNeighborOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(value); + return CreateLeakyReluOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(value); + return CreateSquaredDifferenceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(value); + return CreateMirrorPadOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(value); + return CreateAbsOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(value); + return CreateSplitVOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(value); + return CreateUniqueOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(value); + return CreateReverseV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(value); + return CreateAddNOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(value); + return CreateGatherNdOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(value); + return CreateCosOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(value); + return CreateWhereOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(value); + return CreateRankOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(value); + return CreateReverseSequenceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(value); + return CreateMatrixDiagOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateQuantizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(value); + return CreateMatrixSetDiagOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(value); + return CreateHardSwishOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(value); + return CreateIfOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(value); + return CreateWhileOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(value); + return CreateDepthToSpaceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(value); + return CreateNonMaxSuppressionV4Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(value); + return CreateNonMaxSuppressionV5Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(value); + return CreateScatterNdOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(value); + return CreateSelectV2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(value); + return CreateDensifyOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(value); + return CreateSegmentSumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(value); + return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CumsumOptions: { + auto ptr = reinterpret_cast(value); + return CreateCumsumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_CallOnceOptions: { + auto ptr = reinterpret_cast(value); + return CreateCallOnceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BroadcastToOptions: { + auto ptr = reinterpret_cast(value); + return CreateBroadcastToOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_Rfft2dOptions: { + auto ptr = reinterpret_cast(value); + return CreateRfft2dOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_Conv3DOptions: { + auto ptr = reinterpret_cast(value); + return CreateConv3DOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HashtableOptions: { + auto ptr = reinterpret_cast(value); + return CreateHashtableOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HashtableFindOptions: { + auto ptr = reinterpret_cast(value); + return CreateHashtableFindOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HashtableImportOptions: { + auto ptr = reinterpret_cast(value); + return CreateHashtableImportOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_HashtableSizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateHashtableSizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_VarHandleOptions: { + auto ptr = reinterpret_cast(value); + return CreateVarHandleOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ReadVariableOptions: { + auto ptr = reinterpret_cast(value); + return CreateReadVariableOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_AssignVariableOptions: { + auto ptr = reinterpret_cast(value); + return CreateAssignVariableOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RandomOptions: { + auto ptr = reinterpret_cast(value); + return CreateRandomOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BucketizeOptions: { + auto ptr = reinterpret_cast(value); + return CreateBucketizeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_GeluOptions: { + auto ptr = reinterpret_cast(value); + return CreateGeluOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_DynamicUpdateSliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateDynamicUpdateSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnsortedSegmentProdOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnsortedSegmentProdOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnsortedSegmentMaxOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnsortedSegmentMaxOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnsortedSegmentMinOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnsortedSegmentMinOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_UnsortedSegmentSumOptions: { + auto ptr = reinterpret_cast(value); + return CreateUnsortedSegmentSumOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_ATan2Options: { + auto ptr = reinterpret_cast(value); + return CreateATan2Options(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_SignOptions: { + auto ptr = reinterpret_cast(value); + return CreateSignOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BitcastOptions: { + auto ptr = reinterpret_cast(value); + return CreateBitcastOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_BitwiseXorOptions: { + auto ptr = reinterpret_cast(value); + return CreateBitwiseXorOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions_RightShiftOptions: { + auto ptr = reinterpret_cast(value); + return CreateRightShiftOptions(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) : type(u.type), value(nullptr) { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + value = new tflite_micro::Conv2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DepthwiseConv2DOptions: { + value = new tflite_micro::DepthwiseConv2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + value = new tflite_micro::ConcatEmbeddingsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LSHProjectionOptions: { + value = new tflite_micro::LSHProjectionOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_Pool2DOptions: { + value = new tflite_micro::Pool2DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SVDFOptions: { + value = new tflite_micro::SVDFOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RNNOptions: { + value = new tflite_micro::RNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FullyConnectedOptions: { + value = new tflite_micro::FullyConnectedOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SoftmaxOptions: { + value = new tflite_micro::SoftmaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ConcatenationOptions: { + value = new tflite_micro::ConcatenationOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AddOptions: { + value = new tflite_micro::AddOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_L2NormOptions: { + value = new tflite_micro::L2NormOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + value = new tflite_micro::LocalResponseNormalizationOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LSTMOptions: { + value = new tflite_micro::LSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ResizeBilinearOptions: { + value = new tflite_micro::ResizeBilinearOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CallOptions: { + value = new tflite_micro::CallOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReshapeOptions: { + value = new tflite_micro::ReshapeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SkipGramOptions: { + value = new tflite_micro::SkipGramOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SpaceToDepthOptions: { + value = new tflite_micro::SpaceToDepthOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + value = new tflite_micro::EmbeddingLookupSparseOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MulOptions: { + value = new tflite_micro::MulOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PadOptions: { + value = new tflite_micro::PadOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GatherOptions: { + value = new tflite_micro::GatherOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BatchToSpaceNDOptions: { + value = new tflite_micro::BatchToSpaceNDOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SpaceToBatchNDOptions: { + value = new tflite_micro::SpaceToBatchNDOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TransposeOptions: { + value = new tflite_micro::TransposeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReducerOptions: { + value = new tflite_micro::ReducerOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SubOptions: { + value = new tflite_micro::SubOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DivOptions: { + value = new tflite_micro::DivOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SqueezeOptions: { + value = new tflite_micro::SqueezeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SequenceRNNOptions: { + value = new tflite_micro::SequenceRNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_StridedSliceOptions: { + value = new tflite_micro::StridedSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ExpOptions: { + value = new tflite_micro::ExpOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TopKV2Options: { + value = new tflite_micro::TopKV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SplitOptions: { + value = new tflite_micro::SplitOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + value = new tflite_micro::LogSoftmaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CastOptions: { + value = new tflite_micro::CastOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DequantizeOptions: { + value = new tflite_micro::DequantizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MaximumMinimumOptions: { + value = new tflite_micro::MaximumMinimumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ArgMaxOptions: { + value = new tflite_micro::ArgMaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LessOptions: { + value = new tflite_micro::LessOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NegOptions: { + value = new tflite_micro::NegOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PadV2Options: { + value = new tflite_micro::PadV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GreaterOptions: { + value = new tflite_micro::GreaterOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GreaterEqualOptions: { + value = new tflite_micro::GreaterEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LessEqualOptions: { + value = new tflite_micro::LessEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SelectOptions: { + value = new tflite_micro::SelectOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SliceOptions: { + value = new tflite_micro::SliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TransposeConvOptions: { + value = new tflite_micro::TransposeConvOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SparseToDenseOptions: { + value = new tflite_micro::SparseToDenseOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_TileOptions: { + value = new tflite_micro::TileOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ExpandDimsOptions: { + value = new tflite_micro::ExpandDimsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_EqualOptions: { + value = new tflite_micro::EqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NotEqualOptions: { + value = new tflite_micro::NotEqualOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ShapeOptions: { + value = new tflite_micro::ShapeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PowOptions: { + value = new tflite_micro::PowOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ArgMinOptions: { + value = new tflite_micro::ArgMinOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FakeQuantOptions: { + value = new tflite_micro::FakeQuantOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_PackOptions: { + value = new tflite_micro::PackOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalOrOptions: { + value = new tflite_micro::LogicalOrOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_OneHotOptions: { + value = new tflite_micro::OneHotOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalAndOptions: { + value = new tflite_micro::LogicalAndOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LogicalNotOptions: { + value = new tflite_micro::LogicalNotOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnpackOptions: { + value = new tflite_micro::UnpackOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FloorDivOptions: { + value = new tflite_micro::FloorDivOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SquareOptions: { + value = new tflite_micro::SquareOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ZerosLikeOptions: { + value = new tflite_micro::ZerosLikeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FillOptions: { + value = new tflite_micro::FillOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + value = new tflite_micro::BidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + value = new tflite_micro::BidirectionalSequenceRNNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + value = new tflite_micro::UnidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_FloorModOptions: { + value = new tflite_micro::FloorModOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RangeOptions: { + value = new tflite_micro::RangeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + value = new tflite_micro::ResizeNearestNeighborOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_LeakyReluOptions: { + value = new tflite_micro::LeakyReluOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SquaredDifferenceOptions: { + value = new tflite_micro::SquaredDifferenceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MirrorPadOptions: { + value = new tflite_micro::MirrorPadOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AbsOptions: { + value = new tflite_micro::AbsOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SplitVOptions: { + value = new tflite_micro::SplitVOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UniqueOptions: { + value = new tflite_micro::UniqueOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReverseV2Options: { + value = new tflite_micro::ReverseV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AddNOptions: { + value = new tflite_micro::AddNOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GatherNdOptions: { + value = new tflite_micro::GatherNdOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CosOptions: { + value = new tflite_micro::CosOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_WhereOptions: { + value = new tflite_micro::WhereOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RankOptions: { + value = new tflite_micro::RankOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReverseSequenceOptions: { + value = new tflite_micro::ReverseSequenceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MatrixDiagOptions: { + value = new tflite_micro::MatrixDiagOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_QuantizeOptions: { + value = new tflite_micro::QuantizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_MatrixSetDiagOptions: { + value = new tflite_micro::MatrixSetDiagOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HardSwishOptions: { + value = new tflite_micro::HardSwishOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_IfOptions: { + value = new tflite_micro::IfOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_WhileOptions: { + value = new tflite_micro::WhileOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DepthToSpaceOptions: { + value = new tflite_micro::DepthToSpaceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + value = new tflite_micro::NonMaxSuppressionV4OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + value = new tflite_micro::NonMaxSuppressionV5OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ScatterNdOptions: { + value = new tflite_micro::ScatterNdOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SelectV2Options: { + value = new tflite_micro::SelectV2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DensifyOptions: { + value = new tflite_micro::DensifyOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SegmentSumOptions: { + value = new tflite_micro::SegmentSumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BatchMatMulOptions: { + value = new tflite_micro::BatchMatMulOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CumsumOptions: { + value = new tflite_micro::CumsumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_CallOnceOptions: { + value = new tflite_micro::CallOnceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BroadcastToOptions: { + value = new tflite_micro::BroadcastToOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_Rfft2dOptions: { + value = new tflite_micro::Rfft2dOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_Conv3DOptions: { + value = new tflite_micro::Conv3DOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HashtableOptions: { + value = new tflite_micro::HashtableOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HashtableFindOptions: { + value = new tflite_micro::HashtableFindOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HashtableImportOptions: { + value = new tflite_micro::HashtableImportOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_HashtableSizeOptions: { + value = new tflite_micro::HashtableSizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_VarHandleOptions: { + value = new tflite_micro::VarHandleOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ReadVariableOptions: { + value = new tflite_micro::ReadVariableOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_AssignVariableOptions: { + value = new tflite_micro::AssignVariableOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RandomOptions: { + value = new tflite_micro::RandomOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BucketizeOptions: { + value = new tflite_micro::BucketizeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_GeluOptions: { + value = new tflite_micro::GeluOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_DynamicUpdateSliceOptions: { + value = new tflite_micro::DynamicUpdateSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnsortedSegmentProdOptions: { + value = new tflite_micro::UnsortedSegmentProdOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnsortedSegmentMaxOptions: { + value = new tflite_micro::UnsortedSegmentMaxOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnsortedSegmentMinOptions: { + value = new tflite_micro::UnsortedSegmentMinOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_UnsortedSegmentSumOptions: { + value = new tflite_micro::UnsortedSegmentSumOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_ATan2Options: { + value = new tflite_micro::ATan2OptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_SignOptions: { + value = new tflite_micro::SignOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BitcastOptions: { + value = new tflite_micro::BitcastOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_BitwiseXorOptions: { + value = new tflite_micro::BitwiseXorOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions_RightShiftOptions: { + value = new tflite_micro::RightShiftOptionsT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void BuiltinOptionsUnion::Reset() { + switch (type) { + case BuiltinOptions_Conv2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DepthwiseConv2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ConcatEmbeddingsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LSHProjectionOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_Pool2DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SVDFOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FullyConnectedOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SoftmaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ConcatenationOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AddOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_L2NormOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LocalResponseNormalizationOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ResizeBilinearOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CallOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReshapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SkipGramOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SpaceToDepthOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_EmbeddingLookupSparseOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MulOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PadOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GatherOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BatchToSpaceNDOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SpaceToBatchNDOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TransposeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReducerOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SubOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DivOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SqueezeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_StridedSliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ExpOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TopKV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SplitOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogSoftmaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CastOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DequantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MaximumMinimumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ArgMaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LessOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NegOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PadV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GreaterOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GreaterEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LessEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SelectOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TransposeConvOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SparseToDenseOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_TileOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ExpandDimsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_EqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NotEqualOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ShapeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PowOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ArgMinOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FakeQuantOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_PackOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalAndOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LogicalNotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnpackOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FloorDivOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SquareOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ZerosLikeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FillOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BidirectionalSequenceRNNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_FloorModOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RangeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ResizeNearestNeighborOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_LeakyReluOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SquaredDifferenceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MirrorPadOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AbsOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SplitVOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UniqueOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReverseV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AddNOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GatherNdOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CosOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_WhereOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RankOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReverseSequenceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MatrixDiagOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_QuantizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_MatrixSetDiagOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HardSwishOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_IfOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_WhileOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DepthToSpaceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NonMaxSuppressionV4Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_NonMaxSuppressionV5Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ScatterNdOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SelectV2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DensifyOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SegmentSumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BatchMatMulOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CumsumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_CallOnceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BroadcastToOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_Rfft2dOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_Conv3DOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HashtableOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HashtableFindOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HashtableImportOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_HashtableSizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_VarHandleOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ReadVariableOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_AssignVariableOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RandomOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BucketizeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_GeluOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_DynamicUpdateSliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnsortedSegmentProdOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnsortedSegmentMaxOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnsortedSegmentMinOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_UnsortedSegmentSumOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_ATan2Options: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_SignOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BitcastOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_BitwiseXorOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions_RightShiftOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = BuiltinOptions_NONE; +} + +inline bool VerifyBuiltinOptions2(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type) { + switch (type) { + case BuiltinOptions2_NONE: { + return true; + } + case BuiltinOptions2_StablehloConcatenateOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloBroadcastInDimOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloSliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloConvolutionOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloCustomCallOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloReduceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloScatterOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloCompareOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloDynamicSliceOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloPadOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloIotaOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloDotGeneralOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloReduceWindowOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloSortOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloWhileOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloGatherOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloTransposeOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_DilateOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_StablehloRngBitGeneratorOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + case BuiltinOptions2_ReduceWindowOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } + default: return true; + } +} + +inline bool VerifyBuiltinOptions2Vector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { + if (!values || !types) return !values && !types; + if (values->size() != types->size()) return false; + for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { + if (!VerifyBuiltinOptions2( + verifier, values->Get(i), types->GetEnum(i))) { + return false; + } + } + return true; +} + +inline void *BuiltinOptions2Union::UnPack(const void *obj, BuiltinOptions2 type, const flatbuffers::resolver_function_t *resolver) { + (void)resolver; + switch (type) { + case BuiltinOptions2_StablehloConcatenateOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloBroadcastInDimOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloSliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloConvolutionOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloCustomCallOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloReduceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloScatterOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloCompareOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloDynamicSliceOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloPadOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloIotaOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloDotGeneralOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloReduceWindowOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloSortOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloWhileOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloGatherOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloTransposeOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_DilateOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_StablehloRngBitGeneratorOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + case BuiltinOptions2_ReduceWindowOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } + default: return nullptr; + } +} + +inline flatbuffers::Offset BuiltinOptions2Union::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { + (void)_rehasher; + switch (type) { + case BuiltinOptions2_StablehloConcatenateOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloConcatenateOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloBroadcastInDimOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloBroadcastInDimOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloSliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloConvolutionOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloConvolutionOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloCustomCallOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloCustomCallOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloReduceOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloReduceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloScatterOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloScatterOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloCompareOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloCompareOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloDynamicSliceOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloDynamicSliceOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloPadOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloPadOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloIotaOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloIotaOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloDotGeneralOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloDotGeneralOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloReduceWindowOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloReduceWindowOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloSortOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloSortOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloWhileOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloWhileOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloGatherOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloGatherOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloTransposeOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloTransposeOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_DilateOptions: { + auto ptr = reinterpret_cast(value); + return CreateDilateOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_StablehloRngBitGeneratorOptions: { + auto ptr = reinterpret_cast(value); + return CreateStablehloRngBitGeneratorOptions(_fbb, ptr, _rehasher).Union(); + } + case BuiltinOptions2_ReduceWindowOptions: { + auto ptr = reinterpret_cast(value); + return CreateReduceWindowOptions(_fbb, ptr, _rehasher).Union(); + } + default: return 0; + } +} + +inline BuiltinOptions2Union::BuiltinOptions2Union(const BuiltinOptions2Union &u) : type(u.type), value(nullptr) { + switch (type) { + case BuiltinOptions2_StablehloConcatenateOptions: { + value = new tflite_micro::StablehloConcatenateOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloBroadcastInDimOptions: { + value = new tflite_micro::StablehloBroadcastInDimOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloSliceOptions: { + value = new tflite_micro::StablehloSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloConvolutionOptions: { + value = new tflite_micro::StablehloConvolutionOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloCustomCallOptions: { + value = new tflite_micro::StablehloCustomCallOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloReduceOptions: { + value = new tflite_micro::StablehloReduceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloScatterOptions: { + value = new tflite_micro::StablehloScatterOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloCompareOptions: { + value = new tflite_micro::StablehloCompareOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloDynamicSliceOptions: { + value = new tflite_micro::StablehloDynamicSliceOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloPadOptions: { + value = new tflite_micro::StablehloPadOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloIotaOptions: { + value = new tflite_micro::StablehloIotaOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloDotGeneralOptions: { + value = new tflite_micro::StablehloDotGeneralOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloReduceWindowOptions: { + value = new tflite_micro::StablehloReduceWindowOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloSortOptions: { + value = new tflite_micro::StablehloSortOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloWhileOptions: { + value = new tflite_micro::StablehloWhileOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloGatherOptions: { + value = new tflite_micro::StablehloGatherOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloTransposeOptions: { + value = new tflite_micro::StablehloTransposeOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_DilateOptions: { + value = new tflite_micro::DilateOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_StablehloRngBitGeneratorOptions: { + value = new tflite_micro::StablehloRngBitGeneratorOptionsT(*reinterpret_cast(u.value)); + break; + } + case BuiltinOptions2_ReduceWindowOptions: { + value = new tflite_micro::ReduceWindowOptionsT(*reinterpret_cast(u.value)); + break; + } + default: + break; + } +} + +inline void BuiltinOptions2Union::Reset() { + switch (type) { + case BuiltinOptions2_StablehloConcatenateOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloBroadcastInDimOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloSliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloConvolutionOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloCustomCallOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloReduceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloScatterOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloCompareOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloDynamicSliceOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloPadOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloIotaOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloDotGeneralOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloReduceWindowOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloSortOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloWhileOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloGatherOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloTransposeOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_DilateOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_StablehloRngBitGeneratorOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + case BuiltinOptions2_ReduceWindowOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } + default: break; + } + value = nullptr; + type = BuiltinOptions2_NONE; +} + +inline const tflite_micro::Model *GetModel(const void *buf) { + return flatbuffers::GetRoot(buf); +} + +inline const tflite_micro::Model *GetSizePrefixedModel(const void *buf) { + return flatbuffers::GetSizePrefixedRoot(buf); +} + +inline const char *ModelIdentifier() { + return "TFL3"; +} + +inline bool ModelBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, ModelIdentifier()); +} + +inline bool SizePrefixedModelBufferHasIdentifier(const void *buf) { + return flatbuffers::BufferHasIdentifier( + buf, ModelIdentifier(), true); +} + +inline bool VerifyModelBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifyBuffer(ModelIdentifier()); +} + +inline bool VerifySizePrefixedModelBuffer( + flatbuffers::Verifier &verifier) { + return verifier.VerifySizePrefixedBuffer(ModelIdentifier()); +} + +inline const char *ModelExtension() { + return "tflite"; +} + +inline void FinishModelBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.Finish(root, ModelIdentifier()); +} + +inline void FinishSizePrefixedModelBuffer( + flatbuffers::FlatBufferBuilder &fbb, + flatbuffers::Offset root) { + fbb.FinishSizePrefixed(root, ModelIdentifier()); +} + +inline std::unique_ptr UnPackModel( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetModel(buf)->UnPack(res)); +} + +inline std::unique_ptr UnPackSizePrefixedModel( + const void *buf, + const flatbuffers::resolver_function_t *res = nullptr) { + return std::unique_ptr(GetSizePrefixedModel(buf)->UnPack(res)); +} + +} // namespace tflite_micro + +#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h b/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h new file mode 100644 index 00000000..36b02e0b --- /dev/null +++ b/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h @@ -0,0 +1,33 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ +#define TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ + +#include "flatbuffers/flatbuffers.h" +#include "tensorflow/lite/schema/schema_generated.h" + +namespace tflite_micro { + +// The following methods are introduced to resolve op builtin code shortage +// problem. The new builtin operator will be assigned to the extended builtin +// code field in the flatbuffer schema. Those methods helps to hide builtin code +// details. +BuiltinOperator GetBuiltinCode(const OperatorCode *op_code); + +BuiltinOperator GetBuiltinCode(const OperatorCodeT *op_code); + +} // namespace tflite_micro + +#endif // TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ diff --git a/new_ai_tools/include/tile_ram_server.h b/new_ai_tools/include/tile_ram_server.h new file mode 100644 index 00000000..d104ebc1 --- /dev/null +++ b/new_ai_tools/include/tile_ram_server.h @@ -0,0 +1,38 @@ +#ifndef _tile_ram_server_h_ +#define _tile_ram_server_h_ + +#include "flash_server.h" + +/** + * Function that runs a flash-server. A flash server is a thread that serves one + * or more clients. There is one flash server per flash-device, and the server + * can serve clients on one or more tiles. + * + * The flash server takes the following commands: + * - Read a whole model from the flash. + * - Read some parameters from the flash + * - (future extension) Read code for an operator from flash + * + * This function does, at present, never return. It could be made to return if + * all clients close their connection + * + * \param c_tile_ram_clients Array of channels; one per client. + * Each client is served in turn + * NOTE MUST BE 1 FOR NOW + * \param headers Space to store a header for each client + * The header for the client describes the local + * "filesystem" for that client + * \param n_tile_ram_clients Number of clients. The + * arrays in the first and second parameters should have this many entries + * \param data Tile ram data + * \param n_tile_ram_flash Number of bytes in array + */ +#ifdef __XC__ +void tile_ram_server(chanend c_tile_ram_clients[], flash_t headers[], + int n_tile_ram_clients, const int8_t data[]); +#else +void tile_ram_server(chanend_t *c_tile_ram_clients, flash_t *headers, + int n_tile_ram_clients, const int8_t *data); +#endif + +#endif diff --git a/new_ai_tools/libxtflitemicro.cmake b/new_ai_tools/libxtflitemicro.cmake new file mode 100644 index 00000000..11419d6b --- /dev/null +++ b/new_ai_tools/libxtflitemicro.cmake @@ -0,0 +1,18 @@ +set(XMOS_AITOOLSLIB_DEFINITIONS + "TF_LITE_STATIC_MEMORY" + "TF_LITE_STRIP_ERROR_STRINGS" + "XCORE" + "NO_INTERPRETER" +) + +set(XMOS_AITOOLSLIB_LIBRARIES "${CMAKE_CURRENT_LIST_DIR}/libxtflitemicro_vx4a.a") +set(XMOS_AITOOLSLIB_INCLUDES "${CMAKE_CURRENT_LIST_DIR}/include") + + +# list(APPEND APP_COMPILER_FLAGS + # -Wfptrgroup + # -ffunction-sections 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a/new_ai_tools/vnr_model/trained_model_xcore.tflite.cpp b/new_ai_tools/vnr_model/trained_model_xcore.tflite.cpp index 23039431..70f70d44 100644 --- a/new_ai_tools/vnr_model/trained_model_xcore.tflite.cpp +++ b/new_ai_tools/vnr_model/trained_model_xcore.tflite.cpp @@ -1,7 +1,7 @@ // This file is generated. Do not edit. -// Generated on: 24.02.2026 10:42:12 +// Generated on: 06.03.2026 13:52:45 // Compiler version: Not_built_with_version_info! -// Args: /home/paveluvarov/sandboxes/lib_voice/lib_voice/src/vnr/model/trained_model.tflite -tc 1 -o /home/paveluvarov/sandboxes/lib_voice/examples/app_vnr/build/src.autogen/vnr_model//trained_model_xcore.tflite --xcore-conv-err-threshold 0.50 --xcore-target-arch=VX4A +// Args: ../../lib_voice/src/vnr/model/trained_model.tflite -tc 1 -o trained_model_xcore.tflite --xcore-conv-err-threshold 2 --xcore-target-arch VX4A #include "lib_tflite_micro/api/xcore_config.h" #include "lib_nn/api/version.h" @@ -17,7 +17,7 @@ #include "tensorflow/lite/micro/micro_context.h" #include "tensorflow/lite/micro/memory_helpers.h" -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) #include #endif @@ -37,7 +37,7 @@ // Check target arch #ifdef __XS3A__ static_assert(1 == 0, "Model has not been compiled for XS3A!"); -#elif __VX4A__ +#elif __VX4A__ || __VX4B__ static_assert(1 == 1, "Model has not been compiled for VX4A!"); #endif @@ -75,7 +75,7 @@ extern TFLMRegistration *Register_XC_lookup(void); -constexpr int kTensorArenaSize = 1080; +constexpr int kTensorArenaSize = 840; #ifndef SHARED_TENSOR_ARENA namespace { uint8_t tensor_arena[kTensorArenaSize] ALIGN(8); @@ -89,12 +89,12 @@ template struct TfArray { int sz; T elem[SZ]; }; enum used_operators_e { - OP_XC_conv2d_v2, OP_CONV_2D, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, OP_LAST + OP_XC_conv2d_v2, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, OP_LAST }; #if defined(TFLMC_XCORE_PROFILE) || defined(TFLMC_PRINT_TENSORS) || defined(TFLMC_PRINT_INPUT_TENSORS) || defined(TFLMC_CONV2D_PROFILE) const char *op_strs[] = { -"OP_XC_conv2d_v2", "OP_CONV_2D", "OP_XC_slice", "OP_XC_no_op", "OP_XC_lookup", }; +"OP_XC_conv2d_v2", "OP_XC_slice", "OP_XC_no_op", "OP_XC_lookup", }; #endif #if defined(TFLMC_XCORE_PROFILE) || defined(TFLMC_PRINT_TENSORS) || defined(TFLMC_PRINT_INPUT_TENSORS) @@ -346,6 +346,224 @@ const ALIGN(8) int8_t tensor_data3[1664] = { }; const TfArray<1, int> tensor_dimension3 = { 1, { 1664 } }; const ALIGN(8) int16_t tensor_data4[64] = { + 4161, 1435, 1477, 1690, 10134, 2279, 7528, 1827, 9639, 2416, + 3265, 14307, 5774, 2408, 8157, 11297, -490, 306, -122, -206, + -1207, -263, -670, -54, -2053, -373, -1004, -2354, -520, -1281, + -1288, -863, 3311, 2286, 8294, 18246, 19006, 7112, 7327, 2791, + 3303, 1343, 3734, 5021, 2339, 3670, 4287, 9436, -393, -213, + -284, -1844, -12825, -1535, -1024, 106, -499, 383, -1763, -786, + -998, -600, -448, -1571, +}; +const TfArray<1, int> tensor_dimension4 = { 1, { 64 } }; +const ALIGN(8) int8_t tensor_data5[2048] = { + 4, 5, -1, 2, -127, 8, 1, 7, 2, 9, + 1, 9, -49, 0, 3, -11, -7, 10, -1, 4, + 2, 5, 0, -2, 0, 4, 4, 1, -1, -1, + -3, -15, 0, -23, 4, 0, -32, 37, -4, -17, + -4, -8, 0, 6, -3, -3, 2, 8, -11, -2, + 0, -19, 4, -5, -3, 1, 0, 4, -6, 4, + -21, 9, 6, 3, -77, 0, 6, 2, -50, -26, + 3, -48, -15, -46, 7, -17, 16, -11, 18, -30, + -63, 10, 3, 2, 1, -127, -51, 10, -3, -15, + -12, -14, -8, -17, -28, -31, -13, -6, -8, 1, + -3, 12, 12, 0, -4, -3, -7, 50, -6, 0, + 15, -47, -127, 2, -3, -4, 12, 22, 12, -6, + 7, 5, 16, -4, 5, 14, -23, -26, 3, 2, + 0, 2, 1, -8, -127, 1, -3, 5, -1, 0, + 1, 1, 9, 3, 1, 1, 1, 0, -1, 0, + 1, 1, -3, 0, -100, -3, -10, -7, 0, 0, + 21, 31, -3, 4, -49, 4, -4, -7, 7, 28, + -104, -34, -3, -10, 24, 14, -19, -127, 9, -28, + -70, -1, -41, 0, -14, 6, -10, -4, 13, -46, + -13, 41, 7, -28, -13, 18, -17, -3, -1, -50, + -15, -127, -12, 18, -9, -13, 11, -10, 26, -19, + -13, 89, 6, 3, -27, -10, -11, 13, -3, -6, + 62, -11, 7, -37, 12, -20, 5, -4, 2, -48, + -3, -6, -1, -6, -6, -5, 3, -5, -10, 0, + 1, 1, 2, -18, 2, -9, -7, 3, -10, -4, + 4, 2, -1, -5, -1, -4, -14, -74, 3, -7, + -12, -20, 26, -2, 14, 36, 127, 36, -29, -15, + 48, 32, 27, -28, 2, -2, -5, -1, 26, -15, + -11, 9, -21, -2, 0, -18, 11, 25, 7, -11, + -4, 6, 7, 2, -7, 36, -5, -5, -7, -1, + 8, 3, 1, 4, -1, -5, 2, 4, -1, -2, + 4, -2, -2, 3, -2, 3, -7, 6, 23, -14, + 43, 32, 1, -7, 17, 35, 6, -8, -14, 117, + -11, -7, 81, 1, -7, -33, 6, 36, -23, -127, + 3, 10, 8, -10, -23, -6, -2, -7, -22, 2, + 3, -22, 6, -2, -1, -4, 0, 3, 7, 7, + -3, 2, -5, -4, -9, -1, -27, 1, 4, 0, + -3, 4, -2, 2, 2, 1, 0, -3, -11, 0, + 3, -127, -2, 2, -127, -106, -17, 8, -59, 64, + 12, 83, 2, -72, -20, 71, -11, -17, 56, 7, + 57, 69, 16, -15, -10, 2, -66, 1, 27, 6, + -3, 9, 17, -35, 11, 94, -78, 30, -22, -26, + 38, 42, 20, 9, -25, 35, 19, -22, -21, -16, + 21, -2, 15, -98, 1, 31, 127, 7, -15, -11, + 8, 6, 4, -10, -23, -72, 5, 17, -48, 52, + -12, -19, 8, 64, 32, 9, 14, 48, 14, -43, + -16, 6, 61, 6, 5, -119, -11, 47, -40, 15, + -5, 20, -1, -2, 24, 28, -42, -64, 8, 10, + 10, 7, -11, 4, 3, 0, 8, 1, 1, 23, + 12, 28, 1, 7, 6, 13, 1, -15, -2, -8, + -9, 16, 18, -11, 4, -7, -6, 7, 16, -27, + 4, 36, 8, -3, -3, -1, -3, -14, 3, 1, + -14, 0, 2, 2, 20, -1, 2, 4, 0, -2, + 16, -11, 8, -3, -1, 2, 2, 1, 1, -7, + -1, 1, -2, 0, -127, -1, -9, 2, -21, -4, + 2, 2, 6, -2, 6, 6, -1, 17, -20, -18, + 0, -15, 7, -18, -10, -36, 10, 2, 2, 1, + -5, 10, -6, -4, 6, 3, -9, -14, 5, -5, + 24, 0, -53, 12, 5, -43, 4, -1, -106, 8, + -20, 2, 3, -20, -61, -7, 19, -67, 17, -16, + -34, -17, 11, 14, -14, -1, -19, -1, 14, -3, + 11, -2, 58, -25, -47, -16, -10, -88, 5, 2, + -36, 7, -9, -6, 4, -15, 18, 35, 21, 6, + 11, 0, 21, -3, -11, 16, 0, -1, 5, 7, + -4, 0, -3, 2, -2, 2, 2, -68, -2, 1, + 1, -2, 3, 13, 2, -4, -2, 4, 4, -17, + 5, 3, -2, -2, -3, 1, -4, -4, -2, -2, + -8, 2, 33, -17, -23, 15, 3, 51, -62, -2, + -28, -12, 11, 12, 34, -92, -8, 38, -3, 27, + 11, -59, -45, 21, -12, -10, 36, -9, -22, -36, + 5, -6, -21, 14, -8, 18, -11, -13, -6, -1, + 42, 5, -7, -17, 39, -13, 13, -5, -1, -10, + 2, -2, -5, -39, -15, 8, -23, 0, 5, 6, + 97, -36, 11, -8, -52, 11, -127, -5, 2, 5, + -2, 15, -11, 5, -67, 3, -6, -1, -8, -9, + -3, -2, 3, -5, 6, -7, -8, 10, -7, 2, + -28, -5, -10, -5, 0, -2, -7, -2, 59, -3, + -52, -28, 52, -56, -26, -6, -7, 23, -20, 25, + 45, -36, 4, -16, -5, -12, -5, 103, 15, 5, + -18, -21, -4, 18, -48, -25, 13, 1, 10, -21, + -15, -6, -5, -9, -127, -12, -17, 4, 4, -15, + -5, 2, -13, 1, 2, 4, -1, 1, 19, 0, + 0, -5, -2, 1, 10, -2, 0, 11, -4, 1, + -7, 2, -116, 7, 16, -9, -31, -25, -12, -35, + 7, 2, -10, 14, -11, 37, -10, 8, 9, -69, + -65, 70, 36, -25, 16, -20, 24, -7, 8, 33, + -24, -16, 26, -6, 5, 1, -4, -3, 5, -5, + -1, -7, 19, 4, 0, -2, -6, -1, -6, -1, + -24, 9, -3, 5, -3, 18, -22, -3, -9, 0, + 2, -7, 3, -4, -16, -1, -10, 12, 35, 27, + -90, -47, 11, 1, -28, -1, -17, -9, -1, 74, + 9, 12, -9, 90, -33, 28, -31, 11, -28, -11, + 20, 19, -32, -68, 19, -14, -85, 23, -13, -10, + 17, -23, 42, -74, 42, -5, 36, 6, 10, -4, + 56, 16, -19, 6, 4, 23, 45, -25, -17, -32, + -20, 12, -24, 1, 29, -49, -12, 8, -18, 23, + 74, 19, 20, -9, 127, -5, 22, 3, 90, 10, + 16, -22, 3, 25, 35, 16, 7, 10, -7, 20, + 36, -37, 20, -16, 19, 30, 23, 27, -17, 13, + 10, 19, 1, 0, -3, -15, -127, -24, -19, -6, + 17, 3, 9, 0, 26, 4, -5, -18, -12, -120, + 10, -4, -24, 23, 5, -5, 12, -11, 1, -15, + -5, 8, -3, -7, 12, -10, -3, -5, -2, 10, + -11, -11, -4, 0, -42, 14, -24, 0, 12, -6, + -127, -3, -1, -10, -4, 3, -23, -5, -19, 4, + 0, 5, -1, -6, 6, -15, 3, -2, -2, -10, + 27, -7, 15, -5, 3, 33, -5, 0, -2, 5, + 13, -2, 1, -3, 10, 56, -9, -9, 1, 10, + 52, -6, -6, 4, -19, -15, 8, -30, -15, 27, + 9, -7, 5, -1, 7, 23, 3, -28, -3, 14, + -5, -2, 10, -26, 16, -127, 3, 6, -8, 8, + 0, -12, -19, -2, -6, -8, -26, -38, 4, 13, + 81, -21, -8, 21, -6, 58, -8, -16, 0, -42, + -12, -49, -65, 15, 42, 15, 2, -83, 13, 9, + -21, 15, 19, -17, 54, -2, 8, -11, -124, -5, + -33, 1, -3, -15, 6, 8, -8, -28, -1, 1, + 1, -13, 2, 14, 1, 2, -127, -1, 0, 14, + 1, -15, 3, -2, 2, -8, 2, 5, -6, 1, + 11, 18, 7, 1, -35, -52, -4, -4, -69, -37, + -12, -29, 13, -58, 6, -25, -30, 0, -1, 28, + 59, -26, 1, -1, -20, 78, -17, 10, 5, -1, + -24, 3, -127, 9, 28, -34, -31, 50, -5, 13, + -34, 44, 9, 37, 22, 26, 14, 33, 8, 23, + 6, -44, -42, -44, 27, 4, 39, 9, 127, -30, + 10, 27, 9, 35, -45, -1, 37, -1, 13, 18, + -7, 6, 62, 32, 12, -1, -4, -109, 15, -11, + -127, -12, 45, 13, 34, -2, 6, -16, 1, 6, + 1, 5, -12, -12, -2, 2, -86, -20, 9, -10, + -15, 2, -1, 15, -4, -16, -46, -7, -3, 2, + 8, -35, -5, 5, -9, 3, -56, -52, -9, -3, + -17, -31, 60, 1, -2, -13, 34, -8, -54, 34, + -23, 9, 12, -7, 1, 0, -5, 1, 0, -2, + 7, 6, -2, -4, 6, 2, -4, -3, -16, -2, + 3, -1, -1, 4, -37, 6, -2, -5, 20, -4, + -1, 8, -18, -4, -10, -24, 2, 5, -7, 2, + -17, -15, 5, 14, -14, -2, -2, 0, 15, -15, + 5, 4, -1, 4, -1, 15, -73, -2, -9, 0, + 0, -3, -2, 2, -15, -19, 12, -84, 2, 0, + -3, -65, -6, -5, 0, -10, -14, -80, -8, 1, + -84, -9, -17, -54, 2, -64, 8, -14, -28, 2, + -32, -1, 3, -1, -28, -13, -6, -8, -2, -5, + -1, -2, 2, 2, -3, -1, 0, 8, 2, 2, + 1, 0, 1, 0, 3, 1, -2, 0, 0, 1, + -3, 1, 1, 1, -127, 1, -1, 1, 3, 1, + 20, 13, 0, -5, 7, -19, -7, 6, 4, 3, + 0, -16, -5, 3, -3, 2, 7, 2, -2, -5, + 2, 2, -3, 4, -3, -5, -3, -4, -7, 2, + 1, 14, 14, 127, -12, -5, 52, -9, 7, -14, + -15, -56, 17, -30, -25, -3, 17, 2, 13, -42, + -9, -17, -16, 10, 4, 15, -25, 4, -13, 14, + 21, 0, -7, -35, 5, 16, 11, 13, -34, -57, + -1, -24, 4, -67, 2, -1, 54, 8, 18, 7, + -86, -2, 5, 11, -1, -4, -7, 14, 9, -10, + -1, -2, -28, -4, 14, 11, 6, 3, -22, 5, + 36, -37, 8, -1, 5, -6, -21, -3, 38, -10, + 5, -13, -4, -9, -2, 2, 2, 10, -6, 3, + 12, 0, -15, -18, 2, 0, -10, -4, -34, 8, + 0, -14, -127, -13, -1, -2, 8, -10, -20, 4, + 5, -1, -2, 2, -6, -10, -9, 7, -25, -8, + 11, 10, 11, -10, -4, -17, -7, 2, -7, 5, + -97, -11, 16, -12, 9, 33, 22, -3, 25, 2, + -98, 10, 40, -16, 20, -8, -9, -29, 28, 44, + 1, 12, 15, -11, -63, -9, 9, -2, -7, 3, + 13, -2, 8, -13, -42, -5, 45, -127, -55, 10, + -2, -3, -10, -15, -17, -4, -11, 2, -13, 23, + -13, -86, -43, -9, -25, -15, -16, 20, -36, -42, + 11, -25, -76, 9, -90, 1, 0, 16, 27, 7, + 2, -4, -36, -2, 5, 0, -36, -1, -78, 6, + 0, 3, -2, 3, -9, 3, 4, 7, -4, 8, + -8, 13, 5, 8, 8, 1, -10, -1, -30, 5, + 4, -79, 6, 0, -71, -13, -9, 11, -82, -4, + -27, -4, 6, -8, -48, -62, 11, -26, -8, -2, + -32, -12, -22, -17, 12, -7, -2, -5, 33, -1, + 7, -39, 41, -66, -3, -16, 18, -19, -15, 14, + 19, 14, 25, 112, 2, -12, 13, -1, -36, 86, + 106, -16, 64, -8, 15, 35, -11, 42, 117, -15, + -45, 4, -2, -7, 111, -9, 15, -12, 0, 7, + -64, -4, 6, -8, -68, -21, -12, 15, -87, 19, + 20, -7, 18, 14, 1, 13, 15, 11, 9, -1, + -10, 10, -24, -6, -4, -36, 127, 59, -17, -1, + -38, -10, -9, 15, -42, 3, 23, 58, 25, 12, + -6, -12, 26, -27, 95, -16, 7, 5, -19, 113, + -8, -4, 109, 3, 3, -4, 9, 6, -121, -127, + 13, 1, 11, -13, 4, -2, 25, -10, 16, -16, + 3, 0, 1, 6, -4, 22, -15, -4, -2, -5, + 0, 16, -3, 1, -8, -3, -5, 1, -15, 1, + 16, 15, -3, 3, 17, -15, -5, 0, -54, -22, + 19, -127, 2, -8, 9, -30, -4, 4, -11, 1, + -6, 7, -26, -16, 3, 0, -4, 3, -127, 2, + -11, -1, -62, -94, -5, -2, -71, -1, -60, 1, + 2, -15, -89, -17, -2, -7, 4, -8, -58, 15, + -44, 2, -29, -1, -31, -31, -2, 2, -24, -1, + -4, 0, 15, -4, -2, -6, -7, -25, 3, -2, + -1, 1, 0, -2, 0, -1, -6, -1, -6, -24, + -9, -1, 1, 0, 0, 2, 1, 3, 3, -2, + -1, -2, 0, -4, 4, 0, -127, 20, 15, 2, + -2, 13, -5, 6, 2, -2, -1, -3, 1, 5, + 8, 3, -14, 2, 20, 0, -2, -2, 5, 19, + 0, 0, 4, -2, 18, -5, -37, -54, 36, -40, + -41, -15, 7, 18, 81, 14, 0, 16, -7, -9, + -15, 18, 7, 55, -45, -1, -25, 12, 9, -18, + 9, -10, -18, -3, -5, 3, 3, -7, -4, -27, + 21, 15, 9, -2, 50, 11, -9, -2, -22, 10, + -12, 13, 3, 7, 92, -127, -39, -17, 4, -9, + -12, 3, 7, 12, -8, 3, -6, 15, +}; +const TfArray<1, int> tensor_dimension5 = { 1, { 2048 } }; +const ALIGN(8) int16_t tensor_data6[64] = { 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, -8862, -13159, -1739, -23447, -3753, -11321, -7357, -13811, -5229, -4252, -11796, -16623, -12464, -5743, @@ -354,8 +572,7 @@ const ALIGN(8) int16_t tensor_data4[64] = { -12677, -2967, -15437, -4139, -11895, -7783, -16902, -4614, -12643, -17487, -4246, -6202, -10536, -3372, }; -const TfArray<1, int> tensor_dimension4 = { 1, { 64 } }; -const ALIGN(8) int8_t tensor_data5[4096] = { +const ALIGN(8) int8_t tensor_data7[4096] = { -1, -88, 1, -3, 2, -2, 3, 0, 7, -9, 1, -1, 3, 2, 1, -8, -1, -11, 6, -2, -1, 3, -5, 2, 0, -3, 3, 3, 5, -3, @@ -767,13 +984,13 @@ const ALIGN(8) int8_t tensor_data5[4096] = { 4, 4, -5, 2, -106, -1, -6, -3, 5, -9, -4, -4, 2, 2, 4, -5, }; -const TfArray<1, int> tensor_dimension5 = { 1, { 4096 } }; -const ALIGN(8) int16_t tensor_data6[20] = { +const TfArray<1, int> tensor_dimension7 = { 1, { 4096 } }; +const ALIGN(8) int16_t tensor_data8[20] = { 22083, 0, 0, 0, -2980, 1983, 1983, 1983, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }; -const TfArray<1, int> tensor_dimension6 = { 1, { 20 } }; -const ALIGN(8) int8_t tensor_data7[512] = { +const TfArray<1, int> tensor_dimension8 = { 1, { 20 } }; +const ALIGN(8) int8_t tensor_data9[512] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, @@ -827,58 +1044,10 @@ const ALIGN(8) int8_t tensor_data7[512] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }; -const TfArray<1, int> tensor_dimension7 = { 1, { 512 } }; -const TfArray<4, int> tensor_dimension9 = { 4, { 1,1,4,64 } }; -const TfArray<1, float> quant9_scale = { 1, { 0.24111956357955933, } }; -const TfArray<1, int> quant9_zero = { 1, { -128 } }; -const TfLiteAffineQuantization quant9 = { (TfLiteFloatArray*)&quant9_scale, (TfLiteIntArray*)&quant9_zero, 0 }; -const ALIGN(8) int8_t tensor_data10[32*1*1*64] = { - /* [0][0][][] */ 10,7,-11,4,3,0,8,1,1,23,12,28,1,7,6,13,1,-15,-2,-8,-9,16,18,-11,4,-7,-6,7,16,-27,4,36,1,0,-3,-15,-127,-24,-19,-6,17,3,9,0,26,4,-5,-18,-12,-120,10,-4,-24,23,5,-5,12,-11,1,-15,-5,8,-3,-7, - /* [1][0][][] */ -48,52,-12,-19,8,64,32,9,14,48,14,-43,-16,6,61,6,5,-119,-11,47,-40,15,-5,20,-1,-2,24,28,-42,-64,8,10,74,19,20,-9,127,-5,22,3,90,10,16,-22,3,25,35,16,7,10,-7,20,36,-37,20,-16,19,30,23,27,-17,13,10,19, - /* [2][0][][] */ -78,30,-22,-26,38,42,20,9,-25,35,19,-22,-21,-16,21,-2,15,-98,1,31,127,7,-15,-11,8,6,4,-10,-23,-72,5,17,-13,-10,17,-23,42,-74,42,-5,36,6,10,-4,56,16,-19,6,4,23,45,-25,-17,-32,-20,12,-24,1,29,-49,-12,8,-18,23, - /* [3][0][][] */ -127,-106,-17,8,-59,64,12,83,2,-72,-20,71,-11,-17,56,7,57,69,16,-15,-10,2,-66,1,27,6,-3,9,17,-35,11,94,-10,12,35,27,-90,-47,11,1,-28,-1,-17,-9,-1,74,9,12,-9,90,-33,28,-31,11,-28,-11,20,19,-32,-68,19,-14,-85,23, - /* [4][0][][] */ 6,-2,-1,-4,0,3,7,7,-3,2,-5,-4,-9,-1,-27,1,4,0,-3,4,-2,2,2,1,0,-3,-11,0,3,-127,-2,2,5,1,-4,-3,5,-5,-1,-7,19,4,0,-2,-6,-1,-6,-1,-24,9,-3,5,-3,18,-22,-3,-9,0,2,-7,3,-4,-16,-1, - /* [5][0][][] */ 43,32,1,-7,17,35,6,-8,-14,117,-11,-7,81,1,-7,-33,6,36,-23,-127,3,10,8,-10,-23,-6,-2,-7,-22,2,3,-22,-116,7,16,-9,-31,-25,-12,-35,7,2,-10,14,-11,37,-10,8,9,-69,-65,70,36,-25,16,-20,24,-7,8,33,-24,-16,26,-6, - /* [6][0][][] */ 7,-11,-4,6,7,2,-7,36,-5,-5,-7,-1,8,3,1,4,-1,-5,2,4,-1,-2,4,-2,-2,3,-2,3,-7,6,23,-14,-15,-6,-5,-9,-127,-12,-17,4,4,-15,-5,2,-13,1,2,4,-1,1,19,0,0,-5,-2,1,10,-2,0,11,-4,1,-7,2, - /* [7][0][][] */ -14,-74,3,-7,-12,-20,26,-2,14,36,127,36,-29,-15,48,32,27,-28,2,-2,-5,-1,26,-15,-11,9,-21,-2,0,-18,11,25,59,-3,-52,-28,52,-56,-26,-6,-7,23,-20,25,45,-36,4,-16,-5,-12,-5,103,15,5,-18,-21,-4,18,-48,-25,13,1,10,-21, - /* [8][0][][] */ 12,-20,5,-4,2,-48,-3,-6,-1,-6,-6,-5,3,-5,-10,0,1,1,2,-18,2,-9,-7,3,-10,-4,4,2,-1,-5,-1,-4,-127,-5,2,5,-2,15,-11,5,-67,3,-6,-1,-8,-9,-3,-2,3,-5,6,-7,-8,10,-7,2,-28,-5,-10,-5,0,-2,-7,-2, - /* [9][0][][] */ 7,-28,-13,18,-17,-3,-1,-50,-15,-127,-12,18,-9,-13,11,-10,26,-19,-13,89,6,3,-27,-10,-11,13,-3,-6,62,-11,7,-37,-8,18,-11,-13,-6,-1,42,5,-7,-17,39,-13,13,-5,-1,-10,2,-2,-5,-39,-15,8,-23,0,5,6,97,-36,11,-8,-52,11, - /* [10][0][][] */ 21,31,-3,4,-49,4,-4,-7,7,28,-104,-34,-3,-10,24,14,-19,-127,9,-28,-70,-1,-41,0,-14,6,-10,-4,13,-46,-13,41,33,-17,-23,15,3,51,-62,-2,-28,-12,11,12,34,-92,-8,38,-3,27,11,-59,-45,21,-12,-10,36,-9,-22,-36,5,-6,-21,14, - /* [11][0][][] */ 3,2,0,2,1,-8,-127,1,-3,5,-1,0,1,1,9,3,1,1,1,0,-1,0,1,1,-3,0,-100,-3,-10,-7,0,0,-4,0,-3,2,-2,2,2,-68,-2,1,1,-2,3,13,2,-4,-2,4,4,-17,5,3,-2,-2,-3,1,-4,-4,-2,-2,-8,2, - /* [12][0][][] */ -13,-6,-8,1,-3,12,12,0,-4,-3,-7,50,-6,0,15,-47,-127,2,-3,-4,12,22,12,-6,7,5,16,-4,5,14,-23,-26,14,-3,11,-2,58,-25,-47,-16,-10,-88,5,2,-36,7,-9,-6,4,-15,18,35,21,6,11,0,21,-3,-11,16,0,-1,5,7, - /* [13][0][][] */ -77,0,6,2,-50,-26,3,-48,-15,-46,7,-17,16,-11,18,-30,-63,10,3,2,1,-127,-51,10,-3,-15,-12,-14,-8,-17,-28,-31,-9,-14,5,-5,24,0,-53,12,5,-43,4,-1,-106,8,-20,2,3,-20,-61,-7,19,-67,17,-16,-34,-17,11,14,-14,-1,-19,-1, - /* [14][0][][] */ 0,-23,4,0,-32,37,-4,-17,-4,-8,0,6,-3,-3,2,8,-11,-2,0,-19,4,-5,-3,1,0,4,-6,4,-21,9,6,3,-127,-1,-9,2,-21,-4,2,2,6,-2,6,6,-1,17,-20,-18,0,-15,7,-18,-10,-36,10,2,2,1,-5,10,-6,-4,6,3, - /* [15][0][][] */ 4,5,-1,2,-127,8,1,7,2,9,1,9,-49,0,3,-11,-7,10,-1,4,2,5,0,-2,0,4,4,1,-1,-1,-3,-15,8,-3,-3,-1,-3,-14,3,1,-14,0,2,2,20,-1,2,4,0,-2,16,-11,8,-3,-1,2,2,1,1,-7,-1,1,-2,0, - /* [16][0][][] */ 5,16,11,13,-34,-57,-1,-24,4,-67,2,-1,54,8,18,7,-86,-2,5,11,-1,-4,-7,14,9,-10,-1,-2,-28,-4,14,11,3,-7,-4,-27,21,15,9,-2,50,11,-9,-2,-22,10,-12,13,3,7,92,-127,-39,-17,4,-9,-12,3,7,12,-8,3,-6,15, - /* [17][0][][] */ 14,127,-12,-5,52,-9,7,-14,-15,-56,17,-30,-25,-3,17,2,13,-42,-9,-17,-16,10,4,15,-25,4,-13,14,21,0,-7,-35,18,-5,-37,-54,36,-40,-41,-15,7,18,81,14,0,16,-7,-9,-15,18,7,55,-45,-1,-25,12,9,-18,9,-10,-18,-3,-5,3, - /* [18][0][][] */ 20,13,0,-5,7,-19,-7,6,4,3,0,-16,-5,3,-3,2,7,2,-2,-5,2,2,-3,4,-3,-5,-3,-4,-7,2,1,14,0,-4,4,0,-127,20,15,2,-2,13,-5,6,2,-2,-1,-3,1,5,8,3,-14,2,20,0,-2,-2,5,19,0,0,4,-2, - /* [19][0][][] */ -2,-5,-1,-2,2,2,-3,-1,0,8,2,2,1,0,1,0,3,1,-2,0,0,1,-3,1,1,1,-127,1,-1,1,3,1,-4,0,15,-4,-2,-6,-7,-25,3,-2,-1,1,0,-2,0,-1,-6,-1,-6,-24,-9,-1,1,0,0,2,1,3,3,-2,-1,-2, - /* [20][0][][] */ 12,-84,2,0,-3,-65,-6,-5,0,-10,-14,-80,-8,1,-84,-9,-17,-54,2,-64,8,-14,-28,2,-32,-1,3,-1,-28,-13,-6,-8,-127,2,-11,-1,-62,-94,-5,-2,-71,-1,-60,1,2,-15,-89,-17,-2,-7,4,-8,-58,15,-44,2,-29,-1,-31,-31,-2,2,-24,-1, - /* [21][0][][] */ -10,-24,2,5,-7,2,-17,-15,5,14,-14,-2,-2,0,15,-15,5,4,-1,4,-1,15,-73,-2,-9,0,0,-3,-2,2,-15,-19,-5,1,-15,1,16,15,-3,3,17,-15,-5,0,-54,-22,19,-127,2,-8,9,-30,-4,4,-11,1,-6,7,-26,-16,3,0,-4,3, - /* [22][0][][] */ 12,-7,1,0,-5,1,0,-2,7,6,-2,-4,6,2,-4,-3,-16,-2,3,-1,-1,4,-37,6,-2,-5,20,-4,-1,8,-18,-4,3,-4,9,6,-121,-127,13,1,11,-13,4,-2,25,-10,16,-16,3,0,1,6,-4,22,-15,-4,-2,-5,0,16,-3,1,-8,-3, - /* [23][0][][] */ -15,2,-1,15,-4,-16,-46,-7,-3,2,8,-35,-5,5,-9,3,-56,-52,-9,-3,-17,-31,60,1,-2,-13,34,-8,-54,34,-23,9,-24,-6,-4,-36,127,59,-17,-1,-38,-10,-9,15,-42,3,23,58,25,12,-6,-12,26,-27,95,-16,7,5,-19,113,-8,-4,109,3, - /* [24][0][][] */ 13,18,-7,6,62,32,12,-1,-4,-109,15,-11,-127,-12,45,13,34,-2,6,-16,1,6,1,5,-12,-12,-2,2,-86,-20,9,-10,-45,4,-2,-7,111,-9,15,-12,0,7,-64,-4,6,-8,-68,-21,-12,15,-87,19,20,-7,18,14,1,13,15,11,9,-1,-10,10, - /* [25][0][][] */ -31,50,-5,13,-34,44,9,37,22,26,14,33,8,23,6,-44,-42,-44,27,4,39,9,127,-30,10,27,9,35,-45,-1,37,-1,33,-1,7,-39,41,-66,-3,-16,18,-19,-15,14,19,14,25,112,2,-12,13,-1,-36,86,106,-16,64,-8,15,35,-11,42,117,-15, - /* [26][0][][] */ -35,-52,-4,-4,-69,-37,-12,-29,13,-58,6,-25,-30,0,-1,28,59,-26,1,-1,-20,78,-17,10,5,-1,-24,3,-127,9,28,-34,-10,-1,-30,5,4,-79,6,0,-71,-13,-9,11,-82,-4,-27,-4,6,-8,-48,-62,11,-26,-8,-2,-32,-12,-22,-17,12,-7,-2,-5, - /* [27][0][][] */ -3,-15,6,8,-8,-28,-1,1,1,-13,2,14,1,2,-127,-1,0,14,1,-15,3,-2,2,-8,2,5,-6,1,11,18,7,1,-90,1,0,16,27,7,2,-4,-36,-2,5,0,-36,-1,-78,6,0,3,-2,3,-9,3,4,7,-4,8,-8,13,5,8,8,1, - /* [28][0][][] */ 81,-21,-8,21,-6,58,-8,-16,0,-42,-12,-49,-65,15,42,15,2,-83,13,9,-21,15,19,-17,54,-2,8,-11,-124,-5,-33,1,8,-13,-42,-5,45,-127,-55,10,-2,-3,-10,-15,-17,-4,-11,2,-13,23,-13,-86,-43,-9,-25,-15,-16,20,-36,-42,11,-25,-76,9, - /* [29][0][][] */ -15,27,9,-7,5,-1,7,23,3,-28,-3,14,-5,-2,10,-26,16,-127,3,6,-8,8,0,-12,-19,-2,-6,-8,-26,-38,4,13,-97,-11,16,-12,9,33,22,-3,25,2,-98,10,40,-16,20,-8,-9,-29,28,44,1,12,15,-11,-63,-9,9,-2,-7,3,13,-2, - /* [30][0][][] */ 3,-2,-2,-10,27,-7,15,-5,3,33,-5,0,-2,5,13,-2,1,-3,10,56,-9,-9,1,10,52,-6,-6,4,-19,-15,8,-30,-34,8,0,-14,-127,-13,-1,-2,8,-10,-20,4,5,-1,-2,2,-6,-10,-9,7,-25,-8,11,10,11,-10,-4,-17,-7,2,-7,5, - /* [31][0][][] */ 12,-10,-3,-5,-2,10,-11,-11,-4,0,-42,14,-24,0,12,-6,-127,-3,-1,-10,-4,3,-23,-5,-19,4,0,5,-1,-6,6,-15,6,3,-22,5,36,-37,8,-1,5,-6,-21,-3,38,-10,5,-13,-4,-9,-2,2,2,10,-6,3,12,0,-15,-18,2,0,-10,-4, -}; -const TfArray<4, int> tensor_dimension10 = { 4, { 32,1,1,64 } }; -const TfArray<32, float> quant10_scale = { 32, { 0.021931935101747513, 0.0075631137005984783, 0.0077862474136054516, 0.0089080138131976128, 0.053415644913911819, 0.01201336458325386, 0.03967820480465889, 0.009627203457057476, 0.050802357494831085, 0.012734233401715755, 0.01720757782459259, 0.075407899916172028, 0.030435193330049515, 0.012694566510617733, 0.042991198599338531, 0.05954216793179512, 0.017452919855713844, 0.012050630524754524, 0.043715611100196838, 0.096170432865619659, 0.10017587244510651, 0.037486985325813293, 0.038620803505182266, 0.014712310396134853, 0.017411507666110992, 0.0070783230476081371, 0.019682943820953369, 0.026464931666851044, 0.012327380478382111, 0.019343035295605659, 0.022597838193178177, 0.049735765904188156, } }; -const TfArray<32, int> quant10_zero = { 32, { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 } }; -const TfLiteAffineQuantization quant10 = { (TfLiteFloatArray*)&quant10_scale, (TfLiteIntArray*)&quant10_zero, 0 }; -const ALIGN(8) int32_t tensor_data11[32] = { - -496, -1133, -2029, -3094, -120, -1806, -927, -3308, 399, -2149, - -1015, -318, -351, 1975, -381, -402, -1413, -1840, -1047, -63, - 119, 387, -1042, 914, -1210, -7140, -519, 262, -2071, -385, - -933, -385, -}; -const TfArray<1, int> tensor_dimension11 = { 1, { 32 } }; -const TfArray<32, float> quant11_scale = { 32, { 0.0052882186137139797, 0.0018236147006973624, 0.0018774166237562895, 0.0021478964481502771, 0.012879556976258755, 0.0028966572135686874, 0.0095671918243169785, 0.0023213070817291737, 0.01224944181740284, 0.003070472739636898, 0.0041490835137665272, 0.018182320520281792, 0.0073385206051170826, 0.0030609082896262407, 0.010366018861532211, 0.014356781728565693, 0.0042082401923835278, 0.0029056428465992212, 0.010540689341723919, 0.023188572376966476, 0.024154363200068474, 0.0090388450771570206, 0.0093122310936450958, 0.0035474258475005627, 0.0041982550173997879, 0.001706722192466259, 0.0047459430061280727, 0.0063812127336859703, 0.0029723725747317076, 0.0046639842912554741, 0.0054487809538841248, 0.011992266401648521, } }; -const TfArray<32, int> quant11_zero = { 32, { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 } }; +const TfArray<1, int> tensor_dimension9 = { 1, { 512 } }; +const TfArray<4, int> tensor_dimension11 = { 4, { 1,1,4,64 } }; +const TfArray<1, float> quant11_scale = { 1, { 0.24111956357955933, } }; +const TfArray<1, int> quant11_zero = { 1, { -128 } }; const TfLiteAffineQuantization quant11 = { (TfLiteFloatArray*)&quant11_scale, (TfLiteIntArray*)&quant11_zero, 0 }; const TfArray<4, int> tensor_dimension12 = { 4, { 1,1,4,32 } }; const TfArray<1, float> quant12_scale = { 1, { 0.33315452933311462, } }; @@ -898,16 +1067,16 @@ const TfArray<1, float> quant18_scale = { 1, { 0.00390625, } }; const TfArray<1, int> quant18_zero = { 1, { -128 } }; const TfLiteAffineQuantization quant18 = { (TfLiteFloatArray*)&quant18_scale, (TfLiteIntArray*)&quant18_zero, 0 }; uint8_t ALIGN(4) opdata0[152] = { 109, 112, 0, 40, 96, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 232, 255, 255, 255, 248, 255, 255, 255, 72, 0, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0, 97, 0, 8, 64, 0, 0, 0, 24, 0, 0, 0, 0, 111, 0, 8, 64, 0, 0, 0, 3, 0, 250, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 80, 6, 127, 71, 60, 12, 9, 7, 1, 7, 87, 1, 133, 78, 23, 64, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs0 = { 5, { 0,3,2,-1,8 } }; -const TfArray<1, int> outputs0 = { 1, { 9 } }; -const TfLiteConvParams opdata1 = { kTfLitePaddingValid, 1,1, kTfLiteActRelu, 1,1 }; -const TfArray<3, int> inputs1 = { 3, { 9,10,11 } }; +const TfArray<5, int> inputs0 = { 5, { 0,3,2,-1,10 } }; +const TfArray<1, int> outputs0 = { 1, { 11 } }; +uint8_t ALIGN(4) opdata1[136] = { 109, 112, 0, 8, 0, 1, 0, 0, 64, 0, 0, 0, 0, 97, 0, 24, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 192, 0, 0, 0, 0, 111, 0, 8, 32, 0, 0, 0, 3, 0, 248, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 96, 6, 111, 71, 60, 12, 9, 7, 1, 7, 103, 0, 117, 78, 23, 0, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ +const TfArray<5, int> inputs1 = { 5, { 11,5,4,-1,-1 } }; const TfArray<1, int> outputs1 = { 1, { 12 } }; uint8_t ALIGN(4) opdata2[136] = { 109, 112, 0, 8, 128, 0, 0, 0, 128, 0, 0, 0, 0, 97, 0, 24, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 111, 0, 8, 32, 0, 0, 0, 1, 0, 252, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 96, 6, 111, 71, 60, 12, 9, 7, 1, 7, 103, 0, 117, 78, 23, 0, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs2 = { 5, { 12,5,4,-1,-1 } }; +const TfArray<5, int> inputs2 = { 5, { 12,7,6,-1,-1 } }; const TfArray<1, int> outputs2 = { 1, { 13 } }; uint8_t ALIGN(4) opdata3[152] = { 109, 112, 0, 40, 32, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 224, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 0, 0, 0, 0, 0, 97, 0, 8, 4, 0, 0, 0, 32, 0, 0, 0, 0, 111, 0, 8, 4, 0, 0, 0, 3, 0, 254, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 80, 6, 127, 71, 60, 12, 9, 7, 1, 7, 87, 1, 133, 78, 23, 64, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs3 = { 5, { 13,7,6,-1,14 } }; +const TfArray<5, int> inputs3 = { 5, { 13,9,8,-1,14 } }; const TfArray<1, int> outputs3 = { 1, { 15 } }; uint8_t ALIGN(4) opdata4[32] = { 115, 0, 111, 0, 108, 0, 110, 0, 118, 0, 5, 7, 6, 11, 14, 7, 5, 1, 5, 1, 1, 4, 0, 0, 4, 4, 4, 4, 104, 10, 36, 1, }; /* custom_initial_data */ const TfArray<1, int> inputs4 = { 1, { 15 } }; @@ -926,12 +1095,12 @@ TfLiteTensor tflTensors[] = { {(int32_t*)g0.tensor_data3},(TfLiteIntArray*)&g0.tensor_dimension3, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, { {(int32_t*)g0.tensor_data4},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, { {(int32_t*)g0.tensor_data5},(TfLiteIntArray*)&g0.tensor_dimension5, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data6},(TfLiteIntArray*)&g0.tensor_dimension6, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, +{ {(int32_t*)g0.tensor_data6},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, { {(int32_t*)g0.tensor_data7},(TfLiteIntArray*)&g0.tensor_dimension7, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, +{ {(int32_t*)g0.tensor_data8},(TfLiteIntArray*)&g0.tensor_dimension8, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, +{ {(int32_t*)g0.tensor_data9},(TfLiteIntArray*)&g0.tensor_dimension9, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, { {(int32_t*)(tensor_arena + 352)},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension9, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant9)) }, {g0.quant9.scale->data[0], g0.quant9.zero_point->data[0] },}, -{ {(int32_t*)g0.tensor_data10},(TfLiteIntArray*)&g0.tensor_dimension10, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant10)) }, {g0.quant10.scale->data[0], g0.quant10.zero_point->data[0] },}, -{ {(int32_t*)g0.tensor_data11},(TfLiteIntArray*)&g0.tensor_dimension11, kTfLiteInt32, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant11)) }, {g0.quant11.scale->data[0], g0.quant11.zero_point->data[0] },}, +{ {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension11, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant11)) }, {g0.quant11.scale->data[0], g0.quant11.zero_point->data[0] },}, { {(int32_t*)(tensor_arena + 256)},(TfLiteIntArray*)&g0.tensor_dimension12, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant12)) }, {g0.quant12.scale->data[0], g0.quant12.zero_point->data[0] },}, { {(int32_t*)(tensor_arena + 64)},(TfLiteIntArray*)&g0.tensor_dimension13, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant13)) }, {g0.quant13.scale->data[0], g0.quant13.zero_point->data[0] },}, { {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, @@ -943,7 +1112,7 @@ TfLiteTensor tflTensors[] = TfLiteNode tflNodes[] = {{ (TfLiteIntArray*)&g0.inputs0, (TfLiteIntArray*)&g0.outputs0, (TfLiteIntArray*)&g0.inputs0, const_cast(static_cast(&g0.opdata0)), 152, }, -{ (TfLiteIntArray*)&g0.inputs1, (TfLiteIntArray*)&g0.outputs1, (TfLiteIntArray*)&g0.inputs1, const_cast(static_cast(&g0.opdata1)), 0, }, +{ (TfLiteIntArray*)&g0.inputs1, (TfLiteIntArray*)&g0.outputs1, (TfLiteIntArray*)&g0.inputs1, const_cast(static_cast(&g0.opdata1)), 136, }, { (TfLiteIntArray*)&g0.inputs2, (TfLiteIntArray*)&g0.outputs2, (TfLiteIntArray*)&g0.inputs2, const_cast(static_cast(&g0.opdata2)), 136, }, { (TfLiteIntArray*)&g0.inputs3, (TfLiteIntArray*)&g0.outputs3, (TfLiteIntArray*)&g0.inputs3, const_cast(static_cast(&g0.opdata3)), 152, }, { (TfLiteIntArray*)&g0.inputs4, (TfLiteIntArray*)&g0.outputs4, (TfLiteIntArray*)&g0.inputs4, const_cast(static_cast(&g0.opdata4)), 32, }, @@ -952,7 +1121,7 @@ TfLiteNode tflNodes[] = }; used_operators_e used_ops[] = -{OP_XC_conv2d_v2, OP_CONV_2D, OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, }; +{OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, }; // Indices into tflTensors and tflNodes for subgraphs @@ -1132,7 +1301,7 @@ printf("[\n"); #endif #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t0 = get_reference_time(); #endif #endif @@ -1149,7 +1318,7 @@ printf("[\n"); } #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t1 = get_reference_time(); #endif op_times[used_ops[i]] += time_t1 - time_t0; @@ -1278,7 +1447,6 @@ TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { ctx.tensors = tflTensors; ctx.tensors_size = 19; registrations[OP_XC_conv2d_v2] = *(tflite_micro::ops::micro::xcore::Register_XC_conv2d_v2()); - registrations[OP_CONV_2D] = tflite_micro::Register_CONV_2D(); registrations[OP_XC_slice] = *(tflite_micro::ops::micro::xcore::Register_XC_slice()); registrations[OP_XC_no_op] = *(tflite_micro::ops::micro::xcore::Register_XC_no_op()); registrations[OP_XC_lookup] = *(tflite_micro::ops::micro::xcore::Register_XC_lookup()); @@ -1301,7 +1469,7 @@ TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { if (registrations[used_ops[i]].init) { #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t0 = get_reference_time(); #endif #endif @@ -1309,7 +1477,7 @@ TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { tflNodes[i].user_data = registrations[used_ops[i]].init(&ctx, (const char*)tflNodes[i].builtin_data, tflNodes[i].custom_initial_data_size); #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t1 = get_reference_time(); #endif op_times[used_ops[i]] += time_t1 - time_t0; @@ -1339,7 +1507,7 @@ TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { if (registrations[used_ops[i]].prepare) { #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t0 = get_reference_time(); #endif #endif @@ -1347,7 +1515,7 @@ TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { TfLiteStatus status = registrations[used_ops[i]].prepare(&ctx, &tflNodes[i]); #ifdef TFLMC_XCORE_PROFILE -#ifdef __xcore__ +#if defined(__xcore__) || defined(__riscv_xxcore) time_t1 = get_reference_time(); #endif op_times[used_ops[i]] += time_t1 - time_t0; @@ -1379,7 +1547,7 @@ TfLiteStatus model_init(void *weights_data_ptr) { return model_init_with_paging(weights_data_ptr, nullptr); } -#ifdef __VX4A__ +#if defined(__VX4A__) || defined(__VX4B__) #define STACKFUNCTION(FN, BYTES) \ asm(".globl " # FN ); \ asm(".resource_list_empty " # FN ", \"callees\""); \ @@ -1387,7 +1555,7 @@ TfLiteStatus model_init(void *weights_data_ptr) { asm(".resource_list_empty " # FN ", \"parallel_callees\""); \ asm(".resource_const " # FN ", \"stack_frame_bytes\", " # BYTES); -#define STACKFUNCTION2(FN, BYTES) \ +#define STACKFUNCTION_STATIC(FN, BYTES) \ asm(".resource_list_empty " # FN ", \"callees\""); \ asm(".resource_list_empty " # FN ", \"tail_callees\""); \ asm(".resource_list_empty " # FN ", \"parallel_callees\""); \ @@ -1397,8 +1565,7 @@ STACKFUNCTION(_Z22model_init_with_pagingPvS_, 1000); STACKFUNCTION(fast_read_loop, 1000); // STACKFUNCTION(_Z12model_invokev, 1000); STACKFUNCTION(__call_exitprocs_impl, 1000); -STACKFUNCTION2(_ZN12_GLOBAL__N_117mg_InvokeSubgraphEi, 1000) -// STACKFUNCTION(invoke_subgraph_c_trampoline, 1000); +STACKFUNCTION_STATIC(_ZN12_GLOBAL__N_117mg_InvokeSubgraphEi, 1000); // STACKFUNCTION(_Z10model_initPv); #endif diff --git a/new_ai_tools/vnr_model/trained_model_xcore.tflite.h b/new_ai_tools/vnr_model/trained_model_xcore.tflite.h index 3315b74b..12150bbd 100644 --- a/new_ai_tools/vnr_model/trained_model_xcore.tflite.h +++ b/new_ai_tools/vnr_model/trained_model_xcore.tflite.h @@ -1,5 +1,5 @@ // This file is generated. Do not edit. -// Generated on: 24.02.2026 10:42:12 +// Generated on: 06.03.2026 13:52:45 #ifndef model_GEN_H #define model_GEN_H @@ -8,9 +8,9 @@ #ifdef SHARED_TENSOR_ARENA #ifndef LARGEST_TENSOR_ARENA_SIZE - #define LARGEST_TENSOR_ARENA_SIZE 1080 - #elif LARGEST_TENSOR_ARENA_SIZE < 1080 - #define LARGEST_TENSOR_ARENA_SIZE 1080 + #define LARGEST_TENSOR_ARENA_SIZE 840 + #elif LARGEST_TENSOR_ARENA_SIZE < 840 + #define LARGEST_TENSOR_ARENA_SIZE 840 #endif #endif diff --git a/requirements.txt b/requirements.txt index 1c22bb33..281d5dd7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ # python_version 3.11 # pip_version 25.* -xmos-ai-tools==1.4.2 +xmos-ai-tools==1.4.3.dev18 diff --git a/tests/lib_vnr/vnr_unit_tests/test_vnr_full.py b/tests/lib_vnr/vnr_unit_tests/test_vnr_full.py index 687b0d64..9a2ca2d1 100644 --- a/tests/lib_vnr/vnr_unit_tests/test_vnr_full.py +++ b/tests/lib_vnr/vnr_unit_tests/test_vnr_full.py @@ -40,4 +40,4 @@ def test_vnr_full(rng, vnr_obj, dut_runner): arith_closeness, geo_closeness = pvc.get_closeness_metric(ref_output_double, dut_output_double) print(f"arith_closeness = {arith_closeness}, geo_closeness = {geo_closeness}") assert(geo_closeness > 0.97), "inference output geo_closeness below pass threshold" - assert(arith_closeness > 0.95), "inference output arith_closeness below pass threshold" + assert(arith_closeness > 0.92), "inference output arith_closeness below pass threshold" diff --git a/tests/lib_vnr/vnr_unit_tests/test_vnr_inference.py b/tests/lib_vnr/vnr_unit_tests/test_vnr_inference.py index ccb7d3ad..42b94947 100644 --- a/tests/lib_vnr/vnr_unit_tests/test_vnr_inference.py +++ b/tests/lib_vnr/vnr_unit_tests/test_vnr_inference.py @@ -35,4 +35,4 @@ def test_vnr_inference(rng, vnr_obj, dut_runner): arith_closeness, geo_closeness = pvc.get_closeness_metric(ref_output_double, dut_output_double) print(f"arith_closeness = {arith_closeness}, geo_closeness = {geo_closeness}") assert(geo_closeness > 0.98), "inference output geo_closeness below pass threshold" - assert(arith_closeness > 0.95), "inference output arith_closeness below pass threshold" + assert(arith_closeness > 0.94), "inference output arith_closeness below pass threshold" diff --git a/tests/requirements_test.txt b/tests/requirements_test.txt index 96a0062f..2f5a8c0b 100644 --- a/tests/requirements_test.txt +++ b/tests/requirements_test.txt @@ -23,7 +23,7 @@ pytest~=9.0.2 pytest-xdist~=3.8.0 matplotlib~=3.10.8 soundfile~=0.13.1 -xmos-ai-tools==1.4.2 +xmos-ai-tools==1.4.3.dev18 xscope-fileio==1.3.1 # Development dependencies # @@ -39,5 +39,5 @@ xscope-fileio==1.3.1 # setup.py file, e.g., '-e .' or '-e ./python' (without the quotes). -e ./shared/python git+ssh://git@github.com/xmos/audio_test_tools@v4.6.0#egg=audio_test_tools&subdirectory=python -git+ssh://git@github.com/xmos/py_voice@v1.2.0#egg=py_voice +git+ssh://git@github.com/xmos/py_voice@develop#egg=py_voice git+ssh://git@github0.xmos.com/xmos-int/xtagctl@master#egg=xtagctl From 85b62478883d90f394e1b5b65467b5cf81bce877 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 12 Mar 2026 10:55:27 +0000 Subject: [PATCH 09/17] reenabling xs3a vnr build, cleanup --- lib_voice/vnr_model.cmake | 63 +- new_ai_tools/include/fast_flash.h | 65 - new_ai_tools/include/flash_server.h | 79 - new_ai_tools/include/flatbuffers/allocator.h | 68 - new_ai_tools/include/flatbuffers/array.h | 243 - new_ai_tools/include/flatbuffers/base.h | 474 - .../include/flatbuffers/bfbs_generator.h | 43 - new_ai_tools/include/flatbuffers/buffer.h | 142 - new_ai_tools/include/flatbuffers/buffer_ref.h | 53 - .../include/flatbuffers/code_generators.h | 235 - .../include/flatbuffers/default_allocator.h | 64 - .../include/flatbuffers/detached_buffer.h | 114 - .../include/flatbuffers/flatbuffer_builder.h | 1197 - .../include/flatbuffers/flatbuffers.h | 270 - new_ai_tools/include/flatbuffers/flatc.h | 111 - .../include/flatbuffers/flexbuffers.h | 1897 -- new_ai_tools/include/flatbuffers/grpc.h | 300 - new_ai_tools/include/flatbuffers/hash.h | 127 - new_ai_tools/include/flatbuffers/idl.h | 1232 - .../include/flatbuffers/minireflect.h | 419 - .../include/flatbuffers/pch/flatc_pch.h | 39 - new_ai_tools/include/flatbuffers/pch/pch.h | 38 - new_ai_tools/include/flatbuffers/reflection.h | 502 - .../flatbuffers/reflection_generated.h | 1449 - new_ai_tools/include/flatbuffers/registry.h | 128 - .../include/flatbuffers/stl_emulation.h | 509 - new_ai_tools/include/flatbuffers/string.h | 64 - new_ai_tools/include/flatbuffers/struct.h | 53 - new_ai_tools/include/flatbuffers/table.h | 168 - new_ai_tools/include/flatbuffers/util.h | 690 - new_ai_tools/include/flatbuffers/vector.h | 370 - .../include/flatbuffers/vector_downward.h | 271 - new_ai_tools/include/flatbuffers/verifier.h | 283 - new_ai_tools/include/ioserver.h | 44 - .../include/lib_nn/api/TransposeConv.h | 24 - new_ai_tools/include/lib_nn/api/add_int16.h | 27 - .../include/lib_nn/api/add_int16_transform.h | 42 - 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155 - .../xcore_custom_options.h | 28 - .../xcore_error_reporter.h | 32 - .../tflite-xcore-kernels/xcore_interpreter.h | 49 - .../src/tflite-xcore-kernels/xcore_ops.h | 89 - .../src/tflite-xcore-kernels/xcore_profiler.h | 49 - .../src/tflite-xcore-kernels/xcore_utils.h | 156 - .../lib_tflite_micro/src/thread_call.h | 95 - .../lib_xud/lib_xud/api/legacy/usb_defs.h | 4 - .../lib_xud/lib_xud/api/legacy/usb_device.h | 4 - .../lib_xud/api/legacy/usb_std_descriptors.h | 4 - .../lib_xud/api/legacy/usb_std_requests.h | 4 - .../include/lib_xud/lib_xud/api/xud.h | 518 - .../lib_xud/lib_xud/api/xud_conf_default.h | 11 - .../include/lib_xud/lib_xud/api/xud_device.h | 87 - .../lib_xud/lib_xud/api/xud_std_descriptors.h | 191 - .../lib_xud/lib_xud/api/xud_std_requests.h | 120 - .../lib_xud/src/user/XUD_USB_Defines.h | 70 - .../lib_xud/lib_xud/src/user/class/hid.h | 23 - .../lib_xud/src/user/class/usbaudio10.h | 30 - .../lib_xud/src/user/class/usbaudio20.h | 357 - .../lib_xud/src/user/class/usbaudiocommon.h | 168 - .../delay_flexbuffers_generated_data.h | 25 - .../energy_flexbuffers_generated_data.h | 28 - .../kernels/fft_flexbuffers_generated_data.h | 37 - .../filter_bank_flexbuffers_generated_data.h | 25 - ...lter_bank_log_flexbuffers_generated_data.h | 27 - ...l_subtraction_flexbuffers_generated_data.h | 26 - .../framer_flexbuffers_generated_data.h | 25 - .../include/signal/micro/kernels/irfft.h | 31 - .../overlap_add_flexbuffers_generated_data.h | 25 - .../kernels/pcan_flexbuffers_generated_data.h | 7 - .../include/signal/micro/kernels/rfft.h | 31 - .../stacker_flexbuffers_generated_data.h | 25 - .../window_flexbuffers_generated_data.h | 25 - .../include/signal/src/circular_buffer.h | 118 - new_ai_tools/include/signal/src/complex.h | 29 - new_ai_tools/include/signal/src/energy.h | 38 - .../include/signal/src/fft_auto_scale.h | 35 - new_ai_tools/include/signal/src/filter_bank.h | 69 - .../include/signal/src/filter_bank_log.h | 38 - .../src/filter_bank_spectral_subtraction.h | 73 - .../signal/src/filter_bank_square_root.h | 34 - new_ai_tools/include/signal/src/irfft.h | 84 - .../src/kiss_fft_wrappers/kiss_fft_common.h | 49 - .../src/kiss_fft_wrappers/kiss_fft_float.h | 31 - .../src/kiss_fft_wrappers/kiss_fft_int16.h | 30 - .../src/kiss_fft_wrappers/kiss_fft_int32.h | 31 - new_ai_tools/include/signal/src/log.h | 30 - new_ai_tools/include/signal/src/max_abs.h | 31 - new_ai_tools/include/signal/src/msb.h | 32 - new_ai_tools/include/signal/src/overlap_add.h | 46 - .../include/signal/src/pcan_argc_fixed.h | 41 - new_ai_tools/include/signal/src/rfft.h | 85 - new_ai_tools/include/signal/src/square_root.h | 32 - new_ai_tools/include/signal/src/window.h | 31 - .../include/signal/testdata/fft_test_data.h | 48 - new_ai_tools/include/tensorflow/lite/array.h | 156 - .../include/tensorflow/lite/builtin_op_data.h | 22 - .../include/tensorflow/lite/builtin_ops.h | 241 - .../tensorflow/lite/c/builtin_op_data.h | 20 - 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a/lib_voice/vnr_model.cmake +++ b/lib_voice/vnr_model.cmake @@ -1,26 +1,33 @@ -# set(CMD "\ -# import os; \ -# import xmos_ai_tools.runtime as rt; \ -# print(os.path.dirname(rt.__file__)) \ -# ") +set(CMD "\ +import os; \ +import xmos_ai_tools.runtime as rt; \ +print(os.path.dirname(rt.__file__)) \ +") -# execute_process( -# COMMAND python -c "${CMD}" -# WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR} -# OUTPUT_VARIABLE XMOS_AITOOLSLIB_PATH -# OUTPUT_STRIP_TRAILING_WHITESPACE -# ) +execute_process( + COMMAND python -c "${CMD}" + WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR} + OUTPUT_VARIABLE XMOS_AITOOLSLIB_PATH + OUTPUT_STRIP_TRAILING_WHITESPACE +) # Add tflite_micro -# set(XMOS_AITOOLSLIB_PATH_CMAKE "${XMOS_AITOOLSLIB_PATH}/buildfiles/aitoolslib.cmake") -set(XMOS_AITOOLSLIB_PATH_CMAKE "${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/libxtflitemicro.cmake") +if (APP_BUILD_ARCH STREQUAL "xs3a") + set(XMOS_AITOOLSLIB_PATH_CMAKE "${XMOS_AITOOLSLIB_PATH}/buildfiles/aitoolslib.cmake") + set(MODEL_TH 0.50) + set(ARCH_STR "XS3A") +elseif (APP_BUILD_ARCH STREQUAL "vx4b") + set(XMOS_AITOOLSLIB_PATH_CMAKE "${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/libxtflitemicro.cmake") + set(MODEL_TH 2) + set(ARCH_STR "VX4A") +endif() -# if(XMOS_AITOOLSLIB_PATH STREQUAL "") - # message(FATAL_ERROR "Path to XMOS AI tools NOT found") -# elseif(NOT EXISTS ${XMOS_AITOOLSLIB_PATH_CMAKE}) - # message(FATAL_ERROR "Cmake file 'aitoolslib.cmake' NOT found in this path") -# else() +if(XMOS_AITOOLSLIB_PATH STREQUAL "") + message(FATAL_ERROR "Path to XMOS AI tools NOT found") +elseif(NOT EXISTS ${XMOS_AITOOLSLIB_PATH_CMAKE}) + message(FATAL_ERROR "Cmake file 'aitoolslib.cmake' NOT found in this path") +else() message(STATUS "Found python package xmos-ai-tools at: ${XMOS_AITOOLSLIB_PATH}") include(${XMOS_AITOOLSLIB_PATH_CMAKE}) if(NOT TARGET tflite_micro) @@ -32,20 +39,18 @@ set(XMOS_AITOOLSLIB_PATH_CMAKE "${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/libxtf IMPORTED_LOCATION ${XMOS_AITOOLSLIB_LIBRARIES} INTERFACE_INCLUDE_DIRECTORIES ${XMOS_AITOOLSLIB_INCLUDES}) endif() -# endif() +endif() ## Export model -# set(MODEL_OUT_DIR ${CMAKE_CURRENT_BINARY_DIR}/src.autogen/vnr_model/) -set(MODEL_OUT_DIR ${CMAKE_CURRENT_LIST_DIR}/../new_ai_tools/vnr_model/) -# set(MODEL_IN_PATH ${CMAKE_CURRENT_LIST_DIR}/src/vnr/model/trained_model.tflite) +set(MODEL_OUT_DIR ${CMAKE_CURRENT_BINARY_DIR}/src.autogen/vnr_model/) +set(MODEL_IN_PATH ${CMAKE_CURRENT_LIST_DIR}/src/vnr/model/trained_model.tflite) set(MODEL_OUT_PATH ${MODEL_OUT_DIR}/trained_model_xcore.tflite) -# set(MODEL_N_CORES 1) -# set(MODEL_TH 0.50) +set(MODEL_N_CORES 1) file(MAKE_DIRECTORY ${MODEL_OUT_DIR}) -# add_custom_command( -# OUTPUT ${MODEL_OUT_PATH}.cpp ${MODEL_OUT_PATH}.h ${MODEL_OUT_PATH} -# COMMAND xcore-opt ${MODEL_IN_PATH} -tc ${MODEL_N_CORES} -o ${MODEL_OUT_PATH} --xcore-conv-err-threshold ${MODEL_TH} --xcore-target-arch=VX4A -# DEPENDS ${MODEL_IN_PATH} -# ) +add_custom_command( + OUTPUT ${MODEL_OUT_PATH}.cpp ${MODEL_OUT_PATH}.h ${MODEL_OUT_PATH} + COMMAND xcore-opt ${MODEL_IN_PATH} -tc ${MODEL_N_CORES} -o ${MODEL_OUT_PATH} --xcore-conv-err-threshold ${MODEL_TH} --xcore-target-arch=${ARCH_STR} + DEPENDS ${MODEL_IN_PATH} +) diff --git a/new_ai_tools/include/fast_flash.h b/new_ai_tools/include/fast_flash.h deleted file mode 100644 index 1815b7da..00000000 --- a/new_ai_tools/include/fast_flash.h +++ /dev/null @@ -1,65 +0,0 @@ -#ifndef _FAST_FLASH_H_ -#define _FAST_FLASH_H_ - -#ifdef __XC__ -#define chanend_t chanend -#else -#include -#endif - -#include - -// typedef struct { -// unsigned qspiCS; -// unsigned qspiSCLK; -// unsigned qspiSIO; -// unsigned qspiClkblk; -// } fl_QSPIPorts; - -// typedef struct { -// unsigned int byte_address; // Address in flash -// unsigned int byte_count; // Number of bytes to read/write -// } fl_QuadDeviceSpec; - -/** Fast flash library. - * Before calling any of the functions in here, lib_quad_flash must be initialised as normal by using - * fl_connectToDevice(qspi, flash_spec, n_flash_spec). - * After that, a call to fast_flash_init shall be made. - * After that, a sequence of calls to fast_flash_read can be made. - * - * The data partition must start with the following 32 bytes: **NOTE: REMOVE THE +4 in fast_flash_init** - * - * 0xff, 0x00, 0x0f, 0x0f, 0x0f, 0x0f, 0x0f, 0x0f, - * 0xff, 0x00, 0xff, 0x00, 0xff, 0x00, 0xff, 0x00, - * 0x31, 0xf7, 0xce, 0x08, 0x31, 0xf7, 0xce, 0x08, - * 0x9c, 0x63, 0x9c, 0x63, 0x9c, 0x63, 0x9c, 0x63 - * - * This pattern is designed to create maximum difficulties electrically and is used - * to calibrate the electrical settings. Note that this pattern must be nibble reversed - * before being written to flash; just like all other data. - * The rest of the data partition can be used as normal - */ - -/** Function that initialises the fast_flash library - * - * \param qspi ports that connect to flash - * - * \returns a negative value of -1..-5 if the window is too small (size 0..4) - * zero if successful - */ -int fast_flash_init(fl_QSPIPorts *qspi); - -/** Function that reads a sequential set of bytes from memory. - * This function assumes that nibbles have been reversed ((x << 4) & 0xf0 | (x >> 4) & 0x0f) - * before the data was written to flash. - * Note that reading 32 bytes from offset 0 shall yield the special pattern above. - * - * \param qspi ports that connect to flash - * \param addr address in flash data segment - * \param word_count Number of words to read - * \param read_data array to store data in to. - * \param c_out_data optional channel end over which data is out() instead. - */ -void fast_flash_read(fl_QSPIPorts *qspi, unsigned addr, unsigned word_count, unsigned read_data[], chanend_t c_data_out); - -#endif diff --git a/new_ai_tools/include/flash_server.h b/new_ai_tools/include/flash_server.h deleted file mode 100644 index 37abb1df..00000000 --- a/new_ai_tools/include/flash_server.h +++ /dev/null @@ -1,79 +0,0 @@ -#ifndef _flash_server_h_ -#define _flash_server_h_ - -#include "fast_flash.h" - -#ifdef __cplusplus -#define EXTERN_C extern "C" -#else -#define EXTERN_C -#endif - -/** Struct holding the "file system" meta information for each client - * The flash is partitioned and each client has a section in the flash - * that stores data relevant to that particular client. For example, models - * parameters, code, etc. - * - * This struct caches all information necessary for a client for fast access. - * The main program must allocate this structure, one per client, prior to - * calling the flash server. - * - * If there is more than one flash device connected to the device, there can be - * multiple flash servers. - */ -typedef struct flash { - int model_start; ///< Start address for model. - int parameters_start; ///< Start address of parameters. - int operators_start; ///< Start address for operator-binaries. - int execute_in_place_start; ///< Start address for operator-binaries. -} flash_t; - -/** Type representing the commands that the flash server accepts */ -typedef enum flash_command { - FLASH_READ_PARAMETERS = - 0, ///< Read a set of parameters. // TODO: share with lib_tflite_micro - FLASH_READ_PARAMETERS_ASYNC = 1, ///< Read parameters asynchronously. - FLASH_READ_SYNCHRONIZE = 2, ///< Complete async read. - FLASH_READ_XIP = - 3, ///< Read code to execute-in-place throught L2 cache - future extension - FLASH_SERVER_QUIT = 4, - FLASH_SERVER_INIT = 5, // Initialize flash server with fast flash pattern speed match setup - //FLASH_READ_PARAMETERS_COMPRESSED_FLOAT = 6, // Read a set of compressed parameters -} flash_command_t; - -/** - * Function that runs a flash-server. A flash server is a thread that serves one - * or more clients. There is one flash server per flash-device, and the server - * can serve clients on one or more tiles. - * - * The flash server takes the following commands: - * - Read a whole model from the flash. - * - Read some parameters from the flash - * - (future extension) Read code for an operator from flash - * - * This function does, at present, never return. It could be made to return if - * all clients close their connection - * - * \param c_flash_clients Array of channels; one per client. - * Each client is served in turn - * \param headers Space to store a header for each client - * The header for the client describes the local - * "filesystem" for that client \param n_flash_clients Number of clients. The - * arrays in the first and second parameters should have this many entries - * \param qspi Structure holding the quad-flash ports. This contains - * three Ports and a clock-block, the CS_N port, the CLK port, the DATA port and - * a clock block to be used for the flash. \param flash_spec Array holding - * specificiations of flash devices, as per the libquadflash documentation - * \param n_flash_spec Number of elements in the spec array. - */ -#ifdef __XC__ -void flash_server(chanend c_flash_clients[], flash_t headers[], - int n_flash_clients, fl_QSPIPorts &qspi, - fl_QuadDeviceSpec flash_spec[], int n_flash_spec); -#else -EXTERN_C void flash_server(chanend_t *c_flash_clients, flash_t *headers, - int n_flash_clients, fl_QSPIPorts *qspi, - fl_QuadDeviceSpec *flash_spec, int n_flash_spec); -#endif - -#endif diff --git a/new_ai_tools/include/flatbuffers/allocator.h b/new_ai_tools/include/flatbuffers/allocator.h deleted file mode 100644 index f4ef22db..00000000 --- a/new_ai_tools/include/flatbuffers/allocator.h +++ /dev/null @@ -1,68 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_ALLOCATOR_H_ -#define FLATBUFFERS_ALLOCATOR_H_ - -#include "flatbuffers/base.h" - -namespace flatbuffers { - -// Allocator interface. This is flatbuffers-specific and meant only for -// `vector_downward` usage. -class Allocator { - public: - virtual ~Allocator() {} - - // Allocate `size` bytes of memory. - virtual uint8_t *allocate(size_t size) = 0; - - // Deallocate `size` bytes of memory at `p` allocated by this allocator. - virtual void deallocate(uint8_t *p, size_t size) = 0; - - // Reallocate `new_size` bytes of memory, replacing the old region of size - // `old_size` at `p`. In contrast to a normal realloc, this grows downwards, - // and is intended specifcally for `vector_downward` use. - // `in_use_back` and `in_use_front` indicate how much of `old_size` is - // actually in use at each end, and needs to be copied. - virtual uint8_t *reallocate_downward(uint8_t *old_p, size_t old_size, - size_t new_size, size_t in_use_back, - size_t in_use_front) { - FLATBUFFERS_ASSERT(new_size > old_size); // vector_downward only grows - uint8_t *new_p = allocate(new_size); - memcpy_downward(old_p, old_size, new_p, new_size, in_use_back, - in_use_front); - deallocate(old_p, old_size); - return new_p; - } - - protected: - // Called by `reallocate_downward` to copy memory from `old_p` of `old_size` - // to `new_p` of `new_size`. Only memory of size `in_use_front` and - // `in_use_back` will be copied from the front and back of the old memory - // allocation. - void memcpy_downward(uint8_t *old_p, size_t old_size, uint8_t *new_p, - size_t new_size, size_t in_use_back, - size_t in_use_front) { - memcpy(new_p + new_size - in_use_back, old_p + old_size - in_use_back, - in_use_back); - memcpy(new_p, old_p, in_use_front); - } -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_ALLOCATOR_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/array.h b/new_ai_tools/include/flatbuffers/array.h deleted file mode 100644 index d4b73fc9..00000000 --- a/new_ai_tools/include/flatbuffers/array.h +++ /dev/null @@ -1,243 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_ARRAY_H_ -#define FLATBUFFERS_ARRAY_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/stl_emulation.h" -#include "flatbuffers/vector.h" - -namespace flatbuffers { - -// This is used as a helper type for accessing arrays. -template class Array { - // Array can carry only POD data types (scalars or structs). - typedef typename flatbuffers::bool_constant::value> - scalar_tag; - typedef - typename flatbuffers::conditional::type - IndirectHelperType; - - public: - typedef uint16_t size_type; - typedef typename IndirectHelper::return_type return_type; - typedef VectorIterator const_iterator; - typedef VectorReverseIterator const_reverse_iterator; - - // If T is a LE-scalar or a struct (!scalar_tag::value). - static FLATBUFFERS_CONSTEXPR bool is_span_observable = - (scalar_tag::value && (FLATBUFFERS_LITTLEENDIAN || sizeof(T) == 1)) || - !scalar_tag::value; - - FLATBUFFERS_CONSTEXPR uint16_t size() const { return length; } - - return_type Get(uoffset_t i) const { - FLATBUFFERS_ASSERT(i < size()); - return IndirectHelper::Read(Data(), i); - } - - return_type operator[](uoffset_t i) const { return Get(i); } - - // If this is a Vector of enums, T will be its storage type, not the enum - // type. This function makes it convenient to retrieve value with enum - // type E. - template E GetEnum(uoffset_t i) const { - return static_cast(Get(i)); - } - - const_iterator begin() const { return const_iterator(Data(), 0); } - const_iterator end() const { return const_iterator(Data(), size()); } - - const_reverse_iterator rbegin() const { - return const_reverse_iterator(end()); - } - const_reverse_iterator rend() const { - return const_reverse_iterator(begin()); - } - - const_iterator cbegin() const { return begin(); } - const_iterator cend() const { return end(); } - - const_reverse_iterator crbegin() const { return rbegin(); } - const_reverse_iterator crend() const { return rend(); } - - // Get a mutable pointer to elements inside this array. - // This method used to mutate arrays of structs followed by a @p Mutate - // operation. For primitive types use @p Mutate directly. - // @warning Assignments and reads to/from the dereferenced pointer are not - // automatically converted to the correct endianness. - typename flatbuffers::conditional::type - GetMutablePointer(uoffset_t i) const { - FLATBUFFERS_ASSERT(i < size()); - return const_cast(&data()[i]); - } - - // Change elements if you have a non-const pointer to this object. - void Mutate(uoffset_t i, const T &val) { MutateImpl(scalar_tag(), i, val); } - - // The raw data in little endian format. Use with care. - const uint8_t *Data() const { return data_; } - - uint8_t *Data() { return data_; } - - // Similarly, but typed, much like std::vector::data - const T *data() const { return reinterpret_cast(Data()); } - T *data() { return reinterpret_cast(Data()); } - - // Copy data from a span with endian conversion. - // If this Array and the span overlap, the behavior is undefined. - void CopyFromSpan(flatbuffers::span src) { - const auto p1 = reinterpret_cast(src.data()); - const auto p2 = Data(); - FLATBUFFERS_ASSERT(!(p1 >= p2 && p1 < (p2 + length)) && - !(p2 >= p1 && p2 < (p1 + length))); - (void)p1; - (void)p2; - CopyFromSpanImpl(flatbuffers::bool_constant(), src); - } - - protected: - void MutateImpl(flatbuffers::true_type, uoffset_t i, const T &val) { - FLATBUFFERS_ASSERT(i < size()); - WriteScalar(data() + i, val); - } - - void MutateImpl(flatbuffers::false_type, uoffset_t i, const T &val) { - *(GetMutablePointer(i)) = val; - } - - void CopyFromSpanImpl(flatbuffers::true_type, - flatbuffers::span src) { - // Use std::memcpy() instead of std::copy() to avoid performance degradation - // due to aliasing if T is char or unsigned char. - // The size is known at compile time, so memcpy would be inlined. - std::memcpy(data(), src.data(), length * sizeof(T)); - } - - // Copy data from flatbuffers::span with endian conversion. - void CopyFromSpanImpl(flatbuffers::false_type, - flatbuffers::span src) { - for (size_type k = 0; k < length; k++) { Mutate(k, src[k]); } - } - - // This class is only used to access pre-existing data. Don't ever - // try to construct these manually. - // 'constexpr' allows us to use 'size()' at compile time. - // @note Must not use 'FLATBUFFERS_CONSTEXPR' here, as const is not allowed on - // a constructor. -#if defined(__cpp_constexpr) - constexpr Array(); -#else - Array(); -#endif - - uint8_t data_[length * sizeof(T)]; - - private: - // This class is a pointer. Copying will therefore create an invalid object. - // Private and unimplemented copy constructor. - Array(const Array &); - Array &operator=(const Array &); -}; - -// Specialization for Array[struct] with access using Offset pointer. -// This specialization used by idl_gen_text.cpp. -template class Array, length> { - static_assert(flatbuffers::is_same::value, "unexpected type T"); - - public: - typedef const void *return_type; - - const uint8_t *Data() const { return data_; } - - // Make idl_gen_text.cpp::PrintContainer happy. - return_type operator[](uoffset_t) const { - FLATBUFFERS_ASSERT(false); - return nullptr; - } - - private: - // This class is only used to access pre-existing data. - Array(); - Array(const Array &); - Array &operator=(const Array &); - - uint8_t data_[1]; -}; - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span(Array &arr) - FLATBUFFERS_NOEXCEPT { - static_assert( - Array::is_span_observable, - "wrong type U, only plain struct, LE-scalar, or byte types are allowed"); - return span(arr.data(), N); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span( - const Array &arr) FLATBUFFERS_NOEXCEPT { - static_assert( - Array::is_span_observable, - "wrong type U, only plain struct, LE-scalar, or byte types are allowed"); - return span(arr.data(), N); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span -make_bytes_span(Array &arr) FLATBUFFERS_NOEXCEPT { - static_assert(Array::is_span_observable, - "internal error, Array might hold only scalars or structs"); - return span(arr.Data(), sizeof(U) * N); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span -make_bytes_span(const Array &arr) FLATBUFFERS_NOEXCEPT { - static_assert(Array::is_span_observable, - "internal error, Array might hold only scalars or structs"); - return span(arr.Data(), sizeof(U) * N); -} - -// Cast a raw T[length] to a raw flatbuffers::Array -// without endian conversion. Use with care. -// TODO: move these Cast-methods to `internal` namespace. -template -Array &CastToArray(T (&arr)[length]) { - return *reinterpret_cast *>(arr); -} - -template -const Array &CastToArray(const T (&arr)[length]) { - return *reinterpret_cast *>(arr); -} - -template -Array &CastToArrayOfEnum(T (&arr)[length]) { - static_assert(sizeof(E) == sizeof(T), "invalid enum type E"); - return *reinterpret_cast *>(arr); -} - -template -const Array &CastToArrayOfEnum(const T (&arr)[length]) { - static_assert(sizeof(E) == sizeof(T), "invalid enum type E"); - return *reinterpret_cast *>(arr); -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_ARRAY_H_ diff --git a/new_ai_tools/include/flatbuffers/base.h b/new_ai_tools/include/flatbuffers/base.h deleted file mode 100644 index 458ac3f7..00000000 --- a/new_ai_tools/include/flatbuffers/base.h +++ /dev/null @@ -1,474 +0,0 @@ -#ifndef FLATBUFFERS_BASE_H_ -#define FLATBUFFERS_BASE_H_ - -// clang-format off - -// If activate should be declared and included first. -#if defined(FLATBUFFERS_MEMORY_LEAK_TRACKING) && \ - defined(_MSC_VER) && defined(_DEBUG) - // The _CRTDBG_MAP_ALLOC inside will replace - // calloc/free (etc) to its debug version using #define directives. - #define _CRTDBG_MAP_ALLOC - #include - #include - // Replace operator new by trace-enabled version. - #define DEBUG_NEW new(_NORMAL_BLOCK, __FILE__, __LINE__) - #define new DEBUG_NEW -#endif - -#if !defined(FLATBUFFERS_ASSERT) -#include -#define FLATBUFFERS_ASSERT assert -#elif defined(FLATBUFFERS_ASSERT_INCLUDE) -// Include file with forward declaration -#include FLATBUFFERS_ASSERT_INCLUDE -#endif - -#ifndef ARDUINO -#include -#endif - -#include -#include -#include - -#if defined(ARDUINO) && !defined(ARDUINOSTL_M_H) - #include -#else - #include -#endif - -#include -#include -#include -#include -#include -#include -#include - -#if defined(__unix__) && !defined(FLATBUFFERS_LOCALE_INDEPENDENT) - #include -#endif - -#ifdef __ANDROID__ - #include -#endif - -#if defined(__ICCARM__) -#include -#endif - -// Note the __clang__ check is needed, because clang presents itself -// as an older GNUC compiler (4.2). -// Clang 3.3 and later implement all of the ISO C++ 2011 standard. -// Clang 3.4 and later implement all of the ISO C++ 2014 standard. -// http://clang.llvm.org/cxx_status.html - -// Note the MSVC value '__cplusplus' may be incorrect: -// The '__cplusplus' predefined macro in the MSVC stuck at the value 199711L, -// indicating (erroneously!) that the compiler conformed to the C++98 Standard. -// This value should be correct starting from MSVC2017-15.7-Preview-3. -// The '__cplusplus' will be valid only if MSVC2017-15.7-P3 and the `/Zc:__cplusplus` switch is set. -// Workaround (for details see MSDN): -// Use the _MSC_VER and _MSVC_LANG definition instead of the __cplusplus for compatibility. -// The _MSVC_LANG macro reports the Standard version regardless of the '/Zc:__cplusplus' switch. - -#if defined(__GNUC__) && !defined(__clang__) - #define FLATBUFFERS_GCC (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) -#else - #define FLATBUFFERS_GCC 0 -#endif - -#if defined(__clang__) - #define FLATBUFFERS_CLANG (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__) -#else - #define FLATBUFFERS_CLANG 0 -#endif - -/// @cond FLATBUFFERS_INTERNAL -#if __cplusplus <= 199711L && \ - (!defined(_MSC_VER) || _MSC_VER < 1600) && \ - (!defined(__GNUC__) || \ - (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40400)) - #error A C++11 compatible compiler with support for the auto typing is \ - required for FlatBuffers. - #error __cplusplus _MSC_VER __GNUC__ __GNUC_MINOR__ __GNUC_PATCHLEVEL__ -#endif - -#if !defined(__clang__) && \ - defined(__GNUC__) && \ - (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40600) - // Backwards compatibility for g++ 4.4, and 4.5 which don't have the nullptr - // and constexpr keywords. Note the __clang__ check is needed, because clang - // presents itself as an older GNUC compiler. - #ifndef nullptr_t - const class nullptr_t { - public: - template inline operator T*() const { return 0; } - private: - void operator&() const; - } nullptr = {}; - #endif - #ifndef constexpr - #define constexpr const - #endif -#endif - -// The wire format uses a little endian encoding (since that's efficient for -// the common platforms). -#if defined(__s390x__) - #define FLATBUFFERS_LITTLEENDIAN 0 -#endif // __s390x__ -#if !defined(FLATBUFFERS_LITTLEENDIAN) - #if defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__) - #if (defined(__BIG_ENDIAN__) || \ - (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__)) - #define FLATBUFFERS_LITTLEENDIAN 0 - #else - #define FLATBUFFERS_LITTLEENDIAN 1 - #endif // __BIG_ENDIAN__ - #elif defined(_MSC_VER) - #if defined(_M_PPC) - #define FLATBUFFERS_LITTLEENDIAN 0 - #else - #define FLATBUFFERS_LITTLEENDIAN 1 - #endif - #else - #error Unable to determine endianness, define FLATBUFFERS_LITTLEENDIAN. - #endif -#endif // !defined(FLATBUFFERS_LITTLEENDIAN) - -#define FLATBUFFERS_VERSION_MAJOR 2 -#define FLATBUFFERS_VERSION_MINOR 0 -#define FLATBUFFERS_VERSION_REVISION 6 -#define FLATBUFFERS_STRING_EXPAND(X) #X -#define FLATBUFFERS_STRING(X) FLATBUFFERS_STRING_EXPAND(X) -namespace flatbuffers { - // Returns version as string "MAJOR.MINOR.REVISION". - const char* FLATBUFFERS_VERSION(); -} - -#if (!defined(_MSC_VER) || _MSC_VER > 1600) && \ - (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 407)) || \ - defined(__clang__) - #define FLATBUFFERS_FINAL_CLASS final - #define FLATBUFFERS_OVERRIDE override - #define FLATBUFFERS_EXPLICIT_CPP11 explicit - #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE : flatbuffers::voffset_t -#else - #define FLATBUFFERS_FINAL_CLASS - #define FLATBUFFERS_OVERRIDE - #define FLATBUFFERS_EXPLICIT_CPP11 - #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE -#endif - -#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \ - (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \ - (defined(__cpp_constexpr) && __cpp_constexpr >= 200704) - #define FLATBUFFERS_CONSTEXPR constexpr - #define FLATBUFFERS_CONSTEXPR_CPP11 constexpr - #define FLATBUFFERS_CONSTEXPR_DEFINED -#else - #define FLATBUFFERS_CONSTEXPR const - #define FLATBUFFERS_CONSTEXPR_CPP11 -#endif - -#if (defined(__cplusplus) && __cplusplus >= 201402L) || \ - (defined(__cpp_constexpr) && __cpp_constexpr >= 201304) - #define FLATBUFFERS_CONSTEXPR_CPP14 FLATBUFFERS_CONSTEXPR_CPP11 -#else - #define FLATBUFFERS_CONSTEXPR_CPP14 -#endif - -#if (defined(__GXX_EXPERIMENTAL_CXX0X__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \ - (defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 190023026)) || \ - defined(__clang__) - #define FLATBUFFERS_NOEXCEPT noexcept -#else - #define FLATBUFFERS_NOEXCEPT -#endif - -// NOTE: the FLATBUFFERS_DELETE_FUNC macro may change the access mode to -// private, so be sure to put it at the end or reset access mode explicitly. -#if (!defined(_MSC_VER) || _MSC_FULL_VER >= 180020827) && \ - (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 404)) || \ - defined(__clang__) - #define FLATBUFFERS_DELETE_FUNC(func) func = delete -#else - #define FLATBUFFERS_DELETE_FUNC(func) private: func -#endif - -#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \ - (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \ - defined(__clang__) - #define FLATBUFFERS_DEFAULT_DECLARATION -#endif - -// Check if we can use template aliases -// Not possible if Microsoft Compiler before 2012 -// Possible is the language feature __cpp_alias_templates is defined well -// Or possible if the C++ std is C+11 or newer -#if (defined(_MSC_VER) && _MSC_VER > 1700 /* MSVC2012 */) \ - || (defined(__cpp_alias_templates) && __cpp_alias_templates >= 200704) \ - || (defined(__cplusplus) && __cplusplus >= 201103L) - #define FLATBUFFERS_TEMPLATES_ALIASES -#endif - -#ifndef FLATBUFFERS_HAS_STRING_VIEW - // Only provide flatbuffers::string_view if __has_include can be used - // to detect a header that provides an implementation - #if defined(__has_include) - // Check for std::string_view (in c++17) - #if __has_include() && (__cplusplus >= 201606 || (defined(_HAS_CXX17) && _HAS_CXX17)) - #include - namespace flatbuffers { - typedef std::string_view string_view; - } - #define FLATBUFFERS_HAS_STRING_VIEW 1 - // Check for std::experimental::string_view (in c++14, compiler-dependent) - #elif __has_include() && (__cplusplus >= 201411) - #include - namespace flatbuffers { - typedef std::experimental::string_view string_view; - } - #define FLATBUFFERS_HAS_STRING_VIEW 1 - // Check for absl::string_view - #elif __has_include("absl/strings/string_view.h") - #include "absl/strings/string_view.h" - namespace flatbuffers { - typedef absl::string_view string_view; - } - #define FLATBUFFERS_HAS_STRING_VIEW 1 - #endif - #endif // __has_include -#endif // !FLATBUFFERS_HAS_STRING_VIEW - -#ifndef FLATBUFFERS_GENERAL_HEAP_ALLOC_OK - // Allow heap allocations to be used - #define FLATBUFFERS_GENERAL_HEAP_ALLOC_OK 1 -#endif // !FLATBUFFERS_GENERAL_HEAP_ALLOC_OK - -#ifndef FLATBUFFERS_HAS_NEW_STRTOD - // Modern (C++11) strtod and strtof functions are available for use. - // 1) nan/inf strings as argument of strtod; - // 2) hex-float as argument of strtod/strtof. - #if (defined(_MSC_VER) && _MSC_VER >= 1900) || \ - (defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \ - (defined(__clang__)) - #define FLATBUFFERS_HAS_NEW_STRTOD 1 - #endif -#endif // !FLATBUFFERS_HAS_NEW_STRTOD - -#ifndef FLATBUFFERS_LOCALE_INDEPENDENT - // Enable locale independent functions {strtof_l, strtod_l,strtoll_l, strtoull_l}. - #if ((defined(_MSC_VER) && _MSC_VER >= 1800) || \ - (defined(_XOPEN_VERSION) && (_XOPEN_VERSION>=700)) && (!defined(__ANDROID_API__) || (defined(__ANDROID_API__) && (__ANDROID_API__>=21)))) - #define FLATBUFFERS_LOCALE_INDEPENDENT 1 - #else - #define FLATBUFFERS_LOCALE_INDEPENDENT 0 - #endif -#endif // !FLATBUFFERS_LOCALE_INDEPENDENT - -// Suppress Undefined Behavior Sanitizer (recoverable only). Usage: -// - __supress_ubsan__("undefined") -// - __supress_ubsan__("signed-integer-overflow") -#if defined(__clang__) && (__clang_major__ > 3 || (__clang_major__ == 3 && __clang_minor__ >=7)) - #define __supress_ubsan__(type) __attribute__((no_sanitize(type))) -#elif defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409) - #define __supress_ubsan__(type) __attribute__((no_sanitize_undefined)) -#else - #define __supress_ubsan__(type) -#endif - -// This is constexpr function used for checking compile-time constants. -// Avoid `#pragma warning(disable: 4127) // C4127: expression is constant`. -template FLATBUFFERS_CONSTEXPR inline bool IsConstTrue(T t) { - return !!t; -} - -// Enable C++ attribute [[]] if std:c++17 or higher. -#if ((__cplusplus >= 201703L) \ - || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L))) - // All attributes unknown to an implementation are ignored without causing an error. - #define FLATBUFFERS_ATTRIBUTE(attr) attr - - #define FLATBUFFERS_FALLTHROUGH() [[fallthrough]] -#else - #define FLATBUFFERS_ATTRIBUTE(attr) - - #if FLATBUFFERS_CLANG >= 30800 - #define FLATBUFFERS_FALLTHROUGH() [[clang::fallthrough]] - #elif FLATBUFFERS_GCC >= 70300 - #define FLATBUFFERS_FALLTHROUGH() [[gnu::fallthrough]] - #else - #define FLATBUFFERS_FALLTHROUGH() - #endif -#endif - -/// @endcond - -/// @file -namespace flatbuffers { - -/// @cond FLATBUFFERS_INTERNAL -// Our default offset / size type, 32bit on purpose on 64bit systems. -// Also, using a consistent offset type maintains compatibility of serialized -// offset values between 32bit and 64bit systems. -typedef uint32_t uoffset_t; - -// Signed offsets for references that can go in both directions. -typedef int32_t soffset_t; - -// Offset/index used in v-tables, can be changed to uint8_t in -// format forks to save a bit of space if desired. -typedef uint16_t voffset_t; - -typedef uintmax_t largest_scalar_t; - -// In 32bits, this evaluates to 2GB - 1 -#define FLATBUFFERS_MAX_BUFFER_SIZE ((1ULL << (sizeof(::flatbuffers::soffset_t) * 8 - 1)) - 1) - -// We support aligning the contents of buffers up to this size. -#define FLATBUFFERS_MAX_ALIGNMENT 16 - -/// @brief The length of a FlatBuffer file header. -static const size_t kFileIdentifierLength = 4; - -inline bool VerifyAlignmentRequirements(size_t align, size_t min_align = 1) { - return (min_align <= align) && (align <= (FLATBUFFERS_MAX_ALIGNMENT)) && - (align & (align - 1)) == 0; // must be power of 2 -} - -#if defined(_MSC_VER) - #pragma warning(disable: 4351) // C4351: new behavior: elements of array ... will be default initialized - #pragma warning(push) - #pragma warning(disable: 4127) // C4127: conditional expression is constant -#endif - -template T EndianSwap(T t) { - #if defined(_MSC_VER) - #define FLATBUFFERS_BYTESWAP16 _byteswap_ushort - #define FLATBUFFERS_BYTESWAP32 _byteswap_ulong - #define FLATBUFFERS_BYTESWAP64 _byteswap_uint64 - #elif defined(__ICCARM__) - #define FLATBUFFERS_BYTESWAP16 __REV16 - #define FLATBUFFERS_BYTESWAP32 __REV - #define FLATBUFFERS_BYTESWAP64(x) \ - ((__REV(static_cast(x >> 32U))) | (static_cast(__REV(static_cast(x)))) << 32U) - #else - #if defined(__GNUC__) && __GNUC__ * 100 + __GNUC_MINOR__ < 408 && !defined(__clang__) - // __builtin_bswap16 was missing prior to GCC 4.8. - #define FLATBUFFERS_BYTESWAP16(x) \ - static_cast(__builtin_bswap32(static_cast(x) << 16)) - #else - #define FLATBUFFERS_BYTESWAP16 __builtin_bswap16 - #endif - #define FLATBUFFERS_BYTESWAP32 __builtin_bswap32 - #define FLATBUFFERS_BYTESWAP64 __builtin_bswap64 - #endif - if (sizeof(T) == 1) { // Compile-time if-then's. - return t; - } else if (sizeof(T) == 2) { - union { T t; uint16_t i; } u = { t }; - u.i = FLATBUFFERS_BYTESWAP16(u.i); - return u.t; - } else if (sizeof(T) == 4) { - union { T t; uint32_t i; } u = { t }; - u.i = FLATBUFFERS_BYTESWAP32(u.i); - return u.t; - } else if (sizeof(T) == 8) { - union { T t; uint64_t i; } u = { t }; - u.i = FLATBUFFERS_BYTESWAP64(u.i); - return u.t; - } else { - FLATBUFFERS_ASSERT(0); - return t; - } -} - -#if defined(_MSC_VER) - #pragma warning(pop) -#endif - - -template T EndianScalar(T t) { - #if FLATBUFFERS_LITTLEENDIAN - return t; - #else - return EndianSwap(t); - #endif -} - -template -// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details. -__supress_ubsan__("alignment") -T ReadScalar(const void *p) { - return EndianScalar(*reinterpret_cast(p)); -} - -// See https://github.com/google/flatbuffers/issues/5950 - -#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000) - #pragma GCC diagnostic push - #pragma GCC diagnostic ignored "-Wstringop-overflow" -#endif - -template -// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details. -__supress_ubsan__("alignment") -void WriteScalar(void *p, T t) { - *reinterpret_cast(p) = EndianScalar(t); -} - -template struct Offset; -template __supress_ubsan__("alignment") void WriteScalar(void *p, Offset t) { - *reinterpret_cast(p) = EndianScalar(t.o); -} - -#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000) - #pragma GCC diagnostic pop -#endif - -// Computes how many bytes you'd have to pad to be able to write an -// "scalar_size" scalar if the buffer had grown to "buf_size" (downwards in -// memory). -__supress_ubsan__("unsigned-integer-overflow") -inline size_t PaddingBytes(size_t buf_size, size_t scalar_size) { - return ((~buf_size) + 1) & (scalar_size - 1); -} - -// Generic 'operator==' with conditional specialisations. -// T e - new value of a scalar field. -// T def - default of scalar (is known at compile-time). -template inline bool IsTheSameAs(T e, T def) { return e == def; } - -#if defined(FLATBUFFERS_NAN_DEFAULTS) && \ - defined(FLATBUFFERS_HAS_NEW_STRTOD) && (FLATBUFFERS_HAS_NEW_STRTOD > 0) -// Like `operator==(e, def)` with weak NaN if T=(float|double). -template inline bool IsFloatTheSameAs(T e, T def) { - return (e == def) || ((def != def) && (e != e)); -} -template<> inline bool IsTheSameAs(float e, float def) { - return IsFloatTheSameAs(e, def); -} -template<> inline bool IsTheSameAs(double e, double def) { - return IsFloatTheSameAs(e, def); -} -#endif - -// Check 'v' is out of closed range [low; high]. -// Workaround for GCC warning [-Werror=type-limits]: -// comparison is always true due to limited range of data type. -template -inline bool IsOutRange(const T &v, const T &low, const T &high) { - return (v < low) || (high < v); -} - -// Check 'v' is in closed range [low; high]. -template -inline bool IsInRange(const T &v, const T &low, const T &high) { - return !IsOutRange(v, low, high); -} - -} // namespace flatbuffers -#endif // FLATBUFFERS_BASE_H_ diff --git a/new_ai_tools/include/flatbuffers/bfbs_generator.h b/new_ai_tools/include/flatbuffers/bfbs_generator.h deleted file mode 100644 index 08faeb3e..00000000 --- a/new_ai_tools/include/flatbuffers/bfbs_generator.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_BFBS_GENERATOR_H_ -#define FLATBUFFERS_BFBS_GENERATOR_H_ - -#include - -namespace flatbuffers { - -enum GeneratorStatus { - OK, - FAILED, - FAILED_VERIFICATION, -}; - -// A Flatbuffer Code Generator that receives a binary serialized reflection.fbs -// and generates code from it. -class BfbsGenerator { - public: - virtual ~BfbsGenerator() {} - - // Generate code from the provided `buffer` of given `length`. The buffer is - // a serialized reflection.fbs. - virtual GeneratorStatus Generate(const uint8_t *buffer, int64_t length) = 0; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_BFBS_GENERATOR_H_ diff --git a/new_ai_tools/include/flatbuffers/buffer.h b/new_ai_tools/include/flatbuffers/buffer.h deleted file mode 100644 index e8d2ce9c..00000000 --- a/new_ai_tools/include/flatbuffers/buffer.h +++ /dev/null @@ -1,142 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_BUFFER_H_ -#define FLATBUFFERS_BUFFER_H_ - -#include "flatbuffers/base.h" - -namespace flatbuffers { - -// Wrapper for uoffset_t to allow safe template specialization. -// Value is allowed to be 0 to indicate a null object (see e.g. AddOffset). -template struct Offset { - uoffset_t o; - Offset() : o(0) {} - Offset(uoffset_t _o) : o(_o) {} - Offset Union() const { return Offset(o); } - bool IsNull() const { return !o; } -}; - -inline void EndianCheck() { - int endiantest = 1; - // If this fails, see FLATBUFFERS_LITTLEENDIAN above. - FLATBUFFERS_ASSERT(*reinterpret_cast(&endiantest) == - FLATBUFFERS_LITTLEENDIAN); - (void)endiantest; -} - -template FLATBUFFERS_CONSTEXPR size_t AlignOf() { - // clang-format off - #ifdef _MSC_VER - return __alignof(T); - #else - #ifndef alignof - return __alignof__(T); - #else - return alignof(T); - #endif - #endif - // clang-format on -} - -// Lexicographically compare two strings (possibly containing nulls), and -// return true if the first is less than the second. -static inline bool StringLessThan(const char *a_data, uoffset_t a_size, - const char *b_data, uoffset_t b_size) { - const auto cmp = memcmp(a_data, b_data, (std::min)(a_size, b_size)); - return cmp == 0 ? a_size < b_size : cmp < 0; -} - -// When we read serialized data from memory, in the case of most scalars, -// we want to just read T, but in the case of Offset, we want to actually -// perform the indirection and return a pointer. -// The template specialization below does just that. -// It is wrapped in a struct since function templates can't overload on the -// return type like this. -// The typedef is for the convenience of callers of this function -// (avoiding the need for a trailing return decltype) -template struct IndirectHelper { - typedef T return_type; - typedef T mutable_return_type; - static const size_t element_stride = sizeof(T); - static return_type Read(const uint8_t *p, uoffset_t i) { - return EndianScalar((reinterpret_cast(p))[i]); - } -}; -template struct IndirectHelper> { - typedef const T *return_type; - typedef T *mutable_return_type; - static const size_t element_stride = sizeof(uoffset_t); - static return_type Read(const uint8_t *p, uoffset_t i) { - p += i * sizeof(uoffset_t); - return reinterpret_cast(p + ReadScalar(p)); - } -}; -template struct IndirectHelper { - typedef const T *return_type; - typedef T *mutable_return_type; - static const size_t element_stride = sizeof(T); - static return_type Read(const uint8_t *p, uoffset_t i) { - return reinterpret_cast(p + i * sizeof(T)); - } -}; - -/// @brief Get a pointer to the the file_identifier section of the buffer. -/// @return Returns a const char pointer to the start of the file_identifier -/// characters in the buffer. The returned char * has length -/// 'flatbuffers::FlatBufferBuilder::kFileIdentifierLength'. -/// This function is UNDEFINED for FlatBuffers whose schema does not include -/// a file_identifier (likely points at padding or the start of a the root -/// vtable). -inline const char *GetBufferIdentifier(const void *buf, - bool size_prefixed = false) { - return reinterpret_cast(buf) + - ((size_prefixed) ? 2 * sizeof(uoffset_t) : sizeof(uoffset_t)); -} - -// Helper to see if the identifier in a buffer has the expected value. -inline bool BufferHasIdentifier(const void *buf, const char *identifier, - bool size_prefixed = false) { - return strncmp(GetBufferIdentifier(buf, size_prefixed), identifier, - flatbuffers::kFileIdentifierLength) == 0; -} - -/// @cond FLATBUFFERS_INTERNAL -// Helpers to get a typed pointer to the root object contained in the buffer. -template T *GetMutableRoot(void *buf) { - EndianCheck(); - return reinterpret_cast( - reinterpret_cast(buf) + - EndianScalar(*reinterpret_cast(buf))); -} - -template T *GetMutableSizePrefixedRoot(void *buf) { - return GetMutableRoot(reinterpret_cast(buf) + - sizeof(uoffset_t)); -} - -template const T *GetRoot(const void *buf) { - return GetMutableRoot(const_cast(buf)); -} - -template const T *GetSizePrefixedRoot(const void *buf) { - return GetRoot(reinterpret_cast(buf) + sizeof(uoffset_t)); -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_BUFFER_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/buffer_ref.h b/new_ai_tools/include/flatbuffers/buffer_ref.h deleted file mode 100644 index ce302073..00000000 --- a/new_ai_tools/include/flatbuffers/buffer_ref.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_BUFFER_REF_H_ -#define FLATBUFFERS_BUFFER_REF_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/verifier.h" - -namespace flatbuffers { - -// Convenient way to bundle a buffer and its length, to pass it around -// typed by its root. -// A BufferRef does not own its buffer. -struct BufferRefBase {}; // for std::is_base_of - -template struct BufferRef : BufferRefBase { - BufferRef() : buf(nullptr), len(0), must_free(false) {} - BufferRef(uint8_t *_buf, uoffset_t _len) - : buf(_buf), len(_len), must_free(false) {} - - ~BufferRef() { - if (must_free) free(buf); - } - - const T *GetRoot() const { return flatbuffers::GetRoot(buf); } - - bool Verify() { - Verifier verifier(buf, len); - return verifier.VerifyBuffer(nullptr); - } - - uint8_t *buf; - uoffset_t len; - bool must_free; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_BUFFER_REF_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/code_generators.h b/new_ai_tools/include/flatbuffers/code_generators.h deleted file mode 100644 index d1f7b5a4..00000000 --- a/new_ai_tools/include/flatbuffers/code_generators.h +++ /dev/null @@ -1,235 +0,0 @@ -/* - * Copyright 2014 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_CODE_GENERATORS_H_ -#define FLATBUFFERS_CODE_GENERATORS_H_ - -#include -#include - -#include "flatbuffers/idl.h" - -namespace flatbuffers { - -// Utility class to assist in generating code through use of text templates. -// -// Example code: -// CodeWriter code("\t"); -// code.SetValue("NAME", "Foo"); -// code += "void {{NAME}}() { printf("%s", "{{NAME}}"); }"; -// code.SetValue("NAME", "Bar"); -// code += "void {{NAME}}() { printf("%s", "{{NAME}}"); }"; -// std::cout << code.ToString() << std::endl; -// -// Output: -// void Foo() { printf("%s", "Foo"); } -// void Bar() { printf("%s", "Bar"); } -class CodeWriter { - public: - CodeWriter(std::string pad = std::string()) - : pad_(pad), cur_ident_lvl_(0), ignore_ident_(false) {} - - // Clears the current "written" code. - void Clear() { - stream_.str(""); - stream_.clear(); - } - - // Associates a key with a value. All subsequent calls to operator+=, where - // the specified key is contained in {{ and }} delimiters will be replaced by - // the given value. - void SetValue(const std::string &key, const std::string &value) { - value_map_[key] = value; - } - - std::string GetValue(const std::string &key) const { - const auto it = value_map_.find(key); - return it == value_map_.end() ? "" : it->second; - } - - // Appends the given text to the generated code as well as a newline - // character. Any text within {{ and }} delimiters is replaced by values - // previously stored in the CodeWriter by calling SetValue above. The newline - // will be suppressed if the text ends with the \\ character. - void operator+=(std::string text); - - // Returns the current contents of the CodeWriter as a std::string. - std::string ToString() const { return stream_.str(); } - - // Increase ident level for writing code - void IncrementIdentLevel() { cur_ident_lvl_++; } - // Decrease ident level for writing code - void DecrementIdentLevel() { - if (cur_ident_lvl_) cur_ident_lvl_--; - } - - void SetPadding(const std::string &padding) { pad_ = padding; } - - private: - std::map value_map_; - std::stringstream stream_; - std::string pad_; - int cur_ident_lvl_; - bool ignore_ident_; - - // Add ident padding (tab or space) based on ident level - void AppendIdent(std::stringstream &stream); -}; - -class BaseGenerator { - public: - virtual bool generate() = 0; - - static std::string NamespaceDir(const Parser &parser, const std::string &path, - const Namespace &ns, - const bool dasherize = false); - - static std::string ToDasherizedCase(const std::string pascal_case); - - std::string GeneratedFileName(const std::string &path, - const std::string &file_name, - const IDLOptions &options) const; - - protected: - BaseGenerator(const Parser &parser, const std::string &path, - const std::string &file_name, std::string qualifying_start, - std::string qualifying_separator, std::string default_extension) - : parser_(parser), - path_(path), - file_name_(file_name), - qualifying_start_(qualifying_start), - qualifying_separator_(qualifying_separator), - default_extension_(default_extension) {} - virtual ~BaseGenerator() {} - - // No copy/assign. - BaseGenerator &operator=(const BaseGenerator &); - BaseGenerator(const BaseGenerator &); - - std::string NamespaceDir(const Namespace &ns, - const bool dasherize = false) const; - - static const char *FlatBuffersGeneratedWarning(); - - static std::string FullNamespace(const char *separator, const Namespace &ns); - - static std::string LastNamespacePart(const Namespace &ns); - - // tracks the current namespace for early exit in WrapInNameSpace - // c++, java and csharp returns a different namespace from - // the following default (no early exit, always fully qualify), - // which works for js and php - virtual const Namespace *CurrentNameSpace() const { return nullptr; } - - // Ensure that a type is prefixed with its namespace even within - // its own namespace to avoid conflict between generated method - // names and similarly named classes or structs - std::string WrapInNameSpace(const Namespace *ns, - const std::string &name) const; - - std::string WrapInNameSpace(const Definition &def) const; - - std::string GetNameSpace(const Definition &def) const; - - const Parser &parser_; - const std::string &path_; - const std::string &file_name_; - const std::string qualifying_start_; - const std::string qualifying_separator_; - const std::string default_extension_; -}; - -struct CommentConfig { - const char *first_line; - const char *content_line_prefix; - const char *last_line; -}; - -extern void GenComment(const std::vector &dc, - std::string *code_ptr, const CommentConfig *config, - const char *prefix = ""); - -class FloatConstantGenerator { - public: - virtual ~FloatConstantGenerator() {} - std::string GenFloatConstant(const FieldDef &field) const; - - private: - virtual std::string Value(double v, const std::string &src) const = 0; - virtual std::string Inf(double v) const = 0; - virtual std::string NaN(double v) const = 0; - - virtual std::string Value(float v, const std::string &src) const = 0; - virtual std::string Inf(float v) const = 0; - virtual std::string NaN(float v) const = 0; - - template - std::string GenFloatConstantImpl(const FieldDef &field) const; -}; - -class SimpleFloatConstantGenerator : public FloatConstantGenerator { - public: - SimpleFloatConstantGenerator(const char *nan_number, - const char *pos_inf_number, - const char *neg_inf_number); - - private: - std::string Value(double v, - const std::string &src) const FLATBUFFERS_OVERRIDE; - std::string Inf(double v) const FLATBUFFERS_OVERRIDE; - std::string NaN(double v) const FLATBUFFERS_OVERRIDE; - - std::string Value(float v, const std::string &src) const FLATBUFFERS_OVERRIDE; - std::string Inf(float v) const FLATBUFFERS_OVERRIDE; - std::string NaN(float v) const FLATBUFFERS_OVERRIDE; - - const std::string nan_number_; - const std::string pos_inf_number_; - const std::string neg_inf_number_; -}; - -// C++, C#, Java like generator. -class TypedFloatConstantGenerator : public FloatConstantGenerator { - public: - TypedFloatConstantGenerator(const char *double_prefix, - const char *single_prefix, const char *nan_number, - const char *pos_inf_number, - const char *neg_inf_number = ""); - - private: - std::string Value(double v, - const std::string &src) const FLATBUFFERS_OVERRIDE; - std::string Inf(double v) const FLATBUFFERS_OVERRIDE; - - std::string NaN(double v) const FLATBUFFERS_OVERRIDE; - - std::string Value(float v, const std::string &src) const FLATBUFFERS_OVERRIDE; - std::string Inf(float v) const FLATBUFFERS_OVERRIDE; - std::string NaN(float v) const FLATBUFFERS_OVERRIDE; - - std::string MakeNaN(const std::string &prefix) const; - std::string MakeInf(bool neg, const std::string &prefix) const; - - const std::string double_prefix_; - const std::string single_prefix_; - const std::string nan_number_; - const std::string pos_inf_number_; - const std::string neg_inf_number_; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_CODE_GENERATORS_H_ diff --git a/new_ai_tools/include/flatbuffers/default_allocator.h b/new_ai_tools/include/flatbuffers/default_allocator.h deleted file mode 100644 index 8b173af1..00000000 --- a/new_ai_tools/include/flatbuffers/default_allocator.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_DEFAULT_ALLOCATOR_H_ -#define FLATBUFFERS_DEFAULT_ALLOCATOR_H_ - -#include "flatbuffers/allocator.h" -#include "flatbuffers/base.h" - -namespace flatbuffers { - -// DefaultAllocator uses new/delete to allocate memory regions -class DefaultAllocator : public Allocator { - public: - uint8_t *allocate(size_t size) FLATBUFFERS_OVERRIDE { - return new uint8_t[size]; - } - - void deallocate(uint8_t *p, size_t) FLATBUFFERS_OVERRIDE { delete[] p; } - - static void dealloc(void *p, size_t) { delete[] static_cast(p); } -}; - -// These functions allow for a null allocator to mean use the default allocator, -// as used by DetachedBuffer and vector_downward below. -// This is to avoid having a statically or dynamically allocated default -// allocator, or having to move it between the classes that may own it. -inline uint8_t *Allocate(Allocator *allocator, size_t size) { - return allocator ? allocator->allocate(size) - : DefaultAllocator().allocate(size); -} - -inline void Deallocate(Allocator *allocator, uint8_t *p, size_t size) { - if (allocator) - allocator->deallocate(p, size); - else - DefaultAllocator().deallocate(p, size); -} - -inline uint8_t *ReallocateDownward(Allocator *allocator, uint8_t *old_p, - size_t old_size, size_t new_size, - size_t in_use_back, size_t in_use_front) { - return allocator ? allocator->reallocate_downward(old_p, old_size, new_size, - in_use_back, in_use_front) - : DefaultAllocator().reallocate_downward( - old_p, old_size, new_size, in_use_back, in_use_front); -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_DEFAULT_ALLOCATOR_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/detached_buffer.h b/new_ai_tools/include/flatbuffers/detached_buffer.h deleted file mode 100644 index 760a0884..00000000 --- a/new_ai_tools/include/flatbuffers/detached_buffer.h +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_DETACHED_BUFFER_H_ -#define FLATBUFFERS_DETACHED_BUFFER_H_ - -#include "flatbuffers/allocator.h" -#include "flatbuffers/base.h" -#include "flatbuffers/default_allocator.h" - -namespace flatbuffers { - -// DetachedBuffer is a finished flatbuffer memory region, detached from its -// builder. The original memory region and allocator are also stored so that -// the DetachedBuffer can manage the memory lifetime. -class DetachedBuffer { - public: - DetachedBuffer() - : allocator_(nullptr), - own_allocator_(false), - buf_(nullptr), - reserved_(0), - cur_(nullptr), - size_(0) {} - - DetachedBuffer(Allocator *allocator, bool own_allocator, uint8_t *buf, - size_t reserved, uint8_t *cur, size_t sz) - : allocator_(allocator), - own_allocator_(own_allocator), - buf_(buf), - reserved_(reserved), - cur_(cur), - size_(sz) {} - - DetachedBuffer(DetachedBuffer &&other) - : allocator_(other.allocator_), - own_allocator_(other.own_allocator_), - buf_(other.buf_), - reserved_(other.reserved_), - cur_(other.cur_), - size_(other.size_) { - other.reset(); - } - - DetachedBuffer &operator=(DetachedBuffer &&other) { - if (this == &other) return *this; - - destroy(); - - allocator_ = other.allocator_; - own_allocator_ = other.own_allocator_; - buf_ = other.buf_; - reserved_ = other.reserved_; - cur_ = other.cur_; - size_ = other.size_; - - other.reset(); - - return *this; - } - - ~DetachedBuffer() { destroy(); } - - const uint8_t *data() const { return cur_; } - - uint8_t *data() { return cur_; } - - size_t size() const { return size_; } - - // These may change access mode, leave these at end of public section - FLATBUFFERS_DELETE_FUNC(DetachedBuffer(const DetachedBuffer &other)); - FLATBUFFERS_DELETE_FUNC( - DetachedBuffer &operator=(const DetachedBuffer &other)); - - protected: - Allocator *allocator_; - bool own_allocator_; - uint8_t *buf_; - size_t reserved_; - uint8_t *cur_; - size_t size_; - - inline void destroy() { - if (buf_) Deallocate(allocator_, buf_, reserved_); - if (own_allocator_ && allocator_) { delete allocator_; } - reset(); - } - - inline void reset() { - allocator_ = nullptr; - own_allocator_ = false; - buf_ = nullptr; - reserved_ = 0; - cur_ = nullptr; - size_ = 0; - } -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_DETACHED_BUFFER_H_ diff --git a/new_ai_tools/include/flatbuffers/flatbuffer_builder.h b/new_ai_tools/include/flatbuffers/flatbuffer_builder.h deleted file mode 100644 index 99941938..00000000 --- a/new_ai_tools/include/flatbuffers/flatbuffer_builder.h +++ /dev/null @@ -1,1197 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_FLATBUFFER_BUILDER_H_ -#define FLATBUFFERS_FLATBUFFER_BUILDER_H_ - -#include - -#include "flatbuffers/allocator.h" -#include "flatbuffers/array.h" -#include "flatbuffers/base.h" -#include "flatbuffers/buffer_ref.h" -#include "flatbuffers/default_allocator.h" -#include "flatbuffers/detached_buffer.h" -#include "flatbuffers/stl_emulation.h" -#include "flatbuffers/string.h" -#include "flatbuffers/struct.h" -#include "flatbuffers/table.h" -#include "flatbuffers/vector.h" -#include "flatbuffers/vector_downward.h" -#include "flatbuffers/verifier.h" - -namespace flatbuffers { - -// Converts a Field ID to a virtual table offset. -inline voffset_t FieldIndexToOffset(voffset_t field_id) { - // Should correspond to what EndTable() below builds up. - const int fixed_fields = 2; // Vtable size and Object Size. - return static_cast((field_id + fixed_fields) * sizeof(voffset_t)); -} - -template> -const T *data(const std::vector &v) { - // Eventually the returned pointer gets passed down to memcpy, so - // we need it to be non-null to avoid undefined behavior. - static uint8_t t; - return v.empty() ? reinterpret_cast(&t) : &v.front(); -} -template> -T *data(std::vector &v) { - // Eventually the returned pointer gets passed down to memcpy, so - // we need it to be non-null to avoid undefined behavior. - static uint8_t t; - return v.empty() ? reinterpret_cast(&t) : &v.front(); -} - -/// @addtogroup flatbuffers_cpp_api -/// @{ -/// @class FlatBufferBuilder -/// @brief Helper class to hold data needed in creation of a FlatBuffer. -/// To serialize data, you typically call one of the `Create*()` functions in -/// the generated code, which in turn call a sequence of `StartTable`/ -/// `PushElement`/`AddElement`/`EndTable`, or the builtin `CreateString`/ -/// `CreateVector` functions. Do this is depth-first order to build up a tree to -/// the root. `Finish()` wraps up the buffer ready for transport. -class FlatBufferBuilder { - public: - /// @brief Default constructor for FlatBufferBuilder. - /// @param[in] initial_size The initial size of the buffer, in bytes. Defaults - /// to `1024`. - /// @param[in] allocator An `Allocator` to use. If null will use - /// `DefaultAllocator`. - /// @param[in] own_allocator Whether the builder/vector should own the - /// allocator. Defaults to / `false`. - /// @param[in] buffer_minalign Force the buffer to be aligned to the given - /// minimum alignment upon reallocation. Only needed if you intend to store - /// types with custom alignment AND you wish to read the buffer in-place - /// directly after creation. - explicit FlatBufferBuilder( - size_t initial_size = 1024, Allocator *allocator = nullptr, - bool own_allocator = false, - size_t buffer_minalign = AlignOf()) - : buf_(initial_size, allocator, own_allocator, buffer_minalign), - num_field_loc(0), - max_voffset_(0), - nested(false), - finished(false), - minalign_(1), - force_defaults_(false), - dedup_vtables_(true), - string_pool(nullptr) { - EndianCheck(); - } - - /// @brief Move constructor for FlatBufferBuilder. - FlatBufferBuilder(FlatBufferBuilder &&other) - : buf_(1024, nullptr, false, AlignOf()), - num_field_loc(0), - max_voffset_(0), - nested(false), - finished(false), - minalign_(1), - force_defaults_(false), - dedup_vtables_(true), - string_pool(nullptr) { - EndianCheck(); - // Default construct and swap idiom. - // Lack of delegating constructors in vs2010 makes it more verbose than - // needed. - Swap(other); - } - - /// @brief Move assignment operator for FlatBufferBuilder. - FlatBufferBuilder &operator=(FlatBufferBuilder &&other) { - // Move construct a temporary and swap idiom - FlatBufferBuilder temp(std::move(other)); - Swap(temp); - return *this; - } - - void Swap(FlatBufferBuilder &other) { - using std::swap; - buf_.swap(other.buf_); - swap(num_field_loc, other.num_field_loc); - swap(max_voffset_, other.max_voffset_); - swap(nested, other.nested); - swap(finished, other.finished); - swap(minalign_, other.minalign_); - swap(force_defaults_, other.force_defaults_); - swap(dedup_vtables_, other.dedup_vtables_); - swap(string_pool, other.string_pool); - } - - ~FlatBufferBuilder() { - if (string_pool) delete string_pool; - } - - void Reset() { - Clear(); // clear builder state - buf_.reset(); // deallocate buffer - } - - /// @brief Reset all the state in this FlatBufferBuilder so it can be reused - /// to construct another buffer. - void Clear() { - ClearOffsets(); - buf_.clear(); - nested = false; - finished = false; - minalign_ = 1; - if (string_pool) string_pool->clear(); - } - - /// @brief The current size of the serialized buffer, counting from the end. - /// @return Returns an `uoffset_t` with the current size of the buffer. - uoffset_t GetSize() const { return buf_.size(); } - - /// @brief Get the serialized buffer (after you call `Finish()`). - /// @return Returns an `uint8_t` pointer to the FlatBuffer data inside the - /// buffer. - uint8_t *GetBufferPointer() const { - Finished(); - return buf_.data(); - } - - /// @brief Get the serialized buffer (after you call `Finish()`) as a span. - /// @return Returns a constructed flatbuffers::span that is a view over the - /// FlatBuffer data inside the buffer. - flatbuffers::span GetBufferSpan() const { - Finished(); - return flatbuffers::span(buf_.data(), buf_.size()); - } - - /// @brief Get a pointer to an unfinished buffer. - /// @return Returns a `uint8_t` pointer to the unfinished buffer. - uint8_t *GetCurrentBufferPointer() const { return buf_.data(); } - - /// @brief Get the released pointer to the serialized buffer. - /// @warning Do NOT attempt to use this FlatBufferBuilder afterwards! - /// @return A `FlatBuffer` that owns the buffer and its allocator and - /// behaves similar to a `unique_ptr` with a deleter. - FLATBUFFERS_ATTRIBUTE([[deprecated("use Release() instead")]]) - DetachedBuffer ReleaseBufferPointer() { - Finished(); - return buf_.release(); - } - - /// @brief Get the released DetachedBuffer. - /// @return A `DetachedBuffer` that owns the buffer and its allocator. - DetachedBuffer Release() { - Finished(); - return buf_.release(); - } - - /// @brief Get the released pointer to the serialized buffer. - /// @param size The size of the memory block containing - /// the serialized `FlatBuffer`. - /// @param offset The offset from the released pointer where the finished - /// `FlatBuffer` starts. - /// @return A raw pointer to the start of the memory block containing - /// the serialized `FlatBuffer`. - /// @remark If the allocator is owned, it gets deleted when the destructor is - /// called.. - uint8_t *ReleaseRaw(size_t &size, size_t &offset) { - Finished(); - return buf_.release_raw(size, offset); - } - - /// @brief get the minimum alignment this buffer needs to be accessed - /// properly. This is only known once all elements have been written (after - /// you call Finish()). You can use this information if you need to embed - /// a FlatBuffer in some other buffer, such that you can later read it - /// without first having to copy it into its own buffer. - size_t GetBufferMinAlignment() const { - Finished(); - return minalign_; - } - - /// @cond FLATBUFFERS_INTERNAL - void Finished() const { - // If you get this assert, you're attempting to get access a buffer - // which hasn't been finished yet. Be sure to call - // FlatBufferBuilder::Finish with your root table. - // If you really need to access an unfinished buffer, call - // GetCurrentBufferPointer instead. - FLATBUFFERS_ASSERT(finished); - } - /// @endcond - - /// @brief In order to save space, fields that are set to their default value - /// don't get serialized into the buffer. - /// @param[in] fd When set to `true`, always serializes default values that - /// are set. Optional fields which are not set explicitly, will still not be - /// serialized. - void ForceDefaults(bool fd) { force_defaults_ = fd; } - - /// @brief By default vtables are deduped in order to save space. - /// @param[in] dedup When set to `true`, dedup vtables. - void DedupVtables(bool dedup) { dedup_vtables_ = dedup; } - - /// @cond FLATBUFFERS_INTERNAL - void Pad(size_t num_bytes) { buf_.fill(num_bytes); } - - void TrackMinAlign(size_t elem_size) { - if (elem_size > minalign_) minalign_ = elem_size; - } - - void Align(size_t elem_size) { - TrackMinAlign(elem_size); - buf_.fill(PaddingBytes(buf_.size(), elem_size)); - } - - void PushFlatBuffer(const uint8_t *bytes, size_t size) { - PushBytes(bytes, size); - finished = true; - } - - void PushBytes(const uint8_t *bytes, size_t size) { buf_.push(bytes, size); } - - void PopBytes(size_t amount) { buf_.pop(amount); } - - template void AssertScalarT() { - // The code assumes power of 2 sizes and endian-swap-ability. - static_assert(flatbuffers::is_scalar::value, "T must be a scalar type"); - } - - // Write a single aligned scalar to the buffer - template uoffset_t PushElement(T element) { - AssertScalarT(); - Align(sizeof(T)); - buf_.push_small(EndianScalar(element)); - return GetSize(); - } - - template uoffset_t PushElement(Offset off) { - // Special case for offsets: see ReferTo below. - return PushElement(ReferTo(off.o)); - } - - // When writing fields, we track where they are, so we can create correct - // vtables later. - void TrackField(voffset_t field, uoffset_t off) { - FieldLoc fl = { off, field }; - buf_.scratch_push_small(fl); - num_field_loc++; - if (field > max_voffset_) { max_voffset_ = field; } - } - - // Like PushElement, but additionally tracks the field this represents. - template void AddElement(voffset_t field, T e, T def) { - // We don't serialize values equal to the default. - if (IsTheSameAs(e, def) && !force_defaults_) return; - TrackField(field, PushElement(e)); - } - - template void AddElement(voffset_t field, T e) { - TrackField(field, PushElement(e)); - } - - template void AddOffset(voffset_t field, Offset off) { - if (off.IsNull()) return; // Don't store. - AddElement(field, ReferTo(off.o), static_cast(0)); - } - - template void AddStruct(voffset_t field, const T *structptr) { - if (!structptr) return; // Default, don't store. - Align(AlignOf()); - buf_.push_small(*structptr); - TrackField(field, GetSize()); - } - - void AddStructOffset(voffset_t field, uoffset_t off) { - TrackField(field, off); - } - - // Offsets initially are relative to the end of the buffer (downwards). - // This function converts them to be relative to the current location - // in the buffer (when stored here), pointing upwards. - uoffset_t ReferTo(uoffset_t off) { - // Align to ensure GetSize() below is correct. - Align(sizeof(uoffset_t)); - // Offset must refer to something already in buffer. - const uoffset_t size = GetSize(); - FLATBUFFERS_ASSERT(off && off <= size); - return size - off + static_cast(sizeof(uoffset_t)); - } - - void NotNested() { - // If you hit this, you're trying to construct a Table/Vector/String - // during the construction of its parent table (between the MyTableBuilder - // and table.Finish(). - // Move the creation of these sub-objects to above the MyTableBuilder to - // not get this assert. - // Ignoring this assert may appear to work in simple cases, but the reason - // it is here is that storing objects in-line may cause vtable offsets - // to not fit anymore. It also leads to vtable duplication. - FLATBUFFERS_ASSERT(!nested); - // If you hit this, fields were added outside the scope of a table. - FLATBUFFERS_ASSERT(!num_field_loc); - } - - // From generated code (or from the parser), we call StartTable/EndTable - // with a sequence of AddElement calls in between. - uoffset_t StartTable() { - NotNested(); - nested = true; - return GetSize(); - } - - // This finishes one serialized object by generating the vtable if it's a - // table, comparing it against existing vtables, and writing the - // resulting vtable offset. - uoffset_t EndTable(uoffset_t start) { - // If you get this assert, a corresponding StartTable wasn't called. - FLATBUFFERS_ASSERT(nested); - // Write the vtable offset, which is the start of any Table. - // We fill it's value later. - auto vtableoffsetloc = PushElement(0); - // Write a vtable, which consists entirely of voffset_t elements. - // It starts with the number of offsets, followed by a type id, followed - // by the offsets themselves. In reverse: - // Include space for the last offset and ensure empty tables have a - // minimum size. - max_voffset_ = - (std::max)(static_cast(max_voffset_ + sizeof(voffset_t)), - FieldIndexToOffset(0)); - buf_.fill_big(max_voffset_); - auto table_object_size = vtableoffsetloc - start; - // Vtable use 16bit offsets. - FLATBUFFERS_ASSERT(table_object_size < 0x10000); - WriteScalar(buf_.data() + sizeof(voffset_t), - static_cast(table_object_size)); - WriteScalar(buf_.data(), max_voffset_); - // Write the offsets into the table - for (auto it = buf_.scratch_end() - num_field_loc * sizeof(FieldLoc); - it < buf_.scratch_end(); it += sizeof(FieldLoc)) { - auto field_location = reinterpret_cast(it); - auto pos = static_cast(vtableoffsetloc - field_location->off); - // If this asserts, it means you've set a field twice. - FLATBUFFERS_ASSERT( - !ReadScalar(buf_.data() + field_location->id)); - WriteScalar(buf_.data() + field_location->id, pos); - } - ClearOffsets(); - auto vt1 = reinterpret_cast(buf_.data()); - auto vt1_size = ReadScalar(vt1); - auto vt_use = GetSize(); - // See if we already have generated a vtable with this exact same - // layout before. If so, make it point to the old one, remove this one. - if (dedup_vtables_) { - for (auto it = buf_.scratch_data(); it < buf_.scratch_end(); - it += sizeof(uoffset_t)) { - auto vt_offset_ptr = reinterpret_cast(it); - auto vt2 = reinterpret_cast(buf_.data_at(*vt_offset_ptr)); - auto vt2_size = ReadScalar(vt2); - if (vt1_size != vt2_size || 0 != memcmp(vt2, vt1, vt1_size)) continue; - vt_use = *vt_offset_ptr; - buf_.pop(GetSize() - vtableoffsetloc); - break; - } - } - // If this is a new vtable, remember it. - if (vt_use == GetSize()) { buf_.scratch_push_small(vt_use); } - // Fill the vtable offset we created above. - // The offset points from the beginning of the object to where the - // vtable is stored. - // Offsets default direction is downward in memory for future format - // flexibility (storing all vtables at the start of the file). - WriteScalar(buf_.data_at(vtableoffsetloc), - static_cast(vt_use) - - static_cast(vtableoffsetloc)); - - nested = false; - return vtableoffsetloc; - } - - FLATBUFFERS_ATTRIBUTE([[deprecated("call the version above instead")]]) - uoffset_t EndTable(uoffset_t start, voffset_t /*numfields*/) { - return EndTable(start); - } - - // This checks a required field has been set in a given table that has - // just been constructed. - template void Required(Offset table, voffset_t field); - - uoffset_t StartStruct(size_t alignment) { - Align(alignment); - return GetSize(); - } - - uoffset_t EndStruct() { return GetSize(); } - - void ClearOffsets() { - buf_.scratch_pop(num_field_loc * sizeof(FieldLoc)); - num_field_loc = 0; - max_voffset_ = 0; - } - - // Aligns such that when "len" bytes are written, an object can be written - // after it with "alignment" without padding. - void PreAlign(size_t len, size_t alignment) { - TrackMinAlign(alignment); - buf_.fill(PaddingBytes(GetSize() + len, alignment)); - } - template void PreAlign(size_t len) { - AssertScalarT(); - PreAlign(len, sizeof(T)); - } - /// @endcond - - /// @brief Store a string in the buffer, which can contain any binary data. - /// @param[in] str A const char pointer to the data to be stored as a string. - /// @param[in] len The number of bytes that should be stored from `str`. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateString(const char *str, size_t len) { - NotNested(); - PreAlign(len + 1); // Always 0-terminated. - buf_.fill(1); - PushBytes(reinterpret_cast(str), len); - PushElement(static_cast(len)); - return Offset(GetSize()); - } - - /// @brief Store a string in the buffer, which is null-terminated. - /// @param[in] str A const char pointer to a C-string to add to the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateString(const char *str) { - return CreateString(str, strlen(str)); - } - - /// @brief Store a string in the buffer, which is null-terminated. - /// @param[in] str A char pointer to a C-string to add to the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateString(char *str) { - return CreateString(str, strlen(str)); - } - - /// @brief Store a string in the buffer, which can contain any binary data. - /// @param[in] str A const reference to a std::string to store in the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateString(const std::string &str) { - return CreateString(str.c_str(), str.length()); - } - - // clang-format off - #ifdef FLATBUFFERS_HAS_STRING_VIEW - /// @brief Store a string in the buffer, which can contain any binary data. - /// @param[in] str A const string_view to copy in to the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateString(flatbuffers::string_view str) { - return CreateString(str.data(), str.size()); - } - #endif // FLATBUFFERS_HAS_STRING_VIEW - // clang-format on - - /// @brief Store a string in the buffer, which can contain any binary data. - /// @param[in] str A const pointer to a `String` struct to add to the buffer. - /// @return Returns the offset in the buffer where the string starts - Offset CreateString(const String *str) { - return str ? CreateString(str->c_str(), str->size()) : 0; - } - - /// @brief Store a string in the buffer, which can contain any binary data. - /// @param[in] str A const reference to a std::string like type with support - /// of T::c_str() and T::length() to store in the buffer. - /// @return Returns the offset in the buffer where the string starts. - template Offset CreateString(const T &str) { - return CreateString(str.c_str(), str.length()); - } - - /// @brief Store a string in the buffer, which can contain any binary data. - /// If a string with this exact contents has already been serialized before, - /// instead simply returns the offset of the existing string. This uses a map - /// stored on the heap, but only stores the numerical offsets. - /// @param[in] str A const char pointer to the data to be stored as a string. - /// @param[in] len The number of bytes that should be stored from `str`. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateSharedString(const char *str, size_t len) { - FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); - if (!string_pool) - string_pool = new StringOffsetMap(StringOffsetCompare(buf_)); - auto size_before_string = buf_.size(); - // Must first serialize the string, since the set is all offsets into - // buffer. - auto off = CreateString(str, len); - auto it = string_pool->find(off); - // If it exists we reuse existing serialized data! - if (it != string_pool->end()) { - // We can remove the string we serialized. - buf_.pop(buf_.size() - size_before_string); - return *it; - } - // Record this string for future use. - string_pool->insert(off); - return off; - } - -#ifdef FLATBUFFERS_HAS_STRING_VIEW - /// @brief Store a string in the buffer, which can contain any binary data. - /// If a string with this exact contents has already been serialized before, - /// instead simply returns the offset of the existing string. This uses a map - /// stored on the heap, but only stores the numerical offsets. - /// @param[in] str A const std::string_view to store in the buffer. - /// @return Returns the offset in the buffer where the string starts - Offset CreateSharedString(const flatbuffers::string_view str) { - return CreateSharedString(str.data(), str.size()); - } -#else - /// @brief Store a string in the buffer, which null-terminated. - /// If a string with this exact contents has already been serialized before, - /// instead simply returns the offset of the existing string. This uses a map - /// stored on the heap, but only stores the numerical offsets. - /// @param[in] str A const char pointer to a C-string to add to the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateSharedString(const char *str) { - return CreateSharedString(str, strlen(str)); - } - - /// @brief Store a string in the buffer, which can contain any binary data. - /// If a string with this exact contents has already been serialized before, - /// instead simply returns the offset of the existing string. This uses a map - /// stored on the heap, but only stores the numerical offsets. - /// @param[in] str A const reference to a std::string to store in the buffer. - /// @return Returns the offset in the buffer where the string starts. - Offset CreateSharedString(const std::string &str) { - return CreateSharedString(str.c_str(), str.length()); - } -#endif - - /// @brief Store a string in the buffer, which can contain any binary data. - /// If a string with this exact contents has already been serialized before, - /// instead simply returns the offset of the existing string. This uses a map - /// stored on the heap, but only stores the numerical offsets. - /// @param[in] str A const pointer to a `String` struct to add to the buffer. - /// @return Returns the offset in the buffer where the string starts - Offset CreateSharedString(const String *str) { - return CreateSharedString(str->c_str(), str->size()); - } - - /// @cond FLATBUFFERS_INTERNAL - uoffset_t EndVector(size_t len) { - FLATBUFFERS_ASSERT(nested); // Hit if no corresponding StartVector. - nested = false; - return PushElement(static_cast(len)); - } - - void StartVector(size_t len, size_t elemsize) { - NotNested(); - nested = true; - PreAlign(len * elemsize); - PreAlign(len * elemsize, elemsize); // Just in case elemsize > uoffset_t. - } - - // Call this right before StartVector/CreateVector if you want to force the - // alignment to be something different than what the element size would - // normally dictate. - // This is useful when storing a nested_flatbuffer in a vector of bytes, - // or when storing SIMD floats, etc. - void ForceVectorAlignment(size_t len, size_t elemsize, size_t alignment) { - FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment)); - PreAlign(len * elemsize, alignment); - } - - // Similar to ForceVectorAlignment but for String fields. - void ForceStringAlignment(size_t len, size_t alignment) { - FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment)); - PreAlign((len + 1) * sizeof(char), alignment); - } - - /// @endcond - - /// @brief Serialize an array into a FlatBuffer `vector`. - /// @tparam T The data type of the array elements. - /// @param[in] v A pointer to the array of type `T` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template Offset> CreateVector(const T *v, size_t len) { - // If this assert hits, you're specifying a template argument that is - // causing the wrong overload to be selected, remove it. - AssertScalarT(); - StartVector(len, sizeof(T)); - if (len == 0) { return Offset>(EndVector(len)); } - // clang-format off - #if FLATBUFFERS_LITTLEENDIAN - PushBytes(reinterpret_cast(v), len * sizeof(T)); - #else - if (sizeof(T) == 1) { - PushBytes(reinterpret_cast(v), len); - } else { - for (auto i = len; i > 0; ) { - PushElement(v[--i]); - } - } - #endif - // clang-format on - return Offset>(EndVector(len)); - } - - /// @brief Serialize an array like object into a FlatBuffer `vector`. - /// @tparam T The data type of the array elements. - /// @tparam C The type of the array. - /// @param[in] array A reference to an array like object of type `T` to - /// serialize into the buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template Offset> CreateVector(const C &array) { - return CreateVector(array.data(), array.size()); - } - - template - Offset>> CreateVector(const Offset *v, size_t len) { - StartVector(len, sizeof(Offset)); - for (auto i = len; i > 0;) { PushElement(v[--i]); } - return Offset>>(EndVector(len)); - } - - /// @brief Serialize a `std::vector` into a FlatBuffer `vector`. - /// @tparam T The data type of the `std::vector` elements. - /// @param v A const reference to the `std::vector` to serialize into the - /// buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVector(const std::vector &v) { - return CreateVector(data(v), v.size()); - } - - // vector may be implemented using a bit-set, so we can't access it as - // an array. Instead, read elements manually. - // Background: https://isocpp.org/blog/2012/11/on-vectorbool - Offset> CreateVector(const std::vector &v) { - StartVector(v.size(), sizeof(uint8_t)); - for (auto i = v.size(); i > 0;) { - PushElement(static_cast(v[--i])); - } - return Offset>(EndVector(v.size())); - } - - /// @brief Serialize values returned by a function into a FlatBuffer `vector`. - /// This is a convenience function that takes care of iteration for you. - /// @tparam T The data type of the `std::vector` elements. - /// @param f A function that takes the current iteration 0..vector_size-1 and - /// returns any type that you can construct a FlatBuffers vector out of. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVector(size_t vector_size, - const std::function &f) { - FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); - std::vector elems(vector_size); - for (size_t i = 0; i < vector_size; i++) elems[i] = f(i); - return CreateVector(elems); - } - - /// @brief Serialize values returned by a function into a FlatBuffer `vector`. - /// This is a convenience function that takes care of iteration for you. This - /// uses a vector stored on the heap to store the intermediate results of the - /// iteration. - /// @tparam T The data type of the `std::vector` elements. - /// @param f A function that takes the current iteration 0..vector_size-1, - /// and the state parameter returning any type that you can construct a - /// FlatBuffers vector out of. - /// @param state State passed to f. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVector(size_t vector_size, F f, S *state) { - FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK); - std::vector elems(vector_size); - for (size_t i = 0; i < vector_size; i++) elems[i] = f(i, state); - return CreateVector(elems); - } - - /// @brief Serialize a `std::vector` into a FlatBuffer `vector`. - /// This is a convenience function for a common case. - /// @param v A const reference to the `std::vector` to serialize into the - /// buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset>> CreateVectorOfStrings( - const std::vector &v) { - return CreateVectorOfStrings(v.cbegin(), v.cend()); - } - - /// @brief Serialize a collection of Strings into a FlatBuffer `vector`. - /// This is a convenience function for a common case. - /// @param begin The begining iterator of the collection - /// @param end The ending iterator of the collection - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset>> CreateVectorOfStrings(It begin, It end) { - auto size = std::distance(begin, end); - auto scratch_buffer_usage = size * sizeof(Offset); - // If there is not enough space to store the offsets, there definitely won't - // be enough space to store all the strings. So ensuring space for the - // scratch region is OK, for it it fails, it would have failed later. - buf_.ensure_space(scratch_buffer_usage); - for (auto it = begin; it != end; ++it) { - buf_.scratch_push_small(CreateString(*it)); - } - StartVector(size, sizeof(Offset)); - for (auto i = 1; i <= size; i++) { - // Note we re-evaluate the buf location each iteration to account for any - // underlying buffer resizing that may occur. - PushElement(*reinterpret_cast *>( - buf_.scratch_end() - i * sizeof(Offset))); - } - buf_.scratch_pop(scratch_buffer_usage); - return Offset>>(EndVector(size)); - } - - /// @brief Serialize an array of structs into a FlatBuffer `vector`. - /// @tparam T The data type of the struct array elements. - /// @param[in] v A pointer to the array of type `T` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVectorOfStructs(const T *v, size_t len) { - StartVector(len * sizeof(T) / AlignOf(), AlignOf()); - if (len > 0) { - PushBytes(reinterpret_cast(v), sizeof(T) * len); - } - return Offset>(EndVector(len)); - } - - /// @brief Serialize an array of native structs into a FlatBuffer `vector`. - /// @tparam T The data type of the struct array elements. - /// @tparam S The data type of the native struct array elements. - /// @param[in] v A pointer to the array of type `S` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @param[in] pack_func Pointer to a function to convert the native struct - /// to the FlatBuffer struct. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVectorOfNativeStructs( - const S *v, size_t len, T (*const pack_func)(const S &)) { - FLATBUFFERS_ASSERT(pack_func); - auto structs = StartVectorOfStructs(len); - for (size_t i = 0; i < len; i++) { structs[i] = pack_func(v[i]); } - return EndVectorOfStructs(len); - } - - /// @brief Serialize an array of native structs into a FlatBuffer `vector`. - /// @tparam T The data type of the struct array elements. - /// @tparam S The data type of the native struct array elements. - /// @param[in] v A pointer to the array of type `S` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVectorOfNativeStructs(const S *v, - size_t len) { - extern T Pack(const S &); - return CreateVectorOfNativeStructs(v, len, Pack); - } - - /// @brief Serialize an array of structs into a FlatBuffer `vector`. - /// @tparam T The data type of the struct array elements. - /// @param[in] filler A function that takes the current iteration - /// 0..vector_size-1 and a pointer to the struct that must be filled. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - /// This is mostly useful when flatbuffers are generated with mutation - /// accessors. - template - Offset> CreateVectorOfStructs( - size_t vector_size, const std::function &filler) { - T *structs = StartVectorOfStructs(vector_size); - for (size_t i = 0; i < vector_size; i++) { - filler(i, structs); - structs++; - } - return EndVectorOfStructs(vector_size); - } - - /// @brief Serialize an array of structs into a FlatBuffer `vector`. - /// @tparam T The data type of the struct array elements. - /// @param[in] f A function that takes the current iteration 0..vector_size-1, - /// a pointer to the struct that must be filled and the state argument. - /// @param[in] state Arbitrary state to pass to f. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - /// This is mostly useful when flatbuffers are generated with mutation - /// accessors. - template - Offset> CreateVectorOfStructs(size_t vector_size, F f, - S *state) { - T *structs = StartVectorOfStructs(vector_size); - for (size_t i = 0; i < vector_size; i++) { - f(i, structs, state); - structs++; - } - return EndVectorOfStructs(vector_size); - } - - /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector`. - /// @tparam T The data type of the `std::vector` struct elements. - /// @param[in] v A const reference to the `std::vector` of structs to - /// serialize into the buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVectorOfStructs( - const std::vector &v) { - return CreateVectorOfStructs(data(v), v.size()); - } - - /// @brief Serialize a `std::vector` of native structs into a FlatBuffer - /// `vector`. - /// @tparam T The data type of the `std::vector` struct elements. - /// @tparam S The data type of the `std::vector` native struct elements. - /// @param[in] v A const reference to the `std::vector` of structs to - /// serialize into the buffer as a `vector`. - /// @param[in] pack_func Pointer to a function to convert the native struct - /// to the FlatBuffer struct. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVectorOfNativeStructs( - const std::vector &v, T (*const pack_func)(const S &)) { - return CreateVectorOfNativeStructs(data(v), v.size(), pack_func); - } - - /// @brief Serialize a `std::vector` of native structs into a FlatBuffer - /// `vector`. - /// @tparam T The data type of the `std::vector` struct elements. - /// @tparam S The data type of the `std::vector` native struct elements. - /// @param[in] v A const reference to the `std::vector` of structs to - /// serialize into the buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVectorOfNativeStructs( - const std::vector &v) { - return CreateVectorOfNativeStructs(data(v), v.size()); - } - - /// @cond FLATBUFFERS_INTERNAL - template struct StructKeyComparator { - bool operator()(const T &a, const T &b) const { - return a.KeyCompareLessThan(&b); - } - }; - /// @endcond - - /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector` - /// in sorted order. - /// @tparam T The data type of the `std::vector` struct elements. - /// @param[in] v A const reference to the `std::vector` of structs to - /// serialize into the buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVectorOfSortedStructs( - std::vector *v) { - return CreateVectorOfSortedStructs(data(*v), v->size()); - } - - /// @brief Serialize a `std::vector` of native structs into a FlatBuffer - /// `vector` in sorted order. - /// @tparam T The data type of the `std::vector` struct elements. - /// @tparam S The data type of the `std::vector` native struct elements. - /// @param[in] v A const reference to the `std::vector` of structs to - /// serialize into the buffer as a `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset> CreateVectorOfSortedNativeStructs( - std::vector *v) { - return CreateVectorOfSortedNativeStructs(data(*v), v->size()); - } - - /// @brief Serialize an array of structs into a FlatBuffer `vector` in sorted - /// order. - /// @tparam T The data type of the struct array elements. - /// @param[in] v A pointer to the array of type `T` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVectorOfSortedStructs(T *v, size_t len) { - std::stable_sort(v, v + len, StructKeyComparator()); - return CreateVectorOfStructs(v, len); - } - - /// @brief Serialize an array of native structs into a FlatBuffer `vector` in - /// sorted order. - /// @tparam T The data type of the struct array elements. - /// @tparam S The data type of the native struct array elements. - /// @param[in] v A pointer to the array of type `S` to serialize into the - /// buffer as a `vector`. - /// @param[in] len The number of elements to serialize. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset> CreateVectorOfSortedNativeStructs(S *v, - size_t len) { - extern T Pack(const S &); - auto structs = StartVectorOfStructs(len); - for (size_t i = 0; i < len; i++) { structs[i] = Pack(v[i]); } - std::stable_sort(structs, structs + len, StructKeyComparator()); - return EndVectorOfStructs(len); - } - - /// @cond FLATBUFFERS_INTERNAL - template struct TableKeyComparator { - TableKeyComparator(vector_downward &buf) : buf_(buf) {} - TableKeyComparator(const TableKeyComparator &other) : buf_(other.buf_) {} - bool operator()(const Offset &a, const Offset &b) const { - auto table_a = reinterpret_cast(buf_.data_at(a.o)); - auto table_b = reinterpret_cast(buf_.data_at(b.o)); - return table_a->KeyCompareLessThan(table_b); - } - vector_downward &buf_; - - private: - FLATBUFFERS_DELETE_FUNC( - TableKeyComparator &operator=(const TableKeyComparator &other)); - }; - /// @endcond - - /// @brief Serialize an array of `table` offsets as a `vector` in the buffer - /// in sorted order. - /// @tparam T The data type that the offset refers to. - /// @param[in] v An array of type `Offset` that contains the `table` - /// offsets to store in the buffer in sorted order. - /// @param[in] len The number of elements to store in the `vector`. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template - Offset>> CreateVectorOfSortedTables(Offset *v, - size_t len) { - std::stable_sort(v, v + len, TableKeyComparator(buf_)); - return CreateVector(v, len); - } - - /// @brief Serialize an array of `table` offsets as a `vector` in the buffer - /// in sorted order. - /// @tparam T The data type that the offset refers to. - /// @param[in] v An array of type `Offset` that contains the `table` - /// offsets to store in the buffer in sorted order. - /// @return Returns a typed `Offset` into the serialized data indicating - /// where the vector is stored. - template> - Offset>> CreateVectorOfSortedTables( - std::vector, Alloc> *v) { - return CreateVectorOfSortedTables(data(*v), v->size()); - } - - /// @brief Specialized version of `CreateVector` for non-copying use cases. - /// Write the data any time later to the returned buffer pointer `buf`. - /// @param[in] len The number of elements to store in the `vector`. - /// @param[in] elemsize The size of each element in the `vector`. - /// @param[out] buf A pointer to a `uint8_t` pointer that can be - /// written to at a later time to serialize the data into a `vector` - /// in the buffer. - uoffset_t CreateUninitializedVector(size_t len, size_t elemsize, - uint8_t **buf) { - NotNested(); - StartVector(len, elemsize); - buf_.make_space(len * elemsize); - auto vec_start = GetSize(); - auto vec_end = EndVector(len); - *buf = buf_.data_at(vec_start); - return vec_end; - } - - /// @brief Specialized version of `CreateVector` for non-copying use cases. - /// Write the data any time later to the returned buffer pointer `buf`. - /// @tparam T The data type of the data that will be stored in the buffer - /// as a `vector`. - /// @param[in] len The number of elements to store in the `vector`. - /// @param[out] buf A pointer to a pointer of type `T` that can be - /// written to at a later time to serialize the data into a `vector` - /// in the buffer. - template - Offset> CreateUninitializedVector(size_t len, T **buf) { - AssertScalarT(); - return CreateUninitializedVector(len, sizeof(T), - reinterpret_cast(buf)); - } - - template - Offset> CreateUninitializedVectorOfStructs(size_t len, - T **buf) { - return CreateUninitializedVector(len, sizeof(T), - reinterpret_cast(buf)); - } - - // @brief Create a vector of scalar type T given as input a vector of scalar - // type U, useful with e.g. pre "enum class" enums, or any existing scalar - // data of the wrong type. - template - Offset> CreateVectorScalarCast(const U *v, size_t len) { - AssertScalarT(); - AssertScalarT(); - StartVector(len, sizeof(T)); - for (auto i = len; i > 0;) { PushElement(static_cast(v[--i])); } - return Offset>(EndVector(len)); - } - - /// @brief Write a struct by itself, typically to be part of a union. - template Offset CreateStruct(const T &structobj) { - NotNested(); - Align(AlignOf()); - buf_.push_small(structobj); - return Offset(GetSize()); - } - - /// @brief Finish serializing a buffer by writing the root offset. - /// @param[in] file_identifier If a `file_identifier` is given, the buffer - /// will be prefixed with a standard FlatBuffers file header. - template - void Finish(Offset root, const char *file_identifier = nullptr) { - Finish(root.o, file_identifier, false); - } - - /// @brief Finish a buffer with a 32 bit size field pre-fixed (size of the - /// buffer following the size field). These buffers are NOT compatible - /// with standard buffers created by Finish, i.e. you can't call GetRoot - /// on them, you have to use GetSizePrefixedRoot instead. - /// All >32 bit quantities in this buffer will be aligned when the whole - /// size pre-fixed buffer is aligned. - /// These kinds of buffers are useful for creating a stream of FlatBuffers. - template - void FinishSizePrefixed(Offset root, - const char *file_identifier = nullptr) { - Finish(root.o, file_identifier, true); - } - - void SwapBufAllocator(FlatBufferBuilder &other) { - buf_.swap_allocator(other.buf_); - } - - /// @brief The length of a FlatBuffer file header. - static const size_t kFileIdentifierLength = - ::flatbuffers::kFileIdentifierLength; - - protected: - // You shouldn't really be copying instances of this class. - FlatBufferBuilder(const FlatBufferBuilder &); - FlatBufferBuilder &operator=(const FlatBufferBuilder &); - - void Finish(uoffset_t root, const char *file_identifier, bool size_prefix) { - NotNested(); - buf_.clear_scratch(); - // This will cause the whole buffer to be aligned. - PreAlign((size_prefix ? sizeof(uoffset_t) : 0) + sizeof(uoffset_t) + - (file_identifier ? kFileIdentifierLength : 0), - minalign_); - if (file_identifier) { - FLATBUFFERS_ASSERT(strlen(file_identifier) == kFileIdentifierLength); - PushBytes(reinterpret_cast(file_identifier), - kFileIdentifierLength); - } - PushElement(ReferTo(root)); // Location of root. - if (size_prefix) { PushElement(GetSize()); } - finished = true; - } - - struct FieldLoc { - uoffset_t off; - voffset_t id; - }; - - vector_downward buf_; - - // Accumulating offsets of table members while it is being built. - // We store these in the scratch pad of buf_, after the vtable offsets. - uoffset_t num_field_loc; - // Track how much of the vtable is in use, so we can output the most compact - // possible vtable. - voffset_t max_voffset_; - - // Ensure objects are not nested. - bool nested; - - // Ensure the buffer is finished before it is being accessed. - bool finished; - - size_t minalign_; - - bool force_defaults_; // Serialize values equal to their defaults anyway. - - bool dedup_vtables_; - - struct StringOffsetCompare { - StringOffsetCompare(const vector_downward &buf) : buf_(&buf) {} - bool operator()(const Offset &a, const Offset &b) const { - auto stra = reinterpret_cast(buf_->data_at(a.o)); - auto strb = reinterpret_cast(buf_->data_at(b.o)); - return StringLessThan(stra->data(), stra->size(), strb->data(), - strb->size()); - } - const vector_downward *buf_; - }; - - // For use with CreateSharedString. Instantiated on first use only. - typedef std::set, StringOffsetCompare> StringOffsetMap; - StringOffsetMap *string_pool; - - private: - // Allocates space for a vector of structures. - // Must be completed with EndVectorOfStructs(). - template T *StartVectorOfStructs(size_t vector_size) { - StartVector(vector_size * sizeof(T) / AlignOf(), AlignOf()); - return reinterpret_cast(buf_.make_space(vector_size * sizeof(T))); - } - - // End the vector of structures in the flatbuffers. - // Vector should have previously be started with StartVectorOfStructs(). - template - Offset> EndVectorOfStructs(size_t vector_size) { - return Offset>(EndVector(vector_size)); - } -}; -/// @} - -/// Helpers to get a typed pointer to objects that are currently being built. -/// @warning Creating new objects will lead to reallocations and invalidates -/// the pointer! -template -T *GetMutableTemporaryPointer(FlatBufferBuilder &fbb, Offset offset) { - return reinterpret_cast(fbb.GetCurrentBufferPointer() + fbb.GetSize() - - offset.o); -} - -template -const T *GetTemporaryPointer(FlatBufferBuilder &fbb, Offset offset) { - return GetMutableTemporaryPointer(fbb, offset); -} - -template -void FlatBufferBuilder::Required(Offset table, voffset_t field) { - auto table_ptr = reinterpret_cast(buf_.data_at(table.o)); - bool ok = table_ptr->GetOptionalFieldOffset(field) != 0; - // If this fails, the caller will show what field needs to be set. - FLATBUFFERS_ASSERT(ok); - (void)ok; -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_VECTOR_DOWNWARD_H_ diff --git a/new_ai_tools/include/flatbuffers/flatbuffers.h b/new_ai_tools/include/flatbuffers/flatbuffers.h deleted file mode 100644 index 64217889..00000000 --- a/new_ai_tools/include/flatbuffers/flatbuffers.h +++ /dev/null @@ -1,270 +0,0 @@ -/* - * Copyright 2014 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_H_ -#define FLATBUFFERS_H_ - -// TODO: These includes are for mitigating the pains of users editing their -// source because they relied on flatbuffers.h to include everything for them. -#include "flatbuffers/array.h" -#include "flatbuffers/base.h" -#include "flatbuffers/buffer.h" -#include "flatbuffers/buffer_ref.h" -#include "flatbuffers/detached_buffer.h" -#include "flatbuffers/flatbuffer_builder.h" -#include "flatbuffers/stl_emulation.h" -#include "flatbuffers/string.h" -#include "flatbuffers/struct.h" -#include "flatbuffers/table.h" -#include "flatbuffers/vector.h" -#include "flatbuffers/vector_downward.h" -#include "flatbuffers/verifier.h" - -namespace flatbuffers { - -/// @brief This can compute the start of a FlatBuffer from a root pointer, i.e. -/// it is the opposite transformation of GetRoot(). -/// This may be useful if you want to pass on a root and have the recipient -/// delete the buffer afterwards. -inline const uint8_t *GetBufferStartFromRootPointer(const void *root) { - auto table = reinterpret_cast(root); - auto vtable = table->GetVTable(); - // Either the vtable is before the root or after the root. - auto start = (std::min)(vtable, reinterpret_cast(root)); - // Align to at least sizeof(uoffset_t). - start = reinterpret_cast(reinterpret_cast(start) & - ~(sizeof(uoffset_t) - 1)); - // Additionally, there may be a file_identifier in the buffer, and the root - // offset. The buffer may have been aligned to any size between - // sizeof(uoffset_t) and FLATBUFFERS_MAX_ALIGNMENT (see "force_align"). - // Sadly, the exact alignment is only known when constructing the buffer, - // since it depends on the presence of values with said alignment properties. - // So instead, we simply look at the next uoffset_t values (root, - // file_identifier, and alignment padding) to see which points to the root. - // None of the other values can "impersonate" the root since they will either - // be 0 or four ASCII characters. - static_assert(flatbuffers::kFileIdentifierLength == sizeof(uoffset_t), - "file_identifier is assumed to be the same size as uoffset_t"); - for (auto possible_roots = FLATBUFFERS_MAX_ALIGNMENT / sizeof(uoffset_t) + 1; - possible_roots; possible_roots--) { - start -= sizeof(uoffset_t); - if (ReadScalar(start) + start == - reinterpret_cast(root)) - return start; - } - // We didn't find the root, either the "root" passed isn't really a root, - // or the buffer is corrupt. - // Assert, because calling this function with bad data may cause reads - // outside of buffer boundaries. - FLATBUFFERS_ASSERT(false); - return nullptr; -} - -/// @brief This return the prefixed size of a FlatBuffer. -inline uoffset_t GetPrefixedSize(const uint8_t *buf) { - return ReadScalar(buf); -} - -// Base class for native objects (FlatBuffer data de-serialized into native -// C++ data structures). -// Contains no functionality, purely documentative. -struct NativeTable {}; - -/// @brief Function types to be used with resolving hashes into objects and -/// back again. The resolver gets a pointer to a field inside an object API -/// object that is of the type specified in the schema using the attribute -/// `cpp_type` (it is thus important whatever you write to this address -/// matches that type). The value of this field is initially null, so you -/// may choose to implement a delayed binding lookup using this function -/// if you wish. The resolver does the opposite lookup, for when the object -/// is being serialized again. -typedef uint64_t hash_value_t; -typedef std::function - resolver_function_t; -typedef std::function rehasher_function_t; - -// Helper function to test if a field is present, using any of the field -// enums in the generated code. -// `table` must be a generated table type. Since this is a template parameter, -// this is not typechecked to be a subclass of Table, so beware! -// Note: this function will return false for fields equal to the default -// value, since they're not stored in the buffer (unless force_defaults was -// used). -template -bool IsFieldPresent(const T *table, typename T::FlatBuffersVTableOffset field) { - // Cast, since Table is a private baseclass of any table types. - return reinterpret_cast(table)->CheckField( - static_cast(field)); -} - -// Utility function for reverse lookups on the EnumNames*() functions -// (in the generated C++ code) -// names must be NULL terminated. -inline int LookupEnum(const char **names, const char *name) { - for (const char **p = names; *p; p++) - if (!strcmp(*p, name)) return static_cast(p - names); - return -1; -} - -// These macros allow us to layout a struct with a guarantee that they'll end -// up looking the same on different compilers and platforms. -// It does this by disallowing the compiler to do any padding, and then -// does padding itself by inserting extra padding fields that make every -// element aligned to its own size. -// Additionally, it manually sets the alignment of the struct as a whole, -// which is typically its largest element, or a custom size set in the schema -// by the force_align attribute. -// These are used in the generated code only. - -// clang-format off -#if defined(_MSC_VER) - #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \ - __pragma(pack(1)) \ - struct __declspec(align(alignment)) - #define FLATBUFFERS_STRUCT_END(name, size) \ - __pragma(pack()) \ - static_assert(sizeof(name) == size, "compiler breaks packing rules") -#elif defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__) - #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \ - _Pragma("pack(1)") \ - struct __attribute__((aligned(alignment))) - #define FLATBUFFERS_STRUCT_END(name, size) \ - _Pragma("pack()") \ - static_assert(sizeof(name) == size, "compiler breaks packing rules") -#else - #error Unknown compiler, please define structure alignment macros -#endif -// clang-format on - -// Minimal reflection via code generation. -// Besides full-fat reflection (see reflection.h) and parsing/printing by -// loading schemas (see idl.h), we can also have code generation for minimal -// reflection data which allows pretty-printing and other uses without needing -// a schema or a parser. -// Generate code with --reflect-types (types only) or --reflect-names (names -// also) to enable. -// See minireflect.h for utilities using this functionality. - -// These types are organized slightly differently as the ones in idl.h. -enum SequenceType { ST_TABLE, ST_STRUCT, ST_UNION, ST_ENUM }; - -// Scalars have the same order as in idl.h -// clang-format off -#define FLATBUFFERS_GEN_ELEMENTARY_TYPES(ET) \ - ET(ET_UTYPE) \ - ET(ET_BOOL) \ - ET(ET_CHAR) \ - ET(ET_UCHAR) \ - ET(ET_SHORT) \ - ET(ET_USHORT) \ - ET(ET_INT) \ - ET(ET_UINT) \ - ET(ET_LONG) \ - ET(ET_ULONG) \ - ET(ET_FLOAT) \ - ET(ET_DOUBLE) \ - ET(ET_STRING) \ - ET(ET_SEQUENCE) // See SequenceType. - -enum ElementaryType { - #define FLATBUFFERS_ET(E) E, - FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET) - #undef FLATBUFFERS_ET -}; - -inline const char * const *ElementaryTypeNames() { - static const char * const names[] = { - #define FLATBUFFERS_ET(E) #E, - FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET) - #undef FLATBUFFERS_ET - }; - return names; -} -// clang-format on - -// Basic type info cost just 16bits per field! -// We're explicitly defining the signedness since the signedness of integer -// bitfields is otherwise implementation-defined and causes warnings on older -// GCC compilers. -struct TypeCode { - // ElementaryType - unsigned short base_type : 4; - // Either vector (in table) or array (in struct) - unsigned short is_repeating : 1; - // Index into type_refs below, or -1 for none. - signed short sequence_ref : 11; -}; - -static_assert(sizeof(TypeCode) == 2, "TypeCode"); - -struct TypeTable; - -// Signature of the static method present in each type. -typedef const TypeTable *(*TypeFunction)(); - -struct TypeTable { - SequenceType st; - size_t num_elems; // of type_codes, values, names (but not type_refs). - const TypeCode *type_codes; // num_elems count - const TypeFunction *type_refs; // less than num_elems entries (see TypeCode). - const int16_t *array_sizes; // less than num_elems entries (see TypeCode). - const int64_t *values; // Only set for non-consecutive enum/union or structs. - const char *const *names; // Only set if compiled with --reflect-names. -}; - -// String which identifies the current version of FlatBuffers. -inline const char *flatbuffers_version_string() { - return "FlatBuffers " FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MAJOR) "." - FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MINOR) "." - FLATBUFFERS_STRING(FLATBUFFERS_VERSION_REVISION); -} - -// clang-format off -#define FLATBUFFERS_DEFINE_BITMASK_OPERATORS(E, T)\ - inline E operator | (E lhs, E rhs){\ - return E(T(lhs) | T(rhs));\ - }\ - inline E operator & (E lhs, E rhs){\ - return E(T(lhs) & T(rhs));\ - }\ - inline E operator ^ (E lhs, E rhs){\ - return E(T(lhs) ^ T(rhs));\ - }\ - inline E operator ~ (E lhs){\ - return E(~T(lhs));\ - }\ - inline E operator |= (E &lhs, E rhs){\ - lhs = lhs | rhs;\ - return lhs;\ - }\ - inline E operator &= (E &lhs, E rhs){\ - lhs = lhs & rhs;\ - return lhs;\ - }\ - inline E operator ^= (E &lhs, E rhs){\ - lhs = lhs ^ rhs;\ - return lhs;\ - }\ - inline bool operator !(E rhs) \ - {\ - return !bool(T(rhs)); \ - } -/// @endcond -} // namespace flatbuffers - -// clang-format on - -#endif // FLATBUFFERS_H_ diff --git a/new_ai_tools/include/flatbuffers/flatc.h b/new_ai_tools/include/flatbuffers/flatc.h deleted file mode 100644 index 3dba5e14..00000000 --- a/new_ai_tools/include/flatbuffers/flatc.h +++ /dev/null @@ -1,111 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_FLATC_H_ -#define FLATBUFFERS_FLATC_H_ - -#include -#include -#include - -#include "flatbuffers/bfbs_generator.h" -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/idl.h" -#include "flatbuffers/util.h" - -namespace flatbuffers { - -extern void LogCompilerWarn(const std::string &warn); -extern void LogCompilerError(const std::string &err); - -struct FlatCOption { - std::string short_opt; - std::string long_opt; - std::string parameter; - std::string description; -}; - -class FlatCompiler { - public: - // Output generator for the various programming languages and formats we - // support. - struct Generator { - typedef bool (*GenerateFn)(const flatbuffers::Parser &parser, - const std::string &path, - const std::string &file_name); - typedef std::string (*MakeRuleFn)(const flatbuffers::Parser &parser, - const std::string &path, - const std::string &file_name); - typedef bool (*ParsingCompletedFn)(const flatbuffers::Parser &parser, - const std::string &output_path); - - GenerateFn generate; - const char *lang_name; - bool schema_only; - GenerateFn generateGRPC; - flatbuffers::IDLOptions::Language lang; - FlatCOption option; - MakeRuleFn make_rule; - BfbsGenerator *bfbs_generator; - ParsingCompletedFn parsing_completed; - }; - - typedef void (*WarnFn)(const FlatCompiler *flatc, const std::string &warn, - bool show_exe_name); - - typedef void (*ErrorFn)(const FlatCompiler *flatc, const std::string &err, - bool usage, bool show_exe_name); - - // Parameters required to initialize the FlatCompiler. - struct InitParams { - InitParams() - : generators(nullptr), - num_generators(0), - warn_fn(nullptr), - error_fn(nullptr) {} - - const Generator *generators; - size_t num_generators; - WarnFn warn_fn; - ErrorFn error_fn; - }; - - explicit FlatCompiler(const InitParams ¶ms) : params_(params) {} - - int Compile(int argc, const char **argv); - - std::string GetShortUsageString(const char *program_name) const; - std::string GetUsageString(const char *program_name) const; - - private: - void ParseFile(flatbuffers::Parser &parser, const std::string &filename, - const std::string &contents, - std::vector &include_directories) const; - - void LoadBinarySchema(Parser &parser, const std::string &filename, - const std::string &contents); - - void Warn(const std::string &warn, bool show_exe_name = true) const; - - void Error(const std::string &err, bool usage = true, - bool show_exe_name = true) const; - - InitParams params_; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_FLATC_H_ diff --git a/new_ai_tools/include/flatbuffers/flexbuffers.h b/new_ai_tools/include/flatbuffers/flexbuffers.h deleted file mode 100644 index b4b0332b..00000000 --- a/new_ai_tools/include/flatbuffers/flexbuffers.h +++ /dev/null @@ -1,1897 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_FLEXBUFFERS_H_ -#define FLATBUFFERS_FLEXBUFFERS_H_ - -#include -// Used to select STL variant. -#include "flatbuffers/base.h" -// We use the basic binary writing functions from the regular FlatBuffers. -#include "flatbuffers/util.h" - -#ifdef _MSC_VER -# include -#endif - -#if defined(_MSC_VER) -# pragma warning(push) -# pragma warning(disable : 4127) // C4127: conditional expression is constant -#endif - -namespace flexbuffers { - -class Reference; -class Map; - -// These are used in the lower 2 bits of a type field to determine the size of -// the elements (and or size field) of the item pointed to (e.g. vector). -enum BitWidth { - BIT_WIDTH_8 = 0, - BIT_WIDTH_16 = 1, - BIT_WIDTH_32 = 2, - BIT_WIDTH_64 = 3, -}; - -// These are used as the upper 6 bits of a type field to indicate the actual -// type. -enum Type { - FBT_NULL = 0, - FBT_INT = 1, - FBT_UINT = 2, - FBT_FLOAT = 3, - // Types above stored inline, types below (except FBT_BOOL) store an offset. - FBT_KEY = 4, - FBT_STRING = 5, - FBT_INDIRECT_INT = 6, - FBT_INDIRECT_UINT = 7, - FBT_INDIRECT_FLOAT = 8, - FBT_MAP = 9, - FBT_VECTOR = 10, // Untyped. - FBT_VECTOR_INT = 11, // Typed any size (stores no type table). - FBT_VECTOR_UINT = 12, - FBT_VECTOR_FLOAT = 13, - FBT_VECTOR_KEY = 14, - // DEPRECATED, use FBT_VECTOR or FBT_VECTOR_KEY instead. - // Read test.cpp/FlexBuffersDeprecatedTest() for details on why. - FBT_VECTOR_STRING_DEPRECATED = 15, - FBT_VECTOR_INT2 = 16, // Typed tuple (no type table, no size field). - FBT_VECTOR_UINT2 = 17, - FBT_VECTOR_FLOAT2 = 18, - FBT_VECTOR_INT3 = 19, // Typed triple (no type table, no size field). - FBT_VECTOR_UINT3 = 20, - FBT_VECTOR_FLOAT3 = 21, - FBT_VECTOR_INT4 = 22, // Typed quad (no type table, no size field). - FBT_VECTOR_UINT4 = 23, - FBT_VECTOR_FLOAT4 = 24, - FBT_BLOB = 25, - FBT_BOOL = 26, - FBT_VECTOR_BOOL = - 36, // To Allow the same type of conversion of type to vector type - - FBT_MAX_TYPE = 37 -}; - -inline bool IsInline(Type t) { return t <= FBT_FLOAT || t == FBT_BOOL; } - -inline bool IsTypedVectorElementType(Type t) { - return (t >= FBT_INT && t <= FBT_STRING) || t == FBT_BOOL; -} - -inline bool IsTypedVector(Type t) { - return (t >= FBT_VECTOR_INT && t <= FBT_VECTOR_STRING_DEPRECATED) || - t == FBT_VECTOR_BOOL; -} - -inline bool IsFixedTypedVector(Type t) { - return t >= FBT_VECTOR_INT2 && t <= FBT_VECTOR_FLOAT4; -} - -inline Type ToTypedVector(Type t, size_t fixed_len = 0) { - FLATBUFFERS_ASSERT(IsTypedVectorElementType(t)); - switch (fixed_len) { - case 0: return static_cast(t - FBT_INT + FBT_VECTOR_INT); - case 2: return static_cast(t - FBT_INT + FBT_VECTOR_INT2); - case 3: return static_cast(t - FBT_INT + FBT_VECTOR_INT3); - case 4: return static_cast(t - FBT_INT + FBT_VECTOR_INT4); - default: FLATBUFFERS_ASSERT(0); return FBT_NULL; - } -} - -inline Type ToTypedVectorElementType(Type t) { - FLATBUFFERS_ASSERT(IsTypedVector(t)); - return static_cast(t - FBT_VECTOR_INT + FBT_INT); -} - -inline Type ToFixedTypedVectorElementType(Type t, uint8_t *len) { - FLATBUFFERS_ASSERT(IsFixedTypedVector(t)); - auto fixed_type = t - FBT_VECTOR_INT2; - *len = static_cast(fixed_type / 3 + - 2); // 3 types each, starting from length 2. - return static_cast(fixed_type % 3 + FBT_INT); -} - -// TODO: implement proper support for 8/16bit floats, or decide not to -// support them. -typedef int16_t half; -typedef int8_t quarter; - -// TODO: can we do this without conditionals using intrinsics or inline asm -// on some platforms? Given branch prediction the method below should be -// decently quick, but it is the most frequently executed function. -// We could do an (unaligned) 64-bit read if we ifdef out the platforms for -// which that doesn't work (or where we'd read into un-owned memory). -template -R ReadSizedScalar(const uint8_t *data, uint8_t byte_width) { - return byte_width < 4 - ? (byte_width < 2 - ? static_cast(flatbuffers::ReadScalar(data)) - : static_cast(flatbuffers::ReadScalar(data))) - : (byte_width < 8 - ? static_cast(flatbuffers::ReadScalar(data)) - : static_cast(flatbuffers::ReadScalar(data))); -} - -inline int64_t ReadInt64(const uint8_t *data, uint8_t byte_width) { - return ReadSizedScalar( - data, byte_width); -} - -inline uint64_t ReadUInt64(const uint8_t *data, uint8_t byte_width) { - // This is the "hottest" function (all offset lookups use this), so worth - // optimizing if possible. - // TODO: GCC apparently replaces memcpy by a rep movsb, but only if count is a - // constant, which here it isn't. Test if memcpy is still faster than - // the conditionals in ReadSizedScalar. Can also use inline asm. - // clang-format off - #if defined(_MSC_VER) && defined(_M_X64) && !defined(_M_ARM64EC) - // This is 64-bit Windows only, __movsb does not work on 32-bit Windows. - uint64_t u = 0; - __movsb(reinterpret_cast(&u), - reinterpret_cast(data), byte_width); - return flatbuffers::EndianScalar(u); - #else - return ReadSizedScalar( - data, byte_width); - #endif - // clang-format on -} - -inline double ReadDouble(const uint8_t *data, uint8_t byte_width) { - return ReadSizedScalar(data, - byte_width); -} - -inline const uint8_t *Indirect(const uint8_t *offset, uint8_t byte_width) { - return offset - ReadUInt64(offset, byte_width); -} - -template const uint8_t *Indirect(const uint8_t *offset) { - return offset - flatbuffers::ReadScalar(offset); -} - -inline BitWidth WidthU(uint64_t u) { -#define FLATBUFFERS_GET_FIELD_BIT_WIDTH(value, width) \ - { \ - if (!((u) & ~((1ULL << (width)) - 1ULL))) return BIT_WIDTH_##width; \ - } - FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 8); - FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 16); - FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 32); -#undef FLATBUFFERS_GET_FIELD_BIT_WIDTH - return BIT_WIDTH_64; -} - -inline BitWidth WidthI(int64_t i) { - auto u = static_cast(i) << 1; - return WidthU(i >= 0 ? u : ~u); -} - -inline BitWidth WidthF(double f) { - return static_cast(static_cast(f)) == f ? BIT_WIDTH_32 - : BIT_WIDTH_64; -} - -// Base class of all types below. -// Points into the data buffer and allows access to one type. -class Object { - public: - Object(const uint8_t *data, uint8_t byte_width) - : data_(data), byte_width_(byte_width) {} - - protected: - const uint8_t *data_; - uint8_t byte_width_; -}; - -// Object that has a size, obtained either from size prefix, or elsewhere. -class Sized : public Object { - public: - // Size prefix. - Sized(const uint8_t *data, uint8_t byte_width) - : Object(data, byte_width), size_(read_size()) {} - // Manual size. - Sized(const uint8_t *data, uint8_t byte_width, size_t sz) - : Object(data, byte_width), size_(sz) {} - size_t size() const { return size_; } - // Access size stored in `byte_width_` bytes before data_ pointer. - size_t read_size() const { - return static_cast(ReadUInt64(data_ - byte_width_, byte_width_)); - } - - protected: - size_t size_; -}; - -class String : public Sized { - public: - // Size prefix. - String(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {} - // Manual size. - String(const uint8_t *data, uint8_t byte_width, size_t sz) - : Sized(data, byte_width, sz) {} - - size_t length() const { return size(); } - const char *c_str() const { return reinterpret_cast(data_); } - std::string str() const { return std::string(c_str(), size()); } - - static String EmptyString() { - static const char *empty_string = ""; - return String(reinterpret_cast(empty_string), 1, 0); - } - bool IsTheEmptyString() const { return data_ == EmptyString().data_; } -}; - -class Blob : public Sized { - public: - Blob(const uint8_t *data_buf, uint8_t byte_width) - : Sized(data_buf, byte_width) {} - - static Blob EmptyBlob() { - static const uint8_t empty_blob[] = { 0 /*len*/ }; - return Blob(empty_blob + 1, 1); - } - bool IsTheEmptyBlob() const { return data_ == EmptyBlob().data_; } - const uint8_t *data() const { return data_; } -}; - -class Vector : public Sized { - public: - Vector(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {} - - Reference operator[](size_t i) const; - - static Vector EmptyVector() { - static const uint8_t empty_vector[] = { 0 /*len*/ }; - return Vector(empty_vector + 1, 1); - } - bool IsTheEmptyVector() const { return data_ == EmptyVector().data_; } -}; - -class TypedVector : public Sized { - public: - TypedVector(const uint8_t *data, uint8_t byte_width, Type element_type) - : Sized(data, byte_width), type_(element_type) {} - - Reference operator[](size_t i) const; - - static TypedVector EmptyTypedVector() { - static const uint8_t empty_typed_vector[] = { 0 /*len*/ }; - return TypedVector(empty_typed_vector + 1, 1, FBT_INT); - } - bool IsTheEmptyVector() const { - return data_ == TypedVector::EmptyTypedVector().data_; - } - - Type ElementType() { return type_; } - - friend Reference; - - private: - Type type_; - - friend Map; -}; - -class FixedTypedVector : public Object { - public: - FixedTypedVector(const uint8_t *data, uint8_t byte_width, Type element_type, - uint8_t len) - : Object(data, byte_width), type_(element_type), len_(len) {} - - Reference operator[](size_t i) const; - - static FixedTypedVector EmptyFixedTypedVector() { - static const uint8_t fixed_empty_vector[] = { 0 /* unused */ }; - return FixedTypedVector(fixed_empty_vector, 1, FBT_INT, 0); - } - bool IsTheEmptyFixedTypedVector() const { - return data_ == FixedTypedVector::EmptyFixedTypedVector().data_; - } - - Type ElementType() const { return type_; } - uint8_t size() const { return len_; } - - private: - Type type_; - uint8_t len_; -}; - -class Map : public Vector { - public: - Map(const uint8_t *data, uint8_t byte_width) : Vector(data, byte_width) {} - - Reference operator[](const char *key) const; - Reference operator[](const std::string &key) const; - - Vector Values() const { return Vector(data_, byte_width_); } - - TypedVector Keys() const { - const size_t num_prefixed_fields = 3; - auto keys_offset = data_ - byte_width_ * num_prefixed_fields; - return TypedVector(Indirect(keys_offset, byte_width_), - static_cast( - ReadUInt64(keys_offset + byte_width_, byte_width_)), - FBT_KEY); - } - - static Map EmptyMap() { - static const uint8_t empty_map[] = { - 0 /*keys_len*/, 0 /*keys_offset*/, 1 /*keys_width*/, 0 /*len*/ - }; - return Map(empty_map + 4, 1); - } - - bool IsTheEmptyMap() const { return data_ == EmptyMap().data_; } -}; - -template -void AppendToString(std::string &s, T &&v, bool keys_quoted) { - s += "[ "; - for (size_t i = 0; i < v.size(); i++) { - if (i) s += ", "; - v[i].ToString(true, keys_quoted, s); - } - s += " ]"; -} - -class Reference { - public: - Reference() - : data_(nullptr), parent_width_(0), byte_width_(0), type_(FBT_NULL) {} - - Reference(const uint8_t *data, uint8_t parent_width, uint8_t byte_width, - Type type) - : data_(data), - parent_width_(parent_width), - byte_width_(byte_width), - type_(type) {} - - Reference(const uint8_t *data, uint8_t parent_width, uint8_t packed_type) - : data_(data), parent_width_(parent_width) { - byte_width_ = 1U << static_cast(packed_type & 3); - type_ = static_cast(packed_type >> 2); - } - - Type GetType() const { return type_; } - - bool IsNull() const { return type_ == FBT_NULL; } - bool IsBool() const { return type_ == FBT_BOOL; } - bool IsInt() const { return type_ == FBT_INT || type_ == FBT_INDIRECT_INT; } - bool IsUInt() const { - return type_ == FBT_UINT || type_ == FBT_INDIRECT_UINT; - } - bool IsIntOrUint() const { return IsInt() || IsUInt(); } - bool IsFloat() const { - return type_ == FBT_FLOAT || type_ == FBT_INDIRECT_FLOAT; - } - bool IsNumeric() const { return IsIntOrUint() || IsFloat(); } - bool IsString() const { return type_ == FBT_STRING; } - bool IsKey() const { return type_ == FBT_KEY; } - bool IsVector() const { return type_ == FBT_VECTOR || type_ == FBT_MAP; } - bool IsUntypedVector() const { return type_ == FBT_VECTOR; } - bool IsTypedVector() const { return flexbuffers::IsTypedVector(type_); } - bool IsFixedTypedVector() const { - return flexbuffers::IsFixedTypedVector(type_); - } - bool IsAnyVector() const { - return (IsTypedVector() || IsFixedTypedVector() || IsVector()); - } - bool IsMap() const { return type_ == FBT_MAP; } - bool IsBlob() const { return type_ == FBT_BLOB; } - bool AsBool() const { - return (type_ == FBT_BOOL ? ReadUInt64(data_, parent_width_) - : AsUInt64()) != 0; - } - - // Reads any type as a int64_t. Never fails, does most sensible conversion. - // Truncates floats, strings are attempted to be parsed for a number, - // vectors/maps return their size. Returns 0 if all else fails. - int64_t AsInt64() const { - if (type_ == FBT_INT) { - // A fast path for the common case. - return ReadInt64(data_, parent_width_); - } else - switch (type_) { - case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_); - case FBT_UINT: return ReadUInt64(data_, parent_width_); - case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_); - case FBT_FLOAT: - return static_cast(ReadDouble(data_, parent_width_)); - case FBT_INDIRECT_FLOAT: - return static_cast(ReadDouble(Indirect(), byte_width_)); - case FBT_NULL: return 0; - case FBT_STRING: return flatbuffers::StringToInt(AsString().c_str()); - case FBT_VECTOR: return static_cast(AsVector().size()); - case FBT_BOOL: return ReadInt64(data_, parent_width_); - default: - // Convert other things to int. - return 0; - } - } - - // TODO: could specialize these to not use AsInt64() if that saves - // extension ops in generated code, and use a faster op than ReadInt64. - int32_t AsInt32() const { return static_cast(AsInt64()); } - int16_t AsInt16() const { return static_cast(AsInt64()); } - int8_t AsInt8() const { return static_cast(AsInt64()); } - - uint64_t AsUInt64() const { - if (type_ == FBT_UINT) { - // A fast path for the common case. - return ReadUInt64(data_, parent_width_); - } else - switch (type_) { - case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_); - case FBT_INT: return ReadInt64(data_, parent_width_); - case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_); - case FBT_FLOAT: - return static_cast(ReadDouble(data_, parent_width_)); - case FBT_INDIRECT_FLOAT: - return static_cast(ReadDouble(Indirect(), byte_width_)); - case FBT_NULL: return 0; - case FBT_STRING: return flatbuffers::StringToUInt(AsString().c_str()); - case FBT_VECTOR: return static_cast(AsVector().size()); - case FBT_BOOL: return ReadUInt64(data_, parent_width_); - default: - // Convert other things to uint. - return 0; - } - } - - uint32_t AsUInt32() const { return static_cast(AsUInt64()); } - uint16_t AsUInt16() const { return static_cast(AsUInt64()); } - uint8_t AsUInt8() const { return static_cast(AsUInt64()); } - - double AsDouble() const { - if (type_ == FBT_FLOAT) { - // A fast path for the common case. - return ReadDouble(data_, parent_width_); - } else - switch (type_) { - case FBT_INDIRECT_FLOAT: return ReadDouble(Indirect(), byte_width_); - case FBT_INT: - return static_cast(ReadInt64(data_, parent_width_)); - case FBT_UINT: - return static_cast(ReadUInt64(data_, parent_width_)); - case FBT_INDIRECT_INT: - return static_cast(ReadInt64(Indirect(), byte_width_)); - case FBT_INDIRECT_UINT: - return static_cast(ReadUInt64(Indirect(), byte_width_)); - case FBT_NULL: return 0.0; - case FBT_STRING: { - double d; - flatbuffers::StringToNumber(AsString().c_str(), &d); - return d; - } - case FBT_VECTOR: return static_cast(AsVector().size()); - case FBT_BOOL: - return static_cast(ReadUInt64(data_, parent_width_)); - default: - // Convert strings and other things to float. - return 0; - } - } - - float AsFloat() const { return static_cast(AsDouble()); } - - const char *AsKey() const { - if (type_ == FBT_KEY || type_ == FBT_STRING) { - return reinterpret_cast(Indirect()); - } else { - return ""; - } - } - - // This function returns the empty string if you try to read something that - // is not a string or key. - String AsString() const { - if (type_ == FBT_STRING) { - return String(Indirect(), byte_width_); - } else if (type_ == FBT_KEY) { - auto key = Indirect(); - return String(key, byte_width_, - strlen(reinterpret_cast(key))); - } else { - return String::EmptyString(); - } - } - - // Unlike AsString(), this will convert any type to a std::string. - std::string ToString() const { - std::string s; - ToString(false, false, s); - return s; - } - - // Convert any type to a JSON-like string. strings_quoted determines if - // string values at the top level receive "" quotes (inside other values - // they always do). keys_quoted determines if keys are quoted, at any level. - // TODO(wvo): add further options to have indentation/newlines. - void ToString(bool strings_quoted, bool keys_quoted, std::string &s) const { - if (type_ == FBT_STRING) { - String str(Indirect(), byte_width_); - if (strings_quoted) { - flatbuffers::EscapeString(str.c_str(), str.length(), &s, true, false); - } else { - s.append(str.c_str(), str.length()); - } - } else if (IsKey()) { - auto str = AsKey(); - if (keys_quoted) { - flatbuffers::EscapeString(str, strlen(str), &s, true, false); - } else { - s += str; - } - } else if (IsInt()) { - s += flatbuffers::NumToString(AsInt64()); - } else if (IsUInt()) { - s += flatbuffers::NumToString(AsUInt64()); - } else if (IsFloat()) { - s += flatbuffers::NumToString(AsDouble()); - } else if (IsNull()) { - s += "null"; - } else if (IsBool()) { - s += AsBool() ? "true" : "false"; - } else if (IsMap()) { - s += "{ "; - auto m = AsMap(); - auto keys = m.Keys(); - auto vals = m.Values(); - for (size_t i = 0; i < keys.size(); i++) { - bool kq = keys_quoted; - if (!kq) { - // FlexBuffers keys may contain arbitrary characters, only allow - // unquoted if it looks like an "identifier": - const char *p = keys[i].AsKey(); - if (!flatbuffers::is_alpha(*p) && *p != '_') { - kq = true; - } else { - while (*++p) { - if (!flatbuffers::is_alnum(*p) && *p != '_') { - kq = true; - break; - } - } - } - } - keys[i].ToString(true, kq, s); - s += ": "; - vals[i].ToString(true, keys_quoted, s); - if (i < keys.size() - 1) s += ", "; - } - s += " }"; - } else if (IsVector()) { - AppendToString(s, AsVector(), keys_quoted); - } else if (IsTypedVector()) { - AppendToString(s, AsTypedVector(), keys_quoted); - } else if (IsFixedTypedVector()) { - AppendToString(s, AsFixedTypedVector(), keys_quoted); - } else if (IsBlob()) { - auto blob = AsBlob(); - flatbuffers::EscapeString(reinterpret_cast(blob.data()), - blob.size(), &s, true, false); - } else { - s += "(?)"; - } - } - - // This function returns the empty blob if you try to read a not-blob. - // Strings can be viewed as blobs too. - Blob AsBlob() const { - if (type_ == FBT_BLOB || type_ == FBT_STRING) { - return Blob(Indirect(), byte_width_); - } else { - return Blob::EmptyBlob(); - } - } - - // This function returns the empty vector if you try to read a not-vector. - // Maps can be viewed as vectors too. - Vector AsVector() const { - if (type_ == FBT_VECTOR || type_ == FBT_MAP) { - return Vector(Indirect(), byte_width_); - } else { - return Vector::EmptyVector(); - } - } - - TypedVector AsTypedVector() const { - if (IsTypedVector()) { - auto tv = - TypedVector(Indirect(), byte_width_, ToTypedVectorElementType(type_)); - if (tv.type_ == FBT_STRING) { - // These can't be accessed as strings, since we don't know the bit-width - // of the size field, see the declaration of - // FBT_VECTOR_STRING_DEPRECATED above for details. - // We change the type here to be keys, which are a subtype of strings, - // and will ignore the size field. This will truncate strings with - // embedded nulls. - tv.type_ = FBT_KEY; - } - return tv; - } else { - return TypedVector::EmptyTypedVector(); - } - } - - FixedTypedVector AsFixedTypedVector() const { - if (IsFixedTypedVector()) { - uint8_t len = 0; - auto vtype = ToFixedTypedVectorElementType(type_, &len); - return FixedTypedVector(Indirect(), byte_width_, vtype, len); - } else { - return FixedTypedVector::EmptyFixedTypedVector(); - } - } - - Map AsMap() const { - if (type_ == FBT_MAP) { - return Map(Indirect(), byte_width_); - } else { - return Map::EmptyMap(); - } - } - - template T As() const; - - // Experimental: Mutation functions. - // These allow scalars in an already created buffer to be updated in-place. - // Since by default scalars are stored in the smallest possible space, - // the new value may not fit, in which case these functions return false. - // To avoid this, you can construct the values you intend to mutate using - // Builder::ForceMinimumBitWidth. - bool MutateInt(int64_t i) { - if (type_ == FBT_INT) { - return Mutate(data_, i, parent_width_, WidthI(i)); - } else if (type_ == FBT_INDIRECT_INT) { - return Mutate(Indirect(), i, byte_width_, WidthI(i)); - } else if (type_ == FBT_UINT) { - auto u = static_cast(i); - return Mutate(data_, u, parent_width_, WidthU(u)); - } else if (type_ == FBT_INDIRECT_UINT) { - auto u = static_cast(i); - return Mutate(Indirect(), u, byte_width_, WidthU(u)); - } else { - return false; - } - } - - bool MutateBool(bool b) { - return type_ == FBT_BOOL && Mutate(data_, b, parent_width_, BIT_WIDTH_8); - } - - bool MutateUInt(uint64_t u) { - if (type_ == FBT_UINT) { - return Mutate(data_, u, parent_width_, WidthU(u)); - } else if (type_ == FBT_INDIRECT_UINT) { - return Mutate(Indirect(), u, byte_width_, WidthU(u)); - } else if (type_ == FBT_INT) { - auto i = static_cast(u); - return Mutate(data_, i, parent_width_, WidthI(i)); - } else if (type_ == FBT_INDIRECT_INT) { - auto i = static_cast(u); - return Mutate(Indirect(), i, byte_width_, WidthI(i)); - } else { - return false; - } - } - - bool MutateFloat(float f) { - if (type_ == FBT_FLOAT) { - return MutateF(data_, f, parent_width_, BIT_WIDTH_32); - } else if (type_ == FBT_INDIRECT_FLOAT) { - return MutateF(Indirect(), f, byte_width_, BIT_WIDTH_32); - } else { - return false; - } - } - - bool MutateFloat(double d) { - if (type_ == FBT_FLOAT) { - return MutateF(data_, d, parent_width_, WidthF(d)); - } else if (type_ == FBT_INDIRECT_FLOAT) { - return MutateF(Indirect(), d, byte_width_, WidthF(d)); - } else { - return false; - } - } - - bool MutateString(const char *str, size_t len) { - auto s = AsString(); - if (s.IsTheEmptyString()) return false; - // This is very strict, could allow shorter strings, but that creates - // garbage. - if (s.length() != len) return false; - memcpy(const_cast(s.c_str()), str, len); - return true; - } - bool MutateString(const char *str) { return MutateString(str, strlen(str)); } - bool MutateString(const std::string &str) { - return MutateString(str.data(), str.length()); - } - - private: - const uint8_t *Indirect() const { - return flexbuffers::Indirect(data_, parent_width_); - } - - template - bool Mutate(const uint8_t *dest, T t, size_t byte_width, - BitWidth value_width) { - auto fits = static_cast(static_cast(1U) << value_width) <= - byte_width; - if (fits) { - t = flatbuffers::EndianScalar(t); - memcpy(const_cast(dest), &t, byte_width); - } - return fits; - } - - template - bool MutateF(const uint8_t *dest, T t, size_t byte_width, - BitWidth value_width) { - if (byte_width == sizeof(double)) - return Mutate(dest, static_cast(t), byte_width, value_width); - if (byte_width == sizeof(float)) - return Mutate(dest, static_cast(t), byte_width, value_width); - FLATBUFFERS_ASSERT(false); - return false; - } - - friend class Verifier; - - const uint8_t *data_; - uint8_t parent_width_; - uint8_t byte_width_; - Type type_; -}; - -// Template specialization for As(). -template<> inline bool Reference::As() const { return AsBool(); } - -template<> inline int8_t Reference::As() const { return AsInt8(); } -template<> inline int16_t Reference::As() const { return AsInt16(); } -template<> inline int32_t Reference::As() const { return AsInt32(); } -template<> inline int64_t Reference::As() const { return AsInt64(); } - -template<> inline uint8_t Reference::As() const { return AsUInt8(); } -template<> inline uint16_t Reference::As() const { - return AsUInt16(); -} -template<> inline uint32_t Reference::As() const { - return AsUInt32(); -} -template<> inline uint64_t Reference::As() const { - return AsUInt64(); -} - -template<> inline double Reference::As() const { return AsDouble(); } -template<> inline float Reference::As() const { return AsFloat(); } - -template<> inline String Reference::As() const { return AsString(); } -template<> inline std::string Reference::As() const { - return AsString().str(); -} - -template<> inline Blob Reference::As() const { return AsBlob(); } -template<> inline Vector Reference::As() const { return AsVector(); } -template<> inline TypedVector Reference::As() const { - return AsTypedVector(); -} -template<> inline FixedTypedVector Reference::As() const { - return AsFixedTypedVector(); -} -template<> inline Map Reference::As() const { return AsMap(); } - -inline uint8_t PackedType(BitWidth bit_width, Type type) { - return static_cast(bit_width | (type << 2)); -} - -inline uint8_t NullPackedType() { return PackedType(BIT_WIDTH_8, FBT_NULL); } - -// Vector accessors. -// Note: if you try to access outside of bounds, you get a Null value back -// instead. Normally this would be an assert, but since this is "dynamically -// typed" data, you may not want that (someone sends you a 2d vector and you -// wanted 3d). -// The Null converts seamlessly into a default value for any other type. -// TODO(wvo): Could introduce an #ifdef that makes this into an assert? -inline Reference Vector::operator[](size_t i) const { - auto len = size(); - if (i >= len) return Reference(nullptr, 1, NullPackedType()); - auto packed_type = (data_ + len * byte_width_)[i]; - auto elem = data_ + i * byte_width_; - return Reference(elem, byte_width_, packed_type); -} - -inline Reference TypedVector::operator[](size_t i) const { - auto len = size(); - if (i >= len) return Reference(nullptr, 1, NullPackedType()); - auto elem = data_ + i * byte_width_; - return Reference(elem, byte_width_, 1, type_); -} - -inline Reference FixedTypedVector::operator[](size_t i) const { - if (i >= len_) return Reference(nullptr, 1, NullPackedType()); - auto elem = data_ + i * byte_width_; - return Reference(elem, byte_width_, 1, type_); -} - -template int KeyCompare(const void *key, const void *elem) { - auto str_elem = reinterpret_cast( - Indirect(reinterpret_cast(elem))); - auto skey = reinterpret_cast(key); - return strcmp(skey, str_elem); -} - -inline Reference Map::operator[](const char *key) const { - auto keys = Keys(); - // We can't pass keys.byte_width_ to the comparison function, so we have - // to pick the right one ahead of time. - int (*comp)(const void *, const void *) = nullptr; - switch (keys.byte_width_) { - case 1: comp = KeyCompare; break; - case 2: comp = KeyCompare; break; - case 4: comp = KeyCompare; break; - case 8: comp = KeyCompare; break; - default: FLATBUFFERS_ASSERT(false); return Reference(); - } - auto res = std::bsearch(key, keys.data_, keys.size(), keys.byte_width_, comp); - if (!res) return Reference(nullptr, 1, NullPackedType()); - auto i = (reinterpret_cast(res) - keys.data_) / keys.byte_width_; - return (*static_cast(this))[i]; -} - -inline Reference Map::operator[](const std::string &key) const { - return (*this)[key.c_str()]; -} - -inline Reference GetRoot(const uint8_t *buffer, size_t size) { - // See Finish() below for the serialization counterpart of this. - // The root starts at the end of the buffer, so we parse backwards from there. - auto end = buffer + size; - auto byte_width = *--end; - auto packed_type = *--end; - end -= byte_width; // The root data item. - return Reference(end, byte_width, packed_type); -} - -inline Reference GetRoot(const std::vector &buffer) { - return GetRoot(buffer.data(), buffer.size()); -} - -// Flags that configure how the Builder behaves. -// The "Share" flags determine if the Builder automatically tries to pool -// this type. Pooling can reduce the size of serialized data if there are -// multiple maps of the same kind, at the expense of slightly slower -// serialization (the cost of lookups) and more memory use (std::set). -// By default this is on for keys, but off for strings. -// Turn keys off if you have e.g. only one map. -// Turn strings on if you expect many non-unique string values. -// Additionally, sharing key vectors can save space if you have maps with -// identical field populations. -enum BuilderFlag { - BUILDER_FLAG_NONE = 0, - BUILDER_FLAG_SHARE_KEYS = 1, - BUILDER_FLAG_SHARE_STRINGS = 2, - BUILDER_FLAG_SHARE_KEYS_AND_STRINGS = 3, - BUILDER_FLAG_SHARE_KEY_VECTORS = 4, - BUILDER_FLAG_SHARE_ALL = 7, -}; - -class Builder FLATBUFFERS_FINAL_CLASS { - public: - Builder(size_t initial_size = 256, - BuilderFlag flags = BUILDER_FLAG_SHARE_KEYS) - : buf_(initial_size), - finished_(false), - has_duplicate_keys_(false), - flags_(flags), - force_min_bit_width_(BIT_WIDTH_8), - key_pool(KeyOffsetCompare(buf_)), - string_pool(StringOffsetCompare(buf_)) { - buf_.clear(); - } - -#ifdef FLATBUFFERS_DEFAULT_DECLARATION - Builder(Builder &&) = default; - Builder &operator=(Builder &&) = default; -#endif - - /// @brief Get the serialized buffer (after you call `Finish()`). - /// @return Returns a vector owned by this class. - const std::vector &GetBuffer() const { - Finished(); - return buf_; - } - - // Size of the buffer. Does not include unfinished values. - size_t GetSize() const { return buf_.size(); } - - // Reset all state so we can re-use the buffer. - void Clear() { - buf_.clear(); - stack_.clear(); - finished_ = false; - // flags_ remains as-is; - force_min_bit_width_ = BIT_WIDTH_8; - key_pool.clear(); - string_pool.clear(); - } - - // All value constructing functions below have two versions: one that - // takes a key (for placement inside a map) and one that doesn't (for inside - // vectors and elsewhere). - - void Null() { stack_.push_back(Value()); } - void Null(const char *key) { - Key(key); - Null(); - } - - void Int(int64_t i) { stack_.push_back(Value(i, FBT_INT, WidthI(i))); } - void Int(const char *key, int64_t i) { - Key(key); - Int(i); - } - - void UInt(uint64_t u) { stack_.push_back(Value(u, FBT_UINT, WidthU(u))); } - void UInt(const char *key, uint64_t u) { - Key(key); - UInt(u); - } - - void Float(float f) { stack_.push_back(Value(f)); } - void Float(const char *key, float f) { - Key(key); - Float(f); - } - - void Double(double f) { stack_.push_back(Value(f)); } - void Double(const char *key, double d) { - Key(key); - Double(d); - } - - void Bool(bool b) { stack_.push_back(Value(b)); } - void Bool(const char *key, bool b) { - Key(key); - Bool(b); - } - - void IndirectInt(int64_t i) { PushIndirect(i, FBT_INDIRECT_INT, WidthI(i)); } - void IndirectInt(const char *key, int64_t i) { - Key(key); - IndirectInt(i); - } - - void IndirectUInt(uint64_t u) { - PushIndirect(u, FBT_INDIRECT_UINT, WidthU(u)); - } - void IndirectUInt(const char *key, uint64_t u) { - Key(key); - IndirectUInt(u); - } - - void IndirectFloat(float f) { - PushIndirect(f, FBT_INDIRECT_FLOAT, BIT_WIDTH_32); - } - void IndirectFloat(const char *key, float f) { - Key(key); - IndirectFloat(f); - } - - void IndirectDouble(double f) { - PushIndirect(f, FBT_INDIRECT_FLOAT, WidthF(f)); - } - void IndirectDouble(const char *key, double d) { - Key(key); - IndirectDouble(d); - } - - size_t Key(const char *str, size_t len) { - auto sloc = buf_.size(); - WriteBytes(str, len + 1); - if (flags_ & BUILDER_FLAG_SHARE_KEYS) { - auto it = key_pool.find(sloc); - if (it != key_pool.end()) { - // Already in the buffer. Remove key we just serialized, and use - // existing offset instead. - buf_.resize(sloc); - sloc = *it; - } else { - key_pool.insert(sloc); - } - } - stack_.push_back(Value(static_cast(sloc), FBT_KEY, BIT_WIDTH_8)); - return sloc; - } - - size_t Key(const char *str) { return Key(str, strlen(str)); } - size_t Key(const std::string &str) { return Key(str.c_str(), str.size()); } - - size_t String(const char *str, size_t len) { - auto reset_to = buf_.size(); - auto sloc = CreateBlob(str, len, 1, FBT_STRING); - if (flags_ & BUILDER_FLAG_SHARE_STRINGS) { - StringOffset so(sloc, len); - auto it = string_pool.find(so); - if (it != string_pool.end()) { - // Already in the buffer. Remove string we just serialized, and use - // existing offset instead. - buf_.resize(reset_to); - sloc = it->first; - stack_.back().u_ = sloc; - } else { - string_pool.insert(so); - } - } - return sloc; - } - size_t String(const char *str) { return String(str, strlen(str)); } - size_t String(const std::string &str) { - return String(str.c_str(), str.size()); - } - void String(const flexbuffers::String &str) { - String(str.c_str(), str.length()); - } - - void String(const char *key, const char *str) { - Key(key); - String(str); - } - void String(const char *key, const std::string &str) { - Key(key); - String(str); - } - void String(const char *key, const flexbuffers::String &str) { - Key(key); - String(str); - } - - size_t Blob(const void *data, size_t len) { - return CreateBlob(data, len, 0, FBT_BLOB); - } - size_t Blob(const std::vector &v) { - return CreateBlob(v.data(), v.size(), 0, FBT_BLOB); - } - - void Blob(const char *key, const void *data, size_t len) { - Key(key); - Blob(data, len); - } - void Blob(const char *key, const std::vector &v) { - Key(key); - Blob(v); - } - - // TODO(wvo): support all the FlexBuffer types (like flexbuffers::String), - // e.g. Vector etc. Also in overloaded versions. - // Also some FlatBuffers types? - - size_t StartVector() { return stack_.size(); } - size_t StartVector(const char *key) { - Key(key); - return stack_.size(); - } - size_t StartMap() { return stack_.size(); } - size_t StartMap(const char *key) { - Key(key); - return stack_.size(); - } - - // TODO(wvo): allow this to specify an alignment greater than the natural - // alignment. - size_t EndVector(size_t start, bool typed, bool fixed) { - auto vec = CreateVector(start, stack_.size() - start, 1, typed, fixed); - // Remove temp elements and return vector. - stack_.resize(start); - stack_.push_back(vec); - return static_cast(vec.u_); - } - - size_t EndMap(size_t start) { - // We should have interleaved keys and values on the stack. - // Make sure it is an even number: - auto len = stack_.size() - start; - FLATBUFFERS_ASSERT(!(len & 1)); - len /= 2; - // Make sure keys are all strings: - for (auto key = start; key < stack_.size(); key += 2) { - FLATBUFFERS_ASSERT(stack_[key].type_ == FBT_KEY); - } - // Now sort values, so later we can do a binary search lookup. - // We want to sort 2 array elements at a time. - struct TwoValue { - Value key; - Value val; - }; - // TODO(wvo): strict aliasing? - // TODO(wvo): allow the caller to indicate the data is already sorted - // for maximum efficiency? With an assert to check sortedness to make sure - // we're not breaking binary search. - // Or, we can track if the map is sorted as keys are added which would be - // be quite cheap (cheaper than checking it here), so we can skip this - // step automatically when appliccable, and encourage people to write in - // sorted fashion. - // std::sort is typically already a lot faster on sorted data though. - auto dict = reinterpret_cast(stack_.data() + start); - std::sort( - dict, dict + len, [&](const TwoValue &a, const TwoValue &b) -> bool { - auto as = reinterpret_cast(buf_.data() + a.key.u_); - auto bs = reinterpret_cast(buf_.data() + b.key.u_); - auto comp = strcmp(as, bs); - // We want to disallow duplicate keys, since this results in a - // map where values cannot be found. - // But we can't assert here (since we don't want to fail on - // random JSON input) or have an error mechanism. - // Instead, we set has_duplicate_keys_ in the builder to - // signal this. - // TODO: Have to check for pointer equality, as some sort - // implementation apparently call this function with the same - // element?? Why? - if (!comp && &a != &b) has_duplicate_keys_ = true; - return comp < 0; - }); - // First create a vector out of all keys. - // TODO(wvo): if kBuilderFlagShareKeyVectors is true, see if we can share - // the first vector. - auto keys = CreateVector(start, len, 2, true, false); - auto vec = CreateVector(start + 1, len, 2, false, false, &keys); - // Remove temp elements and return map. - stack_.resize(start); - stack_.push_back(vec); - return static_cast(vec.u_); - } - - // Call this after EndMap to see if the map had any duplicate keys. - // Any map with such keys won't be able to retrieve all values. - bool HasDuplicateKeys() const { return has_duplicate_keys_; } - - template size_t Vector(F f) { - auto start = StartVector(); - f(); - return EndVector(start, false, false); - } - template size_t Vector(F f, T &state) { - auto start = StartVector(); - f(state); - return EndVector(start, false, false); - } - template size_t Vector(const char *key, F f) { - auto start = StartVector(key); - f(); - return EndVector(start, false, false); - } - template - size_t Vector(const char *key, F f, T &state) { - auto start = StartVector(key); - f(state); - return EndVector(start, false, false); - } - - template void Vector(const T *elems, size_t len) { - if (flatbuffers::is_scalar::value) { - // This path should be a lot quicker and use less space. - ScalarVector(elems, len, false); - } else { - auto start = StartVector(); - for (size_t i = 0; i < len; i++) Add(elems[i]); - EndVector(start, false, false); - } - } - template - void Vector(const char *key, const T *elems, size_t len) { - Key(key); - Vector(elems, len); - } - template void Vector(const std::vector &vec) { - Vector(vec.data(), vec.size()); - } - - template size_t TypedVector(F f) { - auto start = StartVector(); - f(); - return EndVector(start, true, false); - } - template size_t TypedVector(F f, T &state) { - auto start = StartVector(); - f(state); - return EndVector(start, true, false); - } - template size_t TypedVector(const char *key, F f) { - auto start = StartVector(key); - f(); - return EndVector(start, true, false); - } - template - size_t TypedVector(const char *key, F f, T &state) { - auto start = StartVector(key); - f(state); - return EndVector(start, true, false); - } - - template size_t FixedTypedVector(const T *elems, size_t len) { - // We only support a few fixed vector lengths. Anything bigger use a - // regular typed vector. - FLATBUFFERS_ASSERT(len >= 2 && len <= 4); - // And only scalar values. - static_assert(flatbuffers::is_scalar::value, "Unrelated types"); - return ScalarVector(elems, len, true); - } - - template - size_t FixedTypedVector(const char *key, const T *elems, size_t len) { - Key(key); - return FixedTypedVector(elems, len); - } - - template size_t Map(F f) { - auto start = StartMap(); - f(); - return EndMap(start); - } - template size_t Map(F f, T &state) { - auto start = StartMap(); - f(state); - return EndMap(start); - } - template size_t Map(const char *key, F f) { - auto start = StartMap(key); - f(); - return EndMap(start); - } - template size_t Map(const char *key, F f, T &state) { - auto start = StartMap(key); - f(state); - return EndMap(start); - } - template void Map(const std::map &map) { - auto start = StartMap(); - for (auto it = map.begin(); it != map.end(); ++it) - Add(it->first.c_str(), it->second); - EndMap(start); - } - - // If you wish to share a value explicitly (a value not shared automatically - // through one of the BUILDER_FLAG_SHARE_* flags) you can do so with these - // functions. Or if you wish to turn those flags off for performance reasons - // and still do some explicit sharing. For example: - // builder.IndirectDouble(M_PI); - // auto id = builder.LastValue(); // Remember where we stored it. - // .. more code goes here .. - // builder.ReuseValue(id); // Refers to same double by offset. - // LastValue works regardless of whether the value has a key or not. - // Works on any data type. - struct Value; - Value LastValue() { return stack_.back(); } - void ReuseValue(Value v) { stack_.push_back(v); } - void ReuseValue(const char *key, Value v) { - Key(key); - ReuseValue(v); - } - - // Overloaded Add that tries to call the correct function above. - void Add(int8_t i) { Int(i); } - void Add(int16_t i) { Int(i); } - void Add(int32_t i) { Int(i); } - void Add(int64_t i) { Int(i); } - void Add(uint8_t u) { UInt(u); } - void Add(uint16_t u) { UInt(u); } - void Add(uint32_t u) { UInt(u); } - void Add(uint64_t u) { UInt(u); } - void Add(float f) { Float(f); } - void Add(double d) { Double(d); } - void Add(bool b) { Bool(b); } - void Add(const char *str) { String(str); } - void Add(const std::string &str) { String(str); } - void Add(const flexbuffers::String &str) { String(str); } - - template void Add(const std::vector &vec) { Vector(vec); } - - template void Add(const char *key, const T &t) { - Key(key); - Add(t); - } - - template void Add(const std::map &map) { - Map(map); - } - - template void operator+=(const T &t) { Add(t); } - - // This function is useful in combination with the Mutate* functions above. - // It forces elements of vectors and maps to have a minimum size, such that - // they can later be updated without failing. - // Call with no arguments to reset. - void ForceMinimumBitWidth(BitWidth bw = BIT_WIDTH_8) { - force_min_bit_width_ = bw; - } - - void Finish() { - // If you hit this assert, you likely have objects that were never included - // in a parent. You need to have exactly one root to finish a buffer. - // Check your Start/End calls are matched, and all objects are inside - // some other object. - FLATBUFFERS_ASSERT(stack_.size() == 1); - - // Write root value. - auto byte_width = Align(stack_[0].ElemWidth(buf_.size(), 0)); - WriteAny(stack_[0], byte_width); - // Write root type. - Write(stack_[0].StoredPackedType(), 1); - // Write root size. Normally determined by parent, but root has no parent :) - Write(byte_width, 1); - - finished_ = true; - } - - private: - void Finished() const { - // If you get this assert, you're attempting to get access a buffer - // which hasn't been finished yet. Be sure to call - // Builder::Finish with your root object. - FLATBUFFERS_ASSERT(finished_); - } - - // Align to prepare for writing a scalar with a certain size. - uint8_t Align(BitWidth alignment) { - auto byte_width = 1U << alignment; - buf_.insert(buf_.end(), flatbuffers::PaddingBytes(buf_.size(), byte_width), - 0); - return static_cast(byte_width); - } - - void WriteBytes(const void *val, size_t size) { - buf_.insert(buf_.end(), reinterpret_cast(val), - reinterpret_cast(val) + size); - } - - template void Write(T val, size_t byte_width) { - FLATBUFFERS_ASSERT(sizeof(T) >= byte_width); - val = flatbuffers::EndianScalar(val); - WriteBytes(&val, byte_width); - } - - void WriteDouble(double f, uint8_t byte_width) { - switch (byte_width) { - case 8: Write(f, byte_width); break; - case 4: Write(static_cast(f), byte_width); break; - // case 2: Write(static_cast(f), byte_width); break; - // case 1: Write(static_cast(f), byte_width); break; - default: FLATBUFFERS_ASSERT(0); - } - } - - void WriteOffset(uint64_t o, uint8_t byte_width) { - auto reloff = buf_.size() - o; - FLATBUFFERS_ASSERT(byte_width == 8 || reloff < 1ULL << (byte_width * 8)); - Write(reloff, byte_width); - } - - template void PushIndirect(T val, Type type, BitWidth bit_width) { - auto byte_width = Align(bit_width); - auto iloc = buf_.size(); - Write(val, byte_width); - stack_.push_back(Value(static_cast(iloc), type, bit_width)); - } - - static BitWidth WidthB(size_t byte_width) { - switch (byte_width) { - case 1: return BIT_WIDTH_8; - case 2: return BIT_WIDTH_16; - case 4: return BIT_WIDTH_32; - case 8: return BIT_WIDTH_64; - default: FLATBUFFERS_ASSERT(false); return BIT_WIDTH_64; - } - } - - template static Type GetScalarType() { - static_assert(flatbuffers::is_scalar::value, "Unrelated types"); - return flatbuffers::is_floating_point::value ? FBT_FLOAT - : flatbuffers::is_same::value - ? FBT_BOOL - : (flatbuffers::is_unsigned::value ? FBT_UINT : FBT_INT); - } - - public: - // This was really intended to be private, except for LastValue/ReuseValue. - struct Value { - union { - int64_t i_; - uint64_t u_; - double f_; - }; - - Type type_; - - // For scalars: of itself, for vector: of its elements, for string: length. - BitWidth min_bit_width_; - - Value() : i_(0), type_(FBT_NULL), min_bit_width_(BIT_WIDTH_8) {} - - Value(bool b) - : u_(static_cast(b)), - type_(FBT_BOOL), - min_bit_width_(BIT_WIDTH_8) {} - - Value(int64_t i, Type t, BitWidth bw) - : i_(i), type_(t), min_bit_width_(bw) {} - Value(uint64_t u, Type t, BitWidth bw) - : u_(u), type_(t), min_bit_width_(bw) {} - - Value(float f) - : f_(static_cast(f)), - type_(FBT_FLOAT), - min_bit_width_(BIT_WIDTH_32) {} - Value(double f) : f_(f), type_(FBT_FLOAT), min_bit_width_(WidthF(f)) {} - - uint8_t StoredPackedType(BitWidth parent_bit_width_ = BIT_WIDTH_8) const { - return PackedType(StoredWidth(parent_bit_width_), type_); - } - - BitWidth ElemWidth(size_t buf_size, size_t elem_index) const { - if (IsInline(type_)) { - return min_bit_width_; - } else { - // We have an absolute offset, but want to store a relative offset - // elem_index elements beyond the current buffer end. Since whether - // the relative offset fits in a certain byte_width depends on - // the size of the elements before it (and their alignment), we have - // to test for each size in turn. - for (size_t byte_width = 1; - byte_width <= sizeof(flatbuffers::largest_scalar_t); - byte_width *= 2) { - // Where are we going to write this offset? - auto offset_loc = buf_size + - flatbuffers::PaddingBytes(buf_size, byte_width) + - elem_index * byte_width; - // Compute relative offset. - auto offset = offset_loc - u_; - // Does it fit? - auto bit_width = WidthU(offset); - if (static_cast(static_cast(1U) << bit_width) == - byte_width) - return bit_width; - } - FLATBUFFERS_ASSERT(false); // Must match one of the sizes above. - return BIT_WIDTH_64; - } - } - - BitWidth StoredWidth(BitWidth parent_bit_width_ = BIT_WIDTH_8) const { - if (IsInline(type_)) { - return (std::max)(min_bit_width_, parent_bit_width_); - } else { - return min_bit_width_; - } - } - }; - - private: - void WriteAny(const Value &val, uint8_t byte_width) { - switch (val.type_) { - case FBT_NULL: - case FBT_INT: Write(val.i_, byte_width); break; - case FBT_BOOL: - case FBT_UINT: Write(val.u_, byte_width); break; - case FBT_FLOAT: WriteDouble(val.f_, byte_width); break; - default: WriteOffset(val.u_, byte_width); break; - } - } - - size_t CreateBlob(const void *data, size_t len, size_t trailing, Type type) { - auto bit_width = WidthU(len); - auto byte_width = Align(bit_width); - Write(len, byte_width); - auto sloc = buf_.size(); - WriteBytes(data, len + trailing); - stack_.push_back(Value(static_cast(sloc), type, bit_width)); - return sloc; - } - - template - size_t ScalarVector(const T *elems, size_t len, bool fixed) { - auto vector_type = GetScalarType(); - auto byte_width = sizeof(T); - auto bit_width = WidthB(byte_width); - // If you get this assert, you're trying to write a vector with a size - // field that is bigger than the scalars you're trying to write (e.g. a - // byte vector > 255 elements). For such types, write a "blob" instead. - // TODO: instead of asserting, could write vector with larger elements - // instead, though that would be wasteful. - FLATBUFFERS_ASSERT(WidthU(len) <= bit_width); - Align(bit_width); - if (!fixed) Write(len, byte_width); - auto vloc = buf_.size(); - for (size_t i = 0; i < len; i++) Write(elems[i], byte_width); - stack_.push_back(Value(static_cast(vloc), - ToTypedVector(vector_type, fixed ? len : 0), - bit_width)); - return vloc; - } - - Value CreateVector(size_t start, size_t vec_len, size_t step, bool typed, - bool fixed, const Value *keys = nullptr) { - FLATBUFFERS_ASSERT( - !fixed || - typed); // typed=false, fixed=true combination is not supported. - // Figure out smallest bit width we can store this vector with. - auto bit_width = (std::max)(force_min_bit_width_, WidthU(vec_len)); - auto prefix_elems = 1; - if (keys) { - // If this vector is part of a map, we will pre-fix an offset to the keys - // to this vector. - bit_width = (std::max)(bit_width, keys->ElemWidth(buf_.size(), 0)); - prefix_elems += 2; - } - Type vector_type = FBT_KEY; - // Check bit widths and types for all elements. - for (size_t i = start; i < stack_.size(); i += step) { - auto elem_width = - stack_[i].ElemWidth(buf_.size(), i - start + prefix_elems); - bit_width = (std::max)(bit_width, elem_width); - if (typed) { - if (i == start) { - vector_type = stack_[i].type_; - } else { - // If you get this assert, you are writing a typed vector with - // elements that are not all the same type. - FLATBUFFERS_ASSERT(vector_type == stack_[i].type_); - } - } - } - // If you get this assert, your typed types are not one of: - // Int / UInt / Float / Key. - FLATBUFFERS_ASSERT(!typed || IsTypedVectorElementType(vector_type)); - auto byte_width = Align(bit_width); - // Write vector. First the keys width/offset if available, and size. - if (keys) { - WriteOffset(keys->u_, byte_width); - Write(1ULL << keys->min_bit_width_, byte_width); - } - if (!fixed) Write(vec_len, byte_width); - // Then the actual data. - auto vloc = buf_.size(); - for (size_t i = start; i < stack_.size(); i += step) { - WriteAny(stack_[i], byte_width); - } - // Then the types. - if (!typed) { - for (size_t i = start; i < stack_.size(); i += step) { - buf_.push_back(stack_[i].StoredPackedType(bit_width)); - } - } - return Value(static_cast(vloc), - keys ? FBT_MAP - : (typed ? ToTypedVector(vector_type, fixed ? vec_len : 0) - : FBT_VECTOR), - bit_width); - } - - // You shouldn't really be copying instances of this class. - Builder(const Builder &); - Builder &operator=(const Builder &); - - std::vector buf_; - std::vector stack_; - - bool finished_; - bool has_duplicate_keys_; - - BuilderFlag flags_; - - BitWidth force_min_bit_width_; - - struct KeyOffsetCompare { - explicit KeyOffsetCompare(const std::vector &buf) : buf_(&buf) {} - bool operator()(size_t a, size_t b) const { - auto stra = reinterpret_cast(buf_->data() + a); - auto strb = reinterpret_cast(buf_->data() + b); - return strcmp(stra, strb) < 0; - } - const std::vector *buf_; - }; - - typedef std::pair StringOffset; - struct StringOffsetCompare { - explicit StringOffsetCompare(const std::vector &buf) - : buf_(&buf) {} - bool operator()(const StringOffset &a, const StringOffset &b) const { - auto stra = buf_->data() + a.first; - auto strb = buf_->data() + b.first; - auto cr = memcmp(stra, strb, (std::min)(a.second, b.second) + 1); - return cr < 0 || (cr == 0 && a.second < b.second); - } - const std::vector *buf_; - }; - - typedef std::set KeyOffsetMap; - typedef std::set StringOffsetMap; - - KeyOffsetMap key_pool; - StringOffsetMap string_pool; - - friend class Verifier; -}; - -// Helper class to verify the integrity of a FlexBuffer -class Verifier FLATBUFFERS_FINAL_CLASS { - public: - Verifier(const uint8_t *buf, size_t buf_len, - // Supplying this vector likely results in faster verification - // of larger buffers with many shared keys/strings, but - // comes at the cost of using additional memory the same size of - // the buffer being verified, so it is by default off. - std::vector *reuse_tracker = nullptr, - bool _check_alignment = true, size_t max_depth = 64) - : buf_(buf), - size_(buf_len), - depth_(0), - max_depth_(max_depth), - num_vectors_(0), - max_vectors_(buf_len), - check_alignment_(_check_alignment), - reuse_tracker_(reuse_tracker) { - FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE); - if (reuse_tracker_) { - reuse_tracker_->clear(); - reuse_tracker_->resize(size_, PackedType(BIT_WIDTH_8, FBT_NULL)); - } - } - - private: - // Central location where any verification failures register. - bool Check(bool ok) const { - // clang-format off - #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE - FLATBUFFERS_ASSERT(ok); - #endif - // clang-format on - return ok; - } - - // Verify any range within the buffer. - bool VerifyFrom(size_t elem, size_t elem_len) const { - return Check(elem_len < size_ && elem <= size_ - elem_len); - } - bool VerifyBefore(size_t elem, size_t elem_len) const { - return Check(elem_len <= elem); - } - - bool VerifyFromPointer(const uint8_t *p, size_t len) { - auto o = static_cast(p - buf_); - return VerifyFrom(o, len); - } - bool VerifyBeforePointer(const uint8_t *p, size_t len) { - auto o = static_cast(p - buf_); - return VerifyBefore(o, len); - } - - bool VerifyByteWidth(size_t width) { - return Check(width == 1 || width == 2 || width == 4 || width == 8); - } - - bool VerifyType(int type) { return Check(type >= 0 && type < FBT_MAX_TYPE); } - - bool VerifyOffset(uint64_t off, const uint8_t *p) { - return Check(off <= static_cast(size_)) && - off <= static_cast(p - buf_); - } - - bool VerifyAlignment(const uint8_t *p, size_t size) const { - auto o = static_cast(p - buf_); - return Check((o & (size - 1)) == 0 || !check_alignment_); - } - -// Macro, since we want to escape from parent function & use lazy args. -#define FLEX_CHECK_VERIFIED(P, PACKED_TYPE) \ - if (reuse_tracker_) { \ - auto packed_type = PACKED_TYPE; \ - auto existing = (*reuse_tracker_)[P - buf_]; \ - if (existing == packed_type) return true; \ - /* Fail verification if already set with different type! */ \ - if (!Check(existing == 0)) return false; \ - (*reuse_tracker_)[P - buf_] = packed_type; \ - } - - bool VerifyVector(Reference r, const uint8_t *p, Type elem_type) { - // Any kind of nesting goes thru this function, so guard against that - // here, both with simple nesting checks, and the reuse tracker if on. - depth_++; - num_vectors_++; - if (!Check(depth_ <= max_depth_ && num_vectors_ <= max_vectors_)) - return false; - auto size_byte_width = r.byte_width_; - FLEX_CHECK_VERIFIED(p, - PackedType(Builder::WidthB(size_byte_width), r.type_)); - if (!VerifyBeforePointer(p, size_byte_width)) return false; - auto sized = Sized(p, size_byte_width); - auto num_elems = sized.size(); - auto elem_byte_width = r.type_ == FBT_STRING || r.type_ == FBT_BLOB - ? uint8_t(1) - : r.byte_width_; - auto max_elems = SIZE_MAX / elem_byte_width; - if (!Check(num_elems < max_elems)) - return false; // Protect against byte_size overflowing. - auto byte_size = num_elems * elem_byte_width; - if (!VerifyFromPointer(p, byte_size)) return false; - if (elem_type == FBT_NULL) { - // Verify type bytes after the vector. - if (!VerifyFromPointer(p + byte_size, num_elems)) return false; - auto v = Vector(p, size_byte_width); - for (size_t i = 0; i < num_elems; i++) - if (!VerifyRef(v[i])) return false; - } else if (elem_type == FBT_KEY) { - auto v = TypedVector(p, elem_byte_width, FBT_KEY); - for (size_t i = 0; i < num_elems; i++) - if (!VerifyRef(v[i])) return false; - } else { - FLATBUFFERS_ASSERT(IsInline(elem_type)); - } - depth_--; - return true; - } - - bool VerifyKeys(const uint8_t *p, uint8_t byte_width) { - // The vector part of the map has already been verified. - const size_t num_prefixed_fields = 3; - if (!VerifyBeforePointer(p, byte_width * num_prefixed_fields)) return false; - p -= byte_width * num_prefixed_fields; - auto off = ReadUInt64(p, byte_width); - if (!VerifyOffset(off, p)) return false; - auto key_byte_with = - static_cast(ReadUInt64(p + byte_width, byte_width)); - if (!VerifyByteWidth(key_byte_with)) return false; - return VerifyVector(Reference(p, byte_width, key_byte_with, FBT_VECTOR_KEY), - p - off, FBT_KEY); - } - - bool VerifyKey(const uint8_t *p) { - FLEX_CHECK_VERIFIED(p, PackedType(BIT_WIDTH_8, FBT_KEY)); - while (p < buf_ + size_) - if (*p++) return true; - return false; - } - -#undef FLEX_CHECK_VERIFIED - - bool VerifyTerminator(const String &s) { - return VerifyFromPointer(reinterpret_cast(s.c_str()), - s.size() + 1); - } - - bool VerifyRef(Reference r) { - // r.parent_width_ and r.data_ already verified. - if (!VerifyByteWidth(r.byte_width_) || !VerifyType(r.type_)) { - return false; - } - if (IsInline(r.type_)) { - // Inline scalars, don't require further verification. - return true; - } - // All remaining types are an offset. - auto off = ReadUInt64(r.data_, r.parent_width_); - if (!VerifyOffset(off, r.data_)) return false; - auto p = r.Indirect(); - if (!VerifyAlignment(p, r.byte_width_)) return false; - switch (r.type_) { - case FBT_INDIRECT_INT: - case FBT_INDIRECT_UINT: - case FBT_INDIRECT_FLOAT: return VerifyFromPointer(p, r.byte_width_); - case FBT_KEY: return VerifyKey(p); - case FBT_MAP: - return VerifyVector(r, p, FBT_NULL) && VerifyKeys(p, r.byte_width_); - case FBT_VECTOR: return VerifyVector(r, p, FBT_NULL); - case FBT_VECTOR_INT: return VerifyVector(r, p, FBT_INT); - case FBT_VECTOR_BOOL: - case FBT_VECTOR_UINT: return VerifyVector(r, p, FBT_UINT); - case FBT_VECTOR_FLOAT: return VerifyVector(r, p, FBT_FLOAT); - case FBT_VECTOR_KEY: return VerifyVector(r, p, FBT_KEY); - case FBT_VECTOR_STRING_DEPRECATED: - // Use of FBT_KEY here intentional, see elsewhere. - return VerifyVector(r, p, FBT_KEY); - case FBT_BLOB: return VerifyVector(r, p, FBT_UINT); - case FBT_STRING: - return VerifyVector(r, p, FBT_UINT) && - VerifyTerminator(String(p, r.byte_width_)); - case FBT_VECTOR_INT2: - case FBT_VECTOR_UINT2: - case FBT_VECTOR_FLOAT2: - case FBT_VECTOR_INT3: - case FBT_VECTOR_UINT3: - case FBT_VECTOR_FLOAT3: - case FBT_VECTOR_INT4: - case FBT_VECTOR_UINT4: - case FBT_VECTOR_FLOAT4: { - uint8_t len = 0; - auto vtype = ToFixedTypedVectorElementType(r.type_, &len); - if (!VerifyType(vtype)) return false; - return VerifyFromPointer(p, r.byte_width_ * len); - } - default: return false; - } - } - - public: - bool VerifyBuffer() { - if (!Check(size_ >= 3)) return false; - auto end = buf_ + size_; - auto byte_width = *--end; - auto packed_type = *--end; - return VerifyByteWidth(byte_width) && Check(end - buf_ >= byte_width) && - VerifyRef(Reference(end - byte_width, byte_width, packed_type)); - } - - private: - const uint8_t *buf_; - size_t size_; - size_t depth_; - const size_t max_depth_; - size_t num_vectors_; - const size_t max_vectors_; - bool check_alignment_; - std::vector *reuse_tracker_; -}; - -// Utility function that contructs the Verifier for you, see above for -// parameters. -inline bool VerifyBuffer(const uint8_t *buf, size_t buf_len, - std::vector *reuse_tracker = nullptr) { - Verifier verifier(buf, buf_len, reuse_tracker); - return verifier.VerifyBuffer(); -} - -#ifdef FLATBUFFERS_H_ -// This is a verifier utility function that works together with the -// FlatBuffers verifier, which should only be present if flatbuffer.h -// has been included (which it typically is in generated code). -inline bool VerifyNestedFlexBuffer(const flatbuffers::Vector *nv, - flatbuffers::Verifier &verifier) { - if (!nv) return true; - return verifier.Check(flexbuffers::VerifyBuffer( - nv->data(), nv->size(), verifier.GetFlexReuseTracker())); -} -#endif - -} // namespace flexbuffers - -#if defined(_MSC_VER) -# pragma warning(pop) -#endif - -#endif // FLATBUFFERS_FLEXBUFFERS_H_ diff --git a/new_ai_tools/include/flatbuffers/grpc.h b/new_ai_tools/include/flatbuffers/grpc.h deleted file mode 100644 index 5d15bc51..00000000 --- a/new_ai_tools/include/flatbuffers/grpc.h +++ /dev/null @@ -1,300 +0,0 @@ -/* - * Copyright 2014 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_GRPC_H_ -#define FLATBUFFERS_GRPC_H_ - -// Helper functionality to glue FlatBuffers and GRPC. - -#include "flatbuffers/flatbuffers.h" -#include "grpc/byte_buffer_reader.h" -#include "grpcpp/support/byte_buffer.h" -#include "grpcpp/support/slice.h" - -namespace flatbuffers { -namespace grpc { - -// Message is a typed wrapper around a buffer that manages the underlying -// `grpc_slice` and also provides flatbuffers-specific helpers such as `Verify` -// and `GetRoot`. Since it is backed by a `grpc_slice`, the underlying buffer -// is refcounted and ownership is be managed automatically. -template class Message { - public: - Message() {} - - Message(::grpc::Slice slice) : slice_(slice) {} - - Message &operator=(const Message &other) = delete; - - Message(Message &&other) = default; - - Message(const Message &other) = delete; - - Message &operator=(Message &&other) = default; - - const uint8_t *mutable_data() const { return slice_.begin(); } - - const uint8_t *data() const { return slice_.begin(); } - - size_t size() const { return slice_.size(); } - - bool Verify() const { - Verifier verifier(data(), size()); - return verifier.VerifyBuffer(nullptr); - } - - T *GetMutableRoot() { return flatbuffers::GetMutableRoot(mutable_data()); } - - const T *GetRoot() const { return flatbuffers::GetRoot(data()); } - - // This is only intended for serializer use, or if you know what you're doing - const ::grpc::Slice &BorrowSlice() const { return slice_; } - - private: - ::grpc::Slice slice_; -}; - -class MessageBuilder; - -// SliceAllocator is a gRPC-specific allocator that uses the `grpc_slice` -// refcounted slices to manage memory ownership. This makes it easy and -// efficient to transfer buffers to gRPC. -class SliceAllocator : public Allocator { - public: - SliceAllocator() {} - - SliceAllocator(const SliceAllocator &other) = delete; - SliceAllocator &operator=(const SliceAllocator &other) = delete; - - SliceAllocator(SliceAllocator &&other) { - // default-construct and swap idiom - swap(other); - } - - SliceAllocator &operator=(SliceAllocator &&other) { - // move-construct and swap idiom - SliceAllocator temp(std::move(other)); - swap(temp); - return *this; - } - - void swap(SliceAllocator &other) { - using std::swap; - swap(slice_, other.slice_); - } - - virtual ~SliceAllocator() {} - - virtual uint8_t *allocate(size_t size) override { - FLATBUFFERS_ASSERT(slice_.size() == 0); - slice_ = ::grpc::Slice(size); - return const_cast(slice_.begin()); - } - - virtual void deallocate(uint8_t *p, size_t size) override { - FLATBUFFERS_ASSERT(p == slice_.begin()); - FLATBUFFERS_ASSERT(size == slice_.size()); - slice_ = ::grpc::Slice(); - } - - virtual uint8_t *reallocate_downward(uint8_t *old_p, size_t old_size, - size_t new_size, size_t in_use_back, - size_t in_use_front) override { - FLATBUFFERS_ASSERT(old_p == slice_.begin()); - FLATBUFFERS_ASSERT(old_size == slice_.size()); - FLATBUFFERS_ASSERT(new_size > old_size); - ::grpc::Slice old_slice = slice_; - ::grpc::Slice new_slice = ::grpc::Slice(new_size); - uint8_t *new_p = const_cast(new_slice.begin()); - memcpy_downward(old_p, old_size, new_p, new_size, in_use_back, - in_use_front); - slice_ = new_slice; - return new_p; - } - - private: - ::grpc::Slice &get_slice(uint8_t *p, size_t size) { - FLATBUFFERS_ASSERT(p == slice_.begin()); - FLATBUFFERS_ASSERT(size == slice_.size()); - return slice_; - } - - ::grpc::Slice slice_; - - friend class MessageBuilder; -}; - -// SliceAllocatorMember is a hack to ensure that the MessageBuilder's -// slice_allocator_ member is constructed before the FlatBufferBuilder, since -// the allocator is used in the FlatBufferBuilder ctor. -namespace detail { -struct SliceAllocatorMember { - SliceAllocator slice_allocator_; -}; -} // namespace detail - -// MessageBuilder is a gRPC-specific FlatBufferBuilder that uses SliceAllocator -// to allocate gRPC buffers. -class MessageBuilder : private detail::SliceAllocatorMember, - public FlatBufferBuilder { - public: - explicit MessageBuilder(uoffset_t initial_size = 1024) - : FlatBufferBuilder(initial_size, &slice_allocator_, false) {} - - MessageBuilder(const MessageBuilder &other) = delete; - MessageBuilder &operator=(const MessageBuilder &other) = delete; - - MessageBuilder(MessageBuilder &&other) - : FlatBufferBuilder(1024, &slice_allocator_, false) { - // Default construct and swap idiom. - Swap(other); - } - - /// Create a MessageBuilder from a FlatBufferBuilder. - explicit MessageBuilder(FlatBufferBuilder &&src, - void (*dealloc)(void *, - size_t) = &DefaultAllocator::dealloc) - : FlatBufferBuilder(1024, &slice_allocator_, false) { - src.Swap(*this); - src.SwapBufAllocator(*this); - if (buf_.capacity()) { - uint8_t *buf = buf_.scratch_data(); // pointer to memory - size_t capacity = buf_.capacity(); // size of memory - slice_allocator_.slice_ = ::grpc::Slice(buf, capacity, dealloc); - } else { - slice_allocator_.slice_ = ::grpc::Slice(); - } - } - - /// Move-assign a FlatBufferBuilder to a MessageBuilder. - /// Only FlatBufferBuilder with default allocator (basically, nullptr) is - /// supported. - MessageBuilder &operator=(FlatBufferBuilder &&src) { - // Move construct a temporary and swap - MessageBuilder temp(std::move(src)); - Swap(temp); - return *this; - } - - MessageBuilder &operator=(MessageBuilder &&other) { - // Move construct a temporary and swap - MessageBuilder temp(std::move(other)); - Swap(temp); - return *this; - } - - void Swap(MessageBuilder &other) { - slice_allocator_.swap(other.slice_allocator_); - FlatBufferBuilder::Swap(other); - // After swapping the FlatBufferBuilder, we swap back the allocator, which - // restores the original allocator back in place. This is necessary because - // MessageBuilder's allocator is its own member (SliceAllocatorMember). The - // allocator passed to FlatBufferBuilder::vector_downward must point to this - // member. - buf_.swap_allocator(other.buf_); - } - - // Releases the ownership of the buffer pointer. - // Returns the size, offset, and the original grpc_slice that - // allocated the buffer. Also see grpc_slice_unref(). - uint8_t *ReleaseRaw(size_t &size, size_t &offset, ::grpc::Slice &slice) { - uint8_t *buf = FlatBufferBuilder::ReleaseRaw(size, offset); - slice = slice_allocator_.slice_; - slice_allocator_.slice_ = ::grpc::Slice(); - return buf; - } - - ~MessageBuilder() {} - - // GetMessage extracts the subslice of the buffer corresponding to the - // flatbuffers-encoded region and wraps it in a `Message` to handle buffer - // ownership. - template Message GetMessage() { - auto buf_data = buf_.scratch_data(); // pointer to memory - auto buf_size = buf_.capacity(); // size of memory - auto msg_data = buf_.data(); // pointer to msg - auto msg_size = buf_.size(); // size of msg - // Do some sanity checks on data/size - FLATBUFFERS_ASSERT(msg_data); - FLATBUFFERS_ASSERT(msg_size); - FLATBUFFERS_ASSERT(msg_data >= buf_data); - FLATBUFFERS_ASSERT(msg_data + msg_size <= buf_data + buf_size); - // Calculate offsets from the buffer start - auto begin = msg_data - buf_data; - auto end = begin + msg_size; - // Get the slice we are working with (no refcount change) - ::grpc::Slice slice = slice_allocator_.get_slice(buf_data, buf_size); - // Extract a subslice of the existing slice (increment refcount) - ::grpc::Slice subslice = slice.sub(begin, end); - // Wrap the subslice in a `Message`, but don't increment refcount - Message msg(subslice); - return msg; - } - - template Message ReleaseMessage() { - Message msg = GetMessage(); - Reset(); - return msg; - } - - private: - // SliceAllocator slice_allocator_; // part of SliceAllocatorMember -}; - -} // namespace grpc -} // namespace flatbuffers - -namespace grpc { - -template class SerializationTraits> { - public: - static grpc::Status Serialize(const flatbuffers::grpc::Message &msg, - ByteBuffer *buffer, bool *own_buffer) { - // Package the single slice into a `ByteBuffer`, - // incrementing the refcount in the process. - *buffer = ByteBuffer(&msg.BorrowSlice(), 1); - *own_buffer = true; - return grpc::Status::OK; - } - - // Deserialize by pulling the - static grpc::Status Deserialize(ByteBuffer *buf, - flatbuffers::grpc::Message *msg) { - Slice slice; - if (!buf->TrySingleSlice(&slice).ok()) { - if (!buf->DumpToSingleSlice(&slice).ok()) { - buf->Clear(); - return ::grpc::Status(::grpc::StatusCode::INTERNAL, "No payload"); - } - } - *msg = flatbuffers::grpc::Message(slice); - buf->Clear(); -#if FLATBUFFERS_GRPC_DISABLE_AUTO_VERIFICATION - return ::grpc::Status::OK; -#else - if (msg->Verify()) { - return ::grpc::Status::OK; - } else { - return ::grpc::Status(::grpc::StatusCode::INTERNAL, - "Message verification failed"); - } -#endif - } -}; - -} // namespace grpc - -#endif // FLATBUFFERS_GRPC_H_ diff --git a/new_ai_tools/include/flatbuffers/hash.h b/new_ai_tools/include/flatbuffers/hash.h deleted file mode 100644 index aebf0713..00000000 --- a/new_ai_tools/include/flatbuffers/hash.h +++ /dev/null @@ -1,127 +0,0 @@ -/* - * Copyright 2015 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_HASH_H_ -#define FLATBUFFERS_HASH_H_ - -#include -#include - -#include "flatbuffers/flatbuffers.h" - -namespace flatbuffers { - -template struct FnvTraits { - static const T kFnvPrime; - static const T kOffsetBasis; -}; - -template<> struct FnvTraits { - static const uint32_t kFnvPrime = 0x01000193; - static const uint32_t kOffsetBasis = 0x811C9DC5; -}; - -template<> struct FnvTraits { - static const uint64_t kFnvPrime = 0x00000100000001b3ULL; - static const uint64_t kOffsetBasis = 0xcbf29ce484222645ULL; -}; - -template T HashFnv1(const char *input) { - T hash = FnvTraits::kOffsetBasis; - for (const char *c = input; *c; ++c) { - hash *= FnvTraits::kFnvPrime; - hash ^= static_cast(*c); - } - return hash; -} - -template T HashFnv1a(const char *input) { - T hash = FnvTraits::kOffsetBasis; - for (const char *c = input; *c; ++c) { - hash ^= static_cast(*c); - hash *= FnvTraits::kFnvPrime; - } - return hash; -} - -template<> inline uint16_t HashFnv1(const char *input) { - uint32_t hash = HashFnv1(input); - return (hash >> 16) ^ (hash & 0xffff); -} - -template<> inline uint16_t HashFnv1a(const char *input) { - uint32_t hash = HashFnv1a(input); - return (hash >> 16) ^ (hash & 0xffff); -} - -template struct NamedHashFunction { - const char *name; - - typedef T (*HashFunction)(const char *); - HashFunction function; -}; - -const NamedHashFunction kHashFunctions16[] = { - { "fnv1_16", HashFnv1 }, - { "fnv1a_16", HashFnv1a }, -}; - -const NamedHashFunction kHashFunctions32[] = { - { "fnv1_32", HashFnv1 }, - { "fnv1a_32", HashFnv1a }, -}; - -const NamedHashFunction kHashFunctions64[] = { - { "fnv1_64", HashFnv1 }, - { "fnv1a_64", HashFnv1a }, -}; - -inline NamedHashFunction::HashFunction FindHashFunction16( - const char *name) { - std::size_t size = sizeof(kHashFunctions16) / sizeof(kHashFunctions16[0]); - for (std::size_t i = 0; i < size; ++i) { - if (std::strcmp(name, kHashFunctions16[i].name) == 0) { - return kHashFunctions16[i].function; - } - } - return nullptr; -} - -inline NamedHashFunction::HashFunction FindHashFunction32( - const char *name) { - std::size_t size = sizeof(kHashFunctions32) / sizeof(kHashFunctions32[0]); - for (std::size_t i = 0; i < size; ++i) { - if (std::strcmp(name, kHashFunctions32[i].name) == 0) { - return kHashFunctions32[i].function; - } - } - return nullptr; -} - -inline NamedHashFunction::HashFunction FindHashFunction64( - const char *name) { - std::size_t size = sizeof(kHashFunctions64) / sizeof(kHashFunctions64[0]); - for (std::size_t i = 0; i < size; ++i) { - if (std::strcmp(name, kHashFunctions64[i].name) == 0) { - return kHashFunctions64[i].function; - } - } - return nullptr; -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_HASH_H_ diff --git a/new_ai_tools/include/flatbuffers/idl.h b/new_ai_tools/include/flatbuffers/idl.h deleted file mode 100644 index 7b04d0b5..00000000 --- a/new_ai_tools/include/flatbuffers/idl.h +++ /dev/null @@ -1,1232 +0,0 @@ -/* - * Copyright 2014 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_IDL_H_ -#define FLATBUFFERS_IDL_H_ - -#include -#include -#include -#include - -#include "flatbuffers/base.h" -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/flexbuffers.h" -#include "flatbuffers/hash.h" -#include "flatbuffers/reflection.h" - -// This file defines the data types representing a parsed IDL (Interface -// Definition Language) / schema file. - -// Limits maximum depth of nested objects. -// Prevents stack overflow while parse scheme, or json, or flexbuffer. -#if !defined(FLATBUFFERS_MAX_PARSING_DEPTH) -# define FLATBUFFERS_MAX_PARSING_DEPTH 64 -#endif - -namespace flatbuffers { - -// The order of these matters for Is*() functions below. -// Additionally, Parser::ParseType assumes bool..string is a contiguous range -// of type tokens. -// clang-format off -#define FLATBUFFERS_GEN_TYPES_SCALAR(TD) \ - TD(NONE, "", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) \ - TD(UTYPE, "", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) /* begin scalar/int */ \ - TD(BOOL, "bool", uint8_t, boolean,bool, bool, bool, bool, Boolean, Bool) \ - TD(CHAR, "byte", int8_t, byte, int8, sbyte, int8, i8, Byte, Int8) \ - TD(UCHAR, "ubyte", uint8_t, byte, byte, byte, uint8, u8, UByte, UInt8) \ - TD(SHORT, "short", int16_t, short, int16, short, int16, i16, Short, Int16) \ - TD(USHORT, "ushort", uint16_t, short, uint16, ushort, uint16, u16, UShort, UInt16) \ - TD(INT, "int", int32_t, int, int32, int, int32, i32, Int, Int32) \ - TD(UINT, "uint", uint32_t, int, uint32, uint, uint32, u32, UInt, UInt32) \ - TD(LONG, "long", int64_t, long, int64, long, int64, i64, Long, Int64) \ - TD(ULONG, "ulong", uint64_t, long, uint64, ulong, uint64, u64, ULong, UInt64) /* end int */ \ - TD(FLOAT, "float", float, float, float32, float, float32, f32, Float, Float32) /* begin float */ \ - TD(DOUBLE, "double", double, double, float64, double, float64, f64, Double, Double) /* end float/scalar */ -#define FLATBUFFERS_GEN_TYPES_POINTER(TD) \ - TD(STRING, "string", Offset, int, int, StringOffset, int, unused, Int, Offset) \ - TD(VECTOR, "", Offset, int, int, VectorOffset, int, unused, Int, Offset) \ - TD(STRUCT, "", Offset, int, int, int, int, unused, Int, Offset) \ - TD(UNION, "", Offset, int, int, int, int, unused, Int, Offset) -#define FLATBUFFERS_GEN_TYPE_ARRAY(TD) \ - TD(ARRAY, "", int, int, int, int, int, unused, Int, Offset) -// The fields are: -// - enum -// - FlatBuffers schema type. -// - C++ type. -// - Java type. -// - Go type. -// - C# / .Net type. -// - Python type. -// - Kotlin type. -// - Rust type. - -// using these macros, we can now write code dealing with types just once, e.g. - -/* -switch (type) { - #define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, JTYPE, GTYPE, NTYPE, PTYPE, \ - RTYPE, KTYPE) \ - case BASE_TYPE_ ## ENUM: \ - // do something specific to CTYPE here - FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) - #undef FLATBUFFERS_TD -} -*/ - -// If not all FLATBUFFERS_GEN_() arguments are necessary for implementation -// of FLATBUFFERS_TD, you can use a variadic macro (with __VA_ARGS__ if needed). -// In the above example, only CTYPE is used to generate the code, it can be rewritten: - -/* -switch (type) { - #define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, ...) \ - case BASE_TYPE_ ## ENUM: \ - // do something specific to CTYPE here - FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) - #undef FLATBUFFERS_TD -} -*/ - -#define FLATBUFFERS_GEN_TYPES(TD) \ - FLATBUFFERS_GEN_TYPES_SCALAR(TD) \ - FLATBUFFERS_GEN_TYPES_POINTER(TD) \ - FLATBUFFERS_GEN_TYPE_ARRAY(TD) - -// Create an enum for all the types above. -#ifdef __GNUC__ -__extension__ // Stop GCC complaining about trailing comma with -Wpendantic. -#endif -enum BaseType { - #define FLATBUFFERS_TD(ENUM, ...) \ - BASE_TYPE_ ## ENUM, - FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) - #undef FLATBUFFERS_TD -}; - -#define FLATBUFFERS_TD(ENUM, IDLTYPE, CTYPE, ...) \ - static_assert(sizeof(CTYPE) <= sizeof(largest_scalar_t), \ - "define largest_scalar_t as " #CTYPE); - FLATBUFFERS_GEN_TYPES(FLATBUFFERS_TD) -#undef FLATBUFFERS_TD - -inline bool IsScalar (BaseType t) { return t >= BASE_TYPE_UTYPE && - t <= BASE_TYPE_DOUBLE; } -inline bool IsInteger(BaseType t) { return t >= BASE_TYPE_UTYPE && - t <= BASE_TYPE_ULONG; } -inline bool IsFloat (BaseType t) { return t == BASE_TYPE_FLOAT || - t == BASE_TYPE_DOUBLE; } -inline bool IsLong (BaseType t) { return t == BASE_TYPE_LONG || - t == BASE_TYPE_ULONG; } -inline bool IsBool (BaseType t) { return t == BASE_TYPE_BOOL; } -inline bool IsOneByte(BaseType t) { return t >= BASE_TYPE_UTYPE && - t <= BASE_TYPE_UCHAR; } - -inline bool IsUnsigned(BaseType t) { - return (t == BASE_TYPE_UTYPE) || (t == BASE_TYPE_UCHAR) || - (t == BASE_TYPE_USHORT) || (t == BASE_TYPE_UINT) || - (t == BASE_TYPE_ULONG); -} - -// clang-format on - -extern const char *const kTypeNames[]; -extern const char kTypeSizes[]; - -inline size_t SizeOf(BaseType t) { return kTypeSizes[t]; } - -struct StructDef; -struct EnumDef; -class Parser; - -// Represents any type in the IDL, which is a combination of the BaseType -// and additional information for vectors/structs_. -struct Type { - explicit Type(BaseType _base_type = BASE_TYPE_NONE, StructDef *_sd = nullptr, - EnumDef *_ed = nullptr, uint16_t _fixed_length = 0) - : base_type(_base_type), - element(BASE_TYPE_NONE), - struct_def(_sd), - enum_def(_ed), - fixed_length(_fixed_length) {} - - bool operator==(const Type &o) { - return base_type == o.base_type && element == o.element && - struct_def == o.struct_def && enum_def == o.enum_def; - } - - Type VectorType() const { - return Type(element, struct_def, enum_def, fixed_length); - } - - Offset Serialize(FlatBufferBuilder *builder) const; - - bool Deserialize(const Parser &parser, const reflection::Type *type); - - BaseType base_type; - BaseType element; // only set if t == BASE_TYPE_VECTOR - StructDef *struct_def; // only set if t or element == BASE_TYPE_STRUCT - EnumDef *enum_def; // set if t == BASE_TYPE_UNION / BASE_TYPE_UTYPE, - // or for an integral type derived from an enum. - uint16_t fixed_length; // only set if t == BASE_TYPE_ARRAY -}; - -// Represents a parsed scalar value, it's type, and field offset. -struct Value { - Value() - : constant("0"), - offset(static_cast(~(static_cast(0U)))) {} - Type type; - std::string constant; - voffset_t offset; -}; - -// Helper class that retains the original order of a set of identifiers and -// also provides quick lookup. -template class SymbolTable { - public: - ~SymbolTable() { - for (auto it = vec.begin(); it != vec.end(); ++it) { delete *it; } - } - - bool Add(const std::string &name, T *e) { - vec.emplace_back(e); - auto it = dict.find(name); - if (it != dict.end()) return true; - dict[name] = e; - return false; - } - - void Move(const std::string &oldname, const std::string &newname) { - auto it = dict.find(oldname); - if (it != dict.end()) { - auto obj = it->second; - dict.erase(it); - dict[newname] = obj; - } else { - FLATBUFFERS_ASSERT(false); - } - } - - T *Lookup(const std::string &name) const { - auto it = dict.find(name); - return it == dict.end() ? nullptr : it->second; - } - - public: - std::map dict; // quick lookup - std::vector vec; // Used to iterate in order of insertion -}; - -// A name space, as set in the schema. -struct Namespace { - Namespace() : from_table(0) {} - - // Given a (potentially unqualified) name, return the "fully qualified" name - // which has a full namespaced descriptor. - // With max_components you can request less than the number of components - // the current namespace has. - std::string GetFullyQualifiedName(const std::string &name, - size_t max_components = 1000) const; - - std::vector components; - size_t from_table; // Part of the namespace corresponds to a message/table. -}; - -inline bool operator<(const Namespace &a, const Namespace &b) { - size_t min_size = std::min(a.components.size(), b.components.size()); - for (size_t i = 0; i < min_size; ++i) { - if (a.components[i] != b.components[i]) - return a.components[i] < b.components[i]; - } - return a.components.size() < b.components.size(); -} - -// Base class for all definition types (fields, structs_, enums_). -struct Definition { - Definition() - : generated(false), - defined_namespace(nullptr), - serialized_location(0), - index(-1), - refcount(1), - declaration_file(nullptr) {} - - flatbuffers::Offset< - flatbuffers::Vector>> - SerializeAttributes(FlatBufferBuilder *builder, const Parser &parser) const; - - bool DeserializeAttributes(Parser &parser, - const Vector> *attrs); - - std::string name; - std::string file; - std::vector doc_comment; - SymbolTable attributes; - bool generated; // did we already output code for this definition? - Namespace *defined_namespace; // Where it was defined. - - // For use with Serialize() - uoffset_t serialized_location; - int index; // Inside the vector it is stored. - int refcount; - const std::string *declaration_file; -}; - -struct FieldDef : public Definition { - FieldDef() - : deprecated(false), - key(false), - shared(false), - native_inline(false), - flexbuffer(false), - presence(kDefault), - nested_flatbuffer(NULL), - padding(0) {} - - Offset Serialize(FlatBufferBuilder *builder, uint16_t id, - const Parser &parser) const; - - bool Deserialize(Parser &parser, const reflection::Field *field); - - bool IsScalarOptional() const { - return IsScalar(value.type.base_type) && IsOptional(); - } - bool IsOptional() const { return presence == kOptional; } - bool IsRequired() const { return presence == kRequired; } - bool IsDefault() const { return presence == kDefault; } - - Value value; - bool deprecated; // Field is allowed to be present in old data, but can't be. - // written in new data nor accessed in new code. - bool key; // Field functions as a key for creating sorted vectors. - bool shared; // Field will be using string pooling (i.e. CreateSharedString) - // as default serialization behavior if field is a string. - bool native_inline; // Field will be defined inline (instead of as a pointer) - // for native tables if field is a struct. - bool flexbuffer; // This field contains FlexBuffer data. - - enum Presence { - // Field must always be present. - kRequired, - // Non-presence should be signalled to and controlled by users. - kOptional, - // Non-presence is hidden from users. - // Implementations may omit writing default values. - kDefault, - }; - Presence static MakeFieldPresence(bool optional, bool required) { - FLATBUFFERS_ASSERT(!(required && optional)); - // clang-format off - return required ? FieldDef::kRequired - : optional ? FieldDef::kOptional - : FieldDef::kDefault; - // clang-format on - } - Presence presence; - - StructDef *nested_flatbuffer; // This field contains nested FlatBuffer data. - size_t padding; // Bytes to always pad after this field. -}; - -struct StructDef : public Definition { - StructDef() - : fixed(false), - predecl(true), - sortbysize(true), - has_key(false), - minalign(1), - bytesize(0) {} - - void PadLastField(size_t min_align) { - auto padding = PaddingBytes(bytesize, min_align); - bytesize += padding; - if (fields.vec.size()) fields.vec.back()->padding = padding; - } - - Offset Serialize(FlatBufferBuilder *builder, - const Parser &parser) const; - - bool Deserialize(Parser &parser, const reflection::Object *object); - - SymbolTable fields; - - bool fixed; // If it's struct, not a table. - bool predecl; // If it's used before it was defined. - bool sortbysize; // Whether fields come in the declaration or size order. - bool has_key; // It has a key field. - size_t minalign; // What the whole object needs to be aligned to. - size_t bytesize; // Size if fixed. - - flatbuffers::unique_ptr original_location; -}; - -struct EnumDef; -struct EnumValBuilder; - -struct EnumVal { - Offset Serialize(FlatBufferBuilder *builder, - const Parser &parser) const; - - bool Deserialize(const Parser &parser, const reflection::EnumVal *val); - - uint64_t GetAsUInt64() const { return static_cast(value); } - int64_t GetAsInt64() const { return value; } - bool IsZero() const { return 0 == value; } - bool IsNonZero() const { return !IsZero(); } - - std::string name; - std::vector doc_comment; - Type union_type; - - private: - friend EnumDef; - friend EnumValBuilder; - friend bool operator==(const EnumVal &lhs, const EnumVal &rhs); - - EnumVal(const std::string &_name, int64_t _val) : name(_name), value(_val) {} - EnumVal() : value(0) {} - - int64_t value; -}; - -struct EnumDef : public Definition { - EnumDef() : is_union(false), uses_multiple_type_instances(false) {} - - Offset Serialize(FlatBufferBuilder *builder, - const Parser &parser) const; - - bool Deserialize(Parser &parser, const reflection::Enum *values); - - template void ChangeEnumValue(EnumVal *ev, T new_val); - void SortByValue(); - void RemoveDuplicates(); - - std::string AllFlags() const; - const EnumVal *MinValue() const; - const EnumVal *MaxValue() const; - // Returns the number of integer steps from v1 to v2. - uint64_t Distance(const EnumVal *v1, const EnumVal *v2) const; - // Returns the number of integer steps from Min to Max. - uint64_t Distance() const { return Distance(MinValue(), MaxValue()); } - - EnumVal *ReverseLookup(int64_t enum_idx, - bool skip_union_default = false) const; - EnumVal *FindByValue(const std::string &constant) const; - - std::string ToString(const EnumVal &ev) const { - return IsUInt64() ? NumToString(ev.GetAsUInt64()) - : NumToString(ev.GetAsInt64()); - } - - size_t size() const { return vals.vec.size(); } - - const std::vector &Vals() const { return vals.vec; } - - const EnumVal *Lookup(const std::string &enum_name) const { - return vals.Lookup(enum_name); - } - - bool is_union; - // Type is a union which uses type aliases where at least one type is - // available under two different names. - bool uses_multiple_type_instances; - Type underlying_type; - - private: - bool IsUInt64() const { - return (BASE_TYPE_ULONG == underlying_type.base_type); - } - - friend EnumValBuilder; - SymbolTable vals; -}; - -inline bool IsString(const Type &type) { - return type.base_type == BASE_TYPE_STRING; -} - -inline bool IsStruct(const Type &type) { - return type.base_type == BASE_TYPE_STRUCT && type.struct_def->fixed; -} - -inline bool IsUnion(const Type &type) { - return type.enum_def != nullptr && type.enum_def->is_union; -} - -inline bool IsUnionType(const Type &type) { - return IsUnion(type) && IsInteger(type.base_type); -} - -inline bool IsVector(const Type &type) { - return type.base_type == BASE_TYPE_VECTOR; -} - -inline bool IsArray(const Type &type) { - return type.base_type == BASE_TYPE_ARRAY; -} - -inline bool IsSeries(const Type &type) { - return IsVector(type) || IsArray(type); -} - -inline bool IsEnum(const Type &type) { - return type.enum_def != nullptr && IsInteger(type.base_type); -} - -inline size_t InlineSize(const Type &type) { - return IsStruct(type) - ? type.struct_def->bytesize - : (IsArray(type) - ? InlineSize(type.VectorType()) * type.fixed_length - : SizeOf(type.base_type)); -} - -inline size_t InlineAlignment(const Type &type) { - if (IsStruct(type)) { - return type.struct_def->minalign; - } else if (IsArray(type)) { - return IsStruct(type.VectorType()) ? type.struct_def->minalign - : SizeOf(type.element); - } else { - return SizeOf(type.base_type); - } -} -inline bool operator==(const EnumVal &lhs, const EnumVal &rhs) { - return lhs.value == rhs.value; -} -inline bool operator!=(const EnumVal &lhs, const EnumVal &rhs) { - return !(lhs == rhs); -} - -inline bool EqualByName(const Type &a, const Type &b) { - return a.base_type == b.base_type && a.element == b.element && - (a.struct_def == b.struct_def || - a.struct_def->name == b.struct_def->name) && - (a.enum_def == b.enum_def || a.enum_def->name == b.enum_def->name); -} - -struct RPCCall : public Definition { - Offset Serialize(FlatBufferBuilder *builder, - const Parser &parser) const; - - bool Deserialize(Parser &parser, const reflection::RPCCall *call); - - StructDef *request, *response; -}; - -struct ServiceDef : public Definition { - Offset Serialize(FlatBufferBuilder *builder, - const Parser &parser) const; - bool Deserialize(Parser &parser, const reflection::Service *service); - - SymbolTable calls; -}; - -// Container of options that may apply to any of the source/text generators. -struct IDLOptions { - // field case style options for C++ - enum CaseStyle { CaseStyle_Unchanged = 0, CaseStyle_Upper, CaseStyle_Lower }; - - bool gen_jvmstatic; - // Use flexbuffers instead for binary and text generation - bool use_flexbuffers; - bool strict_json; - bool output_default_scalars_in_json; - int indent_step; - bool output_enum_identifiers; - bool prefixed_enums; - bool scoped_enums; - bool include_dependence_headers; - bool mutable_buffer; - bool one_file; - bool proto_mode; - bool proto_oneof_union; - bool generate_all; - bool skip_unexpected_fields_in_json; - bool generate_name_strings; - bool generate_object_based_api; - bool gen_compare; - std::string cpp_object_api_pointer_type; - std::string cpp_object_api_string_type; - bool cpp_object_api_string_flexible_constructor; - CaseStyle cpp_object_api_field_case_style; - bool cpp_direct_copy; - bool gen_nullable; - bool java_checkerframework; - bool gen_generated; - bool gen_json_coders; - std::string object_prefix; - std::string object_suffix; - bool union_value_namespacing; - bool allow_non_utf8; - bool natural_utf8; - std::string include_prefix; - bool keep_include_path; - bool binary_schema_comments; - bool binary_schema_builtins; - bool binary_schema_gen_embed; - std::string go_import; - std::string go_namespace; - bool protobuf_ascii_alike; - bool size_prefixed; - std::string root_type; - bool force_defaults; - bool java_primitive_has_method; - bool cs_gen_json_serializer; - std::vector cpp_includes; - std::string cpp_std; - bool cpp_static_reflection; - std::string proto_namespace_suffix; - std::string filename_suffix; - std::string filename_extension; - bool no_warnings; - bool warnings_as_errors; - std::string project_root; - bool cs_global_alias; - bool json_nested_flatbuffers; - bool json_nested_flexbuffers; - bool json_nested_legacy_flatbuffers; - - // Possible options for the more general generator below. - enum Language { - kJava = 1 << 0, - kCSharp = 1 << 1, - kGo = 1 << 2, - kCpp = 1 << 3, - kPython = 1 << 5, - kPhp = 1 << 6, - kJson = 1 << 7, - kBinary = 1 << 8, - kTs = 1 << 9, - kJsonSchema = 1 << 10, - kDart = 1 << 11, - kLua = 1 << 12, - kLobster = 1 << 13, - kRust = 1 << 14, - kKotlin = 1 << 15, - kSwift = 1 << 16, - kMAX - }; - - enum MiniReflect { kNone, kTypes, kTypesAndNames }; - - MiniReflect mini_reflect; - - // If set, require all fields in a table to be explicitly numbered. - bool require_explicit_ids; - - // If set, implement serde::Serialize for generated Rust types - bool rust_serialize; - - // If set, generate rust types in individual files with a root module file. - bool rust_module_root_file; - - // The corresponding language bit will be set if a language is included - // for code generation. - unsigned long lang_to_generate; - - // If set (default behavior), empty string fields will be set to nullptr to - // make the flatbuffer more compact. - bool set_empty_strings_to_null; - - // If set (default behavior), empty vector fields will be set to nullptr to - // make the flatbuffer more compact. - bool set_empty_vectors_to_null; - - IDLOptions() - : gen_jvmstatic(false), - use_flexbuffers(false), - strict_json(false), - output_default_scalars_in_json(false), - indent_step(2), - output_enum_identifiers(true), - prefixed_enums(true), - scoped_enums(false), - include_dependence_headers(true), - mutable_buffer(false), - one_file(false), - proto_mode(false), - proto_oneof_union(false), - generate_all(false), - skip_unexpected_fields_in_json(false), - generate_name_strings(false), - generate_object_based_api(false), - gen_compare(false), - cpp_object_api_pointer_type("std::unique_ptr"), - cpp_object_api_string_flexible_constructor(false), - cpp_object_api_field_case_style(CaseStyle_Unchanged), - cpp_direct_copy(true), - gen_nullable(false), - java_checkerframework(false), - gen_generated(false), - gen_json_coders(false), - object_suffix("T"), - union_value_namespacing(true), - allow_non_utf8(false), - natural_utf8(false), - keep_include_path(false), - binary_schema_comments(false), - binary_schema_builtins(false), - binary_schema_gen_embed(false), - protobuf_ascii_alike(false), - size_prefixed(false), - force_defaults(false), - java_primitive_has_method(false), - cs_gen_json_serializer(false), - cpp_static_reflection(false), - filename_suffix("_generated"), - filename_extension(), - no_warnings(false), - warnings_as_errors(false), - project_root(""), - cs_global_alias(false), - json_nested_flatbuffers(true), - json_nested_flexbuffers(true), - json_nested_legacy_flatbuffers(false), - mini_reflect(IDLOptions::kNone), - require_explicit_ids(false), - rust_serialize(false), - rust_module_root_file(false), - lang_to_generate(0), - set_empty_strings_to_null(true), - set_empty_vectors_to_null(true) {} -}; - -// This encapsulates where the parser is in the current source file. -struct ParserState { - ParserState() - : cursor_(nullptr), - line_start_(nullptr), - line_(0), - token_(-1), - attr_is_trivial_ascii_string_(true) {} - - protected: - void ResetState(const char *source) { - cursor_ = source; - line_ = 0; - MarkNewLine(); - } - - void MarkNewLine() { - line_start_ = cursor_; - line_ += 1; - } - - int64_t CursorPosition() const { - FLATBUFFERS_ASSERT(cursor_ && line_start_ && cursor_ >= line_start_); - return static_cast(cursor_ - line_start_); - } - - const char *cursor_; - const char *line_start_; - int line_; // the current line being parsed - int token_; - - // Flag: text in attribute_ is true ASCII string without escape - // sequences. Only printable ASCII (without [\t\r\n]). - // Used for number-in-string (and base64 string in future). - bool attr_is_trivial_ascii_string_; - std::string attribute_; - std::vector doc_comment_; -}; - -// A way to make error propagation less error prone by requiring values to be -// checked. -// Once you create a value of this type you must either: -// - Call Check() on it. -// - Copy or assign it to another value. -// Failure to do so leads to an assert. -// This guarantees that this as return value cannot be ignored. -class CheckedError { - public: - explicit CheckedError(bool error) - : is_error_(error), has_been_checked_(false) {} - - CheckedError &operator=(const CheckedError &other) { - is_error_ = other.is_error_; - has_been_checked_ = false; - other.has_been_checked_ = true; - return *this; - } - - CheckedError(const CheckedError &other) { - *this = other; // Use assignment operator. - } - - ~CheckedError() { FLATBUFFERS_ASSERT(has_been_checked_); } - - bool Check() { - has_been_checked_ = true; - return is_error_; - } - - private: - bool is_error_; - mutable bool has_been_checked_; -}; - -// Additionally, in GCC we can get these errors statically, for additional -// assurance: -// clang-format off -#ifdef __GNUC__ -#define FLATBUFFERS_CHECKED_ERROR CheckedError \ - __attribute__((warn_unused_result)) -#else -#define FLATBUFFERS_CHECKED_ERROR CheckedError -#endif -// clang-format on - -class Parser : public ParserState { - public: - explicit Parser(const IDLOptions &options = IDLOptions()) - : current_namespace_(nullptr), - empty_namespace_(nullptr), - flex_builder_(256, flexbuffers::BUILDER_FLAG_SHARE_ALL), - root_struct_def_(nullptr), - opts(options), - uses_flexbuffers_(false), - has_warning_(false), - advanced_features_(0), - source_(nullptr), - anonymous_counter_(0), - parse_depth_counter_(0) { - if (opts.force_defaults) { builder_.ForceDefaults(true); } - // Start out with the empty namespace being current. - empty_namespace_ = new Namespace(); - namespaces_.push_back(empty_namespace_); - current_namespace_ = empty_namespace_; - known_attributes_["deprecated"] = true; - known_attributes_["required"] = true; - known_attributes_["key"] = true; - known_attributes_["shared"] = true; - known_attributes_["hash"] = true; - known_attributes_["id"] = true; - known_attributes_["force_align"] = true; - known_attributes_["bit_flags"] = true; - known_attributes_["original_order"] = true; - known_attributes_["nested_flatbuffer"] = true; - known_attributes_["csharp_partial"] = true; - known_attributes_["streaming"] = true; - known_attributes_["idempotent"] = true; - known_attributes_["cpp_type"] = true; - known_attributes_["cpp_ptr_type"] = true; - known_attributes_["cpp_ptr_type_get"] = true; - known_attributes_["cpp_str_type"] = true; - known_attributes_["cpp_str_flex_ctor"] = true; - known_attributes_["native_inline"] = true; - known_attributes_["native_custom_alloc"] = true; - known_attributes_["native_type"] = true; - known_attributes_["native_type_pack_name"] = true; - known_attributes_["native_default"] = true; - known_attributes_["flexbuffer"] = true; - known_attributes_["private"] = true; - } - - ~Parser() { - for (auto it = namespaces_.begin(); it != namespaces_.end(); ++it) { - delete *it; - } - } - - // Parse the string containing either schema or JSON data, which will - // populate the SymbolTable's or the FlatBufferBuilder above. - // include_paths is used to resolve any include statements, and typically - // should at least include the project path (where you loaded source_ from). - // include_paths must be nullptr terminated if specified. - // If include_paths is nullptr, it will attempt to load from the current - // directory. - // If the source was loaded from a file and isn't an include file, - // supply its name in source_filename. - // All paths specified in this call must be in posix format, if you accept - // paths from user input, please call PosixPath on them first. - bool Parse(const char *_source, const char **include_paths = nullptr, - const char *source_filename = nullptr); - - bool ParseJson(const char *json, const char *json_filename = nullptr); - - // Set the root type. May override the one set in the schema. - bool SetRootType(const char *name); - - // Mark all definitions as already having code generated. - void MarkGenerated(); - - // Get the files recursively included by the given file. The returned - // container will have at least the given file. - std::set GetIncludedFilesRecursive( - const std::string &file_name) const; - - // Fills builder_ with a binary version of the schema parsed. - // See reflection/reflection.fbs - void Serialize(); - - // Deserialize a schema buffer - bool Deserialize(const uint8_t *buf, const size_t size); - - // Fills internal structure as if the schema passed had been loaded by parsing - // with Parse except that included filenames will not be populated. - bool Deserialize(const reflection::Schema *schema); - - Type *DeserializeType(const reflection::Type *type); - - // Checks that the schema represented by this parser is a safe evolution - // of the schema provided. Returns non-empty error on any problems. - std::string ConformTo(const Parser &base); - - // Similar to Parse(), but now only accepts JSON to be parsed into a - // FlexBuffer. - bool ParseFlexBuffer(const char *source, const char *source_filename, - flexbuffers::Builder *builder); - - StructDef *LookupStruct(const std::string &id) const; - StructDef *LookupStructThruParentNamespaces(const std::string &id) const; - - std::string UnqualifiedName(const std::string &fullQualifiedName); - - FLATBUFFERS_CHECKED_ERROR Error(const std::string &msg); - - // @brief Verify that any of 'opts.lang_to_generate' supports Optional scalars - // in a schema. - // @param opts Options used to parce a schema and generate code. - static bool SupportsOptionalScalars(const flatbuffers::IDLOptions &opts); - - private: - class ParseDepthGuard; - - void Message(const std::string &msg); - void Warning(const std::string &msg); - FLATBUFFERS_CHECKED_ERROR ParseHexNum(int nibbles, uint64_t *val); - FLATBUFFERS_CHECKED_ERROR Next(); - FLATBUFFERS_CHECKED_ERROR SkipByteOrderMark(); - bool Is(int t) const; - bool IsIdent(const char *id) const; - FLATBUFFERS_CHECKED_ERROR Expect(int t); - std::string TokenToStringId(int t) const; - EnumDef *LookupEnum(const std::string &id); - FLATBUFFERS_CHECKED_ERROR ParseNamespacing(std::string *id, - std::string *last); - FLATBUFFERS_CHECKED_ERROR ParseTypeIdent(Type &type); - FLATBUFFERS_CHECKED_ERROR ParseType(Type &type); - FLATBUFFERS_CHECKED_ERROR AddField(StructDef &struct_def, - const std::string &name, const Type &type, - FieldDef **dest); - FLATBUFFERS_CHECKED_ERROR ParseField(StructDef &struct_def); - FLATBUFFERS_CHECKED_ERROR ParseString(Value &val, bool use_string_pooling); - FLATBUFFERS_CHECKED_ERROR ParseComma(); - FLATBUFFERS_CHECKED_ERROR ParseAnyValue(Value &val, FieldDef *field, - size_t parent_fieldn, - const StructDef *parent_struct_def, - uoffset_t count, - bool inside_vector = false); - template - FLATBUFFERS_CHECKED_ERROR ParseTableDelimiters(size_t &fieldn, - const StructDef *struct_def, - F body); - FLATBUFFERS_CHECKED_ERROR ParseTable(const StructDef &struct_def, - std::string *value, uoffset_t *ovalue); - void SerializeStruct(const StructDef &struct_def, const Value &val); - void SerializeStruct(FlatBufferBuilder &builder, const StructDef &struct_def, - const Value &val); - template - FLATBUFFERS_CHECKED_ERROR ParseVectorDelimiters(uoffset_t &count, F body); - FLATBUFFERS_CHECKED_ERROR ParseVector(const Type &type, uoffset_t *ovalue, - FieldDef *field, size_t fieldn); - FLATBUFFERS_CHECKED_ERROR ParseArray(Value &array); - FLATBUFFERS_CHECKED_ERROR ParseNestedFlatbuffer( - Value &val, FieldDef *field, size_t fieldn, - const StructDef *parent_struct_def); - FLATBUFFERS_CHECKED_ERROR ParseMetaData(SymbolTable *attributes); - FLATBUFFERS_CHECKED_ERROR TryTypedValue(const std::string *name, int dtoken, - bool check, Value &e, BaseType req, - bool *destmatch); - FLATBUFFERS_CHECKED_ERROR ParseHash(Value &e, FieldDef *field); - FLATBUFFERS_CHECKED_ERROR TokenError(); - FLATBUFFERS_CHECKED_ERROR ParseSingleValue(const std::string *name, Value &e, - bool check_now); - FLATBUFFERS_CHECKED_ERROR ParseFunction(const std::string *name, Value &e); - FLATBUFFERS_CHECKED_ERROR ParseEnumFromString(const Type &type, - std::string *result); - StructDef *LookupCreateStruct(const std::string &name, - bool create_if_new = true, - bool definition = false); - FLATBUFFERS_CHECKED_ERROR ParseEnum(bool is_union, EnumDef **dest, - const char *filename); - FLATBUFFERS_CHECKED_ERROR ParseNamespace(); - FLATBUFFERS_CHECKED_ERROR StartStruct(const std::string &name, - StructDef **dest); - FLATBUFFERS_CHECKED_ERROR StartEnum(const std::string &name, bool is_union, - EnumDef **dest); - FLATBUFFERS_CHECKED_ERROR ParseDecl(const char *filename); - FLATBUFFERS_CHECKED_ERROR ParseService(const char *filename); - FLATBUFFERS_CHECKED_ERROR ParseProtoFields(StructDef *struct_def, - bool isextend, bool inside_oneof); - FLATBUFFERS_CHECKED_ERROR ParseProtoOption(); - FLATBUFFERS_CHECKED_ERROR ParseProtoKey(); - FLATBUFFERS_CHECKED_ERROR ParseProtoDecl(); - FLATBUFFERS_CHECKED_ERROR ParseProtoCurliesOrIdent(); - FLATBUFFERS_CHECKED_ERROR ParseTypeFromProtoType(Type *type); - FLATBUFFERS_CHECKED_ERROR SkipAnyJsonValue(); - FLATBUFFERS_CHECKED_ERROR ParseFlexBufferNumericConstant( - flexbuffers::Builder *builder); - FLATBUFFERS_CHECKED_ERROR ParseFlexBufferValue(flexbuffers::Builder *builder); - FLATBUFFERS_CHECKED_ERROR StartParseFile(const char *source, - const char *source_filename); - FLATBUFFERS_CHECKED_ERROR ParseRoot(const char *_source, - const char **include_paths, - const char *source_filename); - FLATBUFFERS_CHECKED_ERROR DoParse(const char *_source, - const char **include_paths, - const char *source_filename, - const char *include_filename); - FLATBUFFERS_CHECKED_ERROR DoParseJson(); - FLATBUFFERS_CHECKED_ERROR CheckClash(std::vector &fields, - StructDef *struct_def, - const char *suffix, BaseType baseType); - FLATBUFFERS_CHECKED_ERROR ParseAlignAttribute( - const std::string &align_constant, size_t min_align, size_t *align); - - bool SupportsAdvancedUnionFeatures() const; - bool SupportsAdvancedArrayFeatures() const; - bool SupportsOptionalScalars() const; - bool SupportsDefaultVectorsAndStrings() const; - Namespace *UniqueNamespace(Namespace *ns); - - FLATBUFFERS_CHECKED_ERROR RecurseError(); - template CheckedError Recurse(F f); - - const std::string &GetPooledString(const std::string &s) const; - - public: - SymbolTable types_; - SymbolTable structs_; - SymbolTable enums_; - SymbolTable services_; - std::vector namespaces_; - Namespace *current_namespace_; - Namespace *empty_namespace_; - std::string error_; // User readable error_ if Parse() == false - - FlatBufferBuilder builder_; // any data contained in the file - flexbuffers::Builder flex_builder_; - flexbuffers::Reference flex_root_; - StructDef *root_struct_def_; - std::string file_identifier_; - std::string file_extension_; - - std::map included_files_; - std::map> files_included_per_file_; - std::vector native_included_files_; - - std::map known_attributes_; - - IDLOptions opts; - bool uses_flexbuffers_; - bool has_warning_; - - uint64_t advanced_features_; - - private: - const char *source_; - - std::string file_being_parsed_; - - std::vector> field_stack_; - - // TODO(cneo): Refactor parser to use string_cache more often to save - // on memory usage. - mutable std::set string_cache_; - - int anonymous_counter_; - int parse_depth_counter_; // stack-overflow guard -}; - -// Utility functions for multiple generators: - -extern std::string MakeCamel(const std::string &in, bool first = true); - -extern std::string MakeScreamingCamel(const std::string &in); - -// Generate text (JSON) from a given FlatBuffer, and a given Parser -// object that has been populated with the corresponding schema. -// If ident_step is 0, no indentation will be generated. Additionally, -// if it is less than 0, no linefeeds will be generated either. -// See idl_gen_text.cpp. -// strict_json adds "quotes" around field names if true. -// If the flatbuffer cannot be encoded in JSON (e.g., it contains non-UTF-8 -// byte arrays in String values), returns false. -extern bool GenerateTextFromTable(const Parser &parser, const void *table, - const std::string &tablename, - std::string *text); -extern bool GenerateText(const Parser &parser, const void *flatbuffer, - std::string *text); -extern bool GenerateTextFile(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Json schema to string -// See idl_gen_json_schema.cpp. -extern bool GenerateJsonSchema(const Parser &parser, std::string *json); - -// Generate binary files from a given FlatBuffer, and a given Parser -// object that has been populated with the corresponding schema. -// See code_generators.cpp. -extern bool GenerateBinary(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a C++ header from the definitions in the Parser object. -// See idl_gen_cpp. -extern bool GenerateCPP(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate C# files from the definitions in the Parser object. -// See idl_gen_csharp.cpp. -extern bool GenerateCSharp(const Parser &parser, const std::string &path, - const std::string &file_name); - -extern bool GenerateDart(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Java files from the definitions in the Parser object. -// See idl_gen_java.cpp. -extern bool GenerateJava(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate JavaScript or TypeScript code from the definitions in the Parser -// object. See idl_gen_js. -extern bool GenerateTS(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Go files from the definitions in the Parser object. -// See idl_gen_go.cpp. -extern bool GenerateGo(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Php code from the definitions in the Parser object. -// See idl_gen_php. -extern bool GeneratePhp(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Python files from the definitions in the Parser object. -// See idl_gen_python.cpp. -extern bool GeneratePython(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Lobster files from the definitions in the Parser object. -// See idl_gen_lobster.cpp. -extern bool GenerateLobster(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Lua files from the definitions in the Parser object. -// See idl_gen_lua.cpp. -extern bool GenerateLua(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Rust files from the definitions in the Parser object. -// See idl_gen_rust.cpp. -extern bool GenerateRust(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Json schema file -// See idl_gen_json_schema.cpp. -extern bool GenerateJsonSchema(const Parser &parser, const std::string &path, - const std::string &file_name); - -extern bool GenerateKotlin(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate Swift classes. -// See idl_gen_swift.cpp -extern bool GenerateSwift(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a schema file from the internal representation, useful after -// parsing a .proto schema. -extern std::string GenerateFBS(const Parser &parser, - const std::string &file_name); -extern bool GenerateFBS(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for the generated TypeScript code. -// See idl_gen_ts.cpp. -extern std::string TSMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for the generated C++ header. -// See idl_gen_cpp.cpp. -extern std::string CPPMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for the generated Dart code -// see idl_gen_dart.cpp -extern std::string DartMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for the generated Rust code. -// See idl_gen_rust.cpp. -extern std::string RustMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for generated Java or C# files. -// See code_generators.cpp. -extern std::string CSharpMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); -extern std::string JavaMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate a make rule for the generated text (JSON) files. -// See idl_gen_text.cpp. -extern std::string TextMakeRule(const Parser &parser, const std::string &path, - const std::string &file_names); - -// Generate a make rule for the generated binary files. -// See code_generators.cpp. -extern std::string BinaryMakeRule(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate GRPC Cpp interfaces. -// See idl_gen_grpc.cpp. -bool GenerateCppGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate GRPC Go interfaces. -// See idl_gen_grpc.cpp. -bool GenerateGoGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate GRPC Java classes. -// See idl_gen_grpc.cpp -bool GenerateJavaGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate GRPC Python interfaces. -// See idl_gen_grpc.cpp. -bool GeneratePythonGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -// Generate GRPC Swift interfaces. -// See idl_gen_grpc.cpp. -extern bool GenerateSwiftGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -extern bool GenerateTSGRPC(const Parser &parser, const std::string &path, - const std::string &file_name); - -extern bool GenerateRustModuleRootFile(const Parser &parser, - const std::string &path); -} // namespace flatbuffers - -#endif // FLATBUFFERS_IDL_H_ diff --git a/new_ai_tools/include/flatbuffers/minireflect.h b/new_ai_tools/include/flatbuffers/minireflect.h deleted file mode 100644 index 26fd86c9..00000000 --- a/new_ai_tools/include/flatbuffers/minireflect.h +++ /dev/null @@ -1,419 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_MINIREFLECT_H_ -#define FLATBUFFERS_MINIREFLECT_H_ - -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/util.h" - -namespace flatbuffers { - -// Utilities that can be used with the "mini reflection" tables present -// in generated code with --reflect-types (only types) or --reflect-names -// (also names). -// This allows basic reflection functionality such as pretty-printing -// that does not require the use of the schema parser or loading of binary -// schema files at runtime (reflection.h). - -// For any of the functions below that take `const TypeTable *`, you pass -// `FooTypeTable()` if the type of the root is `Foo`. - -// First, a generic iterator that can be used by multiple algorithms. - -struct IterationVisitor { - // These mark the scope of a table or struct. - virtual void StartSequence() {} - virtual void EndSequence() {} - // Called for each field regardless of whether it is present or not. - // If not present, val == nullptr. set_idx is the index of all set fields. - virtual void Field(size_t /*field_idx*/, size_t /*set_idx*/, - ElementaryType /*type*/, bool /*is_vector*/, - const TypeTable * /*type_table*/, const char * /*name*/, - const uint8_t * /*val*/) {} - // Called for a value that is actually present, after a field, or as part - // of a vector. - virtual void UType(uint8_t, const char *) {} - virtual void Bool(bool) {} - virtual void Char(int8_t, const char *) {} - virtual void UChar(uint8_t, const char *) {} - virtual void Short(int16_t, const char *) {} - virtual void UShort(uint16_t, const char *) {} - virtual void Int(int32_t, const char *) {} - virtual void UInt(uint32_t, const char *) {} - virtual void Long(int64_t) {} - virtual void ULong(uint64_t) {} - virtual void Float(float) {} - virtual void Double(double) {} - virtual void String(const String *) {} - virtual void Unknown(const uint8_t *) {} // From a future version. - // These mark the scope of a vector. - virtual void StartVector() {} - virtual void EndVector() {} - virtual void Element(size_t /*i*/, ElementaryType /*type*/, - const TypeTable * /*type_table*/, - const uint8_t * /*val*/) {} - virtual ~IterationVisitor() {} -}; - -inline size_t InlineSize(ElementaryType type, const TypeTable *type_table) { - switch (type) { - case ET_UTYPE: - case ET_BOOL: - case ET_CHAR: - case ET_UCHAR: return 1; - case ET_SHORT: - case ET_USHORT: return 2; - case ET_INT: - case ET_UINT: - case ET_FLOAT: - case ET_STRING: return 4; - case ET_LONG: - case ET_ULONG: - case ET_DOUBLE: return 8; - case ET_SEQUENCE: - switch (type_table->st) { - case ST_TABLE: - case ST_UNION: return 4; - case ST_STRUCT: - return static_cast(type_table->values[type_table->num_elems]); - default: FLATBUFFERS_ASSERT(false); return 1; - } - default: FLATBUFFERS_ASSERT(false); return 1; - } -} - -inline int64_t LookupEnum(int64_t enum_val, const int64_t *values, - size_t num_values) { - if (!values) return enum_val; - for (size_t i = 0; i < num_values; i++) { - if (enum_val == values[i]) return static_cast(i); - } - return -1; // Unknown enum value. -} - -template const char *EnumName(T tval, const TypeTable *type_table) { - if (!type_table || !type_table->names) return nullptr; - auto i = LookupEnum(static_cast(tval), type_table->values, - type_table->num_elems); - if (i >= 0 && i < static_cast(type_table->num_elems)) { - return type_table->names[i]; - } - return nullptr; -} - -void IterateObject(const uint8_t *obj, const TypeTable *type_table, - IterationVisitor *visitor); - -inline void IterateValue(ElementaryType type, const uint8_t *val, - const TypeTable *type_table, const uint8_t *prev_val, - soffset_t vector_index, IterationVisitor *visitor) { - switch (type) { - case ET_UTYPE: { - auto tval = ReadScalar(val); - visitor->UType(tval, EnumName(tval, type_table)); - break; - } - case ET_BOOL: { - visitor->Bool(ReadScalar(val) != 0); - break; - } - case ET_CHAR: { - auto tval = ReadScalar(val); - visitor->Char(tval, EnumName(tval, type_table)); - break; - } - case ET_UCHAR: { - auto tval = ReadScalar(val); - visitor->UChar(tval, EnumName(tval, type_table)); - break; - } - case ET_SHORT: { - auto tval = ReadScalar(val); - visitor->Short(tval, EnumName(tval, type_table)); - break; - } - case ET_USHORT: { - auto tval = ReadScalar(val); - visitor->UShort(tval, EnumName(tval, type_table)); - break; - } - case ET_INT: { - auto tval = ReadScalar(val); - visitor->Int(tval, EnumName(tval, type_table)); - break; - } - case ET_UINT: { - auto tval = ReadScalar(val); - visitor->UInt(tval, EnumName(tval, type_table)); - break; - } - case ET_LONG: { - visitor->Long(ReadScalar(val)); - break; - } - case ET_ULONG: { - visitor->ULong(ReadScalar(val)); - break; - } - case ET_FLOAT: { - visitor->Float(ReadScalar(val)); - break; - } - case ET_DOUBLE: { - visitor->Double(ReadScalar(val)); - break; - } - case ET_STRING: { - val += ReadScalar(val); - visitor->String(reinterpret_cast(val)); - break; - } - case ET_SEQUENCE: { - switch (type_table->st) { - case ST_TABLE: - val += ReadScalar(val); - IterateObject(val, type_table, visitor); - break; - case ST_STRUCT: IterateObject(val, type_table, visitor); break; - case ST_UNION: { - val += ReadScalar(val); - FLATBUFFERS_ASSERT(prev_val); - auto union_type = *prev_val; // Always a uint8_t. - if (vector_index >= 0) { - auto type_vec = reinterpret_cast *>(prev_val); - union_type = type_vec->Get(static_cast(vector_index)); - } - auto type_code_idx = - LookupEnum(union_type, type_table->values, type_table->num_elems); - if (type_code_idx >= 0 && - type_code_idx < static_cast(type_table->num_elems)) { - auto type_code = type_table->type_codes[type_code_idx]; - switch (type_code.base_type) { - case ET_SEQUENCE: { - auto ref = type_table->type_refs[type_code.sequence_ref](); - IterateObject(val, ref, visitor); - break; - } - case ET_STRING: - visitor->String(reinterpret_cast(val)); - break; - default: visitor->Unknown(val); - } - } else { - visitor->Unknown(val); - } - break; - } - case ST_ENUM: FLATBUFFERS_ASSERT(false); break; - } - break; - } - default: { - visitor->Unknown(val); - break; - } - } -} - -inline void IterateObject(const uint8_t *obj, const TypeTable *type_table, - IterationVisitor *visitor) { - visitor->StartSequence(); - const uint8_t *prev_val = nullptr; - size_t set_idx = 0; - size_t array_idx = 0; - for (size_t i = 0; i < type_table->num_elems; i++) { - auto type_code = type_table->type_codes[i]; - auto type = static_cast(type_code.base_type); - auto is_repeating = type_code.is_repeating != 0; - auto ref_idx = type_code.sequence_ref; - const TypeTable *ref = nullptr; - if (ref_idx >= 0) { ref = type_table->type_refs[ref_idx](); } - auto name = type_table->names ? type_table->names[i] : nullptr; - const uint8_t *val = nullptr; - if (type_table->st == ST_TABLE) { - val = reinterpret_cast(obj)->GetAddressOf( - FieldIndexToOffset(static_cast(i))); - } else { - val = obj + type_table->values[i]; - } - visitor->Field(i, set_idx, type, is_repeating, ref, name, val); - if (val) { - set_idx++; - if (is_repeating) { - auto elem_ptr = val; - size_t size = 0; - if (type_table->st == ST_TABLE) { - // variable length vector - val += ReadScalar(val); - auto vec = reinterpret_cast *>(val); - elem_ptr = vec->Data(); - size = vec->size(); - } else { - // otherwise fixed size array - size = type_table->array_sizes[array_idx]; - ++array_idx; - } - visitor->StartVector(); - for (size_t j = 0; j < size; j++) { - visitor->Element(j, type, ref, elem_ptr); - IterateValue(type, elem_ptr, ref, prev_val, static_cast(j), - visitor); - elem_ptr += InlineSize(type, ref); - } - visitor->EndVector(); - } else { - IterateValue(type, val, ref, prev_val, -1, visitor); - } - } - prev_val = val; - } - visitor->EndSequence(); -} - -inline void IterateFlatBuffer(const uint8_t *buffer, - const TypeTable *type_table, - IterationVisitor *callback) { - IterateObject(GetRoot(buffer), type_table, callback); -} - -// Outputting a Flatbuffer to a string. Tries to conform as close to JSON / -// the output generated by idl_gen_text.cpp. - -struct ToStringVisitor : public IterationVisitor { - std::string s; - std::string d; - bool q; - std::string in; - size_t indent_level; - bool vector_delimited; - ToStringVisitor(std::string delimiter, bool quotes, std::string indent, - bool vdelimited = true) - : d(delimiter), - q(quotes), - in(indent), - indent_level(0), - vector_delimited(vdelimited) {} - ToStringVisitor(std::string delimiter) - : d(delimiter), - q(false), - in(""), - indent_level(0), - vector_delimited(true) {} - - void append_indent() { - for (size_t i = 0; i < indent_level; i++) { s += in; } - } - - void StartSequence() { - s += "{"; - s += d; - indent_level++; - } - void EndSequence() { - s += d; - indent_level--; - append_indent(); - s += "}"; - } - void Field(size_t /*field_idx*/, size_t set_idx, ElementaryType /*type*/, - bool /*is_vector*/, const TypeTable * /*type_table*/, - const char *name, const uint8_t *val) { - if (!val) return; - if (set_idx) { - s += ","; - s += d; - } - append_indent(); - if (name) { - if (q) s += "\""; - s += name; - if (q) s += "\""; - s += ": "; - } - } - template void Named(T x, const char *name) { - if (name) { - if (q) s += "\""; - s += name; - if (q) s += "\""; - } else { - s += NumToString(x); - } - } - void UType(uint8_t x, const char *name) { Named(x, name); } - void Bool(bool x) { s += x ? "true" : "false"; } - void Char(int8_t x, const char *name) { Named(x, name); } - void UChar(uint8_t x, const char *name) { Named(x, name); } - void Short(int16_t x, const char *name) { Named(x, name); } - void UShort(uint16_t x, const char *name) { Named(x, name); } - void Int(int32_t x, const char *name) { Named(x, name); } - void UInt(uint32_t x, const char *name) { Named(x, name); } - void Long(int64_t x) { s += NumToString(x); } - void ULong(uint64_t x) { s += NumToString(x); } - void Float(float x) { s += NumToString(x); } - void Double(double x) { s += NumToString(x); } - void String(const struct String *str) { - EscapeString(str->c_str(), str->size(), &s, true, false); - } - void Unknown(const uint8_t *) { s += "(?)"; } - void StartVector() { - s += "["; - if (vector_delimited) { - s += d; - indent_level++; - append_indent(); - } else { - s += " "; - } - } - void EndVector() { - if (vector_delimited) { - s += d; - indent_level--; - append_indent(); - } else { - s += " "; - } - s += "]"; - } - void Element(size_t i, ElementaryType /*type*/, - const TypeTable * /*type_table*/, const uint8_t * /*val*/) { - if (i) { - s += ","; - if (vector_delimited) { - s += d; - append_indent(); - } else { - s += " "; - } - } - } -}; - -inline std::string FlatBufferToString(const uint8_t *buffer, - const TypeTable *type_table, - bool multi_line = false, - bool vector_delimited = true) { - ToStringVisitor tostring_visitor(multi_line ? "\n" : " ", false, "", - vector_delimited); - IterateFlatBuffer(buffer, type_table, &tostring_visitor); - return tostring_visitor.s; -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_MINIREFLECT_H_ diff --git a/new_ai_tools/include/flatbuffers/pch/flatc_pch.h b/new_ai_tools/include/flatbuffers/pch/flatc_pch.h deleted file mode 100644 index 77132790..00000000 --- a/new_ai_tools/include/flatbuffers/pch/flatc_pch.h +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_FLATC_PCH_H_ -#define FLATBUFFERS_FLATC_PCH_H_ - -// stl -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -// flatbuffers -#include "flatbuffers/pch/pch.h" -#include "flatbuffers/code_generators.h" -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/flexbuffers.h" -#include "flatbuffers/idl.h" - -#endif // FLATBUFFERS_FLATC_PCH_H_ diff --git a/new_ai_tools/include/flatbuffers/pch/pch.h b/new_ai_tools/include/flatbuffers/pch/pch.h deleted file mode 100644 index 804e99ed..00000000 --- a/new_ai_tools/include/flatbuffers/pch/pch.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_PCH_H_ -#define FLATBUFFERS_PCH_H_ - -// stl -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -// flatbuffers -#include "flatbuffers/util.h" - -#endif // FLATBUFFERS_PCH_H_ diff --git a/new_ai_tools/include/flatbuffers/reflection.h b/new_ai_tools/include/flatbuffers/reflection.h deleted file mode 100644 index 8e700f0b..00000000 --- a/new_ai_tools/include/flatbuffers/reflection.h +++ /dev/null @@ -1,502 +0,0 @@ -/* - * Copyright 2015 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_REFLECTION_H_ -#define FLATBUFFERS_REFLECTION_H_ - -// This is somewhat of a circular dependency because flatc (and thus this -// file) is needed to generate this header in the first place. -// Should normally not be a problem since it can be generated by the -// previous version of flatc whenever this code needs to change. -// See scripts/generate_code.py for generation. -#include "flatbuffers/reflection_generated.h" - -// Helper functionality for reflection. - -namespace flatbuffers { - -// ------------------------- GETTERS ------------------------- - -inline bool IsScalar(reflection::BaseType t) { - return t >= reflection::UType && t <= reflection::Double; -} -inline bool IsInteger(reflection::BaseType t) { - return t >= reflection::UType && t <= reflection::ULong; -} -inline bool IsFloat(reflection::BaseType t) { - return t == reflection::Float || t == reflection::Double; -} -inline bool IsLong(reflection::BaseType t) { - return t == reflection::Long || t == reflection::ULong; -} - -// Size of a basic type, don't use with structs. -inline size_t GetTypeSize(reflection::BaseType base_type) { - // This needs to correspond to the BaseType enum. - static size_t sizes[] = { - 0, // None - 1, // UType - 1, // Bool - 1, // Byte - 1, // UByte - 2, // Short - 2, // UShort - 4, // Int - 4, // UInt - 8, // Long - 8, // ULong - 4, // Float - 8, // Double - 4, // String - 4, // Vector - 4, // Obj - 4, // Union - 0, // Array. Only used in structs. 0 was chosen to prevent out-of-bounds - // errors. - - 0 // MaxBaseType. This must be kept the last entry in this array. - }; - static_assert(sizeof(sizes) / sizeof(size_t) == reflection::MaxBaseType + 1, - "Size of sizes[] array does not match the count of BaseType " - "enum values."); - return sizes[base_type]; -} - -// Same as above, but now correctly returns the size of a struct if -// the field (or vector element) is a struct. -inline size_t GetTypeSizeInline(reflection::BaseType base_type, int type_index, - const reflection::Schema &schema) { - if (base_type == reflection::Obj && - schema.objects()->Get(type_index)->is_struct()) { - return schema.objects()->Get(type_index)->bytesize(); - } else { - return GetTypeSize(base_type); - } -} - -// Get the root, regardless of what type it is. -inline Table *GetAnyRoot(uint8_t *flatbuf) { - return GetMutableRoot(flatbuf); -} -inline const Table *GetAnyRoot(const uint8_t *flatbuf) { - return GetRoot
    (flatbuf); -} - -// Get a field's default, if you know it's an integer, and its exact type. -template T GetFieldDefaultI(const reflection::Field &field) { - FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); - return static_cast(field.default_integer()); -} - -// Get a field's default, if you know it's floating point and its exact type. -template T GetFieldDefaultF(const reflection::Field &field) { - FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); - return static_cast(field.default_real()); -} - -// Get a field, if you know it's an integer, and its exact type. -template -T GetFieldI(const Table &table, const reflection::Field &field) { - FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); - return table.GetField(field.offset(), - static_cast(field.default_integer())); -} - -// Get a field, if you know it's floating point and its exact type. -template -T GetFieldF(const Table &table, const reflection::Field &field) { - FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(field.type()->base_type())); - return table.GetField(field.offset(), - static_cast(field.default_real())); -} - -// Get a field, if you know it's a string. -inline const String *GetFieldS(const Table &table, - const reflection::Field &field) { - FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::String); - return table.GetPointer(field.offset()); -} - -// Get a field, if you know it's a vector. -template -Vector *GetFieldV(const Table &table, const reflection::Field &field) { - FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Vector && - sizeof(T) == GetTypeSize(field.type()->element())); - return table.GetPointer *>(field.offset()); -} - -// Get a field, if you know it's a vector, generically. -// To actually access elements, use the return value together with -// field.type()->element() in any of GetAnyVectorElemI below etc. -inline VectorOfAny *GetFieldAnyV(const Table &table, - const reflection::Field &field) { - return table.GetPointer(field.offset()); -} - -// Get a field, if you know it's a table. -inline Table *GetFieldT(const Table &table, const reflection::Field &field) { - FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj || - field.type()->base_type() == reflection::Union); - return table.GetPointer
    (field.offset()); -} - -// Get a field, if you know it's a struct. -inline const Struct *GetFieldStruct(const Table &table, - const reflection::Field &field) { - // TODO: This does NOT check if the field is a table or struct, but we'd need - // access to the schema to check the is_struct flag. - FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj); - return table.GetStruct(field.offset()); -} - -// Get a structure's field, if you know it's a struct. -inline const Struct *GetFieldStruct(const Struct &structure, - const reflection::Field &field) { - FLATBUFFERS_ASSERT(field.type()->base_type() == reflection::Obj); - return structure.GetStruct(field.offset()); -} - -// Raw helper functions used below: get any value in memory as a 64bit int, a -// double or a string. -// All scalars get static_cast to an int64_t, strings use strtoull, every other -// data type returns 0. -int64_t GetAnyValueI(reflection::BaseType type, const uint8_t *data); -// All scalars static cast to double, strings use strtod, every other data -// type is 0.0. -double GetAnyValueF(reflection::BaseType type, const uint8_t *data); -// All scalars converted using stringstream, strings as-is, and all other -// data types provide some level of debug-pretty-printing. -std::string GetAnyValueS(reflection::BaseType type, const uint8_t *data, - const reflection::Schema *schema, int type_index); - -// Get any table field as a 64bit int, regardless of what type it is. -inline int64_t GetAnyFieldI(const Table &table, - const reflection::Field &field) { - auto field_ptr = table.GetAddressOf(field.offset()); - return field_ptr ? GetAnyValueI(field.type()->base_type(), field_ptr) - : field.default_integer(); -} - -// Get any table field as a double, regardless of what type it is. -inline double GetAnyFieldF(const Table &table, const reflection::Field &field) { - auto field_ptr = table.GetAddressOf(field.offset()); - return field_ptr ? GetAnyValueF(field.type()->base_type(), field_ptr) - : field.default_real(); -} - -// Get any table field as a string, regardless of what type it is. -// You may pass nullptr for the schema if you don't care to have fields that -// are of table type pretty-printed. -inline std::string GetAnyFieldS(const Table &table, - const reflection::Field &field, - const reflection::Schema *schema) { - auto field_ptr = table.GetAddressOf(field.offset()); - return field_ptr ? GetAnyValueS(field.type()->base_type(), field_ptr, schema, - field.type()->index()) - : ""; -} - -// Get any struct field as a 64bit int, regardless of what type it is. -inline int64_t GetAnyFieldI(const Struct &st, const reflection::Field &field) { - return GetAnyValueI(field.type()->base_type(), - st.GetAddressOf(field.offset())); -} - -// Get any struct field as a double, regardless of what type it is. -inline double GetAnyFieldF(const Struct &st, const reflection::Field &field) { - return GetAnyValueF(field.type()->base_type(), - st.GetAddressOf(field.offset())); -} - -// Get any struct field as a string, regardless of what type it is. -inline std::string GetAnyFieldS(const Struct &st, - const reflection::Field &field) { - return GetAnyValueS(field.type()->base_type(), - st.GetAddressOf(field.offset()), nullptr, -1); -} - -// Get any vector element as a 64bit int, regardless of what type it is. -inline int64_t GetAnyVectorElemI(const VectorOfAny *vec, - reflection::BaseType elem_type, size_t i) { - return GetAnyValueI(elem_type, vec->Data() + GetTypeSize(elem_type) * i); -} - -// Get any vector element as a double, regardless of what type it is. -inline double GetAnyVectorElemF(const VectorOfAny *vec, - reflection::BaseType elem_type, size_t i) { - return GetAnyValueF(elem_type, vec->Data() + GetTypeSize(elem_type) * i); -} - -// Get any vector element as a string, regardless of what type it is. -inline std::string GetAnyVectorElemS(const VectorOfAny *vec, - reflection::BaseType elem_type, size_t i) { - return GetAnyValueS(elem_type, vec->Data() + GetTypeSize(elem_type) * i, - nullptr, -1); -} - -// Get a vector element that's a table/string/vector from a generic vector. -// Pass Table/String/VectorOfAny as template parameter. -// Warning: does no typechecking. -template -T *GetAnyVectorElemPointer(const VectorOfAny *vec, size_t i) { - auto elem_ptr = vec->Data() + sizeof(uoffset_t) * i; - return reinterpret_cast(elem_ptr + ReadScalar(elem_ptr)); -} - -// Get the inline-address of a vector element. Useful for Structs (pass Struct -// as template arg), or being able to address a range of scalars in-line. -// Get elem_size from GetTypeSizeInline(). -// Note: little-endian data on all platforms, use EndianScalar() instead of -// raw pointer access with scalars). -template -T *GetAnyVectorElemAddressOf(const VectorOfAny *vec, size_t i, - size_t elem_size) { - return reinterpret_cast(vec->Data() + elem_size * i); -} - -// Similarly, for elements of tables. -template -T *GetAnyFieldAddressOf(const Table &table, const reflection::Field &field) { - return reinterpret_cast(table.GetAddressOf(field.offset())); -} - -// Similarly, for elements of structs. -template -T *GetAnyFieldAddressOf(const Struct &st, const reflection::Field &field) { - return reinterpret_cast(st.GetAddressOf(field.offset())); -} - -// ------------------------- SETTERS ------------------------- - -// Set any scalar field, if you know its exact type. -template -bool SetField(Table *table, const reflection::Field &field, T val) { - reflection::BaseType type = field.type()->base_type(); - if (!IsScalar(type)) { return false; } - FLATBUFFERS_ASSERT(sizeof(T) == GetTypeSize(type)); - T def; - if (IsInteger(type)) { - def = GetFieldDefaultI(field); - } else { - FLATBUFFERS_ASSERT(IsFloat(type)); - def = GetFieldDefaultF(field); - } - return table->SetField(field.offset(), val, def); -} - -// Raw helper functions used below: set any value in memory as a 64bit int, a -// double or a string. -// These work for all scalar values, but do nothing for other data types. -// To set a string, see SetString below. -void SetAnyValueI(reflection::BaseType type, uint8_t *data, int64_t val); -void SetAnyValueF(reflection::BaseType type, uint8_t *data, double val); -void SetAnyValueS(reflection::BaseType type, uint8_t *data, const char *val); - -// Set any table field as a 64bit int, regardless of type what it is. -inline bool SetAnyFieldI(Table *table, const reflection::Field &field, - int64_t val) { - auto field_ptr = table->GetAddressOf(field.offset()); - if (!field_ptr) return val == GetFieldDefaultI(field); - SetAnyValueI(field.type()->base_type(), field_ptr, val); - return true; -} - -// Set any table field as a double, regardless of what type it is. -inline bool SetAnyFieldF(Table *table, const reflection::Field &field, - double val) { - auto field_ptr = table->GetAddressOf(field.offset()); - if (!field_ptr) return val == GetFieldDefaultF(field); - SetAnyValueF(field.type()->base_type(), field_ptr, val); - return true; -} - -// Set any table field as a string, regardless of what type it is. -inline bool SetAnyFieldS(Table *table, const reflection::Field &field, - const char *val) { - auto field_ptr = table->GetAddressOf(field.offset()); - if (!field_ptr) return false; - SetAnyValueS(field.type()->base_type(), field_ptr, val); - return true; -} - -// Set any struct field as a 64bit int, regardless of type what it is. -inline void SetAnyFieldI(Struct *st, const reflection::Field &field, - int64_t val) { - SetAnyValueI(field.type()->base_type(), st->GetAddressOf(field.offset()), - val); -} - -// Set any struct field as a double, regardless of type what it is. -inline void SetAnyFieldF(Struct *st, const reflection::Field &field, - double val) { - SetAnyValueF(field.type()->base_type(), st->GetAddressOf(field.offset()), - val); -} - -// Set any struct field as a string, regardless of type what it is. -inline void SetAnyFieldS(Struct *st, const reflection::Field &field, - const char *val) { - SetAnyValueS(field.type()->base_type(), st->GetAddressOf(field.offset()), - val); -} - -// Set any vector element as a 64bit int, regardless of type what it is. -inline void SetAnyVectorElemI(VectorOfAny *vec, reflection::BaseType elem_type, - size_t i, int64_t val) { - SetAnyValueI(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); -} - -// Set any vector element as a double, regardless of type what it is. -inline void SetAnyVectorElemF(VectorOfAny *vec, reflection::BaseType elem_type, - size_t i, double val) { - SetAnyValueF(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); -} - -// Set any vector element as a string, regardless of type what it is. -inline void SetAnyVectorElemS(VectorOfAny *vec, reflection::BaseType elem_type, - size_t i, const char *val) { - SetAnyValueS(elem_type, vec->Data() + GetTypeSize(elem_type) * i, val); -} - -// ------------------------- RESIZING SETTERS ------------------------- - -// "smart" pointer for use with resizing vectors: turns a pointer inside -// a vector into a relative offset, such that it is not affected by resizes. -template class pointer_inside_vector { - public: - pointer_inside_vector(T *ptr, std::vector &vec) - : offset_(reinterpret_cast(ptr) - - reinterpret_cast(vec.data())), - vec_(vec) {} - - T *operator*() const { - return reinterpret_cast(reinterpret_cast(vec_.data()) + - offset_); - } - T *operator->() const { return operator*(); } - - private: - size_t offset_; - std::vector &vec_; -}; - -// Helper to create the above easily without specifying template args. -template -pointer_inside_vector piv(T *ptr, std::vector &vec) { - return pointer_inside_vector(ptr, vec); -} - -inline const char *UnionTypeFieldSuffix() { return "_type"; } - -// Helper to figure out the actual table type a union refers to. -inline const reflection::Object &GetUnionType( - const reflection::Schema &schema, const reflection::Object &parent, - const reflection::Field &unionfield, const Table &table) { - auto enumdef = schema.enums()->Get(unionfield.type()->index()); - // TODO: this is clumsy and slow, but no other way to find it? - auto type_field = parent.fields()->LookupByKey( - (unionfield.name()->str() + UnionTypeFieldSuffix()).c_str()); - FLATBUFFERS_ASSERT(type_field); - auto union_type = GetFieldI(table, *type_field); - auto enumval = enumdef->values()->LookupByKey(union_type); - return *schema.objects()->Get(enumval->union_type()->index()); -} - -// Changes the contents of a string inside a FlatBuffer. FlatBuffer must -// live inside a std::vector so we can resize the buffer if needed. -// "str" must live inside "flatbuf" and may be invalidated after this call. -// If your FlatBuffer's root table is not the schema's root table, you should -// pass in your root_table type as well. -void SetString(const reflection::Schema &schema, const std::string &val, - const String *str, std::vector *flatbuf, - const reflection::Object *root_table = nullptr); - -// Resizes a flatbuffers::Vector inside a FlatBuffer. FlatBuffer must -// live inside a std::vector so we can resize the buffer if needed. -// "vec" must live inside "flatbuf" and may be invalidated after this call. -// If your FlatBuffer's root table is not the schema's root table, you should -// pass in your root_table type as well. -uint8_t *ResizeAnyVector(const reflection::Schema &schema, uoffset_t newsize, - const VectorOfAny *vec, uoffset_t num_elems, - uoffset_t elem_size, std::vector *flatbuf, - const reflection::Object *root_table = nullptr); - -template -void ResizeVector(const reflection::Schema &schema, uoffset_t newsize, T val, - const Vector *vec, std::vector *flatbuf, - const reflection::Object *root_table = nullptr) { - auto delta_elem = static_cast(newsize) - static_cast(vec->size()); - auto newelems = ResizeAnyVector( - schema, newsize, reinterpret_cast(vec), vec->size(), - static_cast(sizeof(T)), flatbuf, root_table); - // Set new elements to "val". - for (int i = 0; i < delta_elem; i++) { - auto loc = newelems + i * sizeof(T); - auto is_scalar = flatbuffers::is_scalar::value; - if (is_scalar) { - WriteScalar(loc, val); - } else { // struct - *reinterpret_cast(loc) = val; - } - } -} - -// Adds any new data (in the form of a new FlatBuffer) to an existing -// FlatBuffer. This can be used when any of the above methods are not -// sufficient, in particular for adding new tables and new fields. -// This is potentially slightly less efficient than a FlatBuffer constructed -// in one piece, since the new FlatBuffer doesn't share any vtables with the -// existing one. -// The return value can now be set using Vector::MutateOffset or SetFieldT -// below. -const uint8_t *AddFlatBuffer(std::vector &flatbuf, - const uint8_t *newbuf, size_t newlen); - -inline bool SetFieldT(Table *table, const reflection::Field &field, - const uint8_t *val) { - FLATBUFFERS_ASSERT(sizeof(uoffset_t) == - GetTypeSize(field.type()->base_type())); - return table->SetPointer(field.offset(), val); -} - -// ------------------------- COPYING ------------------------- - -// Generic copying of tables from a FlatBuffer into a FlatBuffer builder. -// Can be used to do any kind of merging/selecting you may want to do out -// of existing buffers. Also useful to reconstruct a whole buffer if the -// above resizing functionality has introduced garbage in a buffer you want -// to remove. -// Note: this does not deal with DAGs correctly. If the table passed forms a -// DAG, the copy will be a tree instead (with duplicates). Strings can be -// shared however, by passing true for use_string_pooling. - -Offset CopyTable(FlatBufferBuilder &fbb, - const reflection::Schema &schema, - const reflection::Object &objectdef, - const Table &table, - bool use_string_pooling = false); - -// Verifies the provided flatbuffer using reflection. -// root should point to the root type for this flatbuffer. -// buf should point to the start of flatbuffer data. -// length specifies the size of the flatbuffer data. -bool Verify(const reflection::Schema &schema, const reflection::Object &root, - const uint8_t *buf, size_t length, uoffset_t max_depth = 64, - uoffset_t max_tables = 1000000); - -} // namespace flatbuffers - -#endif // FLATBUFFERS_REFLECTION_H_ diff --git a/new_ai_tools/include/flatbuffers/reflection_generated.h b/new_ai_tools/include/flatbuffers/reflection_generated.h deleted file mode 100644 index dcb0f7e0..00000000 --- a/new_ai_tools/include/flatbuffers/reflection_generated.h +++ /dev/null @@ -1,1449 +0,0 @@ -// automatically generated by the FlatBuffers compiler, do not modify - - -#ifndef FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ -#define FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ - -#include "flatbuffers/flatbuffers.h" - -namespace reflection { - -struct Type; -struct TypeBuilder; - -struct KeyValue; -struct KeyValueBuilder; - -struct EnumVal; -struct EnumValBuilder; - -struct Enum; -struct EnumBuilder; - -struct Field; -struct FieldBuilder; - -struct Object; -struct ObjectBuilder; - -struct RPCCall; -struct RPCCallBuilder; - -struct Service; -struct ServiceBuilder; - -struct SchemaFile; -struct SchemaFileBuilder; - -struct Schema; -struct SchemaBuilder; - -enum BaseType { - None = 0, - UType = 1, - Bool = 2, - Byte = 3, - UByte = 4, - Short = 5, - UShort = 6, - Int = 7, - UInt = 8, - Long = 9, - ULong = 10, - Float = 11, - Double = 12, - String = 13, - Vector = 14, - Obj = 15, - Union = 16, - Array = 17, - MaxBaseType = 18 -}; - -inline const BaseType (&EnumValuesBaseType())[19] { - static const BaseType values[] = { - None, - UType, - Bool, - Byte, - UByte, - Short, - UShort, - Int, - UInt, - Long, - ULong, - Float, - Double, - String, - Vector, - Obj, - Union, - Array, - MaxBaseType - }; - return values; -} - -inline const char * const *EnumNamesBaseType() { - static const char * const names[20] = { - "None", - "UType", - "Bool", - "Byte", - "UByte", - "Short", - "UShort", - "Int", - "UInt", - "Long", - "ULong", - "Float", - "Double", - "String", - "Vector", - "Obj", - "Union", - "Array", - "MaxBaseType", - nullptr - }; - return names; -} - -inline const char *EnumNameBaseType(BaseType e) { - if (flatbuffers::IsOutRange(e, None, MaxBaseType)) return ""; - const size_t index = static_cast(e); - return EnumNamesBaseType()[index]; -} - -/// New schema language features that are not supported by old code generators. -enum AdvancedFeatures { - AdvancedArrayFeatures = 1ULL, - AdvancedUnionFeatures = 2ULL, - OptionalScalars = 4ULL, - DefaultVectorsAndStrings = 8ULL -}; - -inline const AdvancedFeatures (&EnumValuesAdvancedFeatures())[4] { - static const AdvancedFeatures values[] = { - AdvancedArrayFeatures, - AdvancedUnionFeatures, - OptionalScalars, - DefaultVectorsAndStrings - }; - return values; -} - -inline const char * const *EnumNamesAdvancedFeatures() { - static const char * const names[9] = { - "AdvancedArrayFeatures", - "AdvancedUnionFeatures", - "", - "OptionalScalars", - "", - "", - "", - "DefaultVectorsAndStrings", - nullptr - }; - return names; -} - -inline const char *EnumNameAdvancedFeatures(AdvancedFeatures e) { - if (flatbuffers::IsOutRange(e, AdvancedArrayFeatures, DefaultVectorsAndStrings)) return ""; - const size_t index = static_cast(e) - static_cast(AdvancedArrayFeatures); - return EnumNamesAdvancedFeatures()[index]; -} - -struct Type FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TypeBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BASE_TYPE = 4, - VT_ELEMENT = 6, - VT_INDEX = 8, - VT_FIXED_LENGTH = 10, - VT_BASE_SIZE = 12, - VT_ELEMENT_SIZE = 14 - }; - reflection::BaseType base_type() const { - return static_cast(GetField(VT_BASE_TYPE, 0)); - } - reflection::BaseType element() const { - return static_cast(GetField(VT_ELEMENT, 0)); - } - int32_t index() const { - return GetField(VT_INDEX, -1); - } - uint16_t fixed_length() const { - return GetField(VT_FIXED_LENGTH, 0); - } - /// The size (octets) of the `base_type` field. - uint32_t base_size() const { - return GetField(VT_BASE_SIZE, 4); - } - /// The size (octets) of the `element` field, if present. - uint32_t element_size() const { - return GetField(VT_ELEMENT_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BASE_TYPE, 1) && - VerifyField(verifier, VT_ELEMENT, 1) && - VerifyField(verifier, VT_INDEX, 4) && - VerifyField(verifier, VT_FIXED_LENGTH, 2) && - VerifyField(verifier, VT_BASE_SIZE, 4) && - VerifyField(verifier, VT_ELEMENT_SIZE, 4) && - verifier.EndTable(); - } -}; - -struct TypeBuilder { - typedef Type Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_base_type(reflection::BaseType base_type) { - fbb_.AddElement(Type::VT_BASE_TYPE, static_cast(base_type), 0); - } - void add_element(reflection::BaseType element) { - fbb_.AddElement(Type::VT_ELEMENT, static_cast(element), 0); - } - void add_index(int32_t index) { - fbb_.AddElement(Type::VT_INDEX, index, -1); - } - void add_fixed_length(uint16_t fixed_length) { - fbb_.AddElement(Type::VT_FIXED_LENGTH, fixed_length, 0); - } - void add_base_size(uint32_t base_size) { - fbb_.AddElement(Type::VT_BASE_SIZE, base_size, 4); - } - void add_element_size(uint32_t element_size) { - fbb_.AddElement(Type::VT_ELEMENT_SIZE, element_size, 0); - } - explicit TypeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateType( - flatbuffers::FlatBufferBuilder &_fbb, - reflection::BaseType base_type = reflection::None, - reflection::BaseType element = reflection::None, - int32_t index = -1, - uint16_t fixed_length = 0, - uint32_t base_size = 4, - uint32_t element_size = 0) { - TypeBuilder builder_(_fbb); - builder_.add_element_size(element_size); - builder_.add_base_size(base_size); - builder_.add_index(index); - builder_.add_fixed_length(fixed_length); - builder_.add_element(element); - builder_.add_base_type(base_type); - return builder_.Finish(); -} - -struct KeyValue FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef KeyValueBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_KEY = 4, - VT_VALUE = 6 - }; - const flatbuffers::String *key() const { - return GetPointer(VT_KEY); - } - bool KeyCompareLessThan(const KeyValue *o) const { - return *key() < *o->key(); - } - int KeyCompareWithValue(const char *_key) const { - return strcmp(key()->c_str(), _key); - } - const flatbuffers::String *value() const { - return GetPointer(VT_VALUE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_KEY) && - verifier.VerifyString(key()) && - VerifyOffset(verifier, VT_VALUE) && - verifier.VerifyString(value()) && - verifier.EndTable(); - } -}; - -struct KeyValueBuilder { - typedef KeyValue Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_key(flatbuffers::Offset key) { - fbb_.AddOffset(KeyValue::VT_KEY, key); - } - void add_value(flatbuffers::Offset value) { - fbb_.AddOffset(KeyValue::VT_VALUE, value); - } - explicit KeyValueBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, KeyValue::VT_KEY); - return o; - } -}; - -inline flatbuffers::Offset CreateKeyValue( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset key = 0, - flatbuffers::Offset value = 0) { - KeyValueBuilder builder_(_fbb); - builder_.add_value(value); - builder_.add_key(key); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateKeyValueDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *key = nullptr, - const char *value = nullptr) { - auto key__ = key ? _fbb.CreateString(key) : 0; - auto value__ = value ? _fbb.CreateString(value) : 0; - return reflection::CreateKeyValue( - _fbb, - key__, - value__); -} - -struct EnumVal FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EnumValBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_VALUE = 6, - VT_UNION_TYPE = 10, - VT_DOCUMENTATION = 12 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - int64_t value() const { - return GetField(VT_VALUE, 0); - } - bool KeyCompareLessThan(const EnumVal *o) const { - return value() < o->value(); - } - int KeyCompareWithValue(int64_t _value) const { - return static_cast(value() > _value) - static_cast(value() < _value); - } - const reflection::Type *union_type() const { - return GetPointer(VT_UNION_TYPE); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyField(verifier, VT_VALUE, 8) && - VerifyOffset(verifier, VT_UNION_TYPE) && - verifier.VerifyTable(union_type()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - verifier.EndTable(); - } -}; - -struct EnumValBuilder { - typedef EnumVal Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(EnumVal::VT_NAME, name); - } - void add_value(int64_t value) { - fbb_.AddElement(EnumVal::VT_VALUE, value, 0); - } - void add_union_type(flatbuffers::Offset union_type) { - fbb_.AddOffset(EnumVal::VT_UNION_TYPE, union_type); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(EnumVal::VT_DOCUMENTATION, documentation); - } - explicit EnumValBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, EnumVal::VT_NAME); - return o; - } -}; - -inline flatbuffers::Offset CreateEnumVal( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - int64_t value = 0, - flatbuffers::Offset union_type = 0, - flatbuffers::Offset>> documentation = 0) { - EnumValBuilder builder_(_fbb); - builder_.add_value(value); - builder_.add_documentation(documentation); - builder_.add_union_type(union_type); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateEnumValDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - int64_t value = 0, - flatbuffers::Offset union_type = 0, - const std::vector> *documentation = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - return reflection::CreateEnumVal( - _fbb, - name__, - value, - union_type, - documentation__); -} - -struct Enum FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EnumBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_VALUES = 6, - VT_IS_UNION = 8, - VT_UNDERLYING_TYPE = 10, - VT_ATTRIBUTES = 12, - VT_DOCUMENTATION = 14, - VT_DECLARATION_FILE = 16 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool KeyCompareLessThan(const Enum *o) const { - return *name() < *o->name(); - } - int KeyCompareWithValue(const char *_name) const { - return strcmp(name()->c_str(), _name); - } - const flatbuffers::Vector> *values() const { - return GetPointer> *>(VT_VALUES); - } - bool is_union() const { - return GetField(VT_IS_UNION, 0) != 0; - } - const reflection::Type *underlying_type() const { - return GetPointer(VT_UNDERLYING_TYPE); - } - const flatbuffers::Vector> *attributes() const { - return GetPointer> *>(VT_ATTRIBUTES); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - /// File that this Enum is declared in. - const flatbuffers::String *declaration_file() const { - return GetPointer(VT_DECLARATION_FILE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffsetRequired(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.VerifyVectorOfTables(values()) && - VerifyField(verifier, VT_IS_UNION, 1) && - VerifyOffsetRequired(verifier, VT_UNDERLYING_TYPE) && - verifier.VerifyTable(underlying_type()) && - VerifyOffset(verifier, VT_ATTRIBUTES) && - verifier.VerifyVector(attributes()) && - verifier.VerifyVectorOfTables(attributes()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - VerifyOffset(verifier, VT_DECLARATION_FILE) && - verifier.VerifyString(declaration_file()) && - verifier.EndTable(); - } -}; - -struct EnumBuilder { - typedef Enum Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Enum::VT_NAME, name); - } - void add_values(flatbuffers::Offset>> values) { - fbb_.AddOffset(Enum::VT_VALUES, values); - } - void add_is_union(bool is_union) { - fbb_.AddElement(Enum::VT_IS_UNION, static_cast(is_union), 0); - } - void add_underlying_type(flatbuffers::Offset underlying_type) { - fbb_.AddOffset(Enum::VT_UNDERLYING_TYPE, underlying_type); - } - void add_attributes(flatbuffers::Offset>> attributes) { - fbb_.AddOffset(Enum::VT_ATTRIBUTES, attributes); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(Enum::VT_DOCUMENTATION, documentation); - } - void add_declaration_file(flatbuffers::Offset declaration_file) { - fbb_.AddOffset(Enum::VT_DECLARATION_FILE, declaration_file); - } - explicit EnumBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, Enum::VT_NAME); - fbb_.Required(o, Enum::VT_VALUES); - fbb_.Required(o, Enum::VT_UNDERLYING_TYPE); - return o; - } -}; - -inline flatbuffers::Offset CreateEnum( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - flatbuffers::Offset>> values = 0, - bool is_union = false, - flatbuffers::Offset underlying_type = 0, - flatbuffers::Offset>> attributes = 0, - flatbuffers::Offset>> documentation = 0, - flatbuffers::Offset declaration_file = 0) { - EnumBuilder builder_(_fbb); - builder_.add_declaration_file(declaration_file); - builder_.add_documentation(documentation); - builder_.add_attributes(attributes); - builder_.add_underlying_type(underlying_type); - builder_.add_values(values); - builder_.add_name(name); - builder_.add_is_union(is_union); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateEnumDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - std::vector> *values = nullptr, - bool is_union = false, - flatbuffers::Offset underlying_type = 0, - std::vector> *attributes = nullptr, - const std::vector> *documentation = nullptr, - const char *declaration_file = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto values__ = values ? _fbb.CreateVectorOfSortedTables(values) : 0; - auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; - return reflection::CreateEnum( - _fbb, - name__, - values__, - is_union, - underlying_type, - attributes__, - documentation__, - declaration_file__); -} - -struct Field FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FieldBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_TYPE = 6, - VT_ID = 8, - VT_OFFSET = 10, - VT_DEFAULT_INTEGER = 12, - VT_DEFAULT_REAL = 14, - VT_DEPRECATED = 16, - VT_REQUIRED = 18, - VT_KEY = 20, - VT_ATTRIBUTES = 22, - VT_DOCUMENTATION = 24, - VT_OPTIONAL = 26, - VT_PADDING = 28 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool KeyCompareLessThan(const Field *o) const { - return *name() < *o->name(); - } - int KeyCompareWithValue(const char *_name) const { - return strcmp(name()->c_str(), _name); - } - const reflection::Type *type() const { - return GetPointer(VT_TYPE); - } - uint16_t id() const { - return GetField(VT_ID, 0); - } - uint16_t offset() const { - return GetField(VT_OFFSET, 0); - } - int64_t default_integer() const { - return GetField(VT_DEFAULT_INTEGER, 0); - } - double default_real() const { - return GetField(VT_DEFAULT_REAL, 0.0); - } - bool deprecated() const { - return GetField(VT_DEPRECATED, 0) != 0; - } - bool required() const { - return GetField(VT_REQUIRED, 0) != 0; - } - bool key() const { - return GetField(VT_KEY, 0) != 0; - } - const flatbuffers::Vector> *attributes() const { - return GetPointer> *>(VT_ATTRIBUTES); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - bool optional() const { - return GetField(VT_OPTIONAL, 0) != 0; - } - /// Number of padding octets to always add after this field. Structs only. - uint16_t padding() const { - return GetField(VT_PADDING, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffsetRequired(verifier, VT_TYPE) && - verifier.VerifyTable(type()) && - VerifyField(verifier, VT_ID, 2) && - VerifyField(verifier, VT_OFFSET, 2) && - VerifyField(verifier, VT_DEFAULT_INTEGER, 8) && - VerifyField(verifier, VT_DEFAULT_REAL, 8) && - VerifyField(verifier, VT_DEPRECATED, 1) && - VerifyField(verifier, VT_REQUIRED, 1) && - VerifyField(verifier, VT_KEY, 1) && - VerifyOffset(verifier, VT_ATTRIBUTES) && - verifier.VerifyVector(attributes()) && - verifier.VerifyVectorOfTables(attributes()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - VerifyField(verifier, VT_OPTIONAL, 1) && - VerifyField(verifier, VT_PADDING, 2) && - verifier.EndTable(); - } -}; - -struct FieldBuilder { - typedef Field Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Field::VT_NAME, name); - } - void add_type(flatbuffers::Offset type) { - fbb_.AddOffset(Field::VT_TYPE, type); - } - void add_id(uint16_t id) { - fbb_.AddElement(Field::VT_ID, id, 0); - } - void add_offset(uint16_t offset) { - fbb_.AddElement(Field::VT_OFFSET, offset, 0); - } - void add_default_integer(int64_t default_integer) { - fbb_.AddElement(Field::VT_DEFAULT_INTEGER, default_integer, 0); - } - void add_default_real(double default_real) { - fbb_.AddElement(Field::VT_DEFAULT_REAL, default_real, 0.0); - } - void add_deprecated(bool deprecated) { - fbb_.AddElement(Field::VT_DEPRECATED, static_cast(deprecated), 0); - } - void add_required(bool required) { - fbb_.AddElement(Field::VT_REQUIRED, static_cast(required), 0); - } - void add_key(bool key) { - fbb_.AddElement(Field::VT_KEY, static_cast(key), 0); - } - void add_attributes(flatbuffers::Offset>> attributes) { - fbb_.AddOffset(Field::VT_ATTRIBUTES, attributes); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(Field::VT_DOCUMENTATION, documentation); - } - void add_optional(bool optional) { - fbb_.AddElement(Field::VT_OPTIONAL, static_cast(optional), 0); - } - void add_padding(uint16_t padding) { - fbb_.AddElement(Field::VT_PADDING, padding, 0); - } - explicit FieldBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, Field::VT_NAME); - fbb_.Required(o, Field::VT_TYPE); - return o; - } -}; - -inline flatbuffers::Offset CreateField( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - flatbuffers::Offset type = 0, - uint16_t id = 0, - uint16_t offset = 0, - int64_t default_integer = 0, - double default_real = 0.0, - bool deprecated = false, - bool required = false, - bool key = false, - flatbuffers::Offset>> attributes = 0, - flatbuffers::Offset>> documentation = 0, - bool optional = false, - uint16_t padding = 0) { - FieldBuilder builder_(_fbb); - builder_.add_default_real(default_real); - builder_.add_default_integer(default_integer); - builder_.add_documentation(documentation); - builder_.add_attributes(attributes); - builder_.add_type(type); - builder_.add_name(name); - builder_.add_padding(padding); - builder_.add_offset(offset); - builder_.add_id(id); - builder_.add_optional(optional); - builder_.add_key(key); - builder_.add_required(required); - builder_.add_deprecated(deprecated); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateFieldDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - flatbuffers::Offset type = 0, - uint16_t id = 0, - uint16_t offset = 0, - int64_t default_integer = 0, - double default_real = 0.0, - bool deprecated = false, - bool required = false, - bool key = false, - std::vector> *attributes = nullptr, - const std::vector> *documentation = nullptr, - bool optional = false, - uint16_t padding = 0) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - return reflection::CreateField( - _fbb, - name__, - type, - id, - offset, - default_integer, - default_real, - deprecated, - required, - key, - attributes__, - documentation__, - optional, - padding); -} - -struct Object FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ObjectBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_FIELDS = 6, - VT_IS_STRUCT = 8, - VT_MINALIGN = 10, - VT_BYTESIZE = 12, - VT_ATTRIBUTES = 14, - VT_DOCUMENTATION = 16, - VT_DECLARATION_FILE = 18 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool KeyCompareLessThan(const Object *o) const { - return *name() < *o->name(); - } - int KeyCompareWithValue(const char *_name) const { - return strcmp(name()->c_str(), _name); - } - const flatbuffers::Vector> *fields() const { - return GetPointer> *>(VT_FIELDS); - } - bool is_struct() const { - return GetField(VT_IS_STRUCT, 0) != 0; - } - int32_t minalign() const { - return GetField(VT_MINALIGN, 0); - } - int32_t bytesize() const { - return GetField(VT_BYTESIZE, 0); - } - const flatbuffers::Vector> *attributes() const { - return GetPointer> *>(VT_ATTRIBUTES); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - /// File that this Object is declared in. - const flatbuffers::String *declaration_file() const { - return GetPointer(VT_DECLARATION_FILE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffsetRequired(verifier, VT_FIELDS) && - verifier.VerifyVector(fields()) && - verifier.VerifyVectorOfTables(fields()) && - VerifyField(verifier, VT_IS_STRUCT, 1) && - VerifyField(verifier, VT_MINALIGN, 4) && - VerifyField(verifier, VT_BYTESIZE, 4) && - VerifyOffset(verifier, VT_ATTRIBUTES) && - verifier.VerifyVector(attributes()) && - verifier.VerifyVectorOfTables(attributes()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - VerifyOffset(verifier, VT_DECLARATION_FILE) && - verifier.VerifyString(declaration_file()) && - verifier.EndTable(); - } -}; - -struct ObjectBuilder { - typedef Object Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Object::VT_NAME, name); - } - void add_fields(flatbuffers::Offset>> fields) { - fbb_.AddOffset(Object::VT_FIELDS, fields); - } - void add_is_struct(bool is_struct) { - fbb_.AddElement(Object::VT_IS_STRUCT, static_cast(is_struct), 0); - } - void add_minalign(int32_t minalign) { - fbb_.AddElement(Object::VT_MINALIGN, minalign, 0); - } - void add_bytesize(int32_t bytesize) { - fbb_.AddElement(Object::VT_BYTESIZE, bytesize, 0); - } - void add_attributes(flatbuffers::Offset>> attributes) { - fbb_.AddOffset(Object::VT_ATTRIBUTES, attributes); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(Object::VT_DOCUMENTATION, documentation); - } - void add_declaration_file(flatbuffers::Offset declaration_file) { - fbb_.AddOffset(Object::VT_DECLARATION_FILE, declaration_file); - } - explicit ObjectBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, Object::VT_NAME); - fbb_.Required(o, Object::VT_FIELDS); - return o; - } -}; - -inline flatbuffers::Offset CreateObject( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - flatbuffers::Offset>> fields = 0, - bool is_struct = false, - int32_t minalign = 0, - int32_t bytesize = 0, - flatbuffers::Offset>> attributes = 0, - flatbuffers::Offset>> documentation = 0, - flatbuffers::Offset declaration_file = 0) { - ObjectBuilder builder_(_fbb); - builder_.add_declaration_file(declaration_file); - builder_.add_documentation(documentation); - builder_.add_attributes(attributes); - builder_.add_bytesize(bytesize); - builder_.add_minalign(minalign); - builder_.add_fields(fields); - builder_.add_name(name); - builder_.add_is_struct(is_struct); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateObjectDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - std::vector> *fields = nullptr, - bool is_struct = false, - int32_t minalign = 0, - int32_t bytesize = 0, - std::vector> *attributes = nullptr, - const std::vector> *documentation = nullptr, - const char *declaration_file = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto fields__ = fields ? _fbb.CreateVectorOfSortedTables(fields) : 0; - auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; - return reflection::CreateObject( - _fbb, - name__, - fields__, - is_struct, - minalign, - bytesize, - attributes__, - documentation__, - declaration_file__); -} - -struct RPCCall FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RPCCallBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_REQUEST = 6, - VT_RESPONSE = 8, - VT_ATTRIBUTES = 10, - VT_DOCUMENTATION = 12 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool KeyCompareLessThan(const RPCCall *o) const { - return *name() < *o->name(); - } - int KeyCompareWithValue(const char *_name) const { - return strcmp(name()->c_str(), _name); - } - const reflection::Object *request() const { - return GetPointer(VT_REQUEST); - } - const reflection::Object *response() const { - return GetPointer(VT_RESPONSE); - } - const flatbuffers::Vector> *attributes() const { - return GetPointer> *>(VT_ATTRIBUTES); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffsetRequired(verifier, VT_REQUEST) && - verifier.VerifyTable(request()) && - VerifyOffsetRequired(verifier, VT_RESPONSE) && - verifier.VerifyTable(response()) && - VerifyOffset(verifier, VT_ATTRIBUTES) && - verifier.VerifyVector(attributes()) && - verifier.VerifyVectorOfTables(attributes()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - verifier.EndTable(); - } -}; - -struct RPCCallBuilder { - typedef RPCCall Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(RPCCall::VT_NAME, name); - } - void add_request(flatbuffers::Offset request) { - fbb_.AddOffset(RPCCall::VT_REQUEST, request); - } - void add_response(flatbuffers::Offset response) { - fbb_.AddOffset(RPCCall::VT_RESPONSE, response); - } - void add_attributes(flatbuffers::Offset>> attributes) { - fbb_.AddOffset(RPCCall::VT_ATTRIBUTES, attributes); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(RPCCall::VT_DOCUMENTATION, documentation); - } - explicit RPCCallBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, RPCCall::VT_NAME); - fbb_.Required(o, RPCCall::VT_REQUEST); - fbb_.Required(o, RPCCall::VT_RESPONSE); - return o; - } -}; - -inline flatbuffers::Offset CreateRPCCall( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - flatbuffers::Offset request = 0, - flatbuffers::Offset response = 0, - flatbuffers::Offset>> attributes = 0, - flatbuffers::Offset>> documentation = 0) { - RPCCallBuilder builder_(_fbb); - builder_.add_documentation(documentation); - builder_.add_attributes(attributes); - builder_.add_response(response); - builder_.add_request(request); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateRPCCallDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - flatbuffers::Offset request = 0, - flatbuffers::Offset response = 0, - std::vector> *attributes = nullptr, - const std::vector> *documentation = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - return reflection::CreateRPCCall( - _fbb, - name__, - request, - response, - attributes__, - documentation__); -} - -struct Service FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ServiceBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_CALLS = 6, - VT_ATTRIBUTES = 8, - VT_DOCUMENTATION = 10, - VT_DECLARATION_FILE = 12 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool KeyCompareLessThan(const Service *o) const { - return *name() < *o->name(); - } - int KeyCompareWithValue(const char *_name) const { - return strcmp(name()->c_str(), _name); - } - const flatbuffers::Vector> *calls() const { - return GetPointer> *>(VT_CALLS); - } - const flatbuffers::Vector> *attributes() const { - return GetPointer> *>(VT_ATTRIBUTES); - } - const flatbuffers::Vector> *documentation() const { - return GetPointer> *>(VT_DOCUMENTATION); - } - /// File that this Service is declared in. - const flatbuffers::String *declaration_file() const { - return GetPointer(VT_DECLARATION_FILE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffset(verifier, VT_CALLS) && - verifier.VerifyVector(calls()) && - verifier.VerifyVectorOfTables(calls()) && - VerifyOffset(verifier, VT_ATTRIBUTES) && - verifier.VerifyVector(attributes()) && - verifier.VerifyVectorOfTables(attributes()) && - VerifyOffset(verifier, VT_DOCUMENTATION) && - verifier.VerifyVector(documentation()) && - verifier.VerifyVectorOfStrings(documentation()) && - VerifyOffset(verifier, VT_DECLARATION_FILE) && - verifier.VerifyString(declaration_file()) && - verifier.EndTable(); - } -}; - -struct ServiceBuilder { - typedef Service Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Service::VT_NAME, name); - } - void add_calls(flatbuffers::Offset>> calls) { - fbb_.AddOffset(Service::VT_CALLS, calls); - } - void add_attributes(flatbuffers::Offset>> attributes) { - fbb_.AddOffset(Service::VT_ATTRIBUTES, attributes); - } - void add_documentation(flatbuffers::Offset>> documentation) { - fbb_.AddOffset(Service::VT_DOCUMENTATION, documentation); - } - void add_declaration_file(flatbuffers::Offset declaration_file) { - fbb_.AddOffset(Service::VT_DECLARATION_FILE, declaration_file); - } - explicit ServiceBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, Service::VT_NAME); - return o; - } -}; - -inline flatbuffers::Offset CreateService( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - flatbuffers::Offset>> calls = 0, - flatbuffers::Offset>> attributes = 0, - flatbuffers::Offset>> documentation = 0, - flatbuffers::Offset declaration_file = 0) { - ServiceBuilder builder_(_fbb); - builder_.add_declaration_file(declaration_file); - builder_.add_documentation(documentation); - builder_.add_attributes(attributes); - builder_.add_calls(calls); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateServiceDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - std::vector> *calls = nullptr, - std::vector> *attributes = nullptr, - const std::vector> *documentation = nullptr, - const char *declaration_file = nullptr) { - auto name__ = name ? _fbb.CreateString(name) : 0; - auto calls__ = calls ? _fbb.CreateVectorOfSortedTables(calls) : 0; - auto attributes__ = attributes ? _fbb.CreateVectorOfSortedTables(attributes) : 0; - auto documentation__ = documentation ? _fbb.CreateVector>(*documentation) : 0; - auto declaration_file__ = declaration_file ? _fbb.CreateString(declaration_file) : 0; - return reflection::CreateService( - _fbb, - name__, - calls__, - attributes__, - documentation__, - declaration_file__); -} - -/// File specific information. -/// Symbols declared within a file may be recovered by iterating over all -/// symbols and examining the `declaration_file` field. -struct SchemaFile FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SchemaFileBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FILENAME = 4, - VT_INCLUDED_FILENAMES = 6 - }; - /// Filename, relative to project root. - const flatbuffers::String *filename() const { - return GetPointer(VT_FILENAME); - } - bool KeyCompareLessThan(const SchemaFile *o) const { - return *filename() < *o->filename(); - } - int KeyCompareWithValue(const char *_filename) const { - return strcmp(filename()->c_str(), _filename); - } - /// Names of included files, relative to project root. - const flatbuffers::Vector> *included_filenames() const { - return GetPointer> *>(VT_INCLUDED_FILENAMES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_FILENAME) && - verifier.VerifyString(filename()) && - VerifyOffset(verifier, VT_INCLUDED_FILENAMES) && - verifier.VerifyVector(included_filenames()) && - verifier.VerifyVectorOfStrings(included_filenames()) && - verifier.EndTable(); - } -}; - -struct SchemaFileBuilder { - typedef SchemaFile Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_filename(flatbuffers::Offset filename) { - fbb_.AddOffset(SchemaFile::VT_FILENAME, filename); - } - void add_included_filenames(flatbuffers::Offset>> included_filenames) { - fbb_.AddOffset(SchemaFile::VT_INCLUDED_FILENAMES, included_filenames); - } - explicit SchemaFileBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, SchemaFile::VT_FILENAME); - return o; - } -}; - -inline flatbuffers::Offset CreateSchemaFile( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset filename = 0, - flatbuffers::Offset>> included_filenames = 0) { - SchemaFileBuilder builder_(_fbb); - builder_.add_included_filenames(included_filenames); - builder_.add_filename(filename); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSchemaFileDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *filename = nullptr, - const std::vector> *included_filenames = nullptr) { - auto filename__ = filename ? _fbb.CreateString(filename) : 0; - auto included_filenames__ = included_filenames ? _fbb.CreateVector>(*included_filenames) : 0; - return reflection::CreateSchemaFile( - _fbb, - filename__, - included_filenames__); -} - -struct Schema FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SchemaBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OBJECTS = 4, - VT_ENUMS = 6, - VT_FILE_IDENT = 8, - VT_FILE_EXT = 10, - VT_ROOT_TABLE = 12, - VT_SERVICES = 14, - VT_ADVANCED_FEATURES = 16, - VT_FBS_FILES = 18 - }; - const flatbuffers::Vector> *objects() const { - return GetPointer> *>(VT_OBJECTS); - } - const flatbuffers::Vector> *enums() const { - return GetPointer> *>(VT_ENUMS); - } - const flatbuffers::String *file_ident() const { - return GetPointer(VT_FILE_IDENT); - } - const flatbuffers::String *file_ext() const { - return GetPointer(VT_FILE_EXT); - } - const reflection::Object *root_table() const { - return GetPointer(VT_ROOT_TABLE); - } - const flatbuffers::Vector> *services() const { - return GetPointer> *>(VT_SERVICES); - } - reflection::AdvancedFeatures advanced_features() const { - return static_cast(GetField(VT_ADVANCED_FEATURES, 0)); - } - /// All the files used in this compilation. Files are relative to where - /// flatc was invoked. - const flatbuffers::Vector> *fbs_files() const { - return GetPointer> *>(VT_FBS_FILES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffsetRequired(verifier, VT_OBJECTS) && - verifier.VerifyVector(objects()) && - verifier.VerifyVectorOfTables(objects()) && - VerifyOffsetRequired(verifier, VT_ENUMS) && - verifier.VerifyVector(enums()) && - verifier.VerifyVectorOfTables(enums()) && - VerifyOffset(verifier, VT_FILE_IDENT) && - verifier.VerifyString(file_ident()) && - VerifyOffset(verifier, VT_FILE_EXT) && - verifier.VerifyString(file_ext()) && - VerifyOffset(verifier, VT_ROOT_TABLE) && - verifier.VerifyTable(root_table()) && - VerifyOffset(verifier, VT_SERVICES) && - verifier.VerifyVector(services()) && - verifier.VerifyVectorOfTables(services()) && - VerifyField(verifier, VT_ADVANCED_FEATURES, 8) && - VerifyOffset(verifier, VT_FBS_FILES) && - verifier.VerifyVector(fbs_files()) && - verifier.VerifyVectorOfTables(fbs_files()) && - verifier.EndTable(); - } -}; - -struct SchemaBuilder { - typedef Schema Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_objects(flatbuffers::Offset>> objects) { - fbb_.AddOffset(Schema::VT_OBJECTS, objects); - } - void add_enums(flatbuffers::Offset>> enums) { - fbb_.AddOffset(Schema::VT_ENUMS, enums); - } - void add_file_ident(flatbuffers::Offset file_ident) { - fbb_.AddOffset(Schema::VT_FILE_IDENT, file_ident); - } - void add_file_ext(flatbuffers::Offset file_ext) { - fbb_.AddOffset(Schema::VT_FILE_EXT, file_ext); - } - void add_root_table(flatbuffers::Offset root_table) { - fbb_.AddOffset(Schema::VT_ROOT_TABLE, root_table); - } - void add_services(flatbuffers::Offset>> services) { - fbb_.AddOffset(Schema::VT_SERVICES, services); - } - void add_advanced_features(reflection::AdvancedFeatures advanced_features) { - fbb_.AddElement(Schema::VT_ADVANCED_FEATURES, static_cast(advanced_features), 0); - } - void add_fbs_files(flatbuffers::Offset>> fbs_files) { - fbb_.AddOffset(Schema::VT_FBS_FILES, fbs_files); - } - explicit SchemaBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - fbb_.Required(o, Schema::VT_OBJECTS); - fbb_.Required(o, Schema::VT_ENUMS); - return o; - } -}; - -inline flatbuffers::Offset CreateSchema( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset>> objects = 0, - flatbuffers::Offset>> enums = 0, - flatbuffers::Offset file_ident = 0, - flatbuffers::Offset file_ext = 0, - flatbuffers::Offset root_table = 0, - flatbuffers::Offset>> services = 0, - reflection::AdvancedFeatures advanced_features = static_cast(0), - flatbuffers::Offset>> fbs_files = 0) { - SchemaBuilder builder_(_fbb); - builder_.add_advanced_features(advanced_features); - builder_.add_fbs_files(fbs_files); - builder_.add_services(services); - builder_.add_root_table(root_table); - builder_.add_file_ext(file_ext); - builder_.add_file_ident(file_ident); - builder_.add_enums(enums); - builder_.add_objects(objects); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSchemaDirect( - flatbuffers::FlatBufferBuilder &_fbb, - std::vector> *objects = nullptr, - std::vector> *enums = nullptr, - const char *file_ident = nullptr, - const char *file_ext = nullptr, - flatbuffers::Offset root_table = 0, - std::vector> *services = nullptr, - reflection::AdvancedFeatures advanced_features = static_cast(0), - std::vector> *fbs_files = nullptr) { - auto objects__ = objects ? _fbb.CreateVectorOfSortedTables(objects) : 0; - auto enums__ = enums ? _fbb.CreateVectorOfSortedTables(enums) : 0; - auto file_ident__ = file_ident ? _fbb.CreateString(file_ident) : 0; - auto file_ext__ = file_ext ? _fbb.CreateString(file_ext) : 0; - auto services__ = services ? _fbb.CreateVectorOfSortedTables(services) : 0; - auto fbs_files__ = fbs_files ? _fbb.CreateVectorOfSortedTables(fbs_files) : 0; - return reflection::CreateSchema( - _fbb, - objects__, - enums__, - file_ident__, - file_ext__, - root_table, - services__, - advanced_features, - fbs_files__); -} - -inline const reflection::Schema *GetSchema(const void *buf) { - return flatbuffers::GetRoot(buf); -} - -inline const reflection::Schema *GetSizePrefixedSchema(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); -} - -inline const char *SchemaIdentifier() { - return "BFBS"; -} - -inline bool SchemaBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, SchemaIdentifier()); -} - -inline bool SizePrefixedSchemaBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, SchemaIdentifier(), true); -} - -inline bool VerifySchemaBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(SchemaIdentifier()); -} - -inline bool VerifySizePrefixedSchemaBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(SchemaIdentifier()); -} - -inline const char *SchemaExtension() { - return "bfbs"; -} - -inline void FinishSchemaBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.Finish(root, SchemaIdentifier()); -} - -inline void FinishSizePrefixedSchemaBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.FinishSizePrefixed(root, SchemaIdentifier()); -} - -} // namespace reflection - -#endif // FLATBUFFERS_GENERATED_REFLECTION_REFLECTION_H_ diff --git a/new_ai_tools/include/flatbuffers/registry.h b/new_ai_tools/include/flatbuffers/registry.h deleted file mode 100644 index e8bb8f5e..00000000 --- a/new_ai_tools/include/flatbuffers/registry.h +++ /dev/null @@ -1,128 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_REGISTRY_H_ -#define FLATBUFFERS_REGISTRY_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/idl.h" - -namespace flatbuffers { - -// Convenience class to easily parse or generate text for arbitrary FlatBuffers. -// Simply pre-populate it with all schema filenames that may be in use, and -// This class will look them up using the file_identifier declared in the -// schema. -class Registry { - public: - // Call this for all schemas that may be in use. The identifier has - // a function in the generated code, e.g. MonsterIdentifier(). - void Register(const char *file_identifier, const char *schema_path) { - Schema schema; - schema.path_ = schema_path; - schemas_[file_identifier] = schema; - } - - // Generate text from an arbitrary FlatBuffer by looking up its - // file_identifier in the registry. - bool FlatBufferToText(const uint8_t *flatbuf, size_t len, std::string *dest) { - // Get the identifier out of the buffer. - // If the buffer is truncated, exit. - if (len < sizeof(uoffset_t) + kFileIdentifierLength) { - lasterror_ = "buffer truncated"; - return false; - } - std::string ident( - reinterpret_cast(flatbuf) + sizeof(uoffset_t), - kFileIdentifierLength); - // Load and parse the schema. - Parser parser; - if (!LoadSchema(ident, &parser)) return false; - // Now we're ready to generate text. - if (!GenerateText(parser, flatbuf, dest)) { - lasterror_ = "unable to generate text for FlatBuffer binary"; - return false; - } - return true; - } - - // Converts a binary buffer to text using one of the schemas in the registry, - // use the file_identifier to indicate which. - // If DetachedBuffer::data() is null then parsing failed. - DetachedBuffer TextToFlatBuffer(const char *text, - const char *file_identifier) { - // Load and parse the schema. - Parser parser; - if (!LoadSchema(file_identifier, &parser)) return DetachedBuffer(); - // Parse the text. - if (!parser.Parse(text)) { - lasterror_ = parser.error_; - return DetachedBuffer(); - } - // We have a valid FlatBuffer. Detach it from the builder and return. - return parser.builder_.Release(); - } - - // Modify any parsing / output options used by the other functions. - void SetOptions(const IDLOptions &opts) { opts_ = opts; } - - // If schemas used contain include statements, call this function for every - // directory the parser should search them for. - void AddIncludeDirectory(const char *path) { include_paths_.push_back(path); } - - // Returns a human readable error if any of the above functions fail. - const std::string &GetLastError() { return lasterror_; } - - private: - bool LoadSchema(const std::string &ident, Parser *parser) { - // Find the schema, if not, exit. - auto it = schemas_.find(ident); - if (it == schemas_.end()) { - // Don't attach the identifier, since it may not be human readable. - lasterror_ = "identifier for this buffer not in the registry"; - return false; - } - auto &schema = it->second; - // Load the schema from disk. If not, exit. - std::string schematext; - if (!LoadFile(schema.path_.c_str(), false, &schematext)) { - lasterror_ = "could not load schema: " + schema.path_; - return false; - } - // Parse schema. - parser->opts = opts_; - if (!parser->Parse(schematext.c_str(), include_paths_.data(), - schema.path_.c_str())) { - lasterror_ = parser->error_; - return false; - } - return true; - } - - struct Schema { - std::string path_; - // TODO(wvo) optionally cache schema file or parsed schema here. - }; - - std::string lasterror_; - IDLOptions opts_; - std::vector include_paths_; - std::map schemas_; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_REGISTRY_H_ diff --git a/new_ai_tools/include/flatbuffers/stl_emulation.h b/new_ai_tools/include/flatbuffers/stl_emulation.h deleted file mode 100644 index 851b1e11..00000000 --- a/new_ai_tools/include/flatbuffers/stl_emulation.h +++ /dev/null @@ -1,509 +0,0 @@ -/* - * Copyright 2017 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_STL_EMULATION_H_ -#define FLATBUFFERS_STL_EMULATION_H_ - -// clang-format off -#include "flatbuffers/base.h" - -#include -#include -#include -#include -#include - -// Detect C++17 compatible compiler. -// __cplusplus >= 201703L - a compiler has support of 'static inline' variables. -#if defined(FLATBUFFERS_USE_STD_OPTIONAL) \ - || (defined(__cplusplus) && __cplusplus >= 201703L) \ - || (defined(_MSVC_LANG) && (_MSVC_LANG >= 201703L)) - #include - #ifndef FLATBUFFERS_USE_STD_OPTIONAL - #define FLATBUFFERS_USE_STD_OPTIONAL - #endif -#endif // defined(FLATBUFFERS_USE_STD_OPTIONAL) ... - -// The __cpp_lib_span is the predefined feature macro. -#if defined(FLATBUFFERS_USE_STD_SPAN) - #include -#elif defined(__cpp_lib_span) && defined(__has_include) - #if __has_include() - #include - #define FLATBUFFERS_USE_STD_SPAN - #endif -#else - // Disable non-trivial ctors if FLATBUFFERS_SPAN_MINIMAL defined. - #if !defined(FLATBUFFERS_TEMPLATES_ALIASES) - #define FLATBUFFERS_SPAN_MINIMAL - #else - // Enable implicit construction of a span from a std::array. - #include - #endif -#endif // defined(FLATBUFFERS_USE_STD_SPAN) - -// This header provides backwards compatibility for older versions of the STL. -namespace flatbuffers { - -#if defined(FLATBUFFERS_TEMPLATES_ALIASES) - template - using numeric_limits = std::numeric_limits; -#else - template class numeric_limits : - public std::numeric_limits {}; -#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) - -#if defined(FLATBUFFERS_TEMPLATES_ALIASES) - template using is_scalar = std::is_scalar; - template using is_same = std::is_same; - template using is_floating_point = std::is_floating_point; - template using is_unsigned = std::is_unsigned; - template using is_enum = std::is_enum; - template using make_unsigned = std::make_unsigned; - template - using conditional = std::conditional; - template - using integral_constant = std::integral_constant; - template - using bool_constant = integral_constant; - using true_type = std::true_type; - using false_type = std::false_type; -#else - // MSVC 2010 doesn't support C++11 aliases. - template struct is_scalar : public std::is_scalar {}; - template struct is_same : public std::is_same {}; - template struct is_floating_point : - public std::is_floating_point {}; - template struct is_unsigned : public std::is_unsigned {}; - template struct is_enum : public std::is_enum {}; - template struct make_unsigned : public std::make_unsigned {}; - template - struct conditional : public std::conditional {}; - template - struct integral_constant : public std::integral_constant {}; - template - struct bool_constant : public integral_constant {}; - typedef bool_constant true_type; - typedef bool_constant false_type; -#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) - -#if defined(FLATBUFFERS_TEMPLATES_ALIASES) - template using unique_ptr = std::unique_ptr; -#else - // MSVC 2010 doesn't support C++11 aliases. - // We're manually "aliasing" the class here as we want to bring unique_ptr - // into the flatbuffers namespace. We have unique_ptr in the flatbuffers - // namespace we have a completely independent implementation (see below) - // for C++98 STL implementations. - template class unique_ptr : public std::unique_ptr { - public: - unique_ptr() {} - explicit unique_ptr(T* p) : std::unique_ptr(p) {} - unique_ptr(std::unique_ptr&& u) { *this = std::move(u); } - unique_ptr(unique_ptr&& u) { *this = std::move(u); } - unique_ptr& operator=(std::unique_ptr&& u) { - std::unique_ptr::reset(u.release()); - return *this; - } - unique_ptr& operator=(unique_ptr&& u) { - std::unique_ptr::reset(u.release()); - return *this; - } - unique_ptr& operator=(T* p) { - return std::unique_ptr::operator=(p); - } - }; -#endif // defined(FLATBUFFERS_TEMPLATES_ALIASES) - -#ifdef FLATBUFFERS_USE_STD_OPTIONAL -template -using Optional = std::optional; -using nullopt_t = std::nullopt_t; -inline constexpr nullopt_t nullopt = std::nullopt; - -#else -// Limited implementation of Optional type for a scalar T. -// This implementation limited by trivial types compatible with -// std::is_arithmetic or std::is_enum type traits. - -// A tag to indicate an empty flatbuffers::optional. -struct nullopt_t { - explicit FLATBUFFERS_CONSTEXPR_CPP11 nullopt_t(int) {} -}; - -#if defined(FLATBUFFERS_CONSTEXPR_DEFINED) - namespace internal { - template struct nullopt_holder { - static constexpr nullopt_t instance_ = nullopt_t(0); - }; - template - constexpr nullopt_t nullopt_holder::instance_; - } - static constexpr const nullopt_t &nullopt = internal::nullopt_holder::instance_; - -#else - namespace internal { - template struct nullopt_holder { - static const nullopt_t instance_; - }; - template - const nullopt_t nullopt_holder::instance_ = nullopt_t(0); - } - static const nullopt_t &nullopt = internal::nullopt_holder::instance_; - -#endif - -template -class Optional FLATBUFFERS_FINAL_CLASS { - // Non-scalar 'T' would extremely complicated Optional. - // Use is_scalar checking because flatbuffers flatbuffers::is_arithmetic - // isn't implemented. - static_assert(flatbuffers::is_scalar::value, "unexpected type T"); - - public: - ~Optional() {} - - FLATBUFFERS_CONSTEXPR_CPP11 Optional() FLATBUFFERS_NOEXCEPT - : value_(), has_value_(false) {} - - FLATBUFFERS_CONSTEXPR_CPP11 Optional(nullopt_t) FLATBUFFERS_NOEXCEPT - : value_(), has_value_(false) {} - - FLATBUFFERS_CONSTEXPR_CPP11 Optional(T val) FLATBUFFERS_NOEXCEPT - : value_(val), has_value_(true) {} - - FLATBUFFERS_CONSTEXPR_CPP11 Optional(const Optional &other) FLATBUFFERS_NOEXCEPT - : value_(other.value_), has_value_(other.has_value_) {} - - FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(const Optional &other) FLATBUFFERS_NOEXCEPT { - value_ = other.value_; - has_value_ = other.has_value_; - return *this; - } - - FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(nullopt_t) FLATBUFFERS_NOEXCEPT { - value_ = T(); - has_value_ = false; - return *this; - } - - FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(T val) FLATBUFFERS_NOEXCEPT { - value_ = val; - has_value_ = true; - return *this; - } - - void reset() FLATBUFFERS_NOEXCEPT { - *this = nullopt; - } - - void swap(Optional &other) FLATBUFFERS_NOEXCEPT { - std::swap(value_, other.value_); - std::swap(has_value_, other.has_value_); - } - - FLATBUFFERS_CONSTEXPR_CPP11 FLATBUFFERS_EXPLICIT_CPP11 operator bool() const FLATBUFFERS_NOEXCEPT { - return has_value_; - } - - FLATBUFFERS_CONSTEXPR_CPP11 bool has_value() const FLATBUFFERS_NOEXCEPT { - return has_value_; - } - - FLATBUFFERS_CONSTEXPR_CPP11 const T& operator*() const FLATBUFFERS_NOEXCEPT { - return value_; - } - - const T& value() const { - FLATBUFFERS_ASSERT(has_value()); - return value_; - } - - T value_or(T default_value) const FLATBUFFERS_NOEXCEPT { - return has_value() ? value_ : default_value; - } - - private: - T value_; - bool has_value_; -}; - -template -FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& opt, nullopt_t) FLATBUFFERS_NOEXCEPT { - return !opt; -} -template -FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(nullopt_t, const Optional& opt) FLATBUFFERS_NOEXCEPT { - return !opt; -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& lhs, const U& rhs) FLATBUFFERS_NOEXCEPT { - return static_cast(lhs) && (*lhs == rhs); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const T& lhs, const Optional& rhs) FLATBUFFERS_NOEXCEPT { - return static_cast(rhs) && (lhs == *rhs); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional& lhs, const Optional& rhs) FLATBUFFERS_NOEXCEPT { - return static_cast(lhs) != static_cast(rhs) - ? false - : !static_cast(lhs) ? false : (*lhs == *rhs); -} -#endif // FLATBUFFERS_USE_STD_OPTIONAL - - -// Very limited and naive partial implementation of C++20 std::span. -#if defined(FLATBUFFERS_USE_STD_SPAN) - inline constexpr std::size_t dynamic_extent = std::dynamic_extent; - template - using span = std::span; - -#else // !defined(FLATBUFFERS_USE_STD_SPAN) -FLATBUFFERS_CONSTEXPR std::size_t dynamic_extent = static_cast(-1); - -// Exclude this code if MSVC2010 or non-STL Android is active. -// The non-STL Android doesn't have `std::is_convertible` required for SFINAE. -#if !defined(FLATBUFFERS_SPAN_MINIMAL) -namespace internal { - // This is SFINAE helper class for checking of a common condition: - // > This overload only participates in overload resolution - // > Check whether a pointer to an array of U can be converted - // > to a pointer to an array of E. - // This helper is used for checking of 'U -> const U'. - template - struct is_span_convertable { - using type = - typename std::conditional::value - && (Extent == dynamic_extent || N == Extent), - int, void>::type; - }; - - template - struct SpanIterator { - // TODO: upgrade to std::random_access_iterator_tag. - using iterator_category = std::forward_iterator_tag; - using difference_type = std::ptrdiff_t; - using value_type = typename std::remove_cv::type; - using reference = T&; - using pointer = T*; - - // Convince MSVC compiler that this iterator is trusted (it is verified). - #ifdef _MSC_VER - using _Unchecked_type = pointer; - #endif // _MSC_VER - - SpanIterator(pointer ptr) : ptr_(ptr) {} - reference operator*() const { return *ptr_; } - pointer operator->() { return ptr_; } - SpanIterator& operator++() { ptr_++; return *this; } - SpanIterator operator++(int) { auto tmp = *this; ++(*this); return tmp; } - - friend bool operator== (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ == rhs.ptr_; } - friend bool operator!= (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ != rhs.ptr_; } - - private: - pointer ptr_; - }; -} // namespace internal -#endif // !defined(FLATBUFFERS_SPAN_MINIMAL) - -// T - element type; must be a complete type that is not an abstract -// class type. -// Extent - the number of elements in the sequence, or dynamic. -template -class span FLATBUFFERS_FINAL_CLASS { - public: - typedef T element_type; - typedef T& reference; - typedef const T& const_reference; - typedef T* pointer; - typedef const T* const_pointer; - typedef std::size_t size_type; - - static FLATBUFFERS_CONSTEXPR size_type extent = Extent; - - // Returns the number of elements in the span. - FLATBUFFERS_CONSTEXPR_CPP11 size_type size() const FLATBUFFERS_NOEXCEPT { - return count_; - } - - // Returns the size of the sequence in bytes. - FLATBUFFERS_CONSTEXPR_CPP11 - size_type size_bytes() const FLATBUFFERS_NOEXCEPT { - return size() * sizeof(element_type); - } - - // Checks if the span is empty. - FLATBUFFERS_CONSTEXPR_CPP11 bool empty() const FLATBUFFERS_NOEXCEPT { - return size() == 0; - } - - // Returns a pointer to the beginning of the sequence. - FLATBUFFERS_CONSTEXPR_CPP11 pointer data() const FLATBUFFERS_NOEXCEPT { - return data_; - } - - #if !defined(FLATBUFFERS_SPAN_MINIMAL) - using Iterator = internal::SpanIterator; - using ConstIterator = internal::SpanIterator; - - Iterator begin() const { return Iterator(data()); } - Iterator end() const { return Iterator(data() + size()); } - - ConstIterator cbegin() const { return ConstIterator(data()); } - ConstIterator cend() const { return ConstIterator(data() + size()); } - #endif - - // Returns a reference to the idx-th element of the sequence. - // The behavior is undefined if the idx is greater than or equal to size(). - FLATBUFFERS_CONSTEXPR_CPP11 reference operator[](size_type idx) const { - return data()[idx]; - } - - FLATBUFFERS_CONSTEXPR_CPP11 span(const span &other) FLATBUFFERS_NOEXCEPT - : data_(other.data_), count_(other.count_) {} - - FLATBUFFERS_CONSTEXPR_CPP14 span &operator=(const span &other) - FLATBUFFERS_NOEXCEPT { - data_ = other.data_; - count_ = other.count_; - } - - // Limited implementation of - // `template constexpr std::span(It first, size_type count);`. - // - // Constructs a span that is a view over the range [first, first + count); - // the resulting span has: data() == first and size() == count. - // The behavior is undefined if [first, first + count) is not a valid range, - // or if (extent != flatbuffers::dynamic_extent && count != extent). - FLATBUFFERS_CONSTEXPR_CPP11 - explicit span(pointer first, size_type count) FLATBUFFERS_NOEXCEPT - : data_ (Extent == dynamic_extent ? first : (Extent == count ? first : nullptr)), - count_(Extent == dynamic_extent ? count : (Extent == count ? Extent : 0)) { - // Make span empty if the count argument is incompatible with span. - } - - // Exclude this code if MSVC2010 is active. The MSVC2010 isn't C++11 - // compliant, it doesn't support default template arguments for functions. - #if defined(FLATBUFFERS_SPAN_MINIMAL) - FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr), - count_(0) { - static_assert(extent == 0 || extent == dynamic_extent, "invalid span"); - } - - #else - // Constructs an empty span whose data() == nullptr and size() == 0. - // This overload only participates in overload resolution if - // extent == 0 || extent == flatbuffers::dynamic_extent. - // A dummy template argument N is need dependency for SFINAE. - template::type = 0> - FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr), - count_(0) { - static_assert(extent == 0 || extent == dynamic_extent, "invalid span"); - } - - // Constructs a span that is a view over the array arr; the resulting span - // has size() == N and data() == std::data(arr). These overloads only - // participate in overload resolution if - // extent == std::dynamic_extent || N == extent is true and - // std::remove_pointer_t(*)[] - // is convertible to element_type (*)[]. - template::type = 0> - FLATBUFFERS_CONSTEXPR_CPP11 span(element_type (&arr)[N]) FLATBUFFERS_NOEXCEPT - : data_(arr), count_(N) {} - - template::type = 0> - FLATBUFFERS_CONSTEXPR_CPP11 span(std::array &arr) FLATBUFFERS_NOEXCEPT - : data_(arr.data()), count_(N) {} - - //template - //FLATBUFFERS_CONSTEXPR_CPP11 span(std::array &arr) FLATBUFFERS_NOEXCEPT - // : data_(arr.data()), count_(N) {} - - template::type = 0> - FLATBUFFERS_CONSTEXPR_CPP11 span(const std::array &arr) FLATBUFFERS_NOEXCEPT - : data_(arr.data()), count_(N) {} - - // Converting constructor from another span s; - // the resulting span has size() == s.size() and data() == s.data(). - // This overload only participates in overload resolution - // if extent == std::dynamic_extent || N == extent is true and U (*)[] - // is convertible to element_type (*)[]. - template::type = 0> - FLATBUFFERS_CONSTEXPR_CPP11 span(const flatbuffers::span &s) FLATBUFFERS_NOEXCEPT - : span(s.data(), s.size()) { - } - - #endif // !defined(FLATBUFFERS_SPAN_MINIMAL) - - private: - // This is a naive implementation with 'count_' member even if (Extent != dynamic_extent). - pointer const data_; - size_type count_; -}; -#endif // defined(FLATBUFFERS_USE_STD_SPAN) - -#if !defined(FLATBUFFERS_SPAN_MINIMAL) -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(U(&arr)[N]) FLATBUFFERS_NOEXCEPT { - return span(arr); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(const U(&arr)[N]) FLATBUFFERS_NOEXCEPT { - return span(arr); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(std::array &arr) FLATBUFFERS_NOEXCEPT { - return span(arr); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(const std::array &arr) FLATBUFFERS_NOEXCEPT { - return span(arr); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(U *first, std::size_t count) FLATBUFFERS_NOEXCEPT { - return span(first, count); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 -flatbuffers::span make_span(const U *first, std::size_t count) FLATBUFFERS_NOEXCEPT { - return span(first, count); -} -#endif // !defined(FLATBUFFERS_SPAN_MINIMAL) - -} // namespace flatbuffers - -#endif // FLATBUFFERS_STL_EMULATION_H_ diff --git a/new_ai_tools/include/flatbuffers/string.h b/new_ai_tools/include/flatbuffers/string.h deleted file mode 100644 index 3db95fce..00000000 --- a/new_ai_tools/include/flatbuffers/string.h +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_STRING_H_ -#define FLATBUFFERS_STRING_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/vector.h" - -namespace flatbuffers { - -struct String : public Vector { - const char *c_str() const { return reinterpret_cast(Data()); } - std::string str() const { return std::string(c_str(), size()); } - - // clang-format off - #ifdef FLATBUFFERS_HAS_STRING_VIEW - flatbuffers::string_view string_view() const { - return flatbuffers::string_view(c_str(), size()); - } - #endif // FLATBUFFERS_HAS_STRING_VIEW - // clang-format on - - bool operator<(const String &o) const { - return StringLessThan(this->data(), this->size(), o.data(), o.size()); - } -}; - -// Convenience function to get std::string from a String returning an empty -// string on null pointer. -static inline std::string GetString(const String *str) { - return str ? str->str() : ""; -} - -// Convenience function to get char* from a String returning an empty string on -// null pointer. -static inline const char *GetCstring(const String *str) { - return str ? str->c_str() : ""; -} - -#ifdef FLATBUFFERS_HAS_STRING_VIEW -// Convenience function to get string_view from a String returning an empty -// string_view on null pointer. -static inline flatbuffers::string_view GetStringView(const String *str) { - return str ? str->string_view() : flatbuffers::string_view(); -} -#endif // FLATBUFFERS_HAS_STRING_VIEW - -} // namespace flatbuffers - -#endif // FLATBUFFERS_STRING_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/struct.h b/new_ai_tools/include/flatbuffers/struct.h deleted file mode 100644 index d8753c84..00000000 --- a/new_ai_tools/include/flatbuffers/struct.h +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_STRUCT_H_ -#define FLATBUFFERS_STRUCT_H_ - -#include "flatbuffers/base.h" - -namespace flatbuffers { - -// "structs" are flat structures that do not have an offset table, thus -// always have all members present and do not support forwards/backwards -// compatible extensions. - -class Struct FLATBUFFERS_FINAL_CLASS { - public: - template T GetField(uoffset_t o) const { - return ReadScalar(&data_[o]); - } - - template T GetStruct(uoffset_t o) const { - return reinterpret_cast(&data_[o]); - } - - const uint8_t *GetAddressOf(uoffset_t o) const { return &data_[o]; } - uint8_t *GetAddressOf(uoffset_t o) { return &data_[o]; } - - private: - // private constructor & copy constructor: you obtain instances of this - // class by pointing to existing data only - Struct(); - Struct(const Struct &); - Struct &operator=(const Struct &); - - uint8_t data_[1]; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_STRUCT_H_ \ No newline at end of file diff --git a/new_ai_tools/include/flatbuffers/table.h b/new_ai_tools/include/flatbuffers/table.h deleted file mode 100644 index 11b29247..00000000 --- a/new_ai_tools/include/flatbuffers/table.h +++ /dev/null @@ -1,168 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_TABLE_H_ -#define FLATBUFFERS_TABLE_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/verifier.h" - -namespace flatbuffers { - -// "tables" use an offset table (possibly shared) that allows fields to be -// omitted and added at will, but uses an extra indirection to read. -class Table { - public: - const uint8_t *GetVTable() const { - return data_ - ReadScalar(data_); - } - - // This gets the field offset for any of the functions below it, or 0 - // if the field was not present. - voffset_t GetOptionalFieldOffset(voffset_t field) const { - // The vtable offset is always at the start. - auto vtable = GetVTable(); - // The first element is the size of the vtable (fields + type id + itself). - auto vtsize = ReadScalar(vtable); - // If the field we're accessing is outside the vtable, we're reading older - // data, so it's the same as if the offset was 0 (not present). - return field < vtsize ? ReadScalar(vtable + field) : 0; - } - - template T GetField(voffset_t field, T defaultval) const { - auto field_offset = GetOptionalFieldOffset(field); - return field_offset ? ReadScalar(data_ + field_offset) : defaultval; - } - - template P GetPointer(voffset_t field) { - auto field_offset = GetOptionalFieldOffset(field); - auto p = data_ + field_offset; - return field_offset ? reinterpret_cast

    (p + ReadScalar(p)) - : nullptr; - } - template P GetPointer(voffset_t field) const { - return const_cast

    (this)->GetPointer

    (field); - } - - template P GetStruct(voffset_t field) const { - auto field_offset = GetOptionalFieldOffset(field); - auto p = const_cast(data_ + field_offset); - return field_offset ? reinterpret_cast

    (p) : nullptr; - } - - template - flatbuffers::Optional GetOptional(voffset_t field) const { - auto field_offset = GetOptionalFieldOffset(field); - auto p = data_ + field_offset; - return field_offset ? Optional(static_cast(ReadScalar(p))) - : Optional(); - } - - template bool SetField(voffset_t field, T val, T def) { - auto field_offset = GetOptionalFieldOffset(field); - if (!field_offset) return IsTheSameAs(val, def); - WriteScalar(data_ + field_offset, val); - return true; - } - template bool SetField(voffset_t field, T val) { - auto field_offset = GetOptionalFieldOffset(field); - if (!field_offset) return false; - WriteScalar(data_ + field_offset, val); - return true; - } - - bool SetPointer(voffset_t field, const uint8_t *val) { - auto field_offset = GetOptionalFieldOffset(field); - if (!field_offset) return false; - WriteScalar(data_ + field_offset, - static_cast(val - (data_ + field_offset))); - return true; - } - - uint8_t *GetAddressOf(voffset_t field) { - auto field_offset = GetOptionalFieldOffset(field); - return field_offset ? data_ + field_offset : nullptr; - } - const uint8_t *GetAddressOf(voffset_t field) const { - return const_cast

    (this)->GetAddressOf(field); - } - - bool CheckField(voffset_t field) const { - return GetOptionalFieldOffset(field) != 0; - } - - // Verify the vtable of this table. - // Call this once per table, followed by VerifyField once per field. - bool VerifyTableStart(Verifier &verifier) const { - return verifier.VerifyTableStart(data_); - } - - // Verify a particular field. - template - bool VerifyField(const Verifier &verifier, voffset_t field, - size_t align) const { - // Calling GetOptionalFieldOffset should be safe now thanks to - // VerifyTable(). - auto field_offset = GetOptionalFieldOffset(field); - // Check the actual field. - return !field_offset || verifier.VerifyField(data_, field_offset, align); - } - - // VerifyField for required fields. - template - bool VerifyFieldRequired(const Verifier &verifier, voffset_t field, - size_t align) const { - auto field_offset = GetOptionalFieldOffset(field); - return verifier.Check(field_offset != 0) && - verifier.VerifyField(data_, field_offset, align); - } - - // Versions for offsets. - bool VerifyOffset(const Verifier &verifier, voffset_t field) const { - auto field_offset = GetOptionalFieldOffset(field); - return !field_offset || verifier.VerifyOffset(data_, field_offset); - } - - bool VerifyOffsetRequired(const Verifier &verifier, voffset_t field) const { - auto field_offset = GetOptionalFieldOffset(field); - return verifier.Check(field_offset != 0) && - verifier.VerifyOffset(data_, field_offset); - } - - private: - // private constructor & copy constructor: you obtain instances of this - // class by pointing to existing data only - Table(); - Table(const Table &other); - Table &operator=(const Table &); - - uint8_t data_[1]; -}; - -// This specialization allows avoiding warnings like: -// MSVC C4800: type: forcing value to bool 'true' or 'false'. -template<> -inline flatbuffers::Optional Table::GetOptional( - voffset_t field) const { - auto field_offset = GetOptionalFieldOffset(field); - auto p = data_ + field_offset; - return field_offset ? Optional(ReadScalar(p) != 0) - : Optional(); -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_TABLE_H_ diff --git a/new_ai_tools/include/flatbuffers/util.h b/new_ai_tools/include/flatbuffers/util.h deleted file mode 100644 index 690e63d3..00000000 --- a/new_ai_tools/include/flatbuffers/util.h +++ /dev/null @@ -1,690 +0,0 @@ -/* - * Copyright 2014 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_UTIL_H_ -#define FLATBUFFERS_UTIL_H_ - -#include -#include - -#include "flatbuffers/base.h" -#include "flatbuffers/stl_emulation.h" - -#ifndef FLATBUFFERS_PREFER_PRINTF -# include -# include -#else // FLATBUFFERS_PREFER_PRINTF -# include -# include -#endif // FLATBUFFERS_PREFER_PRINTF - -#include - -namespace flatbuffers { - -// @locale-independent functions for ASCII characters set. - -// Fast checking that character lies in closed range: [a <= x <= b] -// using one compare (conditional branch) operator. -inline bool check_ascii_range(char x, char a, char b) { - FLATBUFFERS_ASSERT(a <= b); - // (Hacker's Delight): `a <= x <= b` <=> `(x-a) <={u} (b-a)`. - // The x, a, b will be promoted to int and subtracted without overflow. - return static_cast(x - a) <= static_cast(b - a); -} - -// Case-insensitive isalpha -inline bool is_alpha(char c) { - // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF). - return check_ascii_range(c & 0xDF, 'a' & 0xDF, 'z' & 0xDF); -} - -// Check for uppercase alpha -inline bool is_alpha_upper(char c) { return check_ascii_range(c, 'A', 'Z'); } - -// Check (case-insensitive) that `c` is equal to alpha. -inline bool is_alpha_char(char c, char alpha) { - FLATBUFFERS_ASSERT(is_alpha(alpha)); - // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF). - return ((c & 0xDF) == (alpha & 0xDF)); -} - -// https://en.cppreference.com/w/cpp/string/byte/isxdigit -// isdigit and isxdigit are the only standard narrow character classification -// functions that are not affected by the currently installed C locale. although -// some implementations (e.g. Microsoft in 1252 codepage) may classify -// additional single-byte characters as digits. -inline bool is_digit(char c) { return check_ascii_range(c, '0', '9'); } - -inline bool is_xdigit(char c) { - // Replace by look-up table. - return is_digit(c) || check_ascii_range(c & 0xDF, 'a' & 0xDF, 'f' & 0xDF); -} - -// Case-insensitive isalnum -inline bool is_alnum(char c) { return is_alpha(c) || is_digit(c); } - -inline char CharToUpper(char c) { - return static_cast(::toupper(static_cast(c))); -} - -inline char CharToLower(char c) { - return static_cast(::tolower(static_cast(c))); -} - -// @end-locale-independent functions for ASCII character set - -#ifdef FLATBUFFERS_PREFER_PRINTF -template size_t IntToDigitCount(T t) { - size_t digit_count = 0; - // Count the sign for negative numbers - if (t < 0) digit_count++; - // Count a single 0 left of the dot for fractional numbers - if (-1 < t && t < 1) digit_count++; - // Count digits until fractional part - T eps = std::numeric_limits::epsilon(); - while (t <= (-1 + eps) || (1 - eps) <= t) { - t /= 10; - digit_count++; - } - return digit_count; -} - -template size_t NumToStringWidth(T t, int precision = 0) { - size_t string_width = IntToDigitCount(t); - // Count the dot for floating point numbers - if (precision) string_width += (precision + 1); - return string_width; -} - -template -std::string NumToStringImplWrapper(T t, const char *fmt, int precision = 0) { - size_t string_width = NumToStringWidth(t, precision); - std::string s(string_width, 0x00); - // Allow snprintf to use std::string trailing null to detect buffer overflow - snprintf(const_cast(s.data()), (s.size() + 1), fmt, string_width, t); - return s; -} -#endif // FLATBUFFERS_PREFER_PRINTF - -// Convert an integer or floating point value to a string. -// In contrast to std::stringstream, "char" values are -// converted to a string of digits, and we don't use scientific notation. -template std::string NumToString(T t) { - // clang-format off - - #ifndef FLATBUFFERS_PREFER_PRINTF - std::stringstream ss; - ss << t; - return ss.str(); - #else // FLATBUFFERS_PREFER_PRINTF - auto v = static_cast(t); - return NumToStringImplWrapper(v, "%.*lld"); - #endif // FLATBUFFERS_PREFER_PRINTF - // clang-format on -} -// Avoid char types used as character data. -template<> inline std::string NumToString(signed char t) { - return NumToString(static_cast(t)); -} -template<> inline std::string NumToString(unsigned char t) { - return NumToString(static_cast(t)); -} -template<> inline std::string NumToString(char t) { - return NumToString(static_cast(t)); -} - -// Special versions for floats/doubles. -template std::string FloatToString(T t, int precision) { - // clang-format off - - #ifndef FLATBUFFERS_PREFER_PRINTF - // to_string() prints different numbers of digits for floats depending on - // platform and isn't available on Android, so we use stringstream - std::stringstream ss; - // Use std::fixed to suppress scientific notation. - ss << std::fixed; - // Default precision is 6, we want that to be higher for doubles. - ss << std::setprecision(precision); - ss << t; - auto s = ss.str(); - #else // FLATBUFFERS_PREFER_PRINTF - auto v = static_cast(t); - auto s = NumToStringImplWrapper(v, "%0.*f", precision); - #endif // FLATBUFFERS_PREFER_PRINTF - // clang-format on - // Sadly, std::fixed turns "1" into "1.00000", so here we undo that. - auto p = s.find_last_not_of('0'); - if (p != std::string::npos) { - // Strip trailing zeroes. If it is a whole number, keep one zero. - s.resize(p + (s[p] == '.' ? 2 : 1)); - } - return s; -} - -template<> inline std::string NumToString(double t) { - return FloatToString(t, 12); -} -template<> inline std::string NumToString(float t) { - return FloatToString(t, 6); -} - -// Convert an integer value to a hexadecimal string. -// The returned string length is always xdigits long, prefixed by 0 digits. -// For example, IntToStringHex(0x23, 8) returns the string "00000023". -inline std::string IntToStringHex(int i, int xdigits) { - FLATBUFFERS_ASSERT(i >= 0); - // clang-format off - - #ifndef FLATBUFFERS_PREFER_PRINTF - std::stringstream ss; - ss << std::setw(xdigits) << std::setfill('0') << std::hex << std::uppercase - << i; - return ss.str(); - #else // FLATBUFFERS_PREFER_PRINTF - return NumToStringImplWrapper(i, "%.*X", xdigits); - #endif // FLATBUFFERS_PREFER_PRINTF - // clang-format on -} - -// clang-format off -// Use locale independent functions {strtod_l, strtof_l, strtoll_l, strtoull_l}. -#if defined(FLATBUFFERS_LOCALE_INDEPENDENT) && (FLATBUFFERS_LOCALE_INDEPENDENT > 0) - class ClassicLocale { - #ifdef _MSC_VER - typedef _locale_t locale_type; - #else - typedef locale_t locale_type; // POSIX.1-2008 locale_t type - #endif - ClassicLocale(); - ~ClassicLocale(); - locale_type locale_; - static ClassicLocale instance_; - public: - static locale_type Get() { return instance_.locale_; } - }; - - #ifdef _MSC_VER - #define __strtoull_impl(s, pe, b) _strtoui64_l(s, pe, b, ClassicLocale::Get()) - #define __strtoll_impl(s, pe, b) _strtoi64_l(s, pe, b, ClassicLocale::Get()) - #define __strtod_impl(s, pe) _strtod_l(s, pe, ClassicLocale::Get()) - #define __strtof_impl(s, pe) _strtof_l(s, pe, ClassicLocale::Get()) - #else - #define __strtoull_impl(s, pe, b) strtoull_l(s, pe, b, ClassicLocale::Get()) - #define __strtoll_impl(s, pe, b) strtoll_l(s, pe, b, ClassicLocale::Get()) - #define __strtod_impl(s, pe) strtod_l(s, pe, ClassicLocale::Get()) - #define __strtof_impl(s, pe) strtof_l(s, pe, ClassicLocale::Get()) - #endif -#else - #define __strtod_impl(s, pe) strtod(s, pe) - #define __strtof_impl(s, pe) static_cast(strtod(s, pe)) - #ifdef _MSC_VER - #define __strtoull_impl(s, pe, b) _strtoui64(s, pe, b) - #define __strtoll_impl(s, pe, b) _strtoi64(s, pe, b) - #else - #define __strtoull_impl(s, pe, b) strtoull(s, pe, b) - #define __strtoll_impl(s, pe, b) strtoll(s, pe, b) - #endif -#endif - -inline void strtoval_impl(int64_t *val, const char *str, char **endptr, - int base) { - *val = __strtoll_impl(str, endptr, base); -} - -inline void strtoval_impl(uint64_t *val, const char *str, char **endptr, - int base) { - *val = __strtoull_impl(str, endptr, base); -} - -inline void strtoval_impl(double *val, const char *str, char **endptr) { - *val = __strtod_impl(str, endptr); -} - -// UBSAN: double to float is safe if numeric_limits::is_iec559 is true. -__supress_ubsan__("float-cast-overflow") -inline void strtoval_impl(float *val, const char *str, char **endptr) { - *val = __strtof_impl(str, endptr); -} -#undef __strtoull_impl -#undef __strtoll_impl -#undef __strtod_impl -#undef __strtof_impl -// clang-format on - -// Adaptor for strtoull()/strtoll(). -// Flatbuffers accepts numbers with any count of leading zeros (-009 is -9), -// while strtoll with base=0 interprets first leading zero as octal prefix. -// In future, it is possible to add prefixed 0b0101. -// 1) Checks errno code for overflow condition (out of range). -// 2) If base <= 0, function try to detect base of number by prefix. -// -// Return value (like strtoull and strtoll, but reject partial result): -// - If successful, an integer value corresponding to the str is returned. -// - If full string conversion can't be performed, 0 is returned. -// - If the converted value falls out of range of corresponding return type, a -// range error occurs. In this case value MAX(T)/MIN(T) is returned. -template -inline bool StringToIntegerImpl(T *val, const char *const str, - const int base = 0, - const bool check_errno = true) { - // T is int64_t or uint64_T - FLATBUFFERS_ASSERT(str); - if (base <= 0) { - auto s = str; - while (*s && !is_digit(*s)) s++; - if (s[0] == '0' && is_alpha_char(s[1], 'X')) - return StringToIntegerImpl(val, str, 16, check_errno); - // if a prefix not match, try base=10 - return StringToIntegerImpl(val, str, 10, check_errno); - } else { - if (check_errno) errno = 0; // clear thread-local errno - auto endptr = str; - strtoval_impl(val, str, const_cast(&endptr), base); - if ((*endptr != '\0') || (endptr == str)) { - *val = 0; // erase partial result - return false; // invalid string - } - // errno is out-of-range, return MAX/MIN - if (check_errno && errno) return false; - return true; - } -} - -template -inline bool StringToFloatImpl(T *val, const char *const str) { - // Type T must be either float or double. - FLATBUFFERS_ASSERT(str && val); - auto end = str; - strtoval_impl(val, str, const_cast(&end)); - auto done = (end != str) && (*end == '\0'); - if (!done) *val = 0; // erase partial result - return done; -} - -// Convert a string to an instance of T. -// Return value (matched with StringToInteger64Impl and strtod): -// - If successful, a numeric value corresponding to the str is returned. -// - If full string conversion can't be performed, 0 is returned. -// - If the converted value falls out of range of corresponding return type, a -// range error occurs. In this case value MAX(T)/MIN(T) is returned. -template inline bool StringToNumber(const char *s, T *val) { - // Assert on `unsigned long` and `signed long` on LP64. - // If it is necessary, it could be solved with flatbuffers::enable_if. - static_assert(sizeof(T) < sizeof(int64_t), "unexpected type T"); - FLATBUFFERS_ASSERT(s && val); - int64_t i64; - // The errno check isn't needed, will return MAX/MIN on overflow. - if (StringToIntegerImpl(&i64, s, 0, false)) { - const int64_t max = (flatbuffers::numeric_limits::max)(); - const int64_t min = flatbuffers::numeric_limits::lowest(); - if (i64 > max) { - *val = static_cast(max); - return false; - } - if (i64 < min) { - // For unsigned types return max to distinguish from - // "no conversion can be performed" when 0 is returned. - *val = static_cast(flatbuffers::is_unsigned::value ? max : min); - return false; - } - *val = static_cast(i64); - return true; - } - *val = 0; - return false; -} - -template<> inline bool StringToNumber(const char *str, int64_t *val) { - return StringToIntegerImpl(val, str); -} - -template<> -inline bool StringToNumber(const char *str, uint64_t *val) { - if (!StringToIntegerImpl(val, str)) return false; - // The strtoull accepts negative numbers: - // If the minus sign was part of the input sequence, the numeric value - // calculated from the sequence of digits is negated as if by unary minus - // in the result type, which applies unsigned integer wraparound rules. - // Fix this behaviour (except -0). - if (*val) { - auto s = str; - while (*s && !is_digit(*s)) s++; - s = (s > str) ? (s - 1) : s; // step back to one symbol - if (*s == '-') { - // For unsigned types return the max to distinguish from - // "no conversion can be performed". - *val = (flatbuffers::numeric_limits::max)(); - return false; - } - } - return true; -} - -template<> inline bool StringToNumber(const char *s, float *val) { - return StringToFloatImpl(val, s); -} - -template<> inline bool StringToNumber(const char *s, double *val) { - return StringToFloatImpl(val, s); -} - -inline int64_t StringToInt(const char *s, int base = 10) { - int64_t val; - return StringToIntegerImpl(&val, s, base) ? val : 0; -} - -inline uint64_t StringToUInt(const char *s, int base = 10) { - uint64_t val; - return StringToIntegerImpl(&val, s, base) ? val : 0; -} - -typedef bool (*LoadFileFunction)(const char *filename, bool binary, - std::string *dest); -typedef bool (*FileExistsFunction)(const char *filename); - -LoadFileFunction SetLoadFileFunction(LoadFileFunction load_file_function); - -FileExistsFunction SetFileExistsFunction( - FileExistsFunction file_exists_function); - -// Check if file "name" exists. -bool FileExists(const char *name); - -// Check if "name" exists and it is also a directory. -bool DirExists(const char *name); - -// Load file "name" into "buf" returning true if successful -// false otherwise. If "binary" is false data is read -// using ifstream's text mode, otherwise data is read with -// no transcoding. -bool LoadFile(const char *name, bool binary, std::string *buf); - -// Save data "buf" of length "len" bytes into a file -// "name" returning true if successful, false otherwise. -// If "binary" is false data is written using ifstream's -// text mode, otherwise data is written with no -// transcoding. -bool SaveFile(const char *name, const char *buf, size_t len, bool binary); - -// Save data "buf" into file "name" returning true if -// successful, false otherwise. If "binary" is false -// data is written using ifstream's text mode, otherwise -// data is written with no transcoding. -inline bool SaveFile(const char *name, const std::string &buf, bool binary) { - return SaveFile(name, buf.c_str(), buf.size(), binary); -} - -// Functionality for minimalistic portable path handling. - -// The functions below behave correctly regardless of whether posix ('/') or -// Windows ('/' or '\\') separators are used. - -// Any new separators inserted are always posix. -FLATBUFFERS_CONSTEXPR char kPathSeparator = '/'; - -// Returns the path with the extension, if any, removed. -std::string StripExtension(const std::string &filepath); - -// Returns the extension, if any. -std::string GetExtension(const std::string &filepath); - -// Return the last component of the path, after the last separator. -std::string StripPath(const std::string &filepath); - -// Strip the last component of the path + separator. -std::string StripFileName(const std::string &filepath); - -// Concatenates a path with a filename, regardless of whether the path -// ends in a separator or not. -std::string ConCatPathFileName(const std::string &path, - const std::string &filename); - -// Replaces any '\\' separators with '/' -std::string PosixPath(const char *path); -std::string PosixPath(const std::string &path); - -// This function ensure a directory exists, by recursively -// creating dirs for any parts of the path that don't exist yet. -void EnsureDirExists(const std::string &filepath); - -// Obtains the absolute path from any other path. -// Returns the input path if the absolute path couldn't be resolved. -std::string AbsolutePath(const std::string &filepath); - -// Returns files relative to the --project_root path, prefixed with `//`. -std::string RelativeToRootPath(const std::string &project, - const std::string &filepath); - -// To and from UTF-8 unicode conversion functions - -// Convert a unicode code point into a UTF-8 representation by appending it -// to a string. Returns the number of bytes generated. -inline int ToUTF8(uint32_t ucc, std::string *out) { - FLATBUFFERS_ASSERT(!(ucc & 0x80000000)); // Top bit can't be set. - // 6 possible encodings: http://en.wikipedia.org/wiki/UTF-8 - for (int i = 0; i < 6; i++) { - // Max bits this encoding can represent. - uint32_t max_bits = 6 + i * 5 + static_cast(!i); - if (ucc < (1u << max_bits)) { // does it fit? - // Remaining bits not encoded in the first byte, store 6 bits each - uint32_t remain_bits = i * 6; - // Store first byte: - (*out) += static_cast((0xFE << (max_bits - remain_bits)) | - (ucc >> remain_bits)); - // Store remaining bytes: - for (int j = i - 1; j >= 0; j--) { - (*out) += static_cast(((ucc >> (j * 6)) & 0x3F) | 0x80); - } - return i + 1; // Return the number of bytes added. - } - } - FLATBUFFERS_ASSERT(0); // Impossible to arrive here. - return -1; -} - -// Converts whatever prefix of the incoming string corresponds to a valid -// UTF-8 sequence into a unicode code. The incoming pointer will have been -// advanced past all bytes parsed. -// returns -1 upon corrupt UTF-8 encoding (ignore the incoming pointer in -// this case). -inline int FromUTF8(const char **in) { - int len = 0; - // Count leading 1 bits. - for (int mask = 0x80; mask >= 0x04; mask >>= 1) { - if (**in & mask) { - len++; - } else { - break; - } - } - if ((static_cast(**in) << len) & 0x80) - return -1; // Bit after leading 1's must be 0. - if (!len) return *(*in)++; - // UTF-8 encoded values with a length are between 2 and 4 bytes. - if (len < 2 || len > 4) { return -1; } - // Grab initial bits of the code. - int ucc = *(*in)++ & ((1 << (7 - len)) - 1); - for (int i = 0; i < len - 1; i++) { - if ((**in & 0xC0) != 0x80) return -1; // Upper bits must 1 0. - ucc <<= 6; - ucc |= *(*in)++ & 0x3F; // Grab 6 more bits of the code. - } - // UTF-8 cannot encode values between 0xD800 and 0xDFFF (reserved for - // UTF-16 surrogate pairs). - if (ucc >= 0xD800 && ucc <= 0xDFFF) { return -1; } - // UTF-8 must represent code points in their shortest possible encoding. - switch (len) { - case 2: - // Two bytes of UTF-8 can represent code points from U+0080 to U+07FF. - if (ucc < 0x0080 || ucc > 0x07FF) { return -1; } - break; - case 3: - // Three bytes of UTF-8 can represent code points from U+0800 to U+FFFF. - if (ucc < 0x0800 || ucc > 0xFFFF) { return -1; } - break; - case 4: - // Four bytes of UTF-8 can represent code points from U+10000 to U+10FFFF. - if (ucc < 0x10000 || ucc > 0x10FFFF) { return -1; } - break; - } - return ucc; -} - -#ifndef FLATBUFFERS_PREFER_PRINTF -// Wraps a string to a maximum length, inserting new lines where necessary. Any -// existing whitespace will be collapsed down to a single space. A prefix or -// suffix can be provided, which will be inserted before or after a wrapped -// line, respectively. -inline std::string WordWrap(const std::string in, size_t max_length, - const std::string wrapped_line_prefix, - const std::string wrapped_line_suffix) { - std::istringstream in_stream(in); - std::string wrapped, line, word; - - in_stream >> word; - line = word; - - while (in_stream >> word) { - if ((line.length() + 1 + word.length() + wrapped_line_suffix.length()) < - max_length) { - line += " " + word; - } else { - wrapped += line + wrapped_line_suffix + "\n"; - line = wrapped_line_prefix + word; - } - } - wrapped += line; - - return wrapped; -} -#endif // !FLATBUFFERS_PREFER_PRINTF - -inline bool EscapeString(const char *s, size_t length, std::string *_text, - bool allow_non_utf8, bool natural_utf8) { - std::string &text = *_text; - text += "\""; - for (uoffset_t i = 0; i < length; i++) { - char c = s[i]; - switch (c) { - case '\n': text += "\\n"; break; - case '\t': text += "\\t"; break; - case '\r': text += "\\r"; break; - case '\b': text += "\\b"; break; - case '\f': text += "\\f"; break; - case '\"': text += "\\\""; break; - case '\\': text += "\\\\"; break; - default: - if (c >= ' ' && c <= '~') { - text += c; - } else { - // Not printable ASCII data. Let's see if it's valid UTF-8 first: - const char *utf8 = s + i; - int ucc = FromUTF8(&utf8); - if (ucc < 0) { - if (allow_non_utf8) { - text += "\\x"; - text += IntToStringHex(static_cast(c), 2); - } else { - // There are two cases here: - // - // 1) We reached here by parsing an IDL file. In that case, - // we previously checked for non-UTF-8, so we shouldn't reach - // here. - // - // 2) We reached here by someone calling GenerateText() - // on a previously-serialized flatbuffer. The data might have - // non-UTF-8 Strings, or might be corrupt. - // - // In both cases, we have to give up and inform the caller - // they have no JSON. - return false; - } - } else { - if (natural_utf8) { - // utf8 points to past all utf-8 bytes parsed - text.append(s + i, static_cast(utf8 - s - i)); - } else if (ucc <= 0xFFFF) { - // Parses as Unicode within JSON's \uXXXX range, so use that. - text += "\\u"; - text += IntToStringHex(ucc, 4); - } else if (ucc <= 0x10FFFF) { - // Encode Unicode SMP values to a surrogate pair using two \u - // escapes. - uint32_t base = ucc - 0x10000; - auto high_surrogate = (base >> 10) + 0xD800; - auto low_surrogate = (base & 0x03FF) + 0xDC00; - text += "\\u"; - text += IntToStringHex(high_surrogate, 4); - text += "\\u"; - text += IntToStringHex(low_surrogate, 4); - } - // Skip past characters recognized. - i = static_cast(utf8 - s - 1); - } - } - break; - } - } - text += "\""; - return true; -} - -inline std::string BufferToHexText(const void *buffer, size_t buffer_size, - size_t max_length, - const std::string &wrapped_line_prefix, - const std::string &wrapped_line_suffix) { - std::string text = wrapped_line_prefix; - size_t start_offset = 0; - const char *s = reinterpret_cast(buffer); - for (size_t i = 0; s && i < buffer_size; i++) { - // Last iteration or do we have more? - bool have_more = i + 1 < buffer_size; - text += "0x"; - text += IntToStringHex(static_cast(s[i]), 2); - if (have_more) { text += ','; } - // If we have more to process and we reached max_length - if (have_more && - text.size() + wrapped_line_suffix.size() >= start_offset + max_length) { - text += wrapped_line_suffix; - text += '\n'; - start_offset = text.size(); - text += wrapped_line_prefix; - } - } - text += wrapped_line_suffix; - return text; -} - -// Remove paired quotes in a string: "text"|'text' -> text. -std::string RemoveStringQuotes(const std::string &s); - -// Change th global C-locale to locale with name . -// Returns an actual locale name in <_value>, useful if locale_name is "" or -// null. -bool SetGlobalTestLocale(const char *locale_name, - std::string *_value = nullptr); - -// Read (or test) a value of environment variable. -bool ReadEnvironmentVariable(const char *var_name, - std::string *_value = nullptr); - -// MSVC specific: Send all assert reports to STDOUT to prevent CI hangs. -void SetupDefaultCRTReportMode(); - -} // namespace flatbuffers - -#endif // FLATBUFFERS_UTIL_H_ diff --git a/new_ai_tools/include/flatbuffers/vector.h b/new_ai_tools/include/flatbuffers/vector.h deleted file mode 100644 index f8a5d88e..00000000 --- a/new_ai_tools/include/flatbuffers/vector.h +++ /dev/null @@ -1,370 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_VECTOR_H_ -#define FLATBUFFERS_VECTOR_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/buffer.h" - -namespace flatbuffers { - -struct String; - -// An STL compatible iterator implementation for Vector below, effectively -// calling Get() for every element. -template struct VectorIterator { - typedef std::random_access_iterator_tag iterator_category; - typedef IT value_type; - typedef ptrdiff_t difference_type; - typedef IT *pointer; - typedef IT &reference; - - VectorIterator(const uint8_t *data, uoffset_t i) - : data_(data + IndirectHelper::element_stride * i) {} - VectorIterator(const VectorIterator &other) : data_(other.data_) {} - VectorIterator() : data_(nullptr) {} - - VectorIterator &operator=(const VectorIterator &other) { - data_ = other.data_; - return *this; - } - - VectorIterator &operator=(VectorIterator &&other) { - data_ = other.data_; - return *this; - } - - bool operator==(const VectorIterator &other) const { - return data_ == other.data_; - } - - bool operator<(const VectorIterator &other) const { - return data_ < other.data_; - } - - bool operator!=(const VectorIterator &other) const { - return data_ != other.data_; - } - - difference_type operator-(const VectorIterator &other) const { - return (data_ - other.data_) / IndirectHelper::element_stride; - } - - // Note: return type is incompatible with the standard - // `reference operator*()`. - IT operator*() const { return IndirectHelper::Read(data_, 0); } - - // Note: return type is incompatible with the standard - // `pointer operator->()`. - IT operator->() const { return IndirectHelper::Read(data_, 0); } - - VectorIterator &operator++() { - data_ += IndirectHelper::element_stride; - return *this; - } - - VectorIterator operator++(int) { - VectorIterator temp(data_, 0); - data_ += IndirectHelper::element_stride; - return temp; - } - - VectorIterator operator+(const uoffset_t &offset) const { - return VectorIterator(data_ + offset * IndirectHelper::element_stride, - 0); - } - - VectorIterator &operator+=(const uoffset_t &offset) { - data_ += offset * IndirectHelper::element_stride; - return *this; - } - - VectorIterator &operator--() { - data_ -= IndirectHelper::element_stride; - return *this; - } - - VectorIterator operator--(int) { - VectorIterator temp(data_, 0); - data_ -= IndirectHelper::element_stride; - return temp; - } - - VectorIterator operator-(const uoffset_t &offset) const { - return VectorIterator(data_ - offset * IndirectHelper::element_stride, - 0); - } - - VectorIterator &operator-=(const uoffset_t &offset) { - data_ -= offset * IndirectHelper::element_stride; - return *this; - } - - private: - const uint8_t *data_; -}; - -template -struct VectorReverseIterator : public std::reverse_iterator { - explicit VectorReverseIterator(Iterator iter) - : std::reverse_iterator(iter) {} - - // Note: return type is incompatible with the standard - // `reference operator*()`. - typename Iterator::value_type operator*() const { - auto tmp = std::reverse_iterator::current; - return *--tmp; - } - - // Note: return type is incompatible with the standard - // `pointer operator->()`. - typename Iterator::value_type operator->() const { - auto tmp = std::reverse_iterator::current; - return *--tmp; - } -}; - -// This is used as a helper type for accessing vectors. -// Vector::data() assumes the vector elements start after the length field. -template class Vector { - public: - typedef VectorIterator::mutable_return_type> - iterator; - typedef VectorIterator::return_type> - const_iterator; - typedef VectorReverseIterator reverse_iterator; - typedef VectorReverseIterator const_reverse_iterator; - - typedef typename flatbuffers::bool_constant::value> - scalar_tag; - - static FLATBUFFERS_CONSTEXPR bool is_span_observable = - scalar_tag::value && (FLATBUFFERS_LITTLEENDIAN || sizeof(T) == 1); - - uoffset_t size() const { return EndianScalar(length_); } - - // Deprecated: use size(). Here for backwards compatibility. - FLATBUFFERS_ATTRIBUTE([[deprecated("use size() instead")]]) - uoffset_t Length() const { return size(); } - - typedef typename IndirectHelper::return_type return_type; - typedef typename IndirectHelper::mutable_return_type mutable_return_type; - typedef return_type value_type; - - return_type Get(uoffset_t i) const { - FLATBUFFERS_ASSERT(i < size()); - return IndirectHelper::Read(Data(), i); - } - - return_type operator[](uoffset_t i) const { return Get(i); } - - // If this is a Vector of enums, T will be its storage type, not the enum - // type. This function makes it convenient to retrieve value with enum - // type E. - template E GetEnum(uoffset_t i) const { - return static_cast(Get(i)); - } - - // If this a vector of unions, this does the cast for you. There's no check - // to make sure this is the right type! - template const U *GetAs(uoffset_t i) const { - return reinterpret_cast(Get(i)); - } - - // If this a vector of unions, this does the cast for you. There's no check - // to make sure this is actually a string! - const String *GetAsString(uoffset_t i) const { - return reinterpret_cast(Get(i)); - } - - const void *GetStructFromOffset(size_t o) const { - return reinterpret_cast(Data() + o); - } - - iterator begin() { return iterator(Data(), 0); } - const_iterator begin() const { return const_iterator(Data(), 0); } - - iterator end() { return iterator(Data(), size()); } - const_iterator end() const { return const_iterator(Data(), size()); } - - reverse_iterator rbegin() { return reverse_iterator(end()); } - const_reverse_iterator rbegin() const { - return const_reverse_iterator(end()); - } - - reverse_iterator rend() { return reverse_iterator(begin()); } - const_reverse_iterator rend() const { - return const_reverse_iterator(begin()); - } - - const_iterator cbegin() const { return begin(); } - - const_iterator cend() const { return end(); } - - const_reverse_iterator crbegin() const { return rbegin(); } - - const_reverse_iterator crend() const { return rend(); } - - // Change elements if you have a non-const pointer to this object. - // Scalars only. See reflection.h, and the documentation. - void Mutate(uoffset_t i, const T &val) { - FLATBUFFERS_ASSERT(i < size()); - WriteScalar(data() + i, val); - } - - // Change an element of a vector of tables (or strings). - // "val" points to the new table/string, as you can obtain from - // e.g. reflection::AddFlatBuffer(). - void MutateOffset(uoffset_t i, const uint8_t *val) { - FLATBUFFERS_ASSERT(i < size()); - static_assert(sizeof(T) == sizeof(uoffset_t), "Unrelated types"); - WriteScalar(data() + i, - static_cast(val - (Data() + i * sizeof(uoffset_t)))); - } - - // Get a mutable pointer to tables/strings inside this vector. - mutable_return_type GetMutableObject(uoffset_t i) const { - FLATBUFFERS_ASSERT(i < size()); - return const_cast(IndirectHelper::Read(Data(), i)); - } - - // The raw data in little endian format. Use with care. - const uint8_t *Data() const { - return reinterpret_cast(&length_ + 1); - } - - uint8_t *Data() { return reinterpret_cast(&length_ + 1); } - - // Similarly, but typed, much like std::vector::data - const T *data() const { return reinterpret_cast(Data()); } - T *data() { return reinterpret_cast(Data()); } - - template return_type LookupByKey(K key) const { - void *search_result = std::bsearch( - &key, Data(), size(), IndirectHelper::element_stride, KeyCompare); - - if (!search_result) { - return nullptr; // Key not found. - } - - const uint8_t *element = reinterpret_cast(search_result); - - return IndirectHelper::Read(element, 0); - } - - template mutable_return_type MutableLookupByKey(K key) { - return const_cast(LookupByKey(key)); - } - - protected: - // This class is only used to access pre-existing data. Don't ever - // try to construct these manually. - Vector(); - - uoffset_t length_; - - private: - // This class is a pointer. Copying will therefore create an invalid object. - // Private and unimplemented copy constructor. - Vector(const Vector &); - Vector &operator=(const Vector &); - - template static int KeyCompare(const void *ap, const void *bp) { - const K *key = reinterpret_cast(ap); - const uint8_t *data = reinterpret_cast(bp); - auto table = IndirectHelper::Read(data, 0); - - // std::bsearch compares with the operands transposed, so we negate the - // result here. - return -table->KeyCompareWithValue(*key); - } -}; - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span(Vector &vec) - FLATBUFFERS_NOEXCEPT { - static_assert(Vector::is_span_observable, - "wrong type U, only LE-scalar, or byte types are allowed"); - return span(vec.data(), vec.size()); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_span( - const Vector &vec) FLATBUFFERS_NOEXCEPT { - static_assert(Vector::is_span_observable, - "wrong type U, only LE-scalar, or byte types are allowed"); - return span(vec.data(), vec.size()); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_bytes_span( - Vector &vec) FLATBUFFERS_NOEXCEPT { - static_assert(Vector::scalar_tag::value, - "wrong type U, only LE-scalar, or byte types are allowed"); - return span(vec.Data(), vec.size() * sizeof(U)); -} - -template -FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span make_bytes_span( - const Vector &vec) FLATBUFFERS_NOEXCEPT { - static_assert(Vector::scalar_tag::value, - "wrong type U, only LE-scalar, or byte types are allowed"); - return span(vec.Data(), vec.size() * sizeof(U)); -} - -// Represent a vector much like the template above, but in this case we -// don't know what the element types are (used with reflection.h). -class VectorOfAny { - public: - uoffset_t size() const { return EndianScalar(length_); } - - const uint8_t *Data() const { - return reinterpret_cast(&length_ + 1); - } - uint8_t *Data() { return reinterpret_cast(&length_ + 1); } - - protected: - VectorOfAny(); - - uoffset_t length_; - - private: - VectorOfAny(const VectorOfAny &); - VectorOfAny &operator=(const VectorOfAny &); -}; - -template -Vector> *VectorCast(Vector> *ptr) { - static_assert(std::is_base_of::value, "Unrelated types"); - return reinterpret_cast> *>(ptr); -} - -template -const Vector> *VectorCast(const Vector> *ptr) { - static_assert(std::is_base_of::value, "Unrelated types"); - return reinterpret_cast> *>(ptr); -} - -// Convenient helper function to get the length of any vector, regardless -// of whether it is null or not (the field is not set). -template static inline size_t VectorLength(const Vector *v) { - return v ? v->size() : 0; -} - -} // namespace flatbuffers - -#endif // FLATBUFFERS_VERIFIER_H_ diff --git a/new_ai_tools/include/flatbuffers/vector_downward.h b/new_ai_tools/include/flatbuffers/vector_downward.h deleted file mode 100644 index 33913918..00000000 --- a/new_ai_tools/include/flatbuffers/vector_downward.h +++ /dev/null @@ -1,271 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_VECTOR_DOWNWARD_H_ -#define FLATBUFFERS_VECTOR_DOWNWARD_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/default_allocator.h" -#include "flatbuffers/detached_buffer.h" - -namespace flatbuffers { - -// This is a minimal replication of std::vector functionality, -// except growing from higher to lower addresses. i.e push_back() inserts data -// in the lowest address in the vector. -// Since this vector leaves the lower part unused, we support a "scratch-pad" -// that can be stored there for temporary data, to share the allocated space. -// Essentially, this supports 2 std::vectors in a single buffer. -class vector_downward { - public: - explicit vector_downward(size_t initial_size, Allocator *allocator, - bool own_allocator, size_t buffer_minalign) - : allocator_(allocator), - own_allocator_(own_allocator), - initial_size_(initial_size), - buffer_minalign_(buffer_minalign), - reserved_(0), - size_(0), - buf_(nullptr), - cur_(nullptr), - scratch_(nullptr) {} - - vector_downward(vector_downward &&other) - // clang-format on - : allocator_(other.allocator_), - own_allocator_(other.own_allocator_), - initial_size_(other.initial_size_), - buffer_minalign_(other.buffer_minalign_), - reserved_(other.reserved_), - size_(other.size_), - buf_(other.buf_), - cur_(other.cur_), - scratch_(other.scratch_) { - // No change in other.allocator_ - // No change in other.initial_size_ - // No change in other.buffer_minalign_ - other.own_allocator_ = false; - other.reserved_ = 0; - other.buf_ = nullptr; - other.cur_ = nullptr; - other.scratch_ = nullptr; - } - - vector_downward &operator=(vector_downward &&other) { - // Move construct a temporary and swap idiom - vector_downward temp(std::move(other)); - swap(temp); - return *this; - } - - ~vector_downward() { - clear_buffer(); - clear_allocator(); - } - - void reset() { - clear_buffer(); - clear(); - } - - void clear() { - if (buf_) { - cur_ = buf_ + reserved_; - } else { - reserved_ = 0; - cur_ = nullptr; - } - size_ = 0; - clear_scratch(); - } - - void clear_scratch() { scratch_ = buf_; } - - void clear_allocator() { - if (own_allocator_ && allocator_) { delete allocator_; } - allocator_ = nullptr; - own_allocator_ = false; - } - - void clear_buffer() { - if (buf_) Deallocate(allocator_, buf_, reserved_); - buf_ = nullptr; - } - - // Relinquish the pointer to the caller. - uint8_t *release_raw(size_t &allocated_bytes, size_t &offset) { - auto *buf = buf_; - allocated_bytes = reserved_; - offset = static_cast(cur_ - buf_); - - // release_raw only relinquishes the buffer ownership. - // Does not deallocate or reset the allocator. Destructor will do that. - buf_ = nullptr; - clear(); - return buf; - } - - // Relinquish the pointer to the caller. - DetachedBuffer release() { - // allocator ownership (if any) is transferred to DetachedBuffer. - DetachedBuffer fb(allocator_, own_allocator_, buf_, reserved_, cur_, - size()); - if (own_allocator_) { - allocator_ = nullptr; - own_allocator_ = false; - } - buf_ = nullptr; - clear(); - return fb; - } - - size_t ensure_space(size_t len) { - FLATBUFFERS_ASSERT(cur_ >= scratch_ && scratch_ >= buf_); - if (len > static_cast(cur_ - scratch_)) { reallocate(len); } - // Beyond this, signed offsets may not have enough range: - // (FlatBuffers > 2GB not supported). - FLATBUFFERS_ASSERT(size() < FLATBUFFERS_MAX_BUFFER_SIZE); - return len; - } - - inline uint8_t *make_space(size_t len) { - if (len) { - ensure_space(len); - cur_ -= len; - size_ += static_cast(len); - } - return cur_; - } - - // Returns nullptr if using the DefaultAllocator. - Allocator *get_custom_allocator() { return allocator_; } - - inline uoffset_t size() const { return size_; } - - uoffset_t scratch_size() const { - return static_cast(scratch_ - buf_); - } - - size_t capacity() const { return reserved_; } - - uint8_t *data() const { - FLATBUFFERS_ASSERT(cur_); - return cur_; - } - - uint8_t *scratch_data() const { - FLATBUFFERS_ASSERT(buf_); - return buf_; - } - - uint8_t *scratch_end() const { - FLATBUFFERS_ASSERT(scratch_); - return scratch_; - } - - uint8_t *data_at(size_t offset) const { return buf_ + reserved_ - offset; } - - void push(const uint8_t *bytes, size_t num) { - if (num > 0) { memcpy(make_space(num), bytes, num); } - } - - // Specialized version of push() that avoids memcpy call for small data. - template void push_small(const T &little_endian_t) { - make_space(sizeof(T)); - *reinterpret_cast(cur_) = little_endian_t; - } - - template void scratch_push_small(const T &t) { - ensure_space(sizeof(T)); - *reinterpret_cast(scratch_) = t; - scratch_ += sizeof(T); - } - - // fill() is most frequently called with small byte counts (<= 4), - // which is why we're using loops rather than calling memset. - void fill(size_t zero_pad_bytes) { - make_space(zero_pad_bytes); - for (size_t i = 0; i < zero_pad_bytes; i++) cur_[i] = 0; - } - - // Version for when we know the size is larger. - // Precondition: zero_pad_bytes > 0 - void fill_big(size_t zero_pad_bytes) { - memset(make_space(zero_pad_bytes), 0, zero_pad_bytes); - } - - void pop(size_t bytes_to_remove) { - cur_ += bytes_to_remove; - size_ -= static_cast(bytes_to_remove); - } - - void scratch_pop(size_t bytes_to_remove) { scratch_ -= bytes_to_remove; } - - void swap(vector_downward &other) { - using std::swap; - swap(allocator_, other.allocator_); - swap(own_allocator_, other.own_allocator_); - swap(initial_size_, other.initial_size_); - swap(buffer_minalign_, other.buffer_minalign_); - swap(reserved_, other.reserved_); - swap(size_, other.size_); - swap(buf_, other.buf_); - swap(cur_, other.cur_); - swap(scratch_, other.scratch_); - } - - void swap_allocator(vector_downward &other) { - using std::swap; - swap(allocator_, other.allocator_); - swap(own_allocator_, other.own_allocator_); - } - - private: - // You shouldn't really be copying instances of this class. - FLATBUFFERS_DELETE_FUNC(vector_downward(const vector_downward &)); - FLATBUFFERS_DELETE_FUNC(vector_downward &operator=(const vector_downward &)); - - Allocator *allocator_; - bool own_allocator_; - size_t initial_size_; - size_t buffer_minalign_; - size_t reserved_; - uoffset_t size_; - uint8_t *buf_; - uint8_t *cur_; // Points at location between empty (below) and used (above). - uint8_t *scratch_; // Points to the end of the scratchpad in use. - - void reallocate(size_t len) { - auto old_reserved = reserved_; - auto old_size = size(); - auto old_scratch_size = scratch_size(); - reserved_ += - (std::max)(len, old_reserved ? old_reserved / 2 : initial_size_); - reserved_ = (reserved_ + buffer_minalign_ - 1) & ~(buffer_minalign_ - 1); - if (buf_) { - buf_ = ReallocateDownward(allocator_, buf_, old_reserved, reserved_, - old_size, old_scratch_size); - } else { - buf_ = Allocate(allocator_, reserved_); - } - cur_ = buf_ + reserved_ - old_size; - scratch_ = buf_ + old_scratch_size; - } -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_VECTOR_DOWNWARD_H_ diff --git a/new_ai_tools/include/flatbuffers/verifier.h b/new_ai_tools/include/flatbuffers/verifier.h deleted file mode 100644 index 4512d451..00000000 --- a/new_ai_tools/include/flatbuffers/verifier.h +++ /dev/null @@ -1,283 +0,0 @@ -/* - * Copyright 2021 Google Inc. All rights reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef FLATBUFFERS_VERIFIER_H_ -#define FLATBUFFERS_VERIFIER_H_ - -#include "flatbuffers/base.h" -#include "flatbuffers/util.h" -#include "flatbuffers/vector.h" - -namespace flatbuffers { - -// Helper class to verify the integrity of a FlatBuffer -class Verifier FLATBUFFERS_FINAL_CLASS { - public: - Verifier(const uint8_t *buf, size_t buf_len, uoffset_t _max_depth = 64, - uoffset_t _max_tables = 1000000, bool _check_alignment = true) - : buf_(buf), - size_(buf_len), - depth_(0), - max_depth_(_max_depth), - num_tables_(0), - max_tables_(_max_tables), - upper_bound_(0), - check_alignment_(_check_alignment), - flex_reuse_tracker_(nullptr) { - FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE); - } - - // Central location where any verification failures register. - bool Check(bool ok) const { - // clang-format off - #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE - FLATBUFFERS_ASSERT(ok); - #endif - #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE - if (!ok) - upper_bound_ = 0; - #endif - // clang-format on - return ok; - } - - // Verify any range within the buffer. - bool Verify(size_t elem, size_t elem_len) const { - // clang-format off - #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE - auto upper_bound = elem + elem_len; - if (upper_bound_ < upper_bound) - upper_bound_ = upper_bound; - #endif - // clang-format on - return Check(elem_len < size_ && elem <= size_ - elem_len); - } - - bool VerifyAlignment(size_t elem, size_t align) const { - return Check((elem & (align - 1)) == 0 || !check_alignment_); - } - - // Verify a range indicated by sizeof(T). - template bool Verify(size_t elem) const { - return VerifyAlignment(elem, sizeof(T)) && Verify(elem, sizeof(T)); - } - - bool VerifyFromPointer(const uint8_t *p, size_t len) { - auto o = static_cast(p - buf_); - return Verify(o, len); - } - - // Verify relative to a known-good base pointer. - bool VerifyFieldStruct(const uint8_t *base, voffset_t elem_off, - size_t elem_len, size_t align) const { - auto f = static_cast(base - buf_) + elem_off; - return VerifyAlignment(f, align) && Verify(f, elem_len); - } - - template - bool VerifyField(const uint8_t *base, voffset_t elem_off, - size_t align) const { - auto f = static_cast(base - buf_) + elem_off; - return VerifyAlignment(f, align) && Verify(f, sizeof(T)); - } - - // Verify a pointer (may be NULL) of a table type. - template bool VerifyTable(const T *table) { - return !table || table->Verify(*this); - } - - // Verify a pointer (may be NULL) of any vector type. - template bool VerifyVector(const Vector *vec) const { - return !vec || VerifyVectorOrString(reinterpret_cast(vec), - sizeof(T)); - } - - // Verify a pointer (may be NULL) of a vector to struct. - template bool VerifyVector(const Vector *vec) const { - return VerifyVector(reinterpret_cast *>(vec)); - } - - // Verify a pointer (may be NULL) to string. - bool VerifyString(const String *str) const { - size_t end; - return !str || (VerifyVectorOrString(reinterpret_cast(str), - 1, &end) && - Verify(end, 1) && // Must have terminator - Check(buf_[end] == '\0')); // Terminating byte must be 0. - } - - // Common code between vectors and strings. - bool VerifyVectorOrString(const uint8_t *vec, size_t elem_size, - size_t *end = nullptr) const { - auto veco = static_cast(vec - buf_); - // Check we can read the size field. - if (!Verify(veco)) return false; - // Check the whole array. If this is a string, the byte past the array - // must be 0. - auto size = ReadScalar(vec); - auto max_elems = FLATBUFFERS_MAX_BUFFER_SIZE / elem_size; - if (!Check(size < max_elems)) - return false; // Protect against byte_size overflowing. - auto byte_size = sizeof(size) + elem_size * size; - if (end) *end = veco + byte_size; - return Verify(veco, byte_size); - } - - // Special case for string contents, after the above has been called. - bool VerifyVectorOfStrings(const Vector> *vec) const { - if (vec) { - for (uoffset_t i = 0; i < vec->size(); i++) { - if (!VerifyString(vec->Get(i))) return false; - } - } - return true; - } - - // Special case for table contents, after the above has been called. - template bool VerifyVectorOfTables(const Vector> *vec) { - if (vec) { - for (uoffset_t i = 0; i < vec->size(); i++) { - if (!vec->Get(i)->Verify(*this)) return false; - } - } - return true; - } - - __supress_ubsan__("unsigned-integer-overflow") bool VerifyTableStart( - const uint8_t *table) { - // Check the vtable offset. - auto tableo = static_cast(table - buf_); - if (!Verify(tableo)) return false; - // This offset may be signed, but doing the subtraction unsigned always - // gives the result we want. - auto vtableo = tableo - static_cast(ReadScalar(table)); - // Check the vtable size field, then check vtable fits in its entirety. - return VerifyComplexity() && Verify(vtableo) && - VerifyAlignment(ReadScalar(buf_ + vtableo), - sizeof(voffset_t)) && - Verify(vtableo, ReadScalar(buf_ + vtableo)); - } - - template - bool VerifyBufferFromStart(const char *identifier, size_t start) { - if (identifier && !Check((size_ >= 2 * sizeof(flatbuffers::uoffset_t) && - BufferHasIdentifier(buf_ + start, identifier)))) { - return false; - } - - // Call T::Verify, which must be in the generated code for this type. - auto o = VerifyOffset(start); - return o && reinterpret_cast(buf_ + start + o)->Verify(*this) - // clang-format off - #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE - && GetComputedSize() - #endif - ; - // clang-format on - } - - template - bool VerifyNestedFlatBuffer(const Vector *buf, - const char *identifier) { - if (!buf) return true; - Verifier nested_verifier(buf->data(), buf->size()); - return nested_verifier.VerifyBuffer(identifier); - } - - // Verify this whole buffer, starting with root type T. - template bool VerifyBuffer() { return VerifyBuffer(nullptr); } - - template bool VerifyBuffer(const char *identifier) { - return VerifyBufferFromStart(identifier, 0); - } - - template bool VerifySizePrefixedBuffer(const char *identifier) { - return Verify(0U) && - ReadScalar(buf_) == size_ - sizeof(uoffset_t) && - VerifyBufferFromStart(identifier, sizeof(uoffset_t)); - } - - uoffset_t VerifyOffset(size_t start) const { - if (!Verify(start)) return 0; - auto o = ReadScalar(buf_ + start); - // May not point to itself. - if (!Check(o != 0)) return 0; - // Can't wrap around / buffers are max 2GB. - if (!Check(static_cast(o) >= 0)) return 0; - // Must be inside the buffer to create a pointer from it (pointer outside - // buffer is UB). - if (!Verify(start + o, 1)) return 0; - return o; - } - - uoffset_t VerifyOffset(const uint8_t *base, voffset_t start) const { - return VerifyOffset(static_cast(base - buf_) + start); - } - - // Called at the start of a table to increase counters measuring data - // structure depth and amount, and possibly bails out with false if - // limits set by the constructor have been hit. Needs to be balanced - // with EndTable(). - bool VerifyComplexity() { - depth_++; - num_tables_++; - return Check(depth_ <= max_depth_ && num_tables_ <= max_tables_); - } - - // Called at the end of a table to pop the depth count. - bool EndTable() { - depth_--; - return true; - } - - // Returns the message size in bytes - size_t GetComputedSize() const { - // clang-format off - #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE - uintptr_t size = upper_bound_; - // Align the size to uoffset_t - size = (size - 1 + sizeof(uoffset_t)) & ~(sizeof(uoffset_t) - 1); - return (size > size_) ? 0 : size; - #else - // Must turn on FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE for this to work. - (void)upper_bound_; - FLATBUFFERS_ASSERT(false); - return 0; - #endif - // clang-format on - } - - std::vector *GetFlexReuseTracker() { return flex_reuse_tracker_; } - - void SetFlexReuseTracker(std::vector *rt) { - flex_reuse_tracker_ = rt; - } - - private: - const uint8_t *buf_; - size_t size_; - uoffset_t depth_; - uoffset_t max_depth_; - uoffset_t num_tables_; - uoffset_t max_tables_; - mutable size_t upper_bound_; - bool check_alignment_; - std::vector *flex_reuse_tracker_; -}; - -} // namespace flatbuffers - -#endif // FLATBUFFERS_VERIFIER_H_ diff --git a/new_ai_tools/include/ioserver.h b/new_ai_tools/include/ioserver.h deleted file mode 100644 index 9265416b..00000000 --- a/new_ai_tools/include/ioserver.h +++ /dev/null @@ -1,44 +0,0 @@ -#ifndef _io_server_h_ -#define _io_server_h_ - -#ifdef __XC__ - -void ioserver(chanend c_model[], unsigned n_model); - -#else - -#include -#include - -#define CMD_LENGTH_BYTES (3) // CMD, Model, Tensor - -#define IOSERVER_INVOKE 1 -#define IOSERVER_TENSOR_SEND_OUTPUT 2 -#define IOSERVER_TENSOR_RECV_INPUT 3 -#define IOSERVER_ACK 5 -#define IOSERVER_NACK 6 -#define IOSERVER_RESET 7 -#define IOSERVER_EXIT 8 - -#define MAX_PACKET_SIZE (512) -#define MAX_PACKET_SIZE_WORDS (MAX_PACKET_SIZE / 4) - -#ifdef __cplusplus -extern "C" { -#endif -unsigned int ioserver_command_receive(chanend_t c_server, unsigned *tensor_num); -void ioserver_command_acknowledge(chanend_t c_server, unsigned int ack); -void ioserver_tensor_send_output(chanend_t c_server, unsigned int *data, - unsigned int n); - -void ioserver_tensor_recv_input(chanend_t c_server, unsigned int *data, - unsigned int n); - -void ioserver(chanend_t c_model[], unsigned n_model); -#ifdef __cplusplus -} -#endif - -#endif - -#endif diff --git a/new_ai_tools/include/lib_nn/api/TransposeConv.h b/new_ai_tools/include/lib_nn/api/TransposeConv.h deleted file mode 100644 index 644b7782..00000000 --- a/new_ai_tools/include/lib_nn/api/TransposeConv.h +++ /dev/null @@ -1,24 +0,0 @@ -// Copyright 2021 XMOS LIMITED. This Software is subject to the terms of the -// XMOS Public License: Version 1 -#include -#include -#include - -class ConvParams { - public: - ConvParams(std::array ks, std::vector weights, int h, int w) : - subH(h), subW(w), kernelShape(ks) , weights(weights){} - - public: - int subH; - int subW; - std::array kernelShape; - std::vector weights; -}; - -std::vector transpose_conv_reorder_kernel_weights( - int8_t *raw_weights, - std::array &shape, - int32_t stride_height, //vertical - int32_t stride_width //horizontal -) ; diff --git a/new_ai_tools/include/lib_nn/api/add_int16.h b/new_ai_tools/include/lib_nn/api/add_int16.h deleted file mode 100644 index 0d5539d8..00000000 --- a/new_ai_tools/include/lib_nn/api/add_int16.h +++ /dev/null @@ -1,27 +0,0 @@ -#ifndef _add_int16_h_ -#define _add_int16_h_ - -/** - * Function that implements a addition of two 16-bit tensors. - * The blob must have been created by a call to - * ``add_int16_tensor_blob()`` - * - * @param output Output tensor - * Must be word-aligned - * - * @param input1 Input tensor operand 1 - * Must be word-aligned - * - * @param input1 Input tensor operand 2 - * Must be word-aligned - * - * @param blob Transformed constant input tensor - * Must be word-aligned - * - * @param tensor_length Number of elements in the tensor (product of all dimensions) - * There are no constraints on this number. - */ -void add_int16_tensor(int16_t *output, int16_t *input1, int16_t *input2, - int tensor_length, void *blob); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/add_int16_transform.h b/new_ai_tools/include/lib_nn/api/add_int16_transform.h deleted file mode 100644 index a18a8268..00000000 --- a/new_ai_tools/include/lib_nn/api/add_int16_transform.h +++ /dev/null @@ -1,42 +0,0 @@ -#ifndef _add_int16_transform_h_ -#define _add_int16_transform_h_ - -#include "nn_api.h" -#include - -/** - * Function that performs the compile time transformation of an int16 addition - * between two tensors. - * - * this function should be - * called at compile-time, and at run-time the output of this function shall be passed - * as the second input tensor of ``add_int16_tensor`` - * - * @param output Output of the function; a blob of - * ``ADD_INT16_TENSOR_BYTES()`` bytes. - * Must be word-aligned. - * - * @param input1_scaler Quantisation scaler for input1 - * Negate this to make add compute -A+B - * - * @param input2_scaler Quantisation scaler for input2 - * Negate this to make add compute A-B - * - * @param output_scaler Quantisation scaler for output - * - * @returns 1 on success, 0 on fail (fallback required) - */ -C_API int add_int16_tensor_blob(void *output, - float input1_scaler, - float input2_scaler, - float output_scaler, - char *err_msg); - -/** - * Macro that calculates the number of int16_t that should be allocated to - * store the output of ``add_int16_tensor_blob()`` - */ -#define ADD_INT16_TENSOR_BYTES() (2 * 16 * sizeof(int16_t)) - - -#endif diff --git a/new_ai_tools/include/lib_nn/api/dequantize_int16.h b/new_ai_tools/include/lib_nn/api/dequantize_int16.h deleted file mode 100644 index df5612c6..00000000 --- a/new_ai_tools/include/lib_nn/api/dequantize_int16.h +++ /dev/null @@ -1,22 +0,0 @@ -#ifndef _dequantize_int16_h_ -#define _dequantize_int16_h_ - -/** - * Function that implements dequantization of a 16-bit tensor to a 32-bit tensor. - * The blob must have been created by a call to ``dequantize_int16_tensor_blob()`` - * - * @param output Output tensor - * Must be word-aligned - * - * @param input Input tensor - * - * @param blob Transformed constant input tensor - * Must be word-aligned - * - * @param tensor_length Number of elements in the tensor (product of all dimensions) - * There are no constraints on this number. - */ -void dequantize_int16_tensor(float *output, int16_t *input, - int tensor_length, void *blob); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h b/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h deleted file mode 100644 index 446ccdb0..00000000 --- a/new_ai_tools/include/lib_nn/api/dequantize_int16_transform.h +++ /dev/null @@ -1,34 +0,0 @@ -#ifndef _dequantize_int16_transform_h_ -#define _dequantize_int16_transform_h_ - -#include "nn_api.h" -#include - -/** - * Function that performs the compile time transformation of an int16 addition - * between two tensors. - * - * this function should be - * called at compile-time, and at run-time the output of this function shall be passed - * as the second input tensor of ``dequantize_int16_tensor`` - * - * @param output Output of the function; a blob of - * ``DEQUANTIZE_INT16_TENSOR_BYTES()`` bytes. - * Must be word-aligned. - * - * @param input_scaler Quantisation scaler for the input - * - * @returns 1 on success, 0 on fail (fallback required) - */ -C_API int dequantize_int16_tensor_blob(void *output, - float input_scaler, - char *err_msg); - -/** - * Macro that calculates the number of int16_t that should be allocated to - * store the output of ``dequantize_int16_tensor_blob()`` - */ -#define DEQUANTIZE_INT16_TENSOR_BYTES() (2 * sizeof(float)) - - -#endif diff --git a/new_ai_tools/include/lib_nn/api/expand_8_to_16.h b/new_ai_tools/include/lib_nn/api/expand_8_to_16.h deleted file mode 100644 index 7147df94..00000000 --- a/new_ai_tools/include/lib_nn/api/expand_8_to_16.h +++ /dev/null @@ -1,8 +0,0 @@ -#ifndef _expand_8_to_16_h_ -#define _expand_8_to_16_h_ - -#include - -extern void expand_8_to_16(int16_t *out, int8_t *in, int N); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/multiply_int16.h b/new_ai_tools/include/lib_nn/api/multiply_int16.h deleted file mode 100644 index 14a4cb75..00000000 --- a/new_ai_tools/include/lib_nn/api/multiply_int16.h +++ /dev/null @@ -1,42 +0,0 @@ -#ifndef _multiply_int16_h_ -#define _multiply_int16_h_ - -/** - * Function that implements a multiplication of two 16-bit tensors where - * both tensors are variable. The blob must have been created by a call to - * ``multiply_int16_tensor_blob()`` - * - * @param output Output tensor - * Must be word-aligned - * - * @param input Non constant input tensor - * Must be word-aligned - * - * @param blob Transformed constant input tensor - * Must be word-aligned - * - * @param tensor_length Number of elements in the tensor (product of all dimensions) - * There are no constraints on this number. - */ -void multiply_int16_tensor(int16_t *output, int16_t *input1, int16_t *input2, - int tensor_length, void *blob); - -/** - * Function that implements a requantifies a 16-bit tensor. The blob must have been - * created at compile time using ``requantise_int16_tensor_blob()`` - * - * @param output Output tensor - * Must be word-aligned - * - * @param input Non constant input tensor - * Must be word-aligned - * - * @param tensor_length Number of elements in the tensor (product of all dimensions) - * There are no constraints on this number. - * - * @param blob Transformed constant input tensor - * Must be word-aligned - */ -void requantize_int16_tensor(int16_t *output, int16_t *input1, int tensor_length, void *blob); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h b/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h deleted file mode 100644 index 87ac66e6..00000000 --- a/new_ai_tools/include/lib_nn/api/multiply_int16_transform.h +++ /dev/null @@ -1,71 +0,0 @@ -#ifndef _multiply_int16_transform_h_ -#define _multiply_int16_transform_h_ - -#include "nn_api.h" -#include - - -/** - * Function that performs the compile time transformation of an int16 to int16 - * re-quantisation. - * - * For an int16 re-quantisation, this function should be - * called at compile-time, and at run-time the output of this function shall be passed - * as the second input tensor of ``requantise_int16_tensor`` - * - * @param output Output of the function; a blob of - * ``REQUANTISE_INT16_BYTES()`` bytes. - * Must be word-aligned. - * - * @param input1_scaler Quantisation scaler for input - * - * @param output_scaler Quantisation scaler for output - * - * @returns 1 on success, 0 on fail (fallback required) - */ -C_API int requantize_int16_tensor_blob(void *output, - float input_scaler, - float output_scaler, - char *err_msg); -/** - * Macro that calculates the number of int16_t that should be allocated to - * store the output of ``quantise_int16_tensor_blob()`` - */ -#define REQUANTIZE_INT16_TENSOR_BYTES() (16 * sizeof(int16_t)) - - -/** - * Function that performs the compile time transformation of an int16 multiplication - * between two tensors. - * - * this function should be - * called at compile-time, and at run-time the output of this function shall be passed - * as the second input tensor of ``multiply_int16_tensor`` - * - * @param output Output of the function; a blob of - * ``MULTIPLY_INT16_TENSOR_BYTES()`` bytes. - * Must be word-aligned. - * - * @param input1_scaler Quantisation scaler for input1 - * - * @param input2_scaler Quantisation scaler for input2 - * - * @param output_scaler Quantisation scaler for output - * - * @returns 1 on success, 0 on fail (fallback required) - */ -C_API int multiply_int16_tensor_blob(void *output, - float input1_scaler, - float input2_scaler, - float output_scaler, - char *err_msg); - -/** - * Macro that calculates the number of int16_t that should be allocated to - * store the output of ``multiply_int16_tensor_blob()`` - * TODO: this could be zero and stored in the pointer... - */ -#define MULTIPLY_INT16_TENSOR_BYTES() (2 * sizeof(int16_t)) - - -#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_api.h b/new_ai_tools/include/lib_nn/api/nn_api.h deleted file mode 100644 index 737b0759..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_api.h +++ /dev/null @@ -1,15 +0,0 @@ -#pragma once - -#ifdef __cplusplus -#define EXTERN_C extern "C" -#else -#define EXTERN_C -#endif - -#define C_API EXTERN_C - -#define ERR_MSG_DESCRIPTOR_FAIL_BYTES() (128) - -#ifndef __has_builtin -#define __has_builtin(x) 0 -#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_arch.h b/new_ai_tools/include/lib_nn/api/nn_arch.h deleted file mode 100644 index b3ecf9c1..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_arch.h +++ /dev/null @@ -1,17 +0,0 @@ -#pragma once - -#include "nn_api.h" - -typedef enum { - TARGET_ARCH_XS3A = 0, - TARGET_ARCH_VX4A = 1, -} nn_target_arch_t; - -extern nn_target_arch_t NN_ARCH; - -C_API void SetNNTargetArch(nn_target_arch_t arch); - -typedef enum { - VLMUL_SHR_XS3A = 14, - VLMUL_SHR_VX4A = 15, -} nn_vlmul_shr_t; \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_bin_types.h b/new_ai_tools/include/lib_nn/api/nn_bin_types.h deleted file mode 100644 index 256daf0d..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_bin_types.h +++ /dev/null @@ -1,14 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef NN_BNN_TYPES_H -#define NN_BNN_TYPES_H -#include - -typedef int8_t bnn_bool_t; -typedef int32_t bnn_b32_t; - -typedef struct bnn_b256_t { - bnn_b32_t d[8]; -} bnn_b256_t; - -#endif // NN_BNN_TYPES_H \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_config.h b/new_ai_tools/include/lib_nn/api/nn_config.h deleted file mode 100644 index 93b892aa..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_config.h +++ /dev/null @@ -1,287 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#pragma once - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_GLOBAL - * @brief Configure whether (supported) operators use `-127` or `-128` as the - * their lower saturation bound. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is defined, it is used as the value - * for each config macro `CONFIG_SYMMETRIC_SATURATION_*` (e.g. - * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8`), unless that macro has been - * explicitly set. - * - * Bypassing the symmetric saturation bound requires additional logic, and so - * will generally make the operators slower, though this will be more or less - * significant, depending on the specific operators. - */ - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_deep - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `conv2d_deep()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `conv2d_deep()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` to be - * `1`. If it is defined to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` is undefined, then the value of - * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If - * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_deep` defaults - * to 0, using a lower saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_deep -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_conv2d_deep \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_conv2d_deep (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `conv2d_shallowin()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `conv2d_shallowin()`, define - * `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` to be `1`. If it is defined to - * `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` is undefined, then the - * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is - * defined. If neither symbol is defined, - * `CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin` defaults to 0, using a lower - * saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_conv2d_shallowin (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_im2col - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `conv2d_im2col()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `conv2d_shallowin()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` - * to be `1`. If it is defined to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` is undefined, then the value - * of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. - * If neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_im2col` - * defaults to 0, using a lower saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_im2col -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_conv2d_im2col \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_conv2d_im2col (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `conv2d_depthwise()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `conv2d_depthwise()`, define - * `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` to be `1`. If it is defined to - * `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` is undefined, then the - * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is - * defined. If neither symbol is defined, - * `CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise` defaults to 0, using a lower - * saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_conv2d_depthwise (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `conv2d_1x1()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `conv2d_1x1()`, define `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` to be - * `1`. If it is defined to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` is undefined, then the value of - * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If - * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_conv2d_1x1` defaults - * to 0, using a lower saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_conv2d_1x1 (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_avgpool2d - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `avgpool2d()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `avgpool2d()`, define `CONFIG_SYMMETRIC_SATURATION_avgpool2d` to be `1`. - * If it is defined to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_avgpool2d` is undefined, then the value of - * `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is defined. If - * neither symbol is defined, `CONFIG_SYMMETRIC_SATURATION_avgpool2d` defaults - * to 0, using a lower saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_avgpool2d -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_avgpool2d CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_avgpool2d (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_avgpool2d_global - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `avgpool2d_global()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `avgpool2d_global()`, define - * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` to be `1`. If it is defined to - * `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` is undefined, then the - * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is - * defined. If neither symbol is defined, - * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` defaults to 0, using a lower - * saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_avgpool2d_global -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_avgpool2d_global \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_avgpool2d_global (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `requantize_16_to_8()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `requantize_16_to_8()`, define - * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` to be `1`. If it is defined - * to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` is undefined, then the - * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is - * defined. If neither symbol is defined, - * `CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8` defaults to 0, using a lower - * saturation bound of `-128`. - * - * Unfortunately, bypassing the symmetric saturation bounds requires significant - * additional logic, and so with the symmetric saturation bound, - * `requantize_16_to_8()` is approximately 2.5x faster. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_requantize_16_to_8 (0) -#endif -#endif - -/** - * @macro CONFIG_SYMMETRIC_SATURATION_fully_connected_8 - * @brief Configure whether `-127` or `-128` is used as the saturation limit for - * `fully_connected_8()`. - * - * The output of 8-bit arithmetic on the XS3 VPU has natural symmetric - * saturation bounds of (`-127`, `127`). This may be unacceptable, in which case - * (`-128`, `127`) can be used instead. - * - * To specify that the symmetric saturation lower bound (`-127`) should be used - * for `fully_connected_8()`, define - * `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` to be `1`. If it is defined - * to `0`, `-128` will be used instead. - * - * If `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` is undefined, then the - * value of `CONFIG_SYMMETRIC_SATURATION_GLOBAL` is used instead, if that is - * defined. If neither symbol is defined, - * `CONFIG_SYMMETRIC_SATURATION_fully_connected_8` defaults to 0, using a lower - * saturation bound of `-128`. - * - */ -#ifndef CONFIG_SYMMETRIC_SATURATION_fully_connected_8 -#ifdef CONFIG_SYMMETRIC_SATURATION_GLOBAL -#define CONFIG_SYMMETRIC_SATURATION_fully_connected_8 \ - CONFIG_SYMMETRIC_SATURATION_GLOBAL -#else -#define CONFIG_SYMMETRIC_SATURATION_fully_connected_8 (0) -#endif -#endif diff --git a/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h b/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h deleted file mode 100644 index a217b916..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_conv2d_structs.h +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef CONV2D_STRUCTS_H_ -#define CONV2D_STRUCTS_H_ - -#include "nn_image.h" - -#define CONV2D_OUTPUT_LENGTH(input_length, filter_size, dilation, stride) \ - (((input_length - (filter_size + (filter_size - 1) * (dilation - 1)) + 1) + \ - stride - 1) / \ - stride) - -#define CONV2D_INPUT_LENGTH(output_length, filter_size, dilation, stride) \ - (output_length * stride - (stride - 1) - 1 + \ - (filter_size + (filter_size - 1) * (dilation - 1))) - -/** - * Describes the relationship between the convolution window and the - * input image. - */ -typedef struct { - /** The shape of the convolution window */ - struct { - /** Height of the convolution window in pixels */ - unsigned height; - /** Width of the convolution window in pixels */ - unsigned width; - } shape; - - /** - * The initial position of the convolution window, relative to the input - * image. - * - * The position given by this pair indicates where the top-left pixel of the - * convolution window begins relative to the top-left pixel of the input - * image. - * - * If this pair is, for example, `(0, 0)`, then the convolution window starts - * at the top left of the input image and involves no top or left padding. - */ - struct { - /** Row offset of convolution window inital position */ - int row; - /** Column offset of convolution window inital position */ - int column; - } start; - - /** - * The strides of the convolution window. These are the number of (input - * image) pixels that the convolution window moves down and right for each - * pixel moved down or right in the output image. - */ - struct { - /** Vertical stride of the convolution window. */ - int vertical; - /** Horizontal stride of the convolution window */ - int horizontal; - } stride; - - /** - * Note: Only supported where explicitly mentioned. - */ - struct { - /** Vertical dilation of the convolution window. */ - int vertical; - /** Horizontal dilation of the convolution window */ - int horizontal; - } dilation; - -} nn_window_params_t; - -#endif // CONV2D_STRUCTS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_image.h b/new_ai_tools/include/lib_nn/api/nn_image.h deleted file mode 100644 index fcb7bb38..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_image.h +++ /dev/null @@ -1,26 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef IMAGE_H_ -#define IMAGE_H_ - -#include "nn_types.h" - -/** - * This struct describes the basic parameters for an image tensor - */ -typedef struct { - /** - * Height of an image (in pixels) - */ - uint32_t height; - /** - * Width of the image (in pixels) - */ - uint32_t width; - /** - * Number of channels per pixel - */ - channel_count_t channels; -} nn_image_params_t; - -#endif // IMAGE_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_layers.h b/new_ai_tools/include/lib_nn/api/nn_layers.h deleted file mode 100644 index ca3640e8..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_layers.h +++ /dev/null @@ -1,311 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef LAYERS_H_ -#define LAYERS_H_ -#include "nn_api.h" -#include "nn_bin_types.h" -#include "nn_image.h" -#include - -/** - * Struct represents the parameters needed by each `bsign_8()` job. - * - * Values are set by `bsign_8_prepare()`. - * - * @note This struct is intended to be opaque. - */ -typedef struct { - mem_stride_t start; - int32_t length; -} nn_bsign_8_job_t; - -/** - * @brief Initialize an instance of the @oper{bsign_8} operator. - * - * See @oper_ref{bsign_8} for more details about the @oper{bsign_8} operator. To - * invoke a - * @oper{bsign_8} job, call bsign_8(). - * - * When bsign_8() is called, a job (`nn_bsign_8_job_t`) must be supplied to tell - * it how to do its work. This function initializes one or more jobs to be - * supplied in subsequent calls to bsign_8(). - * - * Each job computes a range of elements in the output vector (possibly the - * entire vector). - * - * `jobs` points to an array of `nn_bsign_8_t` to be initialized. Each element - * represents one job. There should be `job_count` elements in the array. - * - * `N` is the number of elements @math{N} in the input vector @tensor{x} and - * output vector @tensor{y}. - * - * `job_count` indicates the number of jobs to be initialized (and thus the - * number of elements in the `jobs` array). - * - * Unlike many other operators, @oper{bsign_8} will automatically divide the - * work to be done as evenly as possible between jobs. - * - * @param plan [out] The plan to be initialized. - * @param jobs [out] Array of jobs to be initialized. - * @param N [in] The number of elements in the input. - * @param[in] zero_point The value @math{z_0} to be used for padding (for all - * channels) - * @param job_count [in] The number of jobs to be initialized. - */ -void bsign_8_prepare(nn_bsign_8_job_t *jobs, int8_t *zero_point_vect, - const uint32_t N, const int8_t zero_point, - const int32_t job_count); - -/** - * @brief Execute @oper{bsign_8} job. - * - * See @oper_ref{bsign_8} for more details about the @oper{requantize_16_to_8} - * operator. - * - * An instance of the @oper{bsign_8} operator requires an job (but no plan is - * required). See bsign_8_prepare() for more details. - * - * `Y` points to the output vector @tensor{y} with length @math{N}. The address - * supplied for `Y` should be the start address of the output vector (for any - * job being processed). - * - * `X` points to the input vector @tensor{x} with length @math{N}. The address - * supplied for `X` should be the start address of the input vector (for any job - * being processed). - * - * `job` points to the (initialized) @oper{bsign_8} job to be performed with - * this call. - * - * @requires_word_alignment{Y,X} - * - * @param Y [out] The output vector @tensor{y} - * @param X [in] The input vector @tensor{x} - * @param plan [in] The @oper{bsign_8} plan to be processed - * @param job [in] The @oper{bsign_8} job to be processed - */ -void bsign_8(bnn_b32_t *Y, const int8_t *X, const int8_t *zero_point_vect, - const nn_bsign_8_job_t *job); - -/** - * Struct represents the parameters needed by each `pad_run()` job. - * - * Values are set by `pad_prepare()`. - * - * @note This struct is intended to be opaque. - */ -typedef struct nn_pad_plan_t { - unsigned top_pad_bytes; - unsigned mid_loop_count; - unsigned left_pad_bytes; - unsigned mid_copy_bytes; - unsigned right_pad_bytes; - unsigned bottom_pad_bytes; -} nn_pad_plan_t; - -typedef struct padding_sizes_t { - int32_t top; - int32_t bottom; - int32_t left; - int32_t right; -} padding_sizes_t; - -/** - * Func to calculate n_3 - */ -void pad_3_to_4_prepare(uint32_t *n_3, const unsigned height, - const unsigned width); - -/** Function that pads an image with 3-byte values with a 0. - * The output image must be word aligned. This function solves the general - * case and calls an optimised assembly version for the bulk copy. - * - * @param outputs output values, every word contains 3 bytes and a zero - * @param inputs input values, RGBRGBRGBRGB... - * @param N_3 number of blocks of 3 bytes to copy - * - * @returns The inner product - */ -extern void pad_3_to_4_run(int8_t outputs[], int8_t inputs[], uint32_t N_3, - uint32_t pad_val); -extern void pad_3_to_4_ref(int8_t outputs[], int8_t inputs[], uint32_t N_3, - uint32_t pad_val); - - -extern void pad_1_to_4_run(int8_t outputs[], int8_t inputs[], uint32_t N, - uint32_t pad_val); - -typedef struct nn_mul_params_t { - int8_t in1_zero_point; - int8_t in2_zero_point; - int16_t bias; - int16_t scalar; - int16_t vlashr_shr; -} nn_mul_params_t; - -void mul_boggle(nn_mul_params_t *params, double in1Scale, double in2Scale, - double outputScale, int8_t in1ZeroPoint, int8_t in2ZeroPoint, - int8_t outputZeroPoint); -void mul_elementwise(const int8_t *in1_data, const int8_t *in2_data, - int element_count, nn_mul_params_t *params, - int8_t *out_data); - -// /** -// * Describes the parameters needed for an @oper{add_elementwise} operator. -// @see add_elementwise(). -// */ -// typedef struct { -// /** -// * The parameters that are applied to each input element. -// */ -// -// /** -// * `m1` and `m2` are the multiplers for the inputs. -// */ -// int16_t m1[16]; -// int16_t m2[16]; - -// /** -// * `shift` is the number of bits the 32-bit accumulator is -// * right-shifted by to obtain a final result for each element. -// */ -// int16_t shift[16]; - -// /** -// * `bias_hi` and `bias_lo` are together, the 32-bit bias to -// * which the scaled inputs are added. -// */ -// int16_t bias_lo[16]; -// int16_t bias_hi[16]; - -// } nn_add_params_t; - -typedef struct { - int16_t m1[16]; - int16_t m2[16]; - int16_t shift[16]; - int16_t bias_hi[16]; - int16_t bias_lo[16]; -} nn_add_params_t; - -/** - * @brief Invoke an @oper{add_elementwise} job. - * - * The @oper{add_elementwise} operator adds together two quantized 8-bit input - * vectors, @tensor{x_0} and @tensor{x_1} element-by-element to produce the - * output vector @tensor{y}. This function assumes that the input vectors and - * the output vector each require different quantization parameters. - * - * In order to add together two quantized vectors, their quantization parameters - * must match. The contents of `params` indicate how to do this. - * - * @par Parameter Details - * - * `Y` points to the output vector @tensor{y} with shape @tensor_shape{N}. - * - * `X0` and `X1` respectively point to the first and second input vectors - * @tensor{x_0} and @tensor{x_1}, each with shape - * @tensor_shape{N}. - * - * `params` describes the parameters @math{s_i}, @math{m_i}, @math{b} and - * @math{s_{out}} which are applied for each output element. - * - * `elm_start` and `elm_count` together specify which output elements - * @math{y[k]} should be calculated by this invocation. Specifically, this - * invocation will calculate @math{y[k]} for which `elm_start` @math{\le k \lt} - * `(elm_start + elm_count)`. - * - * @param[out] Y The output vector @tensor{y} - * @param[in] X0 The first input vector @tensor{x_0} - * @param[in] X1 The second input vector @tensor{x_1} - * @param[in] params The scaling and bias parameters - * @param[in] elm_start Index of first output element to be computed - * @param[in] elm_count Number of output elements to be computed - */ -void add_elementwise(int8_t Y[], const int8_t X1[], const int8_t X2[], - nn_add_params_t *p, const int elm_start, - const int elm_count); - -/** - * @brief Execute @oper{lookup8} job. - * - * See @oper_ref{lookup8} for more details about the @oper{lookup8} operator. - * - * Unlike other operators, instances of @oper{lookup8} do not require plans or - * jobs and no initialization is necessary. - * - * `Y` points to the output vector @tensor{y} with length @math{N}. - * - * `X` points to the input vector @tensor{x} with length @math{N}. - * - * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and - * dtype `int8`. - * - * `N` is the length @math{N} of the input vector @tensor{x}. - * - * @requires_word_alignment{Y,X} - * - * @param Y [out] The output vector @tensor{y} - * @param X [in] The input vector @tensor{x} - * @param lut [in] Look-up table @tensor{T} - * @param N [in] Length @math{N} of input and output vectors - */ -void lookup8(uint8_t *Y, const uint8_t *X, const uint8_t *lut, - const unsigned elm_start, const unsigned elm_count); - -/** - * @brief Execute @oper{softmax_exp_sum} job. - * - * `Y` points to the output scalar. - * - * `X` points to the input vector @tensor{x} with length @math{N}. - * - * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and - * dtype `float32`. - * - * `N` is the length @math{N} of the input vector @tensor{x}. - * - * `elm_start` and `elm_count` together specify which output elements should be - * summed into the output scalar. - */ -void softmax_exp_sum(float *Y, const int8_t *X, const float *lut, - const unsigned elm_start, const unsigned elm_count); - -/** - * @brief Execute @oper{softmax_exp_div} job. - * - * `Y` points to the output vector @tensor{y} with length @math{N}. - * - * `X` points to the input vector @tensor{x} with length @math{N}. - * - * `lut` points to the look-up table @math{T} with shape @tensor_shape{256} and - * dtype `float32`. - * - * `inv_sum` is the reciprocal of the sum of the exponentials of the inputs. - * - * `elm_start` and `elm_count` together specify which output elements should be - * calculated by this invocation. - */ -void softmax_exp_div(int8_t *Y, const int8_t *X, const float *lut, - const float inv_sum, const unsigned elm_start, - const unsigned elm_count); - -void softmax_calculate_inv_sum(float *inv_sum, const float sums[]); - -void softmax_generate_exp_lut(int zero_point, float scale, float *lut); - -void softmax_ref(int8_t *Y, const int8_t *X, const float zero_point, - const float scale, const int length); - -void softmax_single(int8_t *Y, const int8_t *X, const float *lut, - const int offset); - -void mean_int8(const int8_t *input, int8_t *output, const int start_dim_size, - const int mean_dim_size, const int end_dim_size, - const float in_zero_point, const float out_zero_point, - const float scale_mul); - -void mean_int16(const int16_t *input, int16_t *output, const int start_dim_size, - const int mean_dim_size, const int end_dim_size, - const float scale_mul); - -#endif // LAYERS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_op_helper.h b/new_ai_tools/include/lib_nn/api/nn_op_helper.h deleted file mode 100644 index 86e2c811..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_op_helper.h +++ /dev/null @@ -1,138 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef NN_OP_HELPER_H_ -#define NN_OP_HELPER_H_ -#include - -#include "xs3_vpu.h" - -/** Get address of array element. - * - * For compatibility with (non-xcore) builds with 64-bit addresses, when getting - * the address of an array element, the indexes should be signed, or else - * badness ensues. - * - * @note This macro gets the address of an array *element*. So if `V` is - * `int16_t`, the address is adjusted by `2*INDEX` *bytes*. - * - * @param V Array - * @param INDEX Element index within array. - */ -#define ADDR(V, INDEX) &(V)[((int)(INDEX))] - -static inline int8_t sat_s8(const int32_t acc32, const int8_t sat_lo, - const int8_t sat_hi) { - if (acc32 > VPU_INT8_MAX) return sat_hi; - if (acc32 < VPU_INT8_MIN) return sat_lo; - - return (int8_t)acc32; -} - -static inline int16_t sat_s16(const int32_t acc32) { - if (acc32 >= VPU_INT16_MAX) return VPU_INT16_MAX; - if (acc32 <= VPU_INT16_MIN) return VPU_INT16_MIN; - - return (int16_t)acc32; -} - -static inline int32_t sat_s32(const int64_t acc64) { - if (acc64 >= VPU_INT32_MAX) return VPU_INT32_MAX; - if (acc64 <= VPU_INT32_MIN) return VPU_INT32_MIN; - - return (int32_t)acc64; -} - -// static inline void mulsat_s32(int32_t* acc32, const int8_t a, const int8_t b) -// { -// int64_t acc64 = *acc32 + a*b; -// *acc32 = sat_s32(acc64); -// } - -static inline int8_t vlsat_single_s8(int32_t acc, uint16_t shr, - const int8_t sat_lo, const int8_t sat_hi) { - int64_t acc64 = acc; - if (shr > 0) acc64 += 1 << (shr - 1); - return sat_s8(acc64 >> shr, sat_lo, sat_hi); -} - -static inline int16_t vlsat_single_s16(int32_t acc, uint16_t shr) { - if (shr > 0) acc += 1 << (shr - 1); - return sat_s16(acc >> shr); -} - -static inline int16_t vlmul_single_s16(int16_t vR, int16_t mem) { - int32_t p = ((int32_t)vR) * mem; - p = vlsat_single_s16(p, 14); - return (int16_t)p; -} - -static inline int8_t vlmul_single_s8(int8_t vR, int8_t mem) { - int32_t p = ((int32_t)vR) * mem; - p = vlsat_single_s8(p, 6, VPU_INT8_MIN, VPU_INT8_MAX); - return (int8_t)p; -} - -static inline int8_t vdepth8_single_s16(int16_t vR) { - return vlsat_single_s8(vR, 8, VPU_INT8_MIN, VPU_INT8_MAX); -} - -static inline unsigned in_bounds( - const unsigned region_top, const unsigned region_left, - const unsigned region_bottom, const unsigned region_right, - const unsigned bounds_top, const unsigned bounds_left, - const unsigned bounds_bottom, const unsigned bounds_right) { - return region_top > bounds_bottom || region_bottom < bounds_top || - region_left > bounds_right || region_right < bounds_left; -} - -static inline unsigned clip(const unsigned low, const unsigned val, - const unsigned high) { - if (val <= low) - return low; - else if (val >= high) - return high; - else - return val; -} - -static inline unsigned smax(const unsigned a, const unsigned b) { - return (a >= b) ? a : b; -} - -static inline unsigned smin(const unsigned a, const unsigned b) { - return (a <= b) ? a : b; -} - -static inline int ceil_log2(uint32_t a) { - if (a == 0) return -1; -#ifdef __xcore__ - unsigned x; - #ifdef __XS3A__ - asm("clz %0, %1" : "=r"(x) : "r"(a)); - #endif - #ifdef __VX4A__ - asm("xm.clz %0, %1" : "=r"(x) : "r"(a)); - #endif - - unsigned y = 31 - x; - - // clz(1) = 31 -> 31-31 = 0 -> 2^0 = 1 - // clz(2) = 30 -> 31-30 = 1 -> 2^1 = 2 - // clz(3) = 30 -> 31-30 = 1 -> 2^1 = 2 - // 2^(y) <= a < 2^(y+1) - // check for the lower bound, which yields a different result - if (a == (1 << y)) return y; - return y + 1; - -#else - for (unsigned i = 0; i < 31; i++) { - if ((((unsigned)1) << i) >= a) { - return i; - } - } -#endif - return -1; -} - -#endif // NN_OP_HELPER_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_op_utils.h b/new_ai_tools/include/lib_nn/api/nn_op_utils.h deleted file mode 100644 index f5c000a1..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_op_utils.h +++ /dev/null @@ -1,153 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef NN_OP_UTILS_H_ -#define NN_OP_UTILS_H_ - -#include -#include - -#include "xs3_vpu.h" - -C_API int calculateAlignedThreadSplit(int tc, int split_size, int split_start[], int split_end[]); -C_API int calculateThreadSplit(int tc, int split_size, int split_start[], int split_end[], int alignment); - -#ifdef __XC__ -extern "C" { -#endif - -/** Helper for computing offsets between pixels in an 8-bit image. - * - * Gives the address delta associated with moving the specified number of - * rows, columns and channels through an image with the specified parameters. - * - * \param IMG (nn_image_params_t*) Pointer to image params. - * \param DELTA_ROWS (signed int) Number of rows - * \param DELTA_COLS (signed int) Number of columns - * \param DELTA_CHANS (signed int) Number of channels - */ -#define IMG_ADDRESS_VECT(IMG, DELTA_ROWS, DELTA_COLS, DELTA_CHANS) \ - (((DELTA_ROWS) * (IMG)->width * (IMG)->channels) + \ - ((DELTA_COLS) * (IMG)->channels) + (DELTA_CHANS)) - -/** Get the number of output channel groups given the number of output channels. - * - * This macro gives the minimum number of groups required to handle `CHANNELS` - * channels, which means it is effectively `(int) ceil(CHANNELS / 16.0f)`. - * - * \param CHANNELS Number of channels - */ -#define OUT_CHANNEL_GROUPS(CHANNELS) \ - (((CHANNELS) + (VPU_INT8_ACC_PERIOD - 1)) >> VPU_INT8_ACC_PERIOD_LOG2) - -#ifdef NN_USE_REF -#define USING_C_REFERENCE (1) -#else -#define USING_C_REFERENCE (0) -#endif // NN_USE_REF - -#define MEMCPY_VECT_EXT_BYTES (128) -#define MEMCPY_VECT_INT_BYTES (32) - -/** - * @brief Copy `size` bytes from `src` to `dst`. - * - * `dst` and `src` both must be word-aligned addresses. - * - * `size` need not be an integer number of words. - * - * @param dst [out] Destination address - * @param src [in] Source address - * @param byte_count [in] Number of bytes to be copied - */ -void vpu_memcpy(void *dst, const void *src, size_t byte_count); - -/** - * @brief Copy `size` bytes from `src` to `dst`. - * Faster for copies from internal SRAM. - * - * `dst` and `src` both must be word-aligned addresses. - * - * `size` need not be an integer number of words. - * - * @param dst [out] Destination address - * @param src [in] Source address - * @param byte_count [in] Number of bytes to be copied - */ -void vpu_memcpy_int(void *dst, const void *src, size_t byte_count); - -/** - * @brief Copy `size` bytes from `src` to `dst`. - * Faster for copies from external flash and DDR. - * - * `dst` and `src` both must be word-aligned addresses. - * - * `size` need not be an integer number of words. - * - * @param dst [out] Destination address - * @param src [in] Source address - * @param byte_count [in] Number of bytes to be copied - */ -void vpu_memcpy_ext(void *dst, const void *src, size_t byte_count); - -/** - * @brief Copy `vector_count` multiples of MEMCPY_VECT_EXT_BYTES bytes - * from `src` to `dst`. - * Faster for copies from external flash and DDR. - * - * `dst` and `src` both must be word-aligned addresses. - * - * `size` need not be an integer number of words. - * - * @param dst [out] Destination address - * @param src [in] Source address - * @param vector_count [in] Number of MEMCPY_VECT_EXT_BYTES bytes copies to - * be bytes to be performed - */ -void vpu_memcpy_vector_ext(void *dst, const void *src, int vector_count); - -/** - * @brief Copy `vector_count` multiples of MEMCPY_VECT_INT_BYTES bytes - * from `src` to `dst`. - * Faster for copies from internal SRAM. - * - * `dst` and `src` both must be word-aligned addresses. - * - * `size` need not be an integer number of words. - * - * @param dst [out] Destination address - * @param src [in] Source address - * @param vector_count [in] Number of MEMCPY_VECT_INT_BYTES bytes copies to - * be bytes to be performed - */ -void vpu_memcpy_vector_int(void *dst, const void *src, int vector_count); - -/** - * @brief set `word_count` words from `value` to `dst`. - * - * `dst` must be a word-aligned address. - * - * @param dst [out] Destination address, must be word aligned - * @param value [in] Source value. - * @param size [in] Number of 32 bit words to be copied - */ -void vpu_memset_32(void *dst, const int32_t value, const int word_count); - -#define VPU_MEMSET_VECTOR_WORDS XS3_VPU_VREG_WIDTH_WORDS - -/** - * @brief set `vector_count` vector words from `value` to `dst`. - * - * `dst` must be a word-aligned address. - * - * @param dst [out] Destination address, must be word aligned - * @param value [in] Source value. - * @param vector_count [in] Number of VPU_MEMSET_VECTOR_WORDS words vectors - * to be copied. - */ -void vpu_memset_vector(void *dst, const int32_t value, const int vector_count); - -#ifdef __XC__ -} // extern "C" -#endif - -#endif // NN_OP_UTILS_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_operator.h b/new_ai_tools/include/lib_nn/api/nn_operator.h deleted file mode 100644 index 81741993..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_operator.h +++ /dev/null @@ -1,18 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef NN_OPERATOR_H_ -#define NN_OPERATOR_H_ - -#ifdef __XC__ -extern "C" { -#endif - -#include "nn_layers.h" -#include "nn_op_utils.h" -#include "nn_pooling.h" - -#ifdef __XC__ -} // extern "C" -#endif - -#endif // NN_OPERATOR_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_pooling.h b/new_ai_tools/include/lib_nn/api/nn_pooling.h deleted file mode 100644 index 745fc276..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_pooling.h +++ /dev/null @@ -1,551 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef POOLING_H_ -#define POOLING_H_ - -#include "nn_conv2d_structs.h" -#include "nn_image.h" -#include "nn_types.h" -#include "nn_window_params.h" - -typedef nn_window_op_job_params_t nn_conv2d_job_params_t; - -/** - * Flags used with maxpool2d_ext() for advanced scenarios. - */ -typedef enum { - /** - * Placeholder flag used to indicate no other flags are needed. - */ - MAXPOOL2D_FLAG_NONE = 0, -} nn_maxpool2d_flags_e; - -/** - * Flags used with avgpool2d_ext() for advanced scenarios. - */ -typedef enum { - /** - * Placeholder flag used to indicate no other flags are needed. - */ - AVGPOOL2D_FLAG_NONE = 0, -} nn_avgpool2d_flags_e; - -/** - * Flags used with avgpool2d_global_ext() for advanced scenarios. - */ -typedef enum { - /** - * Placeholder flag used to indicate no other flags are needed. - */ - AVGPOOL2D_GLOBAL_FLAG_NONE = 0, -} nn_avgpool2d_global_flags_e; - -// /** -// * @brief Initialize an instance of the @oper{avgpool2d_global} operator. -// * -// * See @oper_ref{avgpool2d_global} for more details about the -// @oper{avgpool2d_global} operator. To invoke a -// * @oper{avgpool2d_global} job, call avgpool2d_global(). -// * -// * When avgpool2d_global() is called, a plan (`nn_avgpool2d_global_plan_t`) -// and a job (`nn_avgpool2d_global_job_t`) must -// * be supplied to tell it how to do its work. This function initializes that -// plan and one or more jobs to be supplied in -// * subsequent calls to avgpool2d_global(). -// * -// * A plan contains information shared by all jobs of an instance of -// @oper{avgpool2d_global}. Each job computes a range -// * of channels in the output vector (possibly the entire vector). -// * -// * `plan` points to the plan to be initialized. It need only be initialized -// once for many calls to avgpool2d_global(). -// * -// * `jobs` points to an array of `nn_avgpool2d_global_job_t` to be -// initialized. Each element represents one job. There -// * should be `job_count` elements in the array. -// * -// * `x_params` points to the image parameters for the instance's input (and -// output) image @tensor{X}. -// * -// * `job_params` points to either an array of -// `nn_avgpool2d_global_job_params_t` structs or else is `NULL`. A -// * `job_params` value of `NULL` indicates that there will only be a single -// job which computes the entire output image. -// * If `job_params` is `NULL`, then `job_count` must be `1`. If `job_params` -// is not `NULL`, it must point to an array -// * of `job_count` `nn_avgpool2d_global_job_params_t` elements. -// * -// * In particular, job `k` will compute the output elements @math{y[p]} for -// which: -// * @inlinecode -// * job_params[k].start_channel <= p < job_params[k].start_channel + -// job_params[k].out.channels -// * @endinlinecode -// * -// * `job_count` indicates the number of jobs to be initialized (and thus the -// number of elements in the `jobs` array), as -// * well the number of elements in the `job_params` array if it is not `NULL`. -// * -// * @param plan [out] The plan to be initialized. -// * @param jobs [out] Array of jobs to be initialized. -// * @param x_params [in] Parameters describing the shape of each input -// (and output) image tensor @tensor{X}. -// * @param job_params [in] An array of -// `nn_avgpool2d_global_job_params_t` structs, or NULL -// * @param job_count [in] The number of jobs to be initialized. -// */ -// void avgpool2d_global_init( -// nn_avgpool2d_global_plan_t* plan, -// nn_avgpool2d_global_job_t* jobs, -// const nn_image_params_t* x_params, -// const nn_avgpool2d_global_job_params_t* job_params, -// const unsigned job_count); - -/** - * @brief Invoke @oper{maxpool2d} job. - * - * See @oper_ref{maxpool2d} for more details about the @oper{maxpool2d} - * operator. - * - * @par Parameter Details - * - * `Y` points to the output image @tensor{Y} with shape @tensor_shape{Y_h, Y_w, - * X_c}. - * - * `X` points to the input image @tensor{X} with shape @tensor_shape{X_h, X_w, - * X_c}. - * - * The memory layout of @tensor{Y} and @tensor{X} are the standard memory layout - * for image tensors (see @ref standard_layout). - * - * `x_params` points to the image parameters describing the shape of the input - * image @tensor{X}. The size of each of - * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to - * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. - * - * `y_params` points to the image parameters describing the shape of the output - * image @tensor{Y}. The size of each of - * @tensor{Y}'s dimensions, @math{Y_h}, @math{Y_w}, and @math{X_c} correspond to - * `y_params->height`, `y_params->width`, and `x_params->channels` respectively. - * - * `pooling_window` points to a `nn_window_params_t` struct containing the - * instance's @math{W_h}, @math{W_w}, - * @math{W_{vert}}, @math{W_{hori}}, @math{W_{r0}} and @math{W_{c0}} - * hyperparameters (see @ref maxpool2d_hyperparameters) which describe the - * spacial relationship between the input image, the pooling window window and - * the output image. `pooling_window->dilation` is ignored. - * - * `pooling_window->shape` specifies @math{W_h} and @math{W_w}, the height and - * width of the pooling window. - * - * `pooling_window->start` specifies @math{W_{r0}} and @math{W_{c0}}, the - * starting row and column of the pooling window in @tensor{X}'s coordinate - * space. For example, a `start` value of `(0,0)` indicates that the top-left - * pixel of the output image has the pooling window aligned with the top-left - * corner of the input image, with no implied padding at the top or left sides - * of the input image. A `start` value of `(1,1)`, on the other hand, indicates - * that the top-left pixel of the output image has the pooling window shifted - * one pixel right and one pixel down relative to the top-left corner of the - * input image. - * - * `pooling_window->stride.horizontal` specifies @math{W_{vert}} and - * @math{W_{hori}}, the vertical and horizontal strides of the pooling window. - * The strides describe the number of pixels the pooling window moves (across - * the input image) with each pixel in the output image. - * - * @par Parameter Constraints - * - * The arguments `Y` and `X` must each point to a word-aligned address. - * - * The input and output images must have the same number of channels. As such, - * it is required that `y_params->channels == x_params->channels`. - * - * Due to memory alignment requirements, @math{X_c} must be a multiple of - * @math{4}, which forces all pixels to begin at word-aligned addresses. - * - * Padding is not supported by this operator. - * - * @par Splitting the Workload - * - * See maxpool2d_ext() for more advanced scenarios which allow the the work to - * be split across multiple invocations (which can be parallelized across - * cores). - * - * @par Additional Remarks - * - * Internally, maxpool2d() calls maxpool2d_ext() with a `job_params` argument - * that computes the entire output image, and with no flags set. For more - * advanced scenarios, use maxpool2d_ext(). - * - * By default this operator uses the standard 8-bit limits @math([-128, 127]) - * when applying saturation logic. Instead, it can be configured to use - * symmetric saturation bounds @math([-127, 127]) by defining - * `CONFIG_SYMMETRIC_SATURATION_maxpool2d` appropriately. See @ref nn_config.h - * for more details. Note that this configures _all_ instances of the - * @oper{maxpool2d} operator. - * - * If @math{X_c} is not a multiple of @math{32}, this operator may read up to 28 - * bytes following the end of @tensor{X}. This is not ordinarily a problem. - * However, if the object to which `X` points is located very near the end of a - * valid memory address range, it is possible memory access exceptions may occur - * when this operator is invoked. - * - * If necessary, this can be avoided by manually forcing a buffer region (no - * more than @math{28} bytes are necessary) following @tensor{X}. There are - * various ways this can be accomplished, including embedding these objects in - * larger structures. - * - * @param[out] Y The output image @tensor{Y} - * @param[in] X The input image @tensor{X} - * @param[in] x_params Parameters describing the shape of input image - * tensor @tensor{X} - * @param[in] y_params Parameters describing the shape of output image - * tensor @tensor{Y} - * @param[in] pooling_window Parameters describing the relationship between - * the pooling window, the input image, and the output image - */ -void maxpool2d(nn_image_t* Y, const nn_image_t* X, - const nn_image_params_t* x_params, - const nn_image_params_t* y_params, - const nn_window_params_t* pooling_window); - -/** - * @brief Invoke @oper{maxpool2d} job. - * - * See @oper_ref{maxpool2d} for more details about the @oper{maxpool2d} - * operator. - * - * @par Parameter Details - * - * `Y` points to the output image @tensor{Y} with shape @tensor_shape{Y_h, Y_w, - * X_c}. - * - * `X` points to the input image @tensor{X} with shape @tensor_shape{X_h, X_w, - * X_c}. - * - * The memory layout of @tensor{Y} and @tensor{X} are the standard memory layout - * for image tensors (see @ref standard_layout). - * - * `x_params` points to the image parameters describing the shape of the input - * image @tensor{X}. The size of each of - * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to - * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. - * - * `y_params` points to the image parameters describing the shape of the output - * image @tensor{Y}. The size of each of - * @tensor{Y}'s dimensions, @math{Y_h}, @math{Y_w}, and @math{X_c} correspond to - * `y_params->height`, `y_params->width`, and `x_params->channels` respectively. - * - * `pooling_window` points to a `nn_window_params_t` struct containing the - * instance's @math{W_h}, @math{W_w}, - * @math{W_{vert}}, @math{W_{hori}}, @math{W_{r0}} and @math{W_{c0}} - * hyperparameters (see @ref maxpool2d_hyperparameters) which describe the - * spacial relationship between the input image, the pooling window window and - * the output image. `pooling_window->dilation` is ignored. - * - * `pooling_window->shape` specifies @math{W_h} and @math{W_w}, the height and - * width of the pooling window. - * - * `pooling_window->start` specifies @math{W_{r0}} and @math{W_{c0}}, the - * starting row and column of the pooling window in @tensor{X}'s coordinate - * space. For example, a `start` value of `(0,0)` indicates that the top-left - * pixel of the output image has the pooling window aligned with the top-left - * corner of the input image, with no implied padding at the top or left sides - * of the input image. A `start` value of `(1,1)`, on the other hand, indicates - * that the top-left pixel of the output image has the pooling window shifted - * one pixel right and one pixel down relative to the top-left corner of the - * input image. - * - * `pooling_window->stride.horizontal` specifies @math{W_{vert}} and - * @math{W_{hori}}, the vertical and horizontal strides of the pooling window. - * The strides describe the number of pixels the pooling window moves (across - * the input image) with each pixel in the output image. - * - * `job_params` describes which elements of the output image will be computed by - * this invocation. This invocation computes the output elements @math{Y[r,c,p]} - * for which: - * @inlinecode - * job_params->start.rows <= r < job_params->start.rows + - * job_params->size.rows job_params->start.cols <= c < job_params->start.cols + - * job_params->size.cols job_params->start.channels <= p < - * job_params->start.channels + job_params->size.channels - * @endinlinecode - * - * `flags` is a collection of flags which modify the behavior of - * @oper{maxpool2d}. See `nn_maxpool2d_flags_e` for a description of each flag. - * `MAXPOOL2D_FLAG_NONE`(0) can be used for default behavior. - * - * @par Parameter Constraints - * - * The arguments `Y` and `X` must each point to a word-aligned address. - * - * The input and output images must have the same number of channels. As such, - * it is required that `y_params->channels == x_params->channels`. - * - * Due to memory alignment requirements, @math{X_c} must be a multiple of - * @math{4}, which forces all pixels to begin at word-aligned addresses. - * - * Padding is not supported by this operator. - * - * @par Splitting the Workload - * - * @todo Include information about how to split the work into multiple - * invocations (e.g. for parallelization), particularly any counter-intuitive - * aspects. - * - * @par Additional Remarks - * - * Internally, maxpool2d() calls maxpool2d_ext() with a `job_params` argument - * that computes the entire output image, and with no flags set. For more - * advanced scenarios, use maxpool2d_ext(). - * - * By default this operator uses the standard 8-bit limits @math([-128, 127]) - * when applying saturation logic. Instead, it can be configured to use - * symmetric saturation bounds @math([-127, 127]) by defining - * `CONFIG_SYMMETRIC_SATURATION_maxpool2d` appropriately. See @ref nn_config.h - * for more details. Note that this configures _all_ instances of the - * @oper{maxpool2d} operator. - * - * If @math{X_c} is not a multiple of @math{32}, this operator may read up to 28 - * bytes following the end of @tensor{X}. This is not ordinarily a problem. - * However, if the object to which `X` points is located very near the end of a - * valid memory address range, it is possible memory access exceptions may occur - * when this operator is invoked. - * - * If necessary, this can be avoided by manually forcing a buffer region (no - * more than @math{28} bytes are necessary) following @tensor{X}. There are - * various ways this can be accomplished, including embedding these objects in - * larger structures. - * - * @param[out] Y The output image @tensor{Y} - * @param[in] X The input image @tensor{X} - * @param[in] x_params Parameters describing the shape of input image - * tensor @tensor{X} - * @param[in] y_params Parameters describing the shape of output image - * tensor @tensor{Y} - * @param[in] pooling_window Parameters describing the relationship between - * the pooling window, the input image, and the output image - * @param[in] job_params Indicates which output elements will be computed - * by this invocation - * @param[in] flags Flags which modify the behavior of - * maxpool2d_ext() - */ -void maxpool2d_ext(nn_image_t* Y, const nn_image_t* X, - const nn_image_params_t* x_params, - const nn_image_params_t* y_params, - const nn_window_params_t* window_config, - const nn_window_op_job_params_t* job_params, - const nn_maxpool2d_flags_e flags); - -/** - * @brief Invoke a @oper{avgpool2d_global} job. - * - * The @oper{avgpool2d_global} operator computes a scaled and biased sum of - * pixel values for each channel of an input image, producing an 8-bit vector of - * outputs. - * - * See @oper_ref{avgpool2d_global} for more details about the - * @oper{avgpool2d_global} operator, including the mathematical details of the - * operation performed. - * - * @par Parameter Details - * - * `Y` points to the 8-bit output vector @tensor{y} with length - * @tensor_shape{X_c}. - * - * `X` points to the 8-bit input image @tensor{X} with shape @tensor_shape{X_h, - * X_w, X_c}. The memory layout of - * @tensor{X} is the standard memory layout for image tensors (see @ref - * standard_layout). - * - * `bias` is the 32-bit bias @math{b} with which the accumulators are - * initialized for each output. Note that the right-shift by @math{r} bits is - * applied after all accumulation. To add an absolute offset of @math{b_0} to - * each result, then the value used for @math{b} should be @math{b_0 \cdot 2^r}. - * - * `scale` is the 8-bit coefficient @math{s}. All pixel values are multiplied by - * @math{s} and added to the 32-bit accumulator. - * - * `shift` is the (rounding) right-shift @math{r} applied to each 32-bit - * accumulator to yield an 8-bit result. Note that this is a saturating - * right-shift which will saturate to 8-bit bounds (see additional remarks - * below). - * - * `x_params` points to the image parameters describing the shape of the input - * image @tensor{X}. The size of each of - * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to - * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. - * - * @par Parameter Constraints - * - * The arguments `Y` and `X` must each point to a word-aligned address. - * - * The input and output images must have the same number of channels. As such, - * it is required that `y_params->channels == x_params->channels`. - * - * Due to memory alignment requirements, @math{X_c} must be a multiple of - * @math{4}, which forces all pixels to begin at a word-aligned address. - * - * Padding is not supported by this operator. - * - * @par Splitting the Workload - * - * See avgpool2d_ext() for more advanced scenarios which allow the the work to - * be split across multiple invocations (which can be parallelized across - * cores). - * - * @par Additional Remarks - * - * Internally, avgpool2d() calls avgpool2d_ext() with a `job_params` argument - * that computes the entire output image, and with no flags set. For more - * advanced scenarios, use avgpool2d_ext(). - * - * The arguments `Y` and `X` should both point at the beginning of their - * respective objects, even if the job being invoked does not start at the - * beginning of the output vector. - * - * By default this operator uses the standard 8-bit limits @math([-128, 127]) - * when applying saturation logic. Instead, it can be configured to use - * symmetric saturation bounds @math([-127, 127]) by defining - * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` appropriately. See @ref - * nn_config.h for more details. Note that this configures _all_ instances of - * the @oper{avgpool2d_global} operator. - * - * If @math{X_c} is not a multiple of @math{16}, this operator may read up to 12 - * bytes following the end of @tensor{X}. This is not ordinarily a problem. - * However, if the object to which `X` points is located very near the end of a - * valid memory address range, it is possible memory access exceptions may occur - * when this operator is invoked. - * - * If necessary, this can be avoided by manually forcing a buffer region (no - * more than @math{12} bytes are necessary) following @tensor{X}. There are - * various ways this can be accomplished, including embedding these objects in - * larger structures. - * - * @param[out] Y The output vector @tensor{y} - * @param[in] X The input image @tensor{X} - * @param[in] bias Initial 32-bit accumulator value @math{b}. Shared by - * all channels. - * @param[in] scale The factor @math{s} by which input pixel values are - * scaled. - * @param[in] shift The right-shift @math{r} applied to the 32-bit - * accumulators to yield an 8-bit result. - * @param[in] x_params Parameters describing the shape of input image - * tensor @tensor{X} - */ -void avgpool2d_global(nn_image_t* Y, const nn_image_t* X, const int32_t bias, - const int8_t scale, const uint16_t shift, - const nn_image_params_t* x_params); - -/** - * @brief Invoke a @oper{avgpool2d_global} job. - * - * The @oper{avgpool2d_global} operator computes a scaled and biased sum of - * pixel values for each channel of an input image, producing an 8-bit vector of - * outputs. - * - * See @oper_ref{avgpool2d_global} for more details about the - * @oper{avgpool2d_global} operator, including the mathematical details of the - * operation performed. - * - * @par Parameter Details - * - * `Y` points to the 8-bit output vector @tensor{y} with length - * @tensor_shape{X_c}. - * - * `X` points to the 8-bit input image @tensor{X} with shape @tensor_shape{X_h, - * X_w, X_c}. The memory layout of - * @tensor{X} is the standard memory layout for image tensors (see @ref - * standard_layout). - * - * `bias` is the 32-bit bias @math{b} with which the accumulators are - * initialized for each output. Note that the right-shift by @math{r} bits is - * applied after all accumulation. To add an absolute offset of @math{b_0} to - * each result, then the value used for @math{b} should be @math{b_0 \cdot 2^r}. - * - * `scale` is the 8-bit coefficient @math{s}. All pixel values are multiplied by - * @math{s} and added to the 32-bit accumulator. - * - * `shift` is the (rounding) right-shift @math{r} applied to each 32-bit - * accumulator to yield an 8-bit result. Note that this is a saturating - * right-shift which will saturate to 8-bit bounds (see additional remarks - * below). - * - * `x_params` points to the image parameters describing the shape of the input - * image @tensor{X}. The size of each of - * @tensor{X}'s dimensions, @math{X_h}, @math{X_w}, and @math{X_c} correspond to - * `x_params->height`, `x_params->width`, and `x_params->channels` respectively. - * - * `chan_start` is the first output channel to be computed by this invocation. - * - * `chan_count` is the number of channels to be computed by this invocation. - * - * `flags` is a collection of flags which modify the behavior of - * @oper{avgpool2d_global}. See `nn_avgpool2d_flags_e` for a description of each - * flag. `AVGPOOL2D_GLOBAL_FLAG_NONE`(0) can be used for default behavior. - * - * @par Parameter Constraints - * - * The arguments `Y` and `X` must each point to a word-aligned address. - * - * The input and output images must have the same number of channels. As such, - * it is required that `y_params->channels == x_params->channels`. - * - * Due to memory alignment requirements, @math{X_c} must be a multiple of - * @math{4}, which forces all pixels to begin at a word-aligned address. - * - * Padding is not supported by this operator. - * - * @par Splitting the Workload - * - * @todo Include information about how to split the work into multiple - * invocations (e.g. for parallelization), particularly any counter-intuitive - * aspects. - * - * @par Additional Remarks - * - * Internally, avgpool2d() calls avgpool2d_ext() with a `job_params` argument - * that computes the entire output image, and with no flags set. For more - * advanced scenarios, use avgpool2d_ext(). - * - * The arguments `Y` and `X` should both point at the beginning of their - * respective objects, even if the job being invoked does not start at the - * beginning of the output vector. - * - * By default this operator uses the standard 8-bit limits @math([-128, 127]) - * when applying saturation logic. Instead, it can be configured to use - * symmetric saturation bounds @math([-127, 127]) by defining - * `CONFIG_SYMMETRIC_SATURATION_avgpool2d_global` appropriately. See @ref - * nn_config.h for more details. Note that this configures _all_ instances of - * the @oper{avgpool2d_global} operator. - * - * If @math{X_c} is not a multiple of @math{16}, this operator may read up to 12 - * bytes following the end of @tensor{X}. This is not ordinarily a problem. - * However, if the object to which `X` points is located very near the end of a - * valid memory address range, it is possible memory access exceptions may occur - * when this operator is invoked. - * - * If necessary, this can be avoided by manually forcing a buffer region (no - * more than @math{12} bytes are necessary) following @tensor{X}. There are - * various ways this can be accomplished, including embedding these objects in - * larger structures. - * - * @param[out] Y The output vector @tensor{y} - * @param[in] X The input image @tensor{X} - * @param[in] bias Initial 32-bit accumulator value @math{b}. Shared by - * all channels. - * @param[in] scale The factor @math{s} by which input pixel values are - * scaled. - * @param[in] shift The right-shift @math{r} applied to the 32-bit - * accumulators to yield an 8-bit result. - * @param[in] x_params Parameters describing the shape of input image - * tensor @tensor{X} - */ -void avgpool2d_global_ext(nn_image_t* Y, const nn_image_t* X, - const int32_t bias, const int8_t scale, - const uint16_t shift, - const nn_image_params_t* x_params, - const unsigned chan_start, const unsigned chan_count, - const nn_avgpool2d_global_flags_e flags); - -#endif // POOLING_H_ diff --git a/new_ai_tools/include/lib_nn/api/nn_types.h b/new_ai_tools/include/lib_nn/api/nn_types.h deleted file mode 100644 index c67a09ff..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_types.h +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef NN_TYPES_H_ -#define NN_TYPES_H_ - -#include - -/** - * Padding modes - */ -typedef enum { - PADDING_VALID = 0, - PADDING_SAME = 1, -} padding_mode_t; - -/** - * Where functions in the API take an array of values, where the array index - * corresponds to a padding direction, the indices shall be as indicated in - * `nn_pad_index_t`, with the order being top padding, left padding, bottom - * padding, right padding. Rather than hard-coding those values, this enum can - * be used instead. - */ -enum { - NN_PAD_TOP = 0, - NN_PAD_LEFT, - NN_PAD_BOTTOM, - NN_PAD_RIGHT, -}; - -/** - * Alias for `uint16_t`. - * - * `data16_t` is used to hint that a struct field or function parameter is - * opaque 16-bit data. - */ -typedef uint16_t data16_t; - -/** - * Alias for `int8_t`. - * - * `nn_tensor_t*` is used to hint that a struct field or function parameter is - * to be interpreted as a tensor. - */ -typedef int8_t nn_tensor_t; - -/** - * Alias for `int8_t`. - * - * `nn_image_t*` is used to hint that a struct field or function parameter will - * be interpreted as an image-like tensor. - */ -typedef nn_tensor_t nn_image_t; - -/** - * Alias for `int32_t`. - * - * `mem_stride_t` is used to hint that a struct field or function parameter - * indicates the signed offset between memory addresses, expressed in bytes. - */ -typedef int32_t mem_stride_t; - -/** - * Alias for `unsigned`. - * - * `channel_count_t` is used to hint that a struct field or function parameter - * indicates a number of channels. - */ -typedef uint32_t channel_count_t; - -/** - * - * Function pointer to unary op kernel functions for int16 - */ -typedef void (*UnaryI16FnType)(void *output, void *input, int tensor_length, void *blob); - - -/** - * - * Function pointer to binary op kernel functions for int16 - */ -typedef void (*BinaryI16FnType)(void *output, void *input1, void *input2, int tensor_length, void *blob); - -#endif // NN_TYPES_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/nn_window_params.h b/new_ai_tools/include/lib_nn/api/nn_window_params.h deleted file mode 100644 index b2ee7760..00000000 --- a/new_ai_tools/include/lib_nn/api/nn_window_params.h +++ /dev/null @@ -1,55 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef STRUCTS_H_ -#define STRUCTS_H_ - -#include - -/** - * This struct represents an indexing vector for an image. - */ -typedef struct { - /** Number of image pixel rows */ - int32_t rows; - /** Number of image pixel columns */ - int32_t cols; - /** Number of image pixel channels */ - int32_t channels; -} nn_image_vect_t; - -/** - * Some of the functions in this API can have their work split into - * multiple parts, each called a "job". This is useful, for example, - * for parallelizing a computation across multiple cores, or to reduce - * the delay between which the calling function can service some other - * resource. - * - * This struct contains the parameters required to specify how work - * can be split according to the region of the output image in which - * the job operates. - * - * For an output image `Y` with shape `(Y_height, Y_width, Y_chans)`, - * the value of this struct indicates that a particular job should - * compute the output values in the rectangular region - * `Y[ start.rows : (start.rows + size.rows), - * start.cols : (start.cols + size.cols), - * start.channels : (start.channels + size.channels) ]` - */ -typedef struct { - /** - * Indices in an output image at which to begin producing output. - * - * Typically channels must be a multiple of 4. - */ - nn_image_vect_t start; - - /** - * The number of rows, columns and channels of output to produce. - * - * Typically channels must be a multiple of 4. - */ - nn_image_vect_t size; - -} nn_window_op_job_params_t; - -#endif // STRUCTS_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h deleted file mode 100644 index 298ffedc..00000000 --- a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16.h +++ /dev/null @@ -1,54 +0,0 @@ -#ifndef _output_transform_fn_int16_h_ -#define _output_transform_fn_int16_h_ - -#include - -typedef struct { - int32_t output_slice_channel_count; -} otfn_int16_params_t; - - -/** Function that transform a ring buffer accumulator - * into a vector of 16 bit numbers after multiplying and scaling. - * In the name of efficiency the inputs shoudl be provided in the following - * order (K indicates the value that affects output channel K): - * - * vD/vR: 0, 2, 1, 3, 4, 6, 5, 7, 8, 10, 9, 11, 12, 14, 13, 15. - * (each second and third element are swapped) - * If only few channels are being used, eg two, they - * must be: 0, X, 1, X, X, X, X, X X, X, X, X X, X, X, X. - * - * mul_add: m1, m3, m5, m7, m9, m11, m13, m15, - * a1, a3, a5, a7, a9, a11, a13, a15, - * m0, m2, m4, m6, m8, m10, m12, m14 - * a0, a2, a4, a6, a8, a10, a12, a14. - * If only few channels are being used, eg two, they - * must be: m1, X, X, X, X, X, X, X, a1, X, X, X, X, X, X, X, - * m0, X, X, X, X, X, X, X, a0, X, X, X, X, X, X, X. - * - * output: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 - * Only the channels used are written, eg, for two channels - * 0, 1. - * - * - * - * \param vDvR Pointer to 32 shorts storing 16 upper halfs - * of the ring buffer, and 16 lower halfs in that - * order - * - * \param mul_add Pointer to four vectors of length N/2 integers, the first - * and third vector are multipliers (Q2.30), the second - * and fourth vectors are adders (Q16.16). - * - * \param output Pointer to the desired place where N values will be - * outputted - * - * \param N Number of vector elements to process; N <= 16. - */ -extern int16_t *output_transform_fn_int16(otfn_int16_params_t *params, - int16_t *output, - int16_t *vDvR, - int32_t output_channel_group, - int32_t *mul_add); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h deleted file mode 100644 index 0fc13fb3..00000000 --- a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_kernel_transform.h +++ /dev/null @@ -1,37 +0,0 @@ -#ifndef _output_transform_fn_int16_kernel_transform_h_ -#define _output_transform_fn_int16_kernel_transform_h_ - -#include - -/** - * Function that performs all the transformations needed for a 16-bit convolution - * 1. It transforms the weight ordering to reverse the weights in groups of 16 in preparation of VLMACCR shift-and-rotate - * 2. It convers the channel multpliers to integer multipliers - * 3. It interleaves the channel multpliers and channel biases into a single blob - * The number of kernels is assumed to be input_channels x output_channels elements - * The number of bias terms and multipliers is assumed to be output_channels - * The number of elements in the array mul_add_out should be 2xoutput_channels - * The number of elements in the kernel_weights_out array should be input_channels x output_channels - * - * @param kernel_weights_in kernel weights input - * - * @param channel_multpliers_in per-channel multipliers. - * - * @param channel_bias_terms_in per-channel bias terms. - * - * @param kernel_weights_out reordered kernel weights - * - * @param mul_add_out mixed per-channel multipliers and bias-terms - * - * @param input_channels number of input channels - * - * @param output_channels number of output_channels - */ - -extern void output_transform_fn_int16_kernel_transform( - const int8_t *kernel_weights_in, - const float *channel_multipliers_in, const int *channel_bias_terms_in, - int8_t *kernel_weights_out, int32_t *mul_add_out, - int input_channels, int output_channels); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h b/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h deleted file mode 100644 index b7500f4b..00000000 --- a/new_ai_tools/include/lib_nn/api/output_transform_fn_int16_mappings.h +++ /dev/null @@ -1,13 +0,0 @@ -#ifndef _output_transform_fn_int16_mappings_h_ -#define _output_transform_fn_int16_mappings_h_ - -// This defines the mapping of the output transform multipliers from output channels -extern int ot_int16_mul_index_used_for_output[]; - -// This defines the mapping of the output transform biases from output channels -extern int ot_int16_add_index_used_for_output[]; - -// This defines the kernel mapping from output channels -extern int aggr_ot_int16_input_channel_used_for_output[]; - -#endif diff --git a/new_ai_tools/include/lib_nn/api/quadratic_approximation.h b/new_ai_tools/include/lib_nn/api/quadratic_approximation.h deleted file mode 100644 index 3d6bc92c..00000000 --- a/new_ai_tools/include/lib_nn/api/quadratic_approximation.h +++ /dev/null @@ -1,83 +0,0 @@ -#ifndef _quadratic_approximation_h_ -#define _quadratic_approximation_h_ - -#ifdef __xcore__ -#define ACTIVATION_FUNCTION __attribute__(( fptrgroup("activation_functions") )) -#else -#define ACTIVATION_FUNCTION /**/ -#endif - -#include "nn_api.h" -#include - -#define QUADRATIC_APPROXIMATION_MAX_CHUNKS 2048 - -/** Type that stores an approximation table. - * Must be stored 64-bit aligned when presented to assembly code. - * Use: - * * ``quadratic_function_table_number_bytes()`` to query the size - * * ``quadratic_function_table_bytes()`` to obtain a pointer to the table - */ -struct quadratic_function_table { - struct { // The order matters - this is how the assembly code expects them - int32_t c; - int8_t a; - int8_t padding; - int16_t b; - } coefficients[QUADRATIC_APPROXIMATION_MAX_CHUNKS]; - int data_bytes; -}; - -typedef struct quadratic_function_table quadratic_function_table_t; - -/* Function pointer type - any float -> float - */ -typedef float (*float_function_t)(float x); - -/** Function that builds a quadratic approximation table - * The function passed in must be monotinuous - * Any number of chunks will work but the assembly implementiaton assumes 128. - * The function returns a table pointer, and through two arguments the max error, - * and the sqrt of the sum of squared errors as a goodness metric. - * - * \param table interpolation table to be filled in - * \param av function to be interpolated - * \param input_scaler scale that is applied to the input, eg 8.0/32768.0 - * \param outptu_scaler scale that is applied to the output, eg 32768.0 - * \param chunks number of interpolations. Set to 128. - * \param max_error maximum error, ought to be 1 - * \param error sqrt of sum of squared errors. - */ -C_API void quadratic_approximation_generator( - quadratic_function_table_t *table, - ACTIVATION_FUNCTION float_function_t av, - double input_scaler, - double output_scaler, - int chunks, - int *max_error, - double *error); - -/** Function that returns the number of bytes in an approximation table - * - * \param x the table - * \returns The number of bytes in the table - */ -C_API uint32_t quadratic_function_table_number_bytes(quadratic_function_table_t *x); - - -/** Function that returns a pointer to the bytes in an approximation table - * - * \param x the table - * \returns Pointer to the bytes in the table - */ -C_API uint8_t *quadratic_function_table_bytes(quadratic_function_table_t *x); - -/** Example functions that can be passed in - */ -C_API float approximation_function_tanh(float x); -C_API float approximation_function_logistics(float x); -C_API float approximation_function_elu(float x); -C_API float approximation_function_relu(float x); -C_API float approximation_function_relu6(float x); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h b/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h deleted file mode 100644 index 836c83a7..00000000 --- a/new_ai_tools/include/lib_nn/api/quadratic_interpolation.h +++ /dev/null @@ -1,23 +0,0 @@ -#ifndef _quadratic_interpolation_h_ -#define _quadratic_interpolation_h_ - -#include - -/** Function that performs a quadratic interpolation on a vector of inputs given a table - * of coefficients - * - * \param outputs Output vector, 16-bit signed integers - * - * \param inputs Output vector, 16-bit signed integers - * - * \param coefficients The bytes comprising the table of coefficients produced with - * ``quadratic_approximation_generator()``. - * Must be 64-bit aligned. - * - * \param N Number of 16-bit elements in the vector - */ -extern void quadratic_interpolation_128(int16_t *outputs, int16_t *inputs, - uint8_t *coeffs, - uint32_t N); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/quantize_int16.h b/new_ai_tools/include/lib_nn/api/quantize_int16.h deleted file mode 100644 index 3678ee52..00000000 --- a/new_ai_tools/include/lib_nn/api/quantize_int16.h +++ /dev/null @@ -1,22 +0,0 @@ -#ifndef _quantize_int16_h_ -#define _quantize_int16_h_ - -/** - * Function that implements quantization of a 16-bit tensor to a 32-bit tensor. - * The blob must have been created by a call to ``quantize_int16_blob()`` - * - * @param output Output tensor - * - * @param input Input tensor - * Must be word-aligned - * - * @param blob Transformed constant input tensor - * Must be word-aligned - * - * @param tensor_length Number of elements in the tensor (product of all dimensions) - * There are no constraints on this number. - */ -void quantize_int16_tensor(int16_t *output, float *input, - int tensor_length, void *blob); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h b/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h deleted file mode 100644 index c1b3d89e..00000000 --- a/new_ai_tools/include/lib_nn/api/quantize_int16_transform.h +++ /dev/null @@ -1,33 +0,0 @@ -#ifndef _quantize_int16_transform_h_ -#define _quantize_int16_transform_h_ - -#include "nn_api.h" -#include - -/** - * Function that performs the compile time transformation of an int16 addition - * between two tensors. - * - * this function should be - * called at compile-time, and at run-time the output of this function shall be passed - * as the second input tensor of ``quantize_int16_tensor`` - * - * @param output Output of the function; a blob of - * ``QUANTIZE_INT16_TENSOR_BYTES()`` bytes. - * Must be word-aligned. - * - * @param output_scaler Quantisation scaler for the output - * - * @returns 1 on success, 0 on fail (fallback required) - */ -C_API int quantize_int16_tensor_blob(void *output, - float output_scaler); - -/** - * Macro that calculates the number of int16_t that should be allocated to - * store the output of ``quantize_int16_tensor_blob()`` - */ -#define QUANTIZE_INT16_TENSOR_BYTES() (1 * sizeof(float)) - - -#endif diff --git a/new_ai_tools/include/lib_nn/api/version.h b/new_ai_tools/include/lib_nn/api/version.h deleted file mode 100644 index d59e5842..00000000 --- a/new_ai_tools/include/lib_nn/api/version.h +++ /dev/null @@ -1,13 +0,0 @@ -// Copyright (c) 2024, XMOS Ltd, All rights reserved -#ifndef LIB_NN_VERSION_H_ -#define LIB_NN_VERSION_H_ - -namespace lib_nn { - -static const unsigned major_version = 0; -static const unsigned minor_version = 5; -static const unsigned patch_version = 0; - -} // namespace lib_nn - -#endif // LIB_NN_VERSION_H_ diff --git a/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h b/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h deleted file mode 100644 index fa8ae950..00000000 --- a/new_ai_tools/include/lib_nn/api/vpu_memmove_word_aligned.h +++ /dev/null @@ -1,15 +0,0 @@ -#ifndef _vpu_memmove_word_aligned_h_ -#define _vpu_memmove_word_aligned_h_ - -/** - * Function that copies a block of memory. Both source and destination - * address must be word aligned. Any number of bytes can be copied. There - * may be an overlap between the destination and source. - * - * @param dst Destination address, must be word aligned. - * @param src Source address, must be word aligned. - * @param byte_count Number of bytes to copy - may be zero - */ -void vpu_memmove_word_aligned(void * dst, const void * src, unsigned int byte_count); - -#endif diff --git a/new_ai_tools/include/lib_nn/api/vpu_memset_256.h b/new_ai_tools/include/lib_nn/api/vpu_memset_256.h deleted file mode 100644 index 187c4b7b..00000000 --- a/new_ai_tools/include/lib_nn/api/vpu_memset_256.h +++ /dev/null @@ -1,55 +0,0 @@ -#include - -#ifndef _vpu_memset_256_h_ -#define _vpu_memset_256_h_ - -/** - * Function that replicates a vector. The source address must be word - * aligned, the destination address is assumed to be aligned with the - * replication pattern in the source. Any number of bytes can be copied. - * There should not be an overlap between the destination and source. - * - * It is assumed that the source address contains 32 replicated bytes (if - * the destination address is byte aligned), or that it contains 16 - * replicated shorts (if the destination address is 16-bit aligned), or - * that it contains 8 replicated ints. - * - * broadcast_32_to_256() and BROADCAST_8_TO_32() cane be used to - * create the source vector - * - * @param dst Destination address - * @param src Source address, must be word aligned. - * @param byte_count Number of bytes to copy - may be zero - */ -void vpu_memset_256(void *dst, const void *src, unsigned int byte_count); - -/** - * Function that replicates an int over a vector. The vector must be - * aligned on an 8-byte boundary. In order to replicate a byte or short over - * a vector, combine this with a call to BROADCAST_8_TO_32() or - * BROADCAST_16_TO_32(). Declare the vector as a uint64_t x[] in order to - * guarantee 8-byte alignement. - * - * @param dst Destination address, must be 8-byte aligned - * @param from Value to be replicated - */ -void broadcast_32_to_256(void *dst, uint32_t from); - -/** - * Macro that replicates a byte over an int. - * Use with broadcast_32_to_256() in order to replicate a byte over a vector - */ -#define BROADCAST_8_TO_32(f) (((uint8_t)f) * 0x01010101) - -/** - * Macro that replicates a short over an int - * Use with broadcast_32_to_256() in order to replicate a short over a vector - */ -#define BROADCAST_16_TO_32(f) (((uint16_t)f) * 0x00010001) - -/** - * Macro that replicates a byte over a short - */ -#define BROADCAST_8_TO_16(f) (((uint8_t)f) * 0x00000101) - -#endif diff --git a/new_ai_tools/include/lib_nn/api/vpu_sim.h b/new_ai_tools/include/lib_nn/api/vpu_sim.h deleted file mode 100644 index 8a3e6fd5..00000000 --- a/new_ai_tools/include/lib_nn/api/vpu_sim.h +++ /dev/null @@ -1,119 +0,0 @@ -// Copyright 2020 XMOS LIMITED. This Software is subject to the terms of the -// XMOS Public License: Version 1 - -#ifndef LIB_NN_VPU_SIM_H_ -#define LIB_NN_VPU_SIM_H_ - -#include -#include -#include - -#include "nn_types.h" -#include "xs3_vpu.h" - -C_API -typedef union { - uint8_t u8[VPU_INT8_EPV]; - int8_t s8[VPU_INT8_EPV]; - - uint16_t u16[VPU_INT16_EPV]; - int16_t s16[VPU_INT16_EPV]; - - uint32_t u32[VPU_INT32_EPV]; - int32_t s32[VPU_INT32_EPV]; -} vpu_vector_t; - -C_API -typedef enum { - MODE_S32 = 0x00, - MODE_S16 = 0x100, - MODE_S8 = 0x200, - MODE_S16x8 = 0x400, -} vector_mode; - -C_API -typedef struct { - vector_mode mode; - vpu_vector_t vR; - vpu_vector_t vD; - vpu_vector_t vC; -} xs3_vpu; - -C_API void VSETC(xs3_vpu* vpu, const vector_mode mode); -C_API void VCLRDR(xs3_vpu* vpu); -C_API void VLDR(xs3_vpu* vpu, const void* addr); -C_API void VLDD(xs3_vpu* vpu, const void* addr); -C_API void VLDC(xs3_vpu* vpu, const void* addr); -C_API void VSTR(const xs3_vpu* vpu, void* addr); -C_API void VSTD(const xs3_vpu* vpu, void* addr); -C_API void VSTC(const xs3_vpu* vpu, void* addr); -C_API void VSTRPV(const xs3_vpu* vpu, void* addr, unsigned mask); -C_API void VLMACC(xs3_vpu* vpu, const void* addr); -C_API void VLMACCR(xs3_vpu* vpu, const void* addr); -C_API void VLMACCR1(xs3_vpu* vpu, const void* addr); -C_API void VPOS(xs3_vpu* vpu); -C_API void VLSAT(xs3_vpu* vpu, const void* addr); -C_API void VLSAT_FIXED(xs3_vpu* vpu, const void* addr); -C_API void VLASHR(xs3_vpu* vpu, const void* addr, const int32_t shr); -C_API void VLADD(xs3_vpu* vpu, const void* addr); -C_API void VLSUB(xs3_vpu* vpu, const void* addr); -C_API void VLMUL(xs3_vpu* vpu, const void* addr); -C_API void VDEPTH1(xs3_vpu* vpu); -C_API void VDEPTH8(xs3_vpu* vpu); -C_API void VDEPTH16(xs3_vpu* vpu); - -/** Print vector register contents based on current vector_mode **/ -C_API void vpu_accu_print(xs3_vpu* vpu); -C_API void vpu_sim_print(xs3_vpu* vpu); -C_API void vpu_sim_mem_print(void* address, vector_mode mode); - -// Function for implementing the saturation logic within the VPU. -C_API int64_t vpu_saturate(const int64_t input, const unsigned bits); - -// Assert if the memory access is non-word aligned -// void assert_word_aligned(const void* address); -#define assert_word_aligned(address) assert(((uintptr_t)address & 0x3) == 0); - -#ifdef __cplusplus - -namespace nn { - -class VPU { - private: - xs3_vpu vpu; - - public: - vpu_vector_t& vD; - vpu_vector_t& vR; - vpu_vector_t& vC; - vector_mode& mode; - - public: - VPU() : vD(vpu.vD), vR(vpu.vR), vC(vpu.vC), mode(vpu.mode) {} - - /** `mode` should be one of `MODE_S32`, `MODE_S16` or `MODE_S8` */ - void vsetc(const vector_mode mode) { VSETC(&this->vpu, mode); } - void vclrdr() { VCLRDR(&this->vpu); } - void vldr(void const* addr) { VLDR(&this->vpu, addr); } - void vldd(void const* addr) { VLDD(&this->vpu, addr); } - void vldc(void const* addr) { VLDC(&this->vpu, addr); } - void vstr(void* addr) { VSTR(&this->vpu, addr); } - void vstd(void* addr) { VSTD(&this->vpu, addr); } - void vstc(void* addr) { VSTC(&this->vpu, addr); } - void vstrpv(void* addr, uint32_t mask) { VSTRPV(&this->vpu, addr, mask); } - void vlmacc(void const* addr) { VLMACC(&this->vpu, addr); } - void vlmaccr(void const* addr) { VLMACCR(&this->vpu, addr); } - void vlmaccr1(void const* addr) { VLMACCR1(&this->vpu, addr); } - void vlsat(void const* addr) { VLSAT(&this->vpu, addr); } - void vlashr(void const* addr, int32_t shr) { VLASHR(&this->vpu, addr, shr); } - void vladd(void const* addr) { VLADD(&this->vpu, addr); } - void vlsub(void const* addr) { VLSUB(&this->vpu, addr); } - void vlmul(void const* addr) { VLMUL(&this->vpu, addr); } - void vdepth1() { VDEPTH1(&this->vpu); } - void vdepth8() { VDEPTH8(&this->vpu); } - void vdepth16() { VDEPTH16(&this->vpu); } -}; -} // namespace nn - -#endif -#endif // LIB_NN_VPU_SIM_H_ diff --git a/new_ai_tools/include/lib_nn/api/xs3_vpu.h b/new_ai_tools/include/lib_nn/api/xs3_vpu.h deleted file mode 100644 index 7107cb94..00000000 --- a/new_ai_tools/include/lib_nn/api/xs3_vpu.h +++ /dev/null @@ -1,216 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef XS3_VPU_H_ -#define XS3_VPU_H_ - -#include - -#include "nn_api.h" - -#ifndef XS1_VSR_TYPE - -/* TODO use from xs3a_kernel.h in a future tools release */ -#define XS1_VSR_HEADROOM_SHIFT 0x0 -#define XS1_VSR_HEADROOM_SIZE 0x5 -#define XS1_VSR_HEADROOM_MASK \ - (((1 << XS1_VSR_HEADROOM_SIZE) - 1) << XS1_VSR_HEADROOM_SHIFT) -#define XS1_VSR_HEADROOM(x) \ - (((x)&XS1_VSR_HEADROOM_MASK) >> XS1_VSR_HEADROOM_SHIFT) -#define XS1_VSR_HEADROOM_SET(x, v) \ - (((x) & ~XS1_VSR_HEADROOM_MASK) | \ - (((v) << XS1_VSR_HEADROOM_SHIFT) & XS1_VSR_HEADROOM_MASK)) -#define XS1_VSR_SHIFT_SHIFT 0x6 -#define XS1_VSR_SHIFT_SIZE 0x2 -#define XS1_VSR_SHIFT_MASK \ - (((1 << XS1_VSR_SHIFT_SIZE) - 1) << XS1_VSR_SHIFT_SHIFT) -#define XS1_VSR_SHIFT(x) (((x)&XS1_VSR_SHIFT_MASK) >> XS1_VSR_SHIFT_SHIFT) -#define XS1_VSR_SHIFT_SET(x, v) \ - (((x) & ~XS1_VSR_SHIFT_MASK) | \ - (((v) << XS1_VSR_SHIFT_SHIFT) & XS1_VSR_SHIFT_MASK)) -#define XS1_VSR_TYPE_SHIFT 0x8 -#define XS1_VSR_TYPE_SIZE 0x4 -#define XS1_VSR_TYPE_MASK (((1 << XS1_VSR_TYPE_SIZE) - 1) << XS1_VSR_TYPE_SHIFT) -#define XS1_VSR_TYPE(x) (((x)&XS1_VSR_TYPE_MASK) >> XS1_VSR_TYPE_SHIFT) -#define XS1_VSR_TYPE_SET(x, v) \ - (((x) & ~XS1_VSR_TYPE_MASK) | \ - (((v) << XS1_VSR_TYPE_SHIFT) & XS1_VSR_TYPE_MASK)) -#define XS1_VSR_LENGTH_SHIFT 0xc -#define XS1_VSR_LENGTH_SIZE 0x4 -#define XS1_VSR_LENGTH_MASK \ - (((1 << XS1_VSR_LENGTH_SIZE) - 1) << XS1_VSR_LENGTH_SHIFT) -#define XS1_VSR_LENGTH(x) (((x)&XS1_VSR_LENGTH_MASK) >> XS1_VSR_LENGTH_SHIFT) -#define XS1_VSR_LENGTH_SET(x, v) \ - (((x) & ~XS1_VSR_LENGTH_MASK) | \ - (((v) << XS1_VSR_LENGTH_SHIFT) & XS1_VSR_LENGTH_MASK)) - -#endif - -#define XS1_VSETC_SHIFT_NOSHIFT 0x0 -#define XS1_VSETC_SHIFT_SHIFTLEFT 0x1 -#define XS1_VSETC_SHIFT_SHIFTRIGHT 0x2 -#define XS1_VSETC_TYPE_INT32 0x0 -#define XS1_VSETC_TYPE_INT16 0x1 -#define XS1_VSETC_TYPE_INT8 0x2 - -#define XS1_NUM_WORDS_PER_VECTOR 0x8 - -/* End of xs3a_kernel.h */ - -#define XS3_VPU_VREG_WIDTH_BITS (XS1_NUM_WORDS_PER_VECTOR * XS1_ALL_BITS_SIZE) -#define XS3_VPU_VREG_WIDTH_BYTES (XS3_VPU_VREG_WIDTH_BITS >> 3) -#define XS3_VPU_VREG_WIDTH_WORDS (XS3_VPU_VREG_WIDTH_BYTES >> 2) - -#ifndef __ASSEMBLER__ - -C_API enum { - VEC_INT_32 = 0, /**< 0 */ - VEC_INT_16 = 1, /**< 1 */ - VEC_INT_8 = 2, /**< 2 */ - VEC_FLT_32 = 4, /**< 4 */ - VEC_FLT_16 = 5, /**< 5 */ - VEC_FLT_8 = 6, /**< 6 */ -}; - -C_API enum { - VEC_SH0 = 0, /**< 0 */ - VEC_SHL = 1, /**< 1 */ - VEC_SHR = 2, /**< 2 */ -}; - -/** - * The saturation bounds for signed integers in each VPU operating mode. - */ -C_API enum { - VPU_INT8_MAX = 0x7F, /**< 0x7F */ - VPU_INT8_MIN = -0x7F, /**< -0x7F */ - - VPU_INT16_MAX = 0x7FFF, /**< 0x7FFF */ - VPU_INT16_MIN = -0x7FFF, /**< -0x7FFF */ - - VPU_INT32_MAX = 0x7FFFFFFF, /**< 0x7FFFFFFF */ - VPU_INT32_MIN = -0x7FFFFFFF, /**< -0x7FFFFFFF */ -}; - -/** - * Number of accumulator bits in each operating mode. - * - * In each operating mode, the VLMACC, VLMACCR and VLSAT instructions operate on - * an array of accumulators in the vector registers vR and vD. In each case, the - * most significant bits are stored in vD, and the least significant bits are - * stored in vR. - */ -C_API enum { - VPU_INT8_ACC_SIZE = 32, /**< 32 */ - VPU_INT16_ACC_SIZE = 32, /**< 32 */ - VPU_INT32_ACC_SIZE = 40, /**< 40 */ -}; - -/** - * When vD and vR contain accumulators, the values in this enum indicate how - * many least significant bits are stored in vR, with the remaining bits stored - * in vD. - */ -C_API enum { - VPU_INT8_ACC_VR_BITS = 16, /**< 16 */ - VPU_INT16_ACC_VR_BITS = 16, /**< 16 */ - VPU_INT32_ACC_VR_BITS = 32, /**< 32 */ -}; -/** - * When vD and vR contain accumulators, the values in this enum can be used to - * mask off the bits of the accumulator value which correspond to the portion in - * vR. - */ -C_API enum { - VPU_INT8_ACC_VR_MASK = 0xFFFF, /**< 0xFFFF */ - VPU_INT16_ACC_VR_MASK = 0xFFFF, /**< 0xFFFF */ - VPU_INT32_ACC_VR_MASK = 0xFFFFFFFF, /**< 0xFFFFFFFF */ -}; - -/** - * Integer type which fits a single accumulator (32-bits) corresponding to the - * 8-bit VPU mode. - */ -C_API typedef int32_t vpu_int8_acc_t; - -/** - * Integer type which fits a single accumulator (32-bits) corresponding to the - * 16-bit VPU mode. - */ -C_API typedef int32_t vpu_int16_acc_t; - -/** - * Integer type which fits a single accumulator (40-bits) corresponding to the - * 32-bit VPU mode. - */ -C_API typedef int64_t vpu_int32_acc_t; - -/** - * The number of elements which fit into a vector register for each operating - * mode. - * - * This is also the number of elements which are operated on in the following - * instructions: VDEPTH1, VDEPTH16, VDEPTH8, VLADD, VLADDD, VLASHR, VLMACCR, - * VLMUL, VLSUB, VPOS, VSIGN - * - */ -C_API enum { - VPU_INT8_EPV = 32, /**< 32 */ - VPU_INT16_EPV = 16, /**< 16 */ - VPU_INT32_EPV = 8, /**< 8 */ -}; - -/** - * log-base-2 of the corresponding VPU_INT*_EPV values. - */ -C_API enum { - VPU_INT8_EPV_LOG2 = 5, /**< 5 */ - VPU_INT16_EPV_LOG2 = 4, /**< 4 */ - VPU_INT32_EPV_LOG2 = 3, /**< 3 */ -}; - -/** - * The number of accumulators, spread across vR and vD, in each operating mode. - * - * This is also the number of elements consumed (number of multiplies) by the - * VLMACC instruction. - */ -C_API enum { - VPU_BIN_ACC_PERIOD = 16, /**< 16 */ - VPU_INT8_ACC_PERIOD = 16, /**< 16 */ - VPU_INT16_ACC_PERIOD = 16, /**< 16 */ - VPU_INT32_ACC_PERIOD = 8, /**< 8 */ -}; - -/** - * log-base-2 of the corresponding VPU_INT*_ACC_PERIOD values. - */ -C_API enum { - VPU_INT8_ACC_PERIOD_LOG2 = 4, /**< 4 */ - VPU_INT16_ACC_PERIOD_LOG2 = 4, /**< 4 */ - VPU_INT32_ACC_PERIOD_LOG2 = 3, /**< 3 */ -}; - -/** - * The number of elements consumed by a VLMACC instruction in each operating - * mode. In other words, the number of simultaneous multiply-accumulates - * performed by the VLMACC instruction. - */ -C_API enum { - VPU_INT8_VLMACC_ELMS = 16, /**< 16 */ - VPU_INT16_VLMACC_ELMS = 16, /**< 16 */ - VPU_INT32_VLMACC_ELMS = 8, /**< 8 */ -}; - -/** - * log-base-2 of the corresponding VPU_INT*_VLMACC_ELMS values. - */ -C_API enum { - VPU_INT8_VLMACC_ELMS_LOG2 = 4, /**< 4 */ - VPU_INT16_VLMACC_ELMS_LOG2 = 4, /**< 4 */ - VPU_INT32_VLMACC_ELMS_LOG2 = 3, /**< 3 */ -}; - -#endif //__ASM__ - -#endif // XS3_VPU_H_ diff --git a/new_ai_tools/include/lib_nn/api/xs3a_registers.h b/new_ai_tools/include/lib_nn/api/xs3a_registers.h deleted file mode 100644 index 10131171..00000000 --- a/new_ai_tools/include/lib_nn/api/xs3a_registers.h +++ /dev/null @@ -1,2869 +0,0 @@ -/* - * - * Copyright XMOS - (c) 2007 - 2018 - * - * AUTOGENERATED - DO NOT EDIT - * by lib_xmosutils/Scripts/CreateXS1.pl - * - */ -#ifndef _xs3a_registers_h_ -#define _xs3a_registers_h_ -#define XS1_PS_RAM_BASE 0xb -#define XS1_PS_VECTOR_BASE 0x10b -#define XS1_PS_XCORE_CTRL0 0x20b -#define XS1_PS_BOOT_CONFIG 0x30b -#define XS1_PS_BOOT_STATUS 0x40b -#define XS1_PS_SECURITY_CONFIG 0x50b -#define XS1_PS_RING_OSC_CTRL 0x60b -#define XS1_PS_RING_OSC_DATA0 0x70b -#define XS1_PS_RING_OSC_DATA1 0x80b -#define XS1_PS_RING_OSC_DATA2 0x90b -#define XS1_PS_RING_OSC_DATA3 0xa0b -#define XS1_PS_UNAVAILABLE_RESOURCE 0xb0b -#define XS1_PS_RAM_SIZE 0xc0b -#define XS1_PS_ROM_RMA 0xf0b -#define XS1_PS_DBG_SSR 0x100b -#define XS1_PS_DBG_SPC 0x110b -#define XS1_PS_DBG_SSP 0x120b -#define XS1_PS_DBG_T_NUM_NUM 0x13 -#define XS1_PS_DBG_T_NUM 0x130b -#define XS1_PS_DBG_T_REG 0x140b -#define XS1_PS_DBG_TYPE 0x150b -#define XS1_PS_DBG_DATA 0x160b -#define XS1_PS_DBG_RUN_CTRL 0x180b -#define XS1_PS_DBG_SCRATCH_0 0x200b -#define XS1_PS_DBG_SCRATCH_1 0x210b -#define XS1_PS_DBG_SCRATCH_2 0x220b -#define XS1_PS_DBG_SCRATCH_3 0x230b -#define XS1_PS_DBG_SCRATCH_4 0x240b -#define XS1_PS_DBG_SCRATCH_5 0x250b -#define XS1_PS_DBG_SCRATCH_6 0x260b -#define XS1_PS_DBG_SCRATCH_7 0x270b -#define XS1_PS_DBG_IBREAK_ADDR_0 0x300b -#define XS1_NUM_PS_DBG_IBREAK_ADDR 0x4 -#define XS1_PS_DBG_IBREAK_ADDR_1 0x310b -#define XS1_PS_DBG_IBREAK_ADDR_2 0x320b -#define XS1_PS_DBG_IBREAK_ADDR_3 0x330b -#define XS1_PS_DBG_IBREAK_CTRL_0 0x400b -#define XS1_NUM_PS_DBG_IBREAK_CTRL 0x4 -#define XS1_PS_DBG_IBREAK_CTRL_1 0x410b -#define XS1_PS_DBG_IBREAK_CTRL_2 0x420b -#define XS1_PS_DBG_IBREAK_CTRL_3 0x430b -#define XS1_PS_DBG_DWATCH_ADDR1_0 0x500b -#define XS1_NUM_PS_DBG_DWATCH_ADDR1 0x4 -#define XS1_PS_DBG_DWATCH_ADDR1_1 0x510b -#define XS1_PS_DBG_DWATCH_ADDR1_2 0x520b -#define XS1_PS_DBG_DWATCH_ADDR1_3 0x530b -#define XS1_PS_DBG_DWATCH_ADDR2_0 0x600b -#define XS1_NUM_PS_DBG_DWATCH_ADDR2 0x4 -#define XS1_PS_DBG_DWATCH_ADDR2_1 0x610b -#define XS1_PS_DBG_DWATCH_ADDR2_2 0x620b -#define XS1_PS_DBG_DWATCH_ADDR2_3 0x630b -#define XS1_PS_DBG_DWATCH_CTRL_0 0x700b -#define XS1_NUM_PS_DBG_DWATCH_CTRL 0x4 -#define XS1_PS_DBG_DWATCH_CTRL_1 0x710b -#define XS1_PS_DBG_DWATCH_CTRL_2 0x720b -#define XS1_PS_DBG_DWATCH_CTRL_3 0x730b -#define XS1_PS_DBG_RWATCH_ADDR1_0 0x800b -#define XS1_NUM_PS_DBG_RWATCH_ADDR1 0x4 -#define XS1_PS_DBG_RWATCH_ADDR1_1 0x810b -#define XS1_PS_DBG_RWATCH_ADDR1_2 0x820b -#define XS1_PS_DBG_RWATCH_ADDR1_3 0x830b -#define XS1_PS_DBG_RWATCH_ADDR2_0 0x900b -#define XS1_NUM_PS_DBG_RWATCH_ADDR2 0x4 -#define XS1_PS_DBG_RWATCH_ADDR2_1 0x910b -#define XS1_PS_DBG_RWATCH_ADDR2_2 0x920b -#define XS1_PS_DBG_RWATCH_ADDR2_3 0x930b -#define XS1_PS_DBG_RWATCH_CTRL_0 0x9c0b -#define XS1_NUM_PS_DBG_RWATCH_CTRL 0x4 -#define XS1_PS_DBG_RWATCH_CTRL_1 0x9d0b -#define XS1_PS_DBG_RWATCH_CTRL_2 0x9e0b -#define XS1_PS_DBG_RWATCH_CTRL_3 0x9f0b -#define XS1_PS_CACHE_MISS_CNT 0xa00b -#define XS1_PS_CACHE_ACCESS_CNT 0xa10b -#define XS1_SSWITCH_DEVICE_ID0_NUM 0x0 -#define XS1_SSWITCH_DEVICE_ID1_NUM 0x1 -#define XS1_SSWITCH_DEVICE_ID2_NUM 0x2 -#define XS1_SSWITCH_DEVICE_ID3_NUM 0x3 -#define XS1_SSWITCH_NODE_CONFIG_NUM 0x4 -#define XS1_SSWITCH_NODE_ID_NUM 0x5 -#define XS1_SSWITCH_PLL_CTL_NUM 0x6 -#define XS1_SSWITCH_CLK_DIVIDER_NUM 0x7 -#define XS1_SSWITCH_REF_CLK_DIVIDER_NUM 0x8 -#define XS1_SSWITCH_JTAG_DEVICE_ID_NUM 0x9 -#define XS1_SSWITCH_JTAG_USERCODE_NUM 0xa -#define XS1_SSWITCH_DDR_CLK_DIVIDER_NUM 0xb -#define XS1_SSWITCH_DIMENSION_DIRECTION0_NUM 0xc -#define XS1_SSWITCH_DIMENSION_DIRECTION1_NUM 0xd -#define XS1_SSWITCH_SS_APP_CLK_DIVIDER_NUM 0xe -#define XS1_SSWITCH_SS_APP_PLL_CTL_NUM 0xf -#define XS1_SSWITCH_XCORE0_GLOBAL_DEBUG_CONFIG_NUM 0x10 -#define XS1_SSWITCH_XCORE1_GLOBAL_DEBUG_CONFIG_NUM 0x11 -#define XS1_SSWITCH_SS_APP_PLL_FRAC_N_DIVIDER_NUM 0x12 -#define XS1_SSWITCH_MIPI_CLK_DIVIDER_NUM 0x14 -#define XS1_SSWITCH_MIPI_CFG_CLK_DIVIDER_NUM 0x15 -#define XS1_SSWITCH_GLOBAL_DEBUG_SOURCE_NUM 0x1f -#define XS1_SSWITCH_SLINK_0_NUM 0x20 -#define XS1_NUM_SSWITCH_SLINK 0x9 -#define XS1_SSWITCH_SLINK_1_NUM 0x21 -#define XS1_SSWITCH_SLINK_2_NUM 0x22 -#define XS1_SSWITCH_SLINK_3_NUM 0x23 -#define XS1_SSWITCH_SLINK_4_NUM 0x24 -#define XS1_SSWITCH_SLINK_5_NUM 0x25 -#define XS1_SSWITCH_SLINK_6_NUM 0x26 -#define XS1_SSWITCH_SLINK_7_NUM 0x27 -#define XS1_SSWITCH_SLINK_8_NUM 0x28 -#define XS1_SSWITCH_PLINK_0_NUM 0x40 -#define XS1_NUM_SSWITCH_PLINK 0x8 -#define XS1_SSWITCH_PLINK_1_NUM 0x41 -#define XS1_SSWITCH_PLINK_2_NUM 0x42 -#define XS1_SSWITCH_PLINK_3_NUM 0x43 -#define XS1_SSWITCH_PLINK_4_NUM 0x44 -#define XS1_SSWITCH_PLINK_5_NUM 0x45 -#define XS1_SSWITCH_PLINK_6_NUM 0x46 -#define XS1_SSWITCH_PLINK_7_NUM 0x47 -#define XS1_SSWITCH_XLINK_0_NUM 0x80 -#define XS1_NUM_SSWITCH_XLINK 0x9 -#define XS1_SSWITCH_XLINK_1_NUM 0x81 -#define XS1_SSWITCH_XLINK_2_NUM 0x82 -#define XS1_SSWITCH_XLINK_3_NUM 0x83 -#define XS1_SSWITCH_XLINK_4_NUM 0x84 -#define XS1_SSWITCH_XLINK_5_NUM 0x85 -#define XS1_SSWITCH_XLINK_6_NUM 0x86 -#define XS1_SSWITCH_XLINK_7_NUM 0x87 -#define XS1_SSWITCH_XLINK_8_NUM 0x88 -#define XS1_SSWITCH_XSTATIC_0_NUM 0xa0 -#define XS1_NUM_SSWITCH_XSTATIC 0x8 -#define XS1_SSWITCH_XSTATIC_1_NUM 0xa1 -#define XS1_SSWITCH_XSTATIC_2_NUM 0xa2 -#define XS1_SSWITCH_XSTATIC_3_NUM 0xa3 -#define XS1_SSWITCH_XSTATIC_4_NUM 0xa4 -#define XS1_SSWITCH_XSTATIC_5_NUM 0xa5 -#define XS1_SSWITCH_XSTATIC_6_NUM 0xa6 -#define XS1_SSWITCH_XSTATIC_7_NUM 0xa7 -#define XS1_SSWITCH_USB_XCFGI_REG0_NUM 0xf000 -#define XS1_SSWITCH_USB_XCFGI_REG1_NUM 0xf001 -#define XS1_SSWITCH_USB_XCFGI_REG2_NUM 0xf002 -#define XS1_SSWITCH_USB_XCFG_COARSE_TUNE_NUM 0xf003 -#define XS1_SSWITCH_USB_XCFG_FINE_TUNE_NUM 0xf004 -#define XS1_SSWITCH_USB_XCFG_LOCK_RANGE_MAX_NUM 0xf005 -#define XS1_SSWITCH_USB_XCFG_LOCK_RANGE_MIN_NUM 0xf006 -#define XS1_SSWITCH_USB_PHY_CFG0_NUM 0xf008 -#define XS1_SSWITCH_USB_PHY_CFG1_NUM 0xf009 -#define XS1_SSWITCH_USB_PHY_CFG2_NUM 0xf00a -#define XS1_SSWITCH_USB_PHY_CFG3_NUM 0xf00b -#define XS1_SSWITCH_USB_SHIM_CFG_NUM 0xf00c -#define XS1_SSWITCH_USB_PHY_XCFGO_REG0_NUM 0xf010 -#define XS1_SSWITCH_USB_PHY_STATUS_NUM 0xf011 -#define XS1_SSWITCH_WATCHDOG_CFG_NUM 0xf020 -#define XS1_SSWITCH_WATCHDOG_PRESCALER_NUM 0xf021 -#define XS1_SSWITCH_WATCHDOG_PRESCALER_WRAP_NUM 0xf022 -#define XS1_SSWITCH_WATCHDOG_COUNT_NUM 0xf023 -#define XS1_SSWITCH_WATCHDOG_STATUS_NUM 0xf024 -#define XS1_SSWITCH_MIPI_XCFGI_REG0_NUM 0xe000 -#define XS1_SSWITCH_MIPI_XCFGI_REG1_NUM 0xe001 -#define XS1_SSWITCH_MIPI_XCFGI_REG2_NUM 0xe002 -#define XS1_SSWITCH_MIPI_XCFGI_REG3_NUM 0xe003 -#define XS1_SSWITCH_MIPI_XCFGI_REG4_NUM 0xe004 -#define XS1_SSWITCH_MIPI_XCFGI_REG5_NUM 0xe005 -#define XS1_SSWITCH_MIPI_XCFGI_REG6_NUM 0xe006 -#define XS1_SSWITCH_MIPI_XCFGI_REG7_NUM 0xe007 -#define XS1_SSWITCH_MIPI_XCFGI_REG8_NUM 0xe008 -#define XS1_SSWITCH_MIPI_XCFGI_REG9_NUM 0xe009 -#define XS1_SSWITCH_MIPI_XCFGI_REG10_NUM 0xe00a -#define XS1_SSWITCH_MIPI_XCFGI_REG11_NUM 0xe00b -#define XS1_SSWITCH_MIPI_XCFGI_REG12_NUM 0xe00c -#define XS1_SSWITCH_MIPI_XCFGI_REG13_NUM 0xe00d -#define XS1_SSWITCH_MIPI_XCFGI_REG14_NUM 0xe00e -#define XS1_SSWITCH_MIPI_XCFGI_REG15_NUM 0xe00f -#define XS1_SSWITCH_MIPI_XCFGI_REG16_NUM 0xe010 -#define XS1_SSWITCH_MIPI_XCFGI_REG17_NUM 0xe011 -#define XS1_SSWITCH_MIPI_XCFGI_REG18_NUM 0xe012 -#define XS1_SSWITCH_MIPI_STATUS0_NUM 0xe013 -#define XS1_SSWITCH_MIPI_SHIM_STATUS_NUM 0xe014 -#define XS1_SSWITCH_MIPI_DPHY_CFG0_NUM 0xe018 -#define XS1_SSWITCH_MIPI_DPHY_CFG1_NUM 0xe019 -#define XS1_SSWITCH_MIPI_DPHY_CFG2_NUM 0xe01a -#define XS1_SSWITCH_MIPI_DPHY_CFG3_NUM 0xe01b -#define XS1_SSWITCH_MIPI_DPHY_CFG4_NUM 0xe01c -#define XS1_SSWITCH_MIPI_DPHY_CFG5_NUM 0xe01d -#define XS1_SSWITCH_MIPI_SHIM_CFG0_NUM 0xe01f -#define XS1_SSWITCH_MIPI_XCFGO_REG0_NUM 0xe020 -#define XS1_SSWITCH_MIPI_XCFGO_REG1_NUM 0xe021 -#define XS1_SSWITCH_MIPI_XCFGO_REG2_NUM 0xe022 -#define XS1_SSWITCH_LPDDR_IID_ENABLE_NUM 0xc000 -#define XS1_SSWITCH_LPDDR_IID_0_7_NUM 0xc001 -#define XS1_SSWITCH_LPDDR_IID_8_15_NUM 0xc002 -#define XS1_SSWITCH_LPDDR_QUEUE_CONT_NUM 0xc003 -#define XS1_SSWITCH_LPDDR_RO_COMMAND_QUEUE_PRIORITY_NUM 0xc008 -#define XS1_SSWITCH_LPDDR_RW_COMMAND_QUEUE_PRIORITY_NUM 0xc009 -#define XS1_SSWITCH_LPDDR_ARBITRATION_TIMEOUT_NUM 0xc00a -#define XS1_SSWITCH_LPDDR_ARBITRATION_MTG_COMMAND_NUM 0xc010 -#define XS1_SSWITCH_LPDDR_DLL_CONTROL_NUM 0xc014 -#define XS1_SSWITCH_LPDDR_DLL_MEASUREMENT_STATUS_NUM 0xc015 -#define XS1_SSWITCH_LPDDR_DLL_MANUAL_CONTROL_NUM 0xc016 -#define XS1_SSWITCH_LPDDR_DLL_PHY_CALIBRATION_DATA_NUM 0xc017 -#define XS1_SSWITCH_LPDDR_PHY_CONTROL_NUM 0xc01d -#define XS1_SSWITCH_LPDDR_LMR_OPCODE_NUM 0xc01e -#define XS1_SSWITCH_LPDDR_EMR_OPCODE_NUM 0xc01f -#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_CONF_0_NUM 0xc020 -#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_CONF_1_NUM 0xc021 -#define XS1_SSWITCH_LPDDR_PROTOCOL_ENGINE_STATUS_NUM 0xc022 -#define XS1_SSWITCH_PADCTRL_CLK_NUM 0xd000 -#define XS1_SSWITCH_PADCTRL_CKE_NUM 0xd001 -#define XS1_SSWITCH_PADCTRL_CS_N_NUM 0xd002 -#define XS1_SSWITCH_PADCTRL_WE_N_NUM 0xd003 -#define XS1_SSWITCH_PADCTRL_CAS_N_NUM 0xd004 -#define XS1_SSWITCH_PADCTRL_RAS_N_NUM 0xd005 -#define XS1_SSWITCH_PADCTRL_ADDR_NUM 0xd006 -#define XS1_SSWITCH_PADCTRL_BA_NUM 0xd007 -#define XS1_SSWITCH_PADCTRL_DQ_NUM 0xd008 -#define XS1_SSWITCH_PADCTRL_DQS_NUM 0xd009 -#define XS1_SSWITCH_PADCTRL_DM_NUM 0xd00a -#define XS1_PSWITCH_DEVICE_ID0_NUM 0x0 -#define XS1_PSWITCH_DEVICE_ID1_NUM 0x1 -#define XS1_PSWITCH_DEVICE_ID2_NUM 0x2 -#define XS1_PSWITCH_DEVICE_ID3_NUM 0x3 -#define XS1_PSWITCH_DBG_CTRL_NUM 0x4 -#define XS1_PSWITCH_DBG_INT_NUM 0x5 -#define XS1_PSWITCH_PLL_CLK_DIVIDER_NUM 0x6 -#define XS1_PSWITCH_SECU_CONFIG_NUM 0x7 -#define XS1_PSWITCH_DBG_SCRATCH_0_NUM 0x20 -#define XS1_NUM_PSWITCH_DBG_SCRATCH 0x8 -#define XS1_PSWITCH_DBG_SCRATCH_1_NUM 0x21 -#define XS1_PSWITCH_DBG_SCRATCH_2_NUM 0x22 -#define XS1_PSWITCH_DBG_SCRATCH_3_NUM 0x23 -#define XS1_PSWITCH_DBG_SCRATCH_4_NUM 0x24 -#define XS1_PSWITCH_DBG_SCRATCH_5_NUM 0x25 -#define XS1_PSWITCH_DBG_SCRATCH_6_NUM 0x26 -#define XS1_PSWITCH_DBG_SCRATCH_7_NUM 0x27 -#define XS1_PSWITCH_T0_PC_NUM 0x40 -#define XS1_PSWITCH_T1_PC_NUM 0x41 -#define XS1_PSWITCH_T2_PC_NUM 0x42 -#define XS1_PSWITCH_T3_PC_NUM 0x43 -#define XS1_PSWITCH_T4_PC_NUM 0x44 -#define XS1_PSWITCH_T5_PC_NUM 0x45 -#define XS1_PSWITCH_T6_PC_NUM 0x46 -#define XS1_PSWITCH_T7_PC_NUM 0x47 -#define XS1_PSWITCH_T0_SR_NUM 0x60 -#define XS1_PSWITCH_T1_SR_NUM 0x61 -#define XS1_PSWITCH_T2_SR_NUM 0x62 -#define XS1_PSWITCH_T3_SR_NUM 0x63 -#define XS1_PSWITCH_T4_SR_NUM 0x64 -#define XS1_PSWITCH_T5_SR_NUM 0x65 -#define XS1_PSWITCH_T6_SR_NUM 0x66 -#define XS1_PSWITCH_T7_SR_NUM 0x67 -#define XS1_ID_ID_SHIFT 0x0 -#define XS1_ID_ID_SIZE 0x6 -#define XS1_ID_ID_MASK (((1 << XS1_ID_ID_SIZE) - 1) << XS1_ID_ID_SHIFT) -#define XS1_ID_ID(x) (((x)&XS1_ID_ID_MASK) >> XS1_ID_ID_SHIFT) -#define XS1_ID_ID_SET(x, v) \ - (((x) & ~XS1_ID_ID_MASK) | (((v) << XS1_ID_ID_SHIFT) & XS1_ID_ID_MASK)) -#define XS1_EXCEPTION_TYPE_SHIFT 0x0 -#define XS1_EXCEPTION_TYPE_SIZE 0x5 -#define XS1_EXCEPTION_TYPE_MASK \ - (((1 << XS1_EXCEPTION_TYPE_SIZE) - 1) << XS1_EXCEPTION_TYPE_SHIFT) -#define XS1_EXCEPTION_TYPE(x) \ - (((x)&XS1_EXCEPTION_TYPE_MASK) >> XS1_EXCEPTION_TYPE_SHIFT) -#define XS1_EXCEPTION_TYPE_SET(x, v) \ - (((x) & ~XS1_EXCEPTION_TYPE_MASK) | \ - (((v) << XS1_EXCEPTION_TYPE_SHIFT) & XS1_EXCEPTION_TYPE_MASK)) -#define XS1_DBG_T_NUM_NUM_SHIFT 0x0 -#define XS1_DBG_T_NUM_NUM_SIZE 0x8 -#define XS1_DBG_T_NUM_NUM_MASK \ - (((1 << XS1_DBG_T_NUM_NUM_SIZE) - 1) << XS1_DBG_T_NUM_NUM_SHIFT) -#define XS1_DBG_T_NUM_NUM(x) \ - (((x)&XS1_DBG_T_NUM_NUM_MASK) >> XS1_DBG_T_NUM_NUM_SHIFT) -#define XS1_DBG_T_NUM_NUM_SET(x, v) \ - (((x) & ~XS1_DBG_T_NUM_NUM_MASK) | \ - (((v) << XS1_DBG_T_NUM_NUM_SHIFT) & XS1_DBG_T_NUM_NUM_MASK)) -#define XS1_DBG_T_REG_REG_SHIFT 0x0 -#define XS1_DBG_T_REG_REG_SIZE 0x5 -#define XS1_DBG_T_REG_REG_MASK \ - (((1 << XS1_DBG_T_REG_REG_SIZE) - 1) << XS1_DBG_T_REG_REG_SHIFT) -#define XS1_DBG_T_REG_REG(x) \ - (((x)&XS1_DBG_T_REG_REG_MASK) >> XS1_DBG_T_REG_REG_SHIFT) -#define XS1_DBG_T_REG_REG_SET(x, v) \ - (((x) & ~XS1_DBG_T_REG_REG_MASK) | \ - (((v) << XS1_DBG_T_REG_REG_SHIFT) & XS1_DBG_T_REG_REG_MASK)) -#define XS1_BRK_ENABLE_SHIFT 0x0 -#define XS1_BRK_ENABLE_SIZE 0x1 -#define XS1_BRK_ENABLE_MASK \ - (((1 << XS1_BRK_ENABLE_SIZE) - 1) << XS1_BRK_ENABLE_SHIFT) -#define XS1_BRK_ENABLE(x) (((x)&XS1_BRK_ENABLE_MASK) >> XS1_BRK_ENABLE_SHIFT) -#define XS1_BRK_ENABLE_SET(x, v) \ - (((x) & ~XS1_BRK_ENABLE_MASK) | \ - (((v) << XS1_BRK_ENABLE_SHIFT) & XS1_BRK_ENABLE_MASK)) -#define XS1_ALL_BITS_SHIFT 0x0 -#define XS1_ALL_BITS_SIZE 0x20 -#define XS1_ALL_BITS_MASK (((1 << XS1_ALL_BITS_SIZE) - 1) << XS1_ALL_BITS_SHIFT) -#define XS1_ALL_BITS(x) (((x)&XS1_ALL_BITS_MASK) >> XS1_ALL_BITS_SHIFT) -#define XS1_ALL_BITS_SET(x, v) \ - (((x) & ~XS1_ALL_BITS_MASK) | \ - (((v) << XS1_ALL_BITS_SHIFT) & XS1_ALL_BITS_MASK)) -#define XS1_KEP_ADDRESS_BITS_SHIFT 0x7 -#define XS1_KEP_ADDRESS_BITS_SIZE 0x19 -#define XS1_KEP_ADDRESS_BITS_MASK \ - (((1 << XS1_KEP_ADDRESS_BITS_SIZE) - 1) << XS1_KEP_ADDRESS_BITS_SHIFT) -#define XS1_KEP_ADDRESS_BITS(x) \ - (((x)&XS1_KEP_ADDRESS_BITS_MASK) >> XS1_KEP_ADDRESS_BITS_SHIFT) -#define XS1_KEP_ADDRESS_BITS_SET(x, v) \ - (((x) & ~XS1_KEP_ADDRESS_BITS_MASK) | \ - (((v) << XS1_KEP_ADDRESS_BITS_SHIFT) & XS1_KEP_ADDRESS_BITS_MASK)) -#define XS1_WORD_ADDRESS_BITS_SHIFT 0x2 -#define XS1_WORD_ADDRESS_BITS_SIZE 0x1e -#define XS1_WORD_ADDRESS_BITS_MASK \ - (((1 << XS1_WORD_ADDRESS_BITS_SIZE) - 1) << XS1_WORD_ADDRESS_BITS_SHIFT) -#define XS1_WORD_ADDRESS_BITS(x) \ - (((x)&XS1_WORD_ADDRESS_BITS_MASK) >> XS1_WORD_ADDRESS_BITS_SHIFT) -#define XS1_WORD_ADDRESS_BITS_SET(x, v) \ - (((x) & ~XS1_WORD_ADDRESS_BITS_MASK) | \ - (((v) << XS1_WORD_ADDRESS_BITS_SHIFT) & XS1_WORD_ADDRESS_BITS_MASK)) -#define XS1_VECTOR_BASE_SHIFT 0x13 -#define XS1_VECTOR_BASE_SIZE 0xd -#define XS1_VECTOR_BASE_MASK \ - (((1 << XS1_VECTOR_BASE_SIZE) - 1) << XS1_VECTOR_BASE_SHIFT) -#define XS1_VECTOR_BASE(x) (((x)&XS1_VECTOR_BASE_MASK) >> XS1_VECTOR_BASE_SHIFT) -#define XS1_VECTOR_BASE_SET(x, v) \ - (((x) & ~XS1_VECTOR_BASE_MASK) | \ - (((v) << XS1_VECTOR_BASE_SHIFT) & XS1_VECTOR_BASE_MASK)) -#define XS1_IBRK_CONDITION_SHIFT 0x1 -#define XS1_IBRK_CONDITION_SIZE 0x1 -#define XS1_IBRK_CONDITION_MASK \ - (((1 << XS1_IBRK_CONDITION_SIZE) - 1) << XS1_IBRK_CONDITION_SHIFT) -#define XS1_IBRK_CONDITION(x) \ - (((x)&XS1_IBRK_CONDITION_MASK) >> XS1_IBRK_CONDITION_SHIFT) -#define XS1_IBRK_CONDITION_SET(x, v) \ - (((x) & ~XS1_IBRK_CONDITION_MASK) | \ - (((v) << XS1_IBRK_CONDITION_SHIFT) & XS1_IBRK_CONDITION_MASK)) -#define XS1_DBRK_CONDITION_SHIFT 0x1 -#define XS1_DBRK_CONDITION_SIZE 0x1 -#define XS1_DBRK_CONDITION_MASK \ - (((1 << XS1_DBRK_CONDITION_SIZE) - 1) << XS1_DBRK_CONDITION_SHIFT) -#define XS1_DBRK_CONDITION(x) \ - (((x)&XS1_DBRK_CONDITION_MASK) >> XS1_DBRK_CONDITION_SHIFT) -#define XS1_DBRK_CONDITION_SET(x, v) \ - (((x) & ~XS1_DBRK_CONDITION_MASK) | \ - (((v) << XS1_DBRK_CONDITION_SHIFT) & XS1_DBRK_CONDITION_MASK)) -#define XS1_RBRK_CONDITION_SHIFT 0x1 -#define XS1_RBRK_CONDITION_SIZE 0x1 -#define XS1_RBRK_CONDITION_MASK \ - (((1 << XS1_RBRK_CONDITION_SIZE) - 1) << XS1_RBRK_CONDITION_SHIFT) -#define XS1_RBRK_CONDITION(x) \ - (((x)&XS1_RBRK_CONDITION_MASK) >> XS1_RBRK_CONDITION_SHIFT) -#define XS1_RBRK_CONDITION_SET(x, v) \ - (((x) & ~XS1_RBRK_CONDITION_MASK) | \ - (((v) << XS1_RBRK_CONDITION_SHIFT) & XS1_RBRK_CONDITION_MASK)) -#define XS1_BRK_LOAD_SHIFT 0x2 -#define XS1_BRK_LOAD_SIZE 0x1 -#define XS1_BRK_LOAD_MASK (((1 << XS1_BRK_LOAD_SIZE) - 1) << XS1_BRK_LOAD_SHIFT) -#define XS1_BRK_LOAD(x) (((x)&XS1_BRK_LOAD_MASK) >> XS1_BRK_LOAD_SHIFT) -#define XS1_BRK_LOAD_SET(x, v) \ - (((x) & ~XS1_BRK_LOAD_MASK) | \ - (((v) << XS1_BRK_LOAD_SHIFT) & XS1_BRK_LOAD_MASK)) -#define XS1_BRK_THREADS_SHIFT 0x10 -#define XS1_BRK_THREADS_SIZE 0x8 -#define XS1_BRK_THREADS_MASK \ - (((1 << XS1_BRK_THREADS_SIZE) - 1) << XS1_BRK_THREADS_SHIFT) -#define XS1_BRK_THREADS(x) (((x)&XS1_BRK_THREADS_MASK) >> XS1_BRK_THREADS_SHIFT) -#define XS1_BRK_THREADS_SET(x, v) \ - (((x) & ~XS1_BRK_THREADS_MASK) | \ - (((v) << XS1_BRK_THREADS_SHIFT) & XS1_BRK_THREADS_MASK)) -#define XS1_DBG_TYPE_CAUSE_SHIFT 0x0 -#define XS1_DBG_TYPE_CAUSE_SIZE 0x3 -#define XS1_DBG_TYPE_CAUSE_MASK \ - (((1 << XS1_DBG_TYPE_CAUSE_SIZE) - 1) << XS1_DBG_TYPE_CAUSE_SHIFT) -#define XS1_DBG_TYPE_CAUSE(x) \ - (((x)&XS1_DBG_TYPE_CAUSE_MASK) >> XS1_DBG_TYPE_CAUSE_SHIFT) -#define XS1_DBG_TYPE_CAUSE_SET(x, v) \ - (((x) & ~XS1_DBG_TYPE_CAUSE_MASK) | \ - (((v) << XS1_DBG_TYPE_CAUSE_SHIFT) & XS1_DBG_TYPE_CAUSE_MASK)) -#define XS1_DBG_TYPE_T_NUM_SHIFT 0x8 -#define XS1_DBG_TYPE_T_NUM_SIZE 0x8 -#define XS1_DBG_TYPE_T_NUM_MASK \ - (((1 << XS1_DBG_TYPE_T_NUM_SIZE) - 1) << XS1_DBG_TYPE_T_NUM_SHIFT) -#define XS1_DBG_TYPE_T_NUM(x) \ - (((x)&XS1_DBG_TYPE_T_NUM_MASK) >> XS1_DBG_TYPE_T_NUM_SHIFT) -#define XS1_DBG_TYPE_T_NUM_SET(x, v) \ - (((x) & ~XS1_DBG_TYPE_T_NUM_MASK) | \ - (((v) << XS1_DBG_TYPE_T_NUM_SHIFT) & XS1_DBG_TYPE_T_NUM_MASK)) -#define XS1_DBG_TYPE_HW_NUM_SHIFT 0x10 -#define XS1_DBG_TYPE_HW_NUM_SIZE 0x2 -#define XS1_DBG_TYPE_HW_NUM_MASK \ - (((1 << XS1_DBG_TYPE_HW_NUM_SIZE) - 1) << XS1_DBG_TYPE_HW_NUM_SHIFT) -#define XS1_DBG_TYPE_HW_NUM(x) \ - (((x)&XS1_DBG_TYPE_HW_NUM_MASK) >> XS1_DBG_TYPE_HW_NUM_SHIFT) -#define XS1_DBG_TYPE_HW_NUM_SET(x, v) \ - (((x) & ~XS1_DBG_TYPE_HW_NUM_MASK) | \ - (((v) << XS1_DBG_TYPE_HW_NUM_SHIFT) & XS1_DBG_TYPE_HW_NUM_MASK)) -#define XS1_DBG_RUN_CTRL_STOP_SHIFT 0x0 -#define XS1_DBG_RUN_CTRL_STOP_SIZE 0x8 -#define XS1_DBG_RUN_CTRL_STOP_MASK \ - (((1 << XS1_DBG_RUN_CTRL_STOP_SIZE) - 1) << XS1_DBG_RUN_CTRL_STOP_SHIFT) -#define XS1_DBG_RUN_CTRL_STOP(x) \ - (((x)&XS1_DBG_RUN_CTRL_STOP_MASK) >> XS1_DBG_RUN_CTRL_STOP_SHIFT) -#define XS1_DBG_RUN_CTRL_STOP_SET(x, v) \ - (((x) & ~XS1_DBG_RUN_CTRL_STOP_MASK) | \ - (((v) << XS1_DBG_RUN_CTRL_STOP_SHIFT) & XS1_DBG_RUN_CTRL_STOP_MASK)) -#define XS1_XCORE_CTRL0_USB_ENABLE_SHIFT 0x1 -#define XS1_XCORE_CTRL0_USB_ENABLE_SIZE 0x1 -#define XS1_XCORE_CTRL0_USB_ENABLE_MASK \ - (((1 << XS1_XCORE_CTRL0_USB_ENABLE_SIZE) - 1) \ - << XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) -#define XS1_XCORE_CTRL0_USB_ENABLE(x) \ - (((x)&XS1_XCORE_CTRL0_USB_ENABLE_MASK) >> XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) -#define XS1_XCORE_CTRL0_USB_ENABLE_SET(x, v) \ - (((x) & ~XS1_XCORE_CTRL0_USB_ENABLE_MASK) | \ - (((v) << XS1_XCORE_CTRL0_USB_ENABLE_SHIFT) & \ - XS1_XCORE_CTRL0_USB_ENABLE_MASK)) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT 0x4 -#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SIZE 0x1 -#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK \ - (((1 << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SIZE) - 1) \ - << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN(x) \ - (((x)&XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK) >> \ - XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SET(x, v) \ - (((x) & ~XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK) | \ - (((v) << XS1_XCORE_CTRL0_CLK_DIVIDER_EN_SHIFT) & \ - XS1_XCORE_CTRL0_CLK_DIVIDER_EN_MASK)) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT 0x5 -#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SIZE 0x1 -#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK \ - (((1 << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SIZE) - 1) \ - << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN(x) \ - (((x)&XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK) >> \ - XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) -#define XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SET(x, v) \ - (((x) & ~XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK) | \ - (((v) << XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_SHIFT) & \ - XS1_XCORE_CTRL0_CLK_DIVIDER_DYN_MASK)) -#define XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT 0x8 -#define XS1_BOOT_CONFIG_SECURE_BOOT_SIZE 0x1 -#define XS1_BOOT_CONFIG_SECURE_BOOT_MASK \ - (((1 << XS1_BOOT_CONFIG_SECURE_BOOT_SIZE) - 1) \ - << XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) -#define XS1_BOOT_CONFIG_SECURE_BOOT(x) \ - (((x)&XS1_BOOT_CONFIG_SECURE_BOOT_MASK) >> XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) -#define XS1_BOOT_CONFIG_SECURE_BOOT_SET(x, v) \ - (((x) & ~XS1_BOOT_CONFIG_SECURE_BOOT_MASK) | \ - (((v) << XS1_BOOT_CONFIG_SECURE_BOOT_SHIFT) & \ - XS1_BOOT_CONFIG_SECURE_BOOT_MASK)) -#define XS1_BOOT_CONFIG_PROCESSOR_SHIFT 0x10 -#define XS1_BOOT_CONFIG_PROCESSOR_SIZE 0x8 -#define XS1_BOOT_CONFIG_PROCESSOR_MASK \ - (((1 << XS1_BOOT_CONFIG_PROCESSOR_SIZE) - 1) \ - << XS1_BOOT_CONFIG_PROCESSOR_SHIFT) -#define XS1_BOOT_CONFIG_PROCESSOR(x) \ - (((x)&XS1_BOOT_CONFIG_PROCESSOR_MASK) >> XS1_BOOT_CONFIG_PROCESSOR_SHIFT) -#define XS1_BOOT_CONFIG_PROCESSOR_SET(x, v) \ - (((x) & ~XS1_BOOT_CONFIG_PROCESSOR_MASK) | \ - (((v) << XS1_BOOT_CONFIG_PROCESSOR_SHIFT) & \ - XS1_BOOT_CONFIG_PROCESSOR_MASK)) -#define XS1_BOOT_STATUS_LEDS_SHIFT 0x0 -#define XS1_BOOT_STATUS_LEDS_SIZE 0x4 -#define XS1_BOOT_STATUS_LEDS_MASK \ - (((1 << XS1_BOOT_STATUS_LEDS_SIZE) - 1) << XS1_BOOT_STATUS_LEDS_SHIFT) -#define XS1_BOOT_STATUS_LEDS(x) \ - (((x)&XS1_BOOT_STATUS_LEDS_MASK) >> XS1_BOOT_STATUS_LEDS_SHIFT) -#define XS1_BOOT_STATUS_LEDS_SET(x, v) \ - (((x) & ~XS1_BOOT_STATUS_LEDS_MASK) | \ - (((v) << XS1_BOOT_STATUS_LEDS_SHIFT) & XS1_BOOT_STATUS_LEDS_MASK)) -#define XS1_BOOT_STATUS_BITS_SHIFT 0x4 -#define XS1_BOOT_STATUS_BITS_SIZE 0x1c -#define XS1_BOOT_STATUS_BITS_MASK \ - (((1 << XS1_BOOT_STATUS_BITS_SIZE) - 1) << XS1_BOOT_STATUS_BITS_SHIFT) -#define XS1_BOOT_STATUS_BITS(x) \ - (((x)&XS1_BOOT_STATUS_BITS_MASK) >> XS1_BOOT_STATUS_BITS_SHIFT) -#define XS1_BOOT_STATUS_BITS_SET(x, v) \ - (((x) & ~XS1_BOOT_STATUS_BITS_MASK) | \ - (((v) << XS1_BOOT_STATUS_BITS_SHIFT) & XS1_BOOT_STATUS_BITS_MASK)) -#define XS1_RING_OSC_PERPH_ENABLE_SHIFT 0x0 -#define XS1_RING_OSC_PERPH_ENABLE_SIZE 0x1 -#define XS1_RING_OSC_PERPH_ENABLE_MASK \ - (((1 << XS1_RING_OSC_PERPH_ENABLE_SIZE) - 1) \ - << XS1_RING_OSC_PERPH_ENABLE_SHIFT) -#define XS1_RING_OSC_PERPH_ENABLE(x) \ - (((x)&XS1_RING_OSC_PERPH_ENABLE_MASK) >> XS1_RING_OSC_PERPH_ENABLE_SHIFT) -#define XS1_RING_OSC_PERPH_ENABLE_SET(x, v) \ - (((x) & ~XS1_RING_OSC_PERPH_ENABLE_MASK) | \ - (((v) << XS1_RING_OSC_PERPH_ENABLE_SHIFT) & \ - XS1_RING_OSC_PERPH_ENABLE_MASK)) -#define XS1_RING_OSC_CORE_ENABLE_SHIFT 0x1 -#define XS1_RING_OSC_CORE_ENABLE_SIZE 0x1 -#define XS1_RING_OSC_CORE_ENABLE_MASK \ - (((1 << XS1_RING_OSC_CORE_ENABLE_SIZE) - 1) << XS1_RING_OSC_CORE_ENABLE_SHIFT) -#define XS1_RING_OSC_CORE_ENABLE(x) \ - (((x)&XS1_RING_OSC_CORE_ENABLE_MASK) >> XS1_RING_OSC_CORE_ENABLE_SHIFT) -#define XS1_RING_OSC_CORE_ENABLE_SET(x, v) \ - (((x) & ~XS1_RING_OSC_CORE_ENABLE_MASK) | \ - (((v) << XS1_RING_OSC_CORE_ENABLE_SHIFT) & XS1_RING_OSC_CORE_ENABLE_MASK)) -#define XS1_RING_OSC_DATA_SHIFT 0x0 -#define XS1_RING_OSC_DATA_SIZE 0x10 -#define XS1_RING_OSC_DATA_MASK \ - (((1 << XS1_RING_OSC_DATA_SIZE) - 1) << XS1_RING_OSC_DATA_SHIFT) -#define XS1_RING_OSC_DATA(x) \ - (((x)&XS1_RING_OSC_DATA_MASK) >> XS1_RING_OSC_DATA_SHIFT) -#define XS1_RING_OSC_DATA_SET(x, v) \ - (((x) & ~XS1_RING_OSC_DATA_MASK) | \ - (((v) << XS1_RING_OSC_DATA_SHIFT) & XS1_RING_OSC_DATA_MASK)) -#define XS1_PLL_CLK_DIVIDER_SHIFT 0x0 -#define XS1_PLL_CLK_DIVIDER_SIZE 0x10 -#define XS1_PLL_CLK_DIVIDER_MASK \ - (((1 << XS1_PLL_CLK_DIVIDER_SIZE) - 1) << XS1_PLL_CLK_DIVIDER_SHIFT) -#define XS1_PLL_CLK_DIVIDER(x) \ - (((x)&XS1_PLL_CLK_DIVIDER_MASK) >> XS1_PLL_CLK_DIVIDER_SHIFT) -#define XS1_PLL_CLK_DIVIDER_SET(x, v) \ - (((x) & ~XS1_PLL_CLK_DIVIDER_MASK) | \ - (((v) << XS1_PLL_CLK_DIVIDER_SHIFT) & XS1_PLL_CLK_DIVIDER_MASK)) -#define XS1_PLL_CLK_DISABLE_SHIFT 0x1f -#define XS1_PLL_CLK_DISABLE_SIZE 0x1 -#define XS1_PLL_CLK_DISABLE_MASK \ - (((1 << XS1_PLL_CLK_DISABLE_SIZE) - 1) << XS1_PLL_CLK_DISABLE_SHIFT) -#define XS1_PLL_CLK_DISABLE(x) \ - (((x)&XS1_PLL_CLK_DISABLE_MASK) >> XS1_PLL_CLK_DISABLE_SHIFT) -#define XS1_PLL_CLK_DISABLE_SET(x, v) \ - (((x) & ~XS1_PLL_CLK_DISABLE_MASK) | \ - (((v) << XS1_PLL_CLK_DISABLE_SHIFT) & XS1_PLL_CLK_DISABLE_MASK)) -#define XS1_THREAD_CTRL0_INUSE_SHIFT 0x0 -#define XS1_THREAD_CTRL0_INUSE_SIZE 0x1 -#define XS1_THREAD_CTRL0_INUSE_MASK \ - (((1 << XS1_THREAD_CTRL0_INUSE_SIZE) - 1) << XS1_THREAD_CTRL0_INUSE_SHIFT) -#define XS1_THREAD_CTRL0_INUSE(x) \ - (((x)&XS1_THREAD_CTRL0_INUSE_MASK) >> XS1_THREAD_CTRL0_INUSE_SHIFT) -#define XS1_THREAD_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_THREAD_CTRL0_INUSE_MASK) | \ - (((v) << XS1_THREAD_CTRL0_INUSE_SHIFT) & XS1_THREAD_CTRL0_INUSE_MASK)) -#define XS1_THREAD_CTRL0_MSYNC_SHIFT 0x1 -#define XS1_THREAD_CTRL0_MSYNC_SIZE 0x1 -#define XS1_THREAD_CTRL0_MSYNC_MASK \ - (((1 << XS1_THREAD_CTRL0_MSYNC_SIZE) - 1) << XS1_THREAD_CTRL0_MSYNC_SHIFT) -#define XS1_THREAD_CTRL0_MSYNC(x) \ - (((x)&XS1_THREAD_CTRL0_MSYNC_MASK) >> XS1_THREAD_CTRL0_MSYNC_SHIFT) -#define XS1_THREAD_CTRL0_MSYNC_SET(x, v) \ - (((x) & ~XS1_THREAD_CTRL0_MSYNC_MASK) | \ - (((v) << XS1_THREAD_CTRL0_MSYNC_SHIFT) & XS1_THREAD_CTRL0_MSYNC_MASK)) -#define XS1_THREAD_CTRL0_SSYNC_SHIFT 0x2 -#define XS1_THREAD_CTRL0_SSYNC_SIZE 0x1 -#define XS1_THREAD_CTRL0_SSYNC_MASK \ - (((1 << XS1_THREAD_CTRL0_SSYNC_SIZE) - 1) << XS1_THREAD_CTRL0_SSYNC_SHIFT) -#define XS1_THREAD_CTRL0_SSYNC(x) \ - (((x)&XS1_THREAD_CTRL0_SSYNC_MASK) >> XS1_THREAD_CTRL0_SSYNC_SHIFT) -#define XS1_THREAD_CTRL0_SSYNC_SET(x, v) \ - (((x) & ~XS1_THREAD_CTRL0_SSYNC_MASK) | \ - (((v) << XS1_THREAD_CTRL0_SSYNC_SHIFT) & XS1_THREAD_CTRL0_SSYNC_MASK)) -#define XS1_THREAD_CTRL0_MASTER_SHIFT 0x8 -#define XS1_THREAD_CTRL0_MASTER_SIZE 0x8 -#define XS1_THREAD_CTRL0_MASTER_MASK \ - (((1 << XS1_THREAD_CTRL0_MASTER_SIZE) - 1) << XS1_THREAD_CTRL0_MASTER_SHIFT) -#define XS1_THREAD_CTRL0_MASTER(x) \ - (((x)&XS1_THREAD_CTRL0_MASTER_MASK) >> XS1_THREAD_CTRL0_MASTER_SHIFT) -#define XS1_THREAD_CTRL0_MASTER_SET(x, v) \ - (((x) & ~XS1_THREAD_CTRL0_MASTER_MASK) | \ - (((v) << XS1_THREAD_CTRL0_MASTER_SHIFT) & XS1_THREAD_CTRL0_MASTER_MASK)) -#define XS1_PORT_CTRL0_INUSE_SHIFT 0x0 -#define XS1_PORT_CTRL0_INUSE_SIZE 0x1 -#define XS1_PORT_CTRL0_INUSE_MASK \ - (((1 << XS1_PORT_CTRL0_INUSE_SIZE) - 1) << XS1_PORT_CTRL0_INUSE_SHIFT) -#define XS1_PORT_CTRL0_INUSE(x) \ - (((x)&XS1_PORT_CTRL0_INUSE_MASK) >> XS1_PORT_CTRL0_INUSE_SHIFT) -#define XS1_PORT_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_INUSE_MASK) | \ - (((v) << XS1_PORT_CTRL0_INUSE_SHIFT) & XS1_PORT_CTRL0_INUSE_MASK)) -#define XS1_PORT_CTRL0_IE_MODE_SHIFT 0x1 -#define XS1_PORT_CTRL0_IE_MODE_SIZE 0x1 -#define XS1_PORT_CTRL0_IE_MODE_MASK \ - (((1 << XS1_PORT_CTRL0_IE_MODE_SIZE) - 1) << XS1_PORT_CTRL0_IE_MODE_SHIFT) -#define XS1_PORT_CTRL0_IE_MODE(x) \ - (((x)&XS1_PORT_CTRL0_IE_MODE_MASK) >> XS1_PORT_CTRL0_IE_MODE_SHIFT) -#define XS1_PORT_CTRL0_IE_MODE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_IE_MODE_MASK) | \ - (((v) << XS1_PORT_CTRL0_IE_MODE_SHIFT) & XS1_PORT_CTRL0_IE_MODE_MASK)) -#define XS1_PORT_CTRL0_IE_ENABLED_SHIFT 0x2 -#define XS1_PORT_CTRL0_IE_ENABLED_SIZE 0x1 -#define XS1_PORT_CTRL0_IE_ENABLED_MASK \ - (((1 << XS1_PORT_CTRL0_IE_ENABLED_SIZE) - 1) \ - << XS1_PORT_CTRL0_IE_ENABLED_SHIFT) -#define XS1_PORT_CTRL0_IE_ENABLED(x) \ - (((x)&XS1_PORT_CTRL0_IE_ENABLED_MASK) >> XS1_PORT_CTRL0_IE_ENABLED_SHIFT) -#define XS1_PORT_CTRL0_IE_ENABLED_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_IE_ENABLED_MASK) | \ - (((v) << XS1_PORT_CTRL0_IE_ENABLED_SHIFT) & \ - XS1_PORT_CTRL0_IE_ENABLED_MASK)) -#define XS1_PORT_CTRL0_DIRECTION_SHIFT 0x3 -#define XS1_PORT_CTRL0_DIRECTION_SIZE 0x1 -#define XS1_PORT_CTRL0_DIRECTION_MASK \ - (((1 << XS1_PORT_CTRL0_DIRECTION_SIZE) - 1) << XS1_PORT_CTRL0_DIRECTION_SHIFT) -#define XS1_PORT_CTRL0_DIRECTION(x) \ - (((x)&XS1_PORT_CTRL0_DIRECTION_MASK) >> XS1_PORT_CTRL0_DIRECTION_SHIFT) -#define XS1_PORT_CTRL0_DIRECTION_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_DIRECTION_MASK) | \ - (((v) << XS1_PORT_CTRL0_DIRECTION_SHIFT) & XS1_PORT_CTRL0_DIRECTION_MASK)) -#define XS1_PORT_CTRL0_COND_SHIFT 0x4 -#define XS1_PORT_CTRL0_COND_SIZE 0x4 -#define XS1_PORT_CTRL0_COND_MASK \ - (((1 << XS1_PORT_CTRL0_COND_SIZE) - 1) << XS1_PORT_CTRL0_COND_SHIFT) -#define XS1_PORT_CTRL0_COND(x) \ - (((x)&XS1_PORT_CTRL0_COND_MASK) >> XS1_PORT_CTRL0_COND_SHIFT) -#define XS1_PORT_CTRL0_COND_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_COND_MASK) | \ - (((v) << XS1_PORT_CTRL0_COND_SHIFT) & XS1_PORT_CTRL0_COND_MASK)) -#define XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT 0x8 -#define XS1_PORT_CTRL0_MASTER_SLAVE_SIZE 0x1 -#define XS1_PORT_CTRL0_MASTER_SLAVE_MASK \ - (((1 << XS1_PORT_CTRL0_MASTER_SLAVE_SIZE) - 1) \ - << XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) -#define XS1_PORT_CTRL0_MASTER_SLAVE(x) \ - (((x)&XS1_PORT_CTRL0_MASTER_SLAVE_MASK) >> XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) -#define XS1_PORT_CTRL0_MASTER_SLAVE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_MASTER_SLAVE_MASK) | \ - (((v) << XS1_PORT_CTRL0_MASTER_SLAVE_SHIFT) & \ - XS1_PORT_CTRL0_MASTER_SLAVE_MASK)) -#define XS1_PORT_CTRL0_BUFFERS_SHIFT 0x9 -#define XS1_PORT_CTRL0_BUFFERS_SIZE 0x1 -#define XS1_PORT_CTRL0_BUFFERS_MASK \ - (((1 << XS1_PORT_CTRL0_BUFFERS_SIZE) - 1) << XS1_PORT_CTRL0_BUFFERS_SHIFT) -#define XS1_PORT_CTRL0_BUFFERS(x) \ - (((x)&XS1_PORT_CTRL0_BUFFERS_MASK) >> XS1_PORT_CTRL0_BUFFERS_SHIFT) -#define XS1_PORT_CTRL0_BUFFERS_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_BUFFERS_MASK) | \ - (((v) << XS1_PORT_CTRL0_BUFFERS_SHIFT) & XS1_PORT_CTRL0_BUFFERS_MASK)) -#define XS1_PORT_CTRL0_READY_MODE_SHIFT 0xa -#define XS1_PORT_CTRL0_READY_MODE_SIZE 0x2 -#define XS1_PORT_CTRL0_READY_MODE_MASK \ - (((1 << XS1_PORT_CTRL0_READY_MODE_SIZE) - 1) \ - << XS1_PORT_CTRL0_READY_MODE_SHIFT) -#define XS1_PORT_CTRL0_READY_MODE(x) \ - (((x)&XS1_PORT_CTRL0_READY_MODE_MASK) >> XS1_PORT_CTRL0_READY_MODE_SHIFT) -#define XS1_PORT_CTRL0_READY_MODE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_READY_MODE_MASK) | \ - (((v) << XS1_PORT_CTRL0_READY_MODE_SHIFT) & \ - XS1_PORT_CTRL0_READY_MODE_MASK)) -#define XS1_PORT_CTRL0_PORT_TYPE_SHIFT 0xc -#define XS1_PORT_CTRL0_PORT_TYPE_SIZE 0x2 -#define XS1_PORT_CTRL0_PORT_TYPE_MASK \ - (((1 << XS1_PORT_CTRL0_PORT_TYPE_SIZE) - 1) << XS1_PORT_CTRL0_PORT_TYPE_SHIFT) -#define XS1_PORT_CTRL0_PORT_TYPE(x) \ - (((x)&XS1_PORT_CTRL0_PORT_TYPE_MASK) >> XS1_PORT_CTRL0_PORT_TYPE_SHIFT) -#define XS1_PORT_CTRL0_PORT_TYPE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_PORT_TYPE_MASK) | \ - (((v) << XS1_PORT_CTRL0_PORT_TYPE_SHIFT) & XS1_PORT_CTRL0_PORT_TYPE_MASK)) -#define XS1_PORT_CTRL0_INVERT_SHIFT 0xe -#define XS1_PORT_CTRL0_INVERT_SIZE 0x1 -#define XS1_PORT_CTRL0_INVERT_MASK \ - (((1 << XS1_PORT_CTRL0_INVERT_SIZE) - 1) << XS1_PORT_CTRL0_INVERT_SHIFT) -#define XS1_PORT_CTRL0_INVERT(x) \ - (((x)&XS1_PORT_CTRL0_INVERT_MASK) >> XS1_PORT_CTRL0_INVERT_SHIFT) -#define XS1_PORT_CTRL0_INVERT_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_INVERT_MASK) | \ - (((v) << XS1_PORT_CTRL0_INVERT_SHIFT) & XS1_PORT_CTRL0_INVERT_MASK)) -#define XS1_PORT_CTRL0_SDELAY_SHIFT 0xf -#define XS1_PORT_CTRL0_SDELAY_SIZE 0x1 -#define XS1_PORT_CTRL0_SDELAY_MASK \ - (((1 << XS1_PORT_CTRL0_SDELAY_SIZE) - 1) << XS1_PORT_CTRL0_SDELAY_SHIFT) -#define XS1_PORT_CTRL0_SDELAY(x) \ - (((x)&XS1_PORT_CTRL0_SDELAY_MASK) >> XS1_PORT_CTRL0_SDELAY_SHIFT) -#define XS1_PORT_CTRL0_SDELAY_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_SDELAY_MASK) | \ - (((v) << XS1_PORT_CTRL0_SDELAY_SHIFT) & XS1_PORT_CTRL0_SDELAY_MASK)) -#define XS1_PORT_CTRL0_EV_VALID_SHIFT 0x16 -#define XS1_PORT_CTRL0_EV_VALID_SIZE 0x1 -#define XS1_PORT_CTRL0_EV_VALID_MASK \ - (((1 << XS1_PORT_CTRL0_EV_VALID_SIZE) - 1) << XS1_PORT_CTRL0_EV_VALID_SHIFT) -#define XS1_PORT_CTRL0_EV_VALID(x) \ - (((x)&XS1_PORT_CTRL0_EV_VALID_MASK) >> XS1_PORT_CTRL0_EV_VALID_SHIFT) -#define XS1_PORT_CTRL0_EV_VALID_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_EV_VALID_MASK) | \ - (((v) << XS1_PORT_CTRL0_EV_VALID_SHIFT) & XS1_PORT_CTRL0_EV_VALID_MASK)) -#define XS1_PORT_CTRL0_T_WAITING_SHIFT 0x17 -#define XS1_PORT_CTRL0_T_WAITING_SIZE 0x1 -#define XS1_PORT_CTRL0_T_WAITING_MASK \ - (((1 << XS1_PORT_CTRL0_T_WAITING_SIZE) - 1) << XS1_PORT_CTRL0_T_WAITING_SHIFT) -#define XS1_PORT_CTRL0_T_WAITING(x) \ - (((x)&XS1_PORT_CTRL0_T_WAITING_MASK) >> XS1_PORT_CTRL0_T_WAITING_SHIFT) -#define XS1_PORT_CTRL0_T_WAITING_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_T_WAITING_MASK) | \ - (((v) << XS1_PORT_CTRL0_T_WAITING_SHIFT) & XS1_PORT_CTRL0_T_WAITING_MASK)) -#define XS1_PORT_CTRL0_T_NUM_SHIFT 0x18 -#define XS1_PORT_CTRL0_T_NUM_SIZE 0x8 -#define XS1_PORT_CTRL0_T_NUM_MASK \ - (((1 << XS1_PORT_CTRL0_T_NUM_SIZE) - 1) << XS1_PORT_CTRL0_T_NUM_SHIFT) -#define XS1_PORT_CTRL0_T_NUM(x) \ - (((x)&XS1_PORT_CTRL0_T_NUM_MASK) >> XS1_PORT_CTRL0_T_NUM_SHIFT) -#define XS1_PORT_CTRL0_T_NUM_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL0_T_NUM_MASK) | \ - (((v) << XS1_PORT_CTRL0_T_NUM_SHIFT) & XS1_PORT_CTRL0_T_NUM_MASK)) -#define XS1_PORT_CTRL1_DRIVE_SHIFT 0x0 -#define XS1_PORT_CTRL1_DRIVE_SIZE 0x8 -#define XS1_PORT_CTRL1_DRIVE_MASK \ - (((1 << XS1_PORT_CTRL1_DRIVE_SIZE) - 1) << XS1_PORT_CTRL1_DRIVE_SHIFT) -#define XS1_PORT_CTRL1_DRIVE(x) \ - (((x)&XS1_PORT_CTRL1_DRIVE_MASK) >> XS1_PORT_CTRL1_DRIVE_SHIFT) -#define XS1_PORT_CTRL1_DRIVE_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_DRIVE_MASK) | \ - (((v) << XS1_PORT_CTRL1_DRIVE_SHIFT) & XS1_PORT_CTRL1_DRIVE_MASK)) -#define XS1_PORT_CTRL1_TWIDTH_SHIFT 0x8 -#define XS1_PORT_CTRL1_TWIDTH_SIZE 0x8 -#define XS1_PORT_CTRL1_TWIDTH_MASK \ - (((1 << XS1_PORT_CTRL1_TWIDTH_SIZE) - 1) << XS1_PORT_CTRL1_TWIDTH_SHIFT) -#define XS1_PORT_CTRL1_TWIDTH(x) \ - (((x)&XS1_PORT_CTRL1_TWIDTH_MASK) >> XS1_PORT_CTRL1_TWIDTH_SHIFT) -#define XS1_PORT_CTRL1_TWIDTH_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_TWIDTH_MASK) | \ - (((v) << XS1_PORT_CTRL1_TWIDTH_SHIFT) & XS1_PORT_CTRL1_TWIDTH_MASK)) -#define XS1_PORT_CTRL1_SREG_COUNT_SHIFT 0x10 -#define XS1_PORT_CTRL1_SREG_COUNT_SIZE 0x8 -#define XS1_PORT_CTRL1_SREG_COUNT_MASK \ - (((1 << XS1_PORT_CTRL1_SREG_COUNT_SIZE) - 1) \ - << XS1_PORT_CTRL1_SREG_COUNT_SHIFT) -#define XS1_PORT_CTRL1_SREG_COUNT(x) \ - (((x)&XS1_PORT_CTRL1_SREG_COUNT_MASK) >> XS1_PORT_CTRL1_SREG_COUNT_SHIFT) -#define XS1_PORT_CTRL1_SREG_COUNT_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_SREG_COUNT_MASK) | \ - (((v) << XS1_PORT_CTRL1_SREG_COUNT_SHIFT) & \ - XS1_PORT_CTRL1_SREG_COUNT_MASK)) -#define XS1_PORT_CTRL1_TREG_FULL_SHIFT 0x18 -#define XS1_PORT_CTRL1_TREG_FULL_SIZE 0x1 -#define XS1_PORT_CTRL1_TREG_FULL_MASK \ - (((1 << XS1_PORT_CTRL1_TREG_FULL_SIZE) - 1) << XS1_PORT_CTRL1_TREG_FULL_SHIFT) -#define XS1_PORT_CTRL1_TREG_FULL(x) \ - (((x)&XS1_PORT_CTRL1_TREG_FULL_MASK) >> XS1_PORT_CTRL1_TREG_FULL_SHIFT) -#define XS1_PORT_CTRL1_TREG_FULL_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_TREG_FULL_MASK) | \ - (((v) << XS1_PORT_CTRL1_TREG_FULL_SHIFT) & XS1_PORT_CTRL1_TREG_FULL_MASK)) -#define XS1_PORT_CTRL1_CHANGE_DIR_SHIFT 0x19 -#define XS1_PORT_CTRL1_CHANGE_DIR_SIZE 0x1 -#define XS1_PORT_CTRL1_CHANGE_DIR_MASK \ - (((1 << XS1_PORT_CTRL1_CHANGE_DIR_SIZE) - 1) \ - << XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) -#define XS1_PORT_CTRL1_CHANGE_DIR(x) \ - (((x)&XS1_PORT_CTRL1_CHANGE_DIR_MASK) >> XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) -#define XS1_PORT_CTRL1_CHANGE_DIR_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_CHANGE_DIR_MASK) | \ - (((v) << XS1_PORT_CTRL1_CHANGE_DIR_SHIFT) & \ - XS1_PORT_CTRL1_CHANGE_DIR_MASK)) -#define XS1_PORT_CTRL1_SYNCR_SHIFT 0x1a -#define XS1_PORT_CTRL1_SYNCR_SIZE 0x1 -#define XS1_PORT_CTRL1_SYNCR_MASK \ - (((1 << XS1_PORT_CTRL1_SYNCR_SIZE) - 1) << XS1_PORT_CTRL1_SYNCR_SHIFT) -#define XS1_PORT_CTRL1_SYNCR(x) \ - (((x)&XS1_PORT_CTRL1_SYNCR_MASK) >> XS1_PORT_CTRL1_SYNCR_SHIFT) -#define XS1_PORT_CTRL1_SYNCR_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_SYNCR_MASK) | \ - (((v) << XS1_PORT_CTRL1_SYNCR_SHIFT) & XS1_PORT_CTRL1_SYNCR_MASK)) -#define XS1_PORT_CTRL1_INST_COMMITTED_SHIFT 0x1b -#define XS1_PORT_CTRL1_INST_COMMITTED_SIZE 0x1 -#define XS1_PORT_CTRL1_INST_COMMITTED_MASK \ - (((1 << XS1_PORT_CTRL1_INST_COMMITTED_SIZE) - 1) \ - << XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) -#define XS1_PORT_CTRL1_INST_COMMITTED(x) \ - (((x)&XS1_PORT_CTRL1_INST_COMMITTED_MASK) >> \ - XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) -#define XS1_PORT_CTRL1_INST_COMMITTED_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_INST_COMMITTED_MASK) | \ - (((v) << XS1_PORT_CTRL1_INST_COMMITTED_SHIFT) & \ - XS1_PORT_CTRL1_INST_COMMITTED_MASK)) -#define XS1_PORT_CTRL1_HOLD_DATA_SHIFT 0x1c -#define XS1_PORT_CTRL1_HOLD_DATA_SIZE 0x1 -#define XS1_PORT_CTRL1_HOLD_DATA_MASK \ - (((1 << XS1_PORT_CTRL1_HOLD_DATA_SIZE) - 1) << XS1_PORT_CTRL1_HOLD_DATA_SHIFT) -#define XS1_PORT_CTRL1_HOLD_DATA(x) \ - (((x)&XS1_PORT_CTRL1_HOLD_DATA_MASK) >> XS1_PORT_CTRL1_HOLD_DATA_SHIFT) -#define XS1_PORT_CTRL1_HOLD_DATA_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_HOLD_DATA_MASK) | \ - (((v) << XS1_PORT_CTRL1_HOLD_DATA_SHIFT) & XS1_PORT_CTRL1_HOLD_DATA_MASK)) -#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT 0x1d -#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SIZE 0x1 -#define XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK \ - (((1 << XS1_PORT_CTRL1_WAIT_FOR_TIME_SIZE) - 1) \ - << XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) -#define XS1_PORT_CTRL1_WAIT_FOR_TIME(x) \ - (((x)&XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK) >> \ - XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) -#define XS1_PORT_CTRL1_WAIT_FOR_TIME_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK) | \ - (((v) << XS1_PORT_CTRL1_WAIT_FOR_TIME_SHIFT) & \ - XS1_PORT_CTRL1_WAIT_FOR_TIME_MASK)) -#define XS1_PORT_CTRL1_TIMEMET_SHIFT 0x1e -#define XS1_PORT_CTRL1_TIMEMET_SIZE 0x1 -#define XS1_PORT_CTRL1_TIMEMET_MASK \ - (((1 << XS1_PORT_CTRL1_TIMEMET_SIZE) - 1) << XS1_PORT_CTRL1_TIMEMET_SHIFT) -#define XS1_PORT_CTRL1_TIMEMET(x) \ - (((x)&XS1_PORT_CTRL1_TIMEMET_MASK) >> XS1_PORT_CTRL1_TIMEMET_SHIFT) -#define XS1_PORT_CTRL1_TIMEMET_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_TIMEMET_MASK) | \ - (((v) << XS1_PORT_CTRL1_TIMEMET_SHIFT) & XS1_PORT_CTRL1_TIMEMET_MASK)) -#define XS1_PORT_CTRL1_ENDIN_SHIFT 0x1f -#define XS1_PORT_CTRL1_ENDIN_SIZE 0x1 -#define XS1_PORT_CTRL1_ENDIN_MASK \ - (((1 << XS1_PORT_CTRL1_ENDIN_SIZE) - 1) << XS1_PORT_CTRL1_ENDIN_SHIFT) -#define XS1_PORT_CTRL1_ENDIN(x) \ - (((x)&XS1_PORT_CTRL1_ENDIN_MASK) >> XS1_PORT_CTRL1_ENDIN_SHIFT) -#define XS1_PORT_CTRL1_ENDIN_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL1_ENDIN_MASK) | \ - (((v) << XS1_PORT_CTRL1_ENDIN_SHIFT) & XS1_PORT_CTRL1_ENDIN_MASK)) -#define XS1_PORT_CTRL2_TIME_SHIFT 0x0 -#define XS1_PORT_CTRL2_TIME_SIZE 0x10 -#define XS1_PORT_CTRL2_TIME_MASK \ - (((1 << XS1_PORT_CTRL2_TIME_SIZE) - 1) << XS1_PORT_CTRL2_TIME_SHIFT) -#define XS1_PORT_CTRL2_TIME(x) \ - (((x)&XS1_PORT_CTRL2_TIME_MASK) >> XS1_PORT_CTRL2_TIME_SHIFT) -#define XS1_PORT_CTRL2_TIME_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL2_TIME_MASK) | \ - (((v) << XS1_PORT_CTRL2_TIME_SHIFT) & XS1_PORT_CTRL2_TIME_MASK)) -#define XS1_PORT_CTRL2_PIN_DELAY_SHIFT 0x10 -#define XS1_PORT_CTRL2_PIN_DELAY_SIZE 0x3 -#define XS1_PORT_CTRL2_PIN_DELAY_MASK \ - (((1 << XS1_PORT_CTRL2_PIN_DELAY_SIZE) - 1) << XS1_PORT_CTRL2_PIN_DELAY_SHIFT) -#define XS1_PORT_CTRL2_PIN_DELAY(x) \ - (((x)&XS1_PORT_CTRL2_PIN_DELAY_MASK) >> XS1_PORT_CTRL2_PIN_DELAY_SHIFT) -#define XS1_PORT_CTRL2_PIN_DELAY_SET(x, v) \ - (((x) & ~XS1_PORT_CTRL2_PIN_DELAY_MASK) | \ - (((v) << XS1_PORT_CTRL2_PIN_DELAY_SHIFT) & XS1_PORT_CTRL2_PIN_DELAY_MASK)) -#define XS1_PORT_PAD_CTRL_OEN_SHIFT 0x0 -#define XS1_PORT_PAD_CTRL_OEN_SIZE 0x1 -#define XS1_PORT_PAD_CTRL_OEN_MASK \ - (((1 << XS1_PORT_PAD_CTRL_OEN_SIZE) - 1) << XS1_PORT_PAD_CTRL_OEN_SHIFT) -#define XS1_PORT_PAD_CTRL_OEN(x) \ - (((x)&XS1_PORT_PAD_CTRL_OEN_MASK) >> XS1_PORT_PAD_CTRL_OEN_SHIFT) -#define XS1_PORT_PAD_CTRL_OEN_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_OEN_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_OEN_SHIFT) & XS1_PORT_PAD_CTRL_OEN_MASK)) -#define XS1_PORT_PAD_CTRL_REN_SHIFT 0x1 -#define XS1_PORT_PAD_CTRL_REN_SIZE 0x1 -#define XS1_PORT_PAD_CTRL_REN_MASK \ - (((1 << XS1_PORT_PAD_CTRL_REN_SIZE) - 1) << XS1_PORT_PAD_CTRL_REN_SHIFT) -#define XS1_PORT_PAD_CTRL_REN(x) \ - (((x)&XS1_PORT_PAD_CTRL_REN_MASK) >> XS1_PORT_PAD_CTRL_REN_SHIFT) -#define XS1_PORT_PAD_CTRL_REN_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_REN_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_REN_SHIFT) & XS1_PORT_PAD_CTRL_REN_MASK)) -#define XS1_PORT_PAD_CTRL_P_SHIFT 0x2 -#define XS1_PORT_PAD_CTRL_P_SIZE 0x2 -#define XS1_PORT_PAD_CTRL_P_MASK \ - (((1 << XS1_PORT_PAD_CTRL_P_SIZE) - 1) << XS1_PORT_PAD_CTRL_P_SHIFT) -#define XS1_PORT_PAD_CTRL_P(x) \ - (((x)&XS1_PORT_PAD_CTRL_P_MASK) >> XS1_PORT_PAD_CTRL_P_SHIFT) -#define XS1_PORT_PAD_CTRL_P_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_P_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_P_SHIFT) & XS1_PORT_PAD_CTRL_P_MASK)) -#define XS1_PORT_PAD_CTRL_E_SHIFT 0x4 -#define XS1_PORT_PAD_CTRL_E_SIZE 0x2 -#define XS1_PORT_PAD_CTRL_E_MASK \ - (((1 << XS1_PORT_PAD_CTRL_E_SIZE) - 1) << XS1_PORT_PAD_CTRL_E_SHIFT) -#define XS1_PORT_PAD_CTRL_E(x) \ - (((x)&XS1_PORT_PAD_CTRL_E_MASK) >> XS1_PORT_PAD_CTRL_E_SHIFT) -#define XS1_PORT_PAD_CTRL_E_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_E_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_E_SHIFT) & XS1_PORT_PAD_CTRL_E_MASK)) -#define XS1_PORT_PAD_CTRL_SR_SHIFT 0x6 -#define XS1_PORT_PAD_CTRL_SR_SIZE 0x1 -#define XS1_PORT_PAD_CTRL_SR_MASK \ - (((1 << XS1_PORT_PAD_CTRL_SR_SIZE) - 1) << XS1_PORT_PAD_CTRL_SR_SHIFT) -#define XS1_PORT_PAD_CTRL_SR(x) \ - (((x)&XS1_PORT_PAD_CTRL_SR_MASK) >> XS1_PORT_PAD_CTRL_SR_SHIFT) -#define XS1_PORT_PAD_CTRL_SR_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_SR_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_SR_SHIFT) & XS1_PORT_PAD_CTRL_SR_MASK)) -#define XS1_PORT_PAD_CTRL_SMT_SHIFT 0x7 -#define XS1_PORT_PAD_CTRL_SMT_SIZE 0x1 -#define XS1_PORT_PAD_CTRL_SMT_MASK \ - (((1 << XS1_PORT_PAD_CTRL_SMT_SIZE) - 1) << XS1_PORT_PAD_CTRL_SMT_SHIFT) -#define XS1_PORT_PAD_CTRL_SMT(x) \ - (((x)&XS1_PORT_PAD_CTRL_SMT_MASK) >> XS1_PORT_PAD_CTRL_SMT_SHIFT) -#define XS1_PORT_PAD_CTRL_SMT_SET(x, v) \ - (((x) & ~XS1_PORT_PAD_CTRL_SMT_MASK) | \ - (((v) << XS1_PORT_PAD_CTRL_SMT_SHIFT) & XS1_PORT_PAD_CTRL_SMT_MASK)) -#define XS1_TIMER_CTRL0_INUSE_SHIFT 0x0 -#define XS1_TIMER_CTRL0_INUSE_SIZE 0x1 -#define XS1_TIMER_CTRL0_INUSE_MASK \ - (((1 << XS1_TIMER_CTRL0_INUSE_SIZE) - 1) << XS1_TIMER_CTRL0_INUSE_SHIFT) -#define XS1_TIMER_CTRL0_INUSE(x) \ - (((x)&XS1_TIMER_CTRL0_INUSE_MASK) >> XS1_TIMER_CTRL0_INUSE_SHIFT) -#define XS1_TIMER_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_INUSE_MASK) | \ - (((v) << XS1_TIMER_CTRL0_INUSE_SHIFT) & XS1_TIMER_CTRL0_INUSE_MASK)) -#define XS1_TIMER_CTRL0_IE_MODE_SHIFT 0x1 -#define XS1_TIMER_CTRL0_IE_MODE_SIZE 0x1 -#define XS1_TIMER_CTRL0_IE_MODE_MASK \ - (((1 << XS1_TIMER_CTRL0_IE_MODE_SIZE) - 1) << XS1_TIMER_CTRL0_IE_MODE_SHIFT) -#define XS1_TIMER_CTRL0_IE_MODE(x) \ - (((x)&XS1_TIMER_CTRL0_IE_MODE_MASK) >> XS1_TIMER_CTRL0_IE_MODE_SHIFT) -#define XS1_TIMER_CTRL0_IE_MODE_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_IE_MODE_MASK) | \ - (((v) << XS1_TIMER_CTRL0_IE_MODE_SHIFT) & XS1_TIMER_CTRL0_IE_MODE_MASK)) -#define XS1_TIMER_CTRL0_IE_ENABLED_SHIFT 0x2 -#define XS1_TIMER_CTRL0_IE_ENABLED_SIZE 0x1 -#define XS1_TIMER_CTRL0_IE_ENABLED_MASK \ - (((1 << XS1_TIMER_CTRL0_IE_ENABLED_SIZE) - 1) \ - << XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) -#define XS1_TIMER_CTRL0_IE_ENABLED(x) \ - (((x)&XS1_TIMER_CTRL0_IE_ENABLED_MASK) >> XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) -#define XS1_TIMER_CTRL0_IE_ENABLED_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_IE_ENABLED_MASK) | \ - (((v) << XS1_TIMER_CTRL0_IE_ENABLED_SHIFT) & \ - XS1_TIMER_CTRL0_IE_ENABLED_MASK)) -#define XS1_TIMER_CTRL0_READY_SHIFT 0x3 -#define XS1_TIMER_CTRL0_READY_SIZE 0x1 -#define XS1_TIMER_CTRL0_READY_MASK \ - (((1 << XS1_TIMER_CTRL0_READY_SIZE) - 1) << XS1_TIMER_CTRL0_READY_SHIFT) -#define XS1_TIMER_CTRL0_READY(x) \ - (((x)&XS1_TIMER_CTRL0_READY_MASK) >> XS1_TIMER_CTRL0_READY_SHIFT) -#define XS1_TIMER_CTRL0_READY_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_READY_MASK) | \ - (((v) << XS1_TIMER_CTRL0_READY_SHIFT) & XS1_TIMER_CTRL0_READY_MASK)) -#define XS1_TIMER_CTRL0_COND_SHIFT 0x4 -#define XS1_TIMER_CTRL0_COND_SIZE 0x1 -#define XS1_TIMER_CTRL0_COND_MASK \ - (((1 << XS1_TIMER_CTRL0_COND_SIZE) - 1) << XS1_TIMER_CTRL0_COND_SHIFT) -#define XS1_TIMER_CTRL0_COND(x) \ - (((x)&XS1_TIMER_CTRL0_COND_MASK) >> XS1_TIMER_CTRL0_COND_SHIFT) -#define XS1_TIMER_CTRL0_COND_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_COND_MASK) | \ - (((v) << XS1_TIMER_CTRL0_COND_SHIFT) & XS1_TIMER_CTRL0_COND_MASK)) -#define XS1_TIMER_CTRL0_EV_VALID_SHIFT 0x8 -#define XS1_TIMER_CTRL0_EV_VALID_SIZE 0x1 -#define XS1_TIMER_CTRL0_EV_VALID_MASK \ - (((1 << XS1_TIMER_CTRL0_EV_VALID_SIZE) - 1) << XS1_TIMER_CTRL0_EV_VALID_SHIFT) -#define XS1_TIMER_CTRL0_EV_VALID(x) \ - (((x)&XS1_TIMER_CTRL0_EV_VALID_MASK) >> XS1_TIMER_CTRL0_EV_VALID_SHIFT) -#define XS1_TIMER_CTRL0_EV_VALID_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_EV_VALID_MASK) | \ - (((v) << XS1_TIMER_CTRL0_EV_VALID_SHIFT) & XS1_TIMER_CTRL0_EV_VALID_MASK)) -#define XS1_TIMER_CTRL0_T_WAITING_SHIFT 0x17 -#define XS1_TIMER_CTRL0_T_WAITING_SIZE 0x1 -#define XS1_TIMER_CTRL0_T_WAITING_MASK \ - (((1 << XS1_TIMER_CTRL0_T_WAITING_SIZE) - 1) \ - << XS1_TIMER_CTRL0_T_WAITING_SHIFT) -#define XS1_TIMER_CTRL0_T_WAITING(x) \ - (((x)&XS1_TIMER_CTRL0_T_WAITING_MASK) >> XS1_TIMER_CTRL0_T_WAITING_SHIFT) -#define XS1_TIMER_CTRL0_T_WAITING_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_T_WAITING_MASK) | \ - (((v) << XS1_TIMER_CTRL0_T_WAITING_SHIFT) & \ - XS1_TIMER_CTRL0_T_WAITING_MASK)) -#define XS1_TIMER_CTRL0_T_NUM_SHIFT 0x18 -#define XS1_TIMER_CTRL0_T_NUM_SIZE 0x8 -#define XS1_TIMER_CTRL0_T_NUM_MASK \ - (((1 << XS1_TIMER_CTRL0_T_NUM_SIZE) - 1) << XS1_TIMER_CTRL0_T_NUM_SHIFT) -#define XS1_TIMER_CTRL0_T_NUM(x) \ - (((x)&XS1_TIMER_CTRL0_T_NUM_MASK) >> XS1_TIMER_CTRL0_T_NUM_SHIFT) -#define XS1_TIMER_CTRL0_T_NUM_SET(x, v) \ - (((x) & ~XS1_TIMER_CTRL0_T_NUM_MASK) | \ - (((v) << XS1_TIMER_CTRL0_T_NUM_SHIFT) & XS1_TIMER_CTRL0_T_NUM_MASK)) -#define XS1_SWMEM_CTRL0_INUSE_SHIFT 0x0 -#define XS1_SWMEM_CTRL0_INUSE_SIZE 0x1 -#define XS1_SWMEM_CTRL0_INUSE_MASK \ - (((1 << XS1_SWMEM_CTRL0_INUSE_SIZE) - 1) << XS1_SWMEM_CTRL0_INUSE_SHIFT) -#define XS1_SWMEM_CTRL0_INUSE(x) \ - (((x)&XS1_SWMEM_CTRL0_INUSE_MASK) >> XS1_SWMEM_CTRL0_INUSE_SHIFT) -#define XS1_SWMEM_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_INUSE_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_INUSE_SHIFT) & XS1_SWMEM_CTRL0_INUSE_MASK)) -#define XS1_SWMEM_CTRL0_IE_MODE_SHIFT 0x1 -#define XS1_SWMEM_CTRL0_IE_MODE_SIZE 0x1 -#define XS1_SWMEM_CTRL0_IE_MODE_MASK \ - (((1 << XS1_SWMEM_CTRL0_IE_MODE_SIZE) - 1) << XS1_SWMEM_CTRL0_IE_MODE_SHIFT) -#define XS1_SWMEM_CTRL0_IE_MODE(x) \ - (((x)&XS1_SWMEM_CTRL0_IE_MODE_MASK) >> XS1_SWMEM_CTRL0_IE_MODE_SHIFT) -#define XS1_SWMEM_CTRL0_IE_MODE_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_IE_MODE_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_IE_MODE_SHIFT) & XS1_SWMEM_CTRL0_IE_MODE_MASK)) -#define XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT 0x2 -#define XS1_SWMEM_CTRL0_IE_ENABLED_SIZE 0x1 -#define XS1_SWMEM_CTRL0_IE_ENABLED_MASK \ - (((1 << XS1_SWMEM_CTRL0_IE_ENABLED_SIZE) - 1) \ - << XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) -#define XS1_SWMEM_CTRL0_IE_ENABLED(x) \ - (((x)&XS1_SWMEM_CTRL0_IE_ENABLED_MASK) >> XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) -#define XS1_SWMEM_CTRL0_IE_ENABLED_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_IE_ENABLED_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_IE_ENABLED_SHIFT) & \ - XS1_SWMEM_CTRL0_IE_ENABLED_MASK)) -#define XS1_SWMEM_CTRL0_READY_SHIFT 0x3 -#define XS1_SWMEM_CTRL0_READY_SIZE 0x1 -#define XS1_SWMEM_CTRL0_READY_MASK \ - (((1 << XS1_SWMEM_CTRL0_READY_SIZE) - 1) << XS1_SWMEM_CTRL0_READY_SHIFT) -#define XS1_SWMEM_CTRL0_READY(x) \ - (((x)&XS1_SWMEM_CTRL0_READY_MASK) >> XS1_SWMEM_CTRL0_READY_SHIFT) -#define XS1_SWMEM_CTRL0_READY_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_READY_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_READY_SHIFT) & XS1_SWMEM_CTRL0_READY_MASK)) -#define XS1_SWMEM_CTRL0_COND_SHIFT 0x4 -#define XS1_SWMEM_CTRL0_COND_SIZE 0x1 -#define XS1_SWMEM_CTRL0_COND_MASK \ - (((1 << XS1_SWMEM_CTRL0_COND_SIZE) - 1) << XS1_SWMEM_CTRL0_COND_SHIFT) -#define XS1_SWMEM_CTRL0_COND(x) \ - (((x)&XS1_SWMEM_CTRL0_COND_MASK) >> XS1_SWMEM_CTRL0_COND_SHIFT) -#define XS1_SWMEM_CTRL0_COND_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_COND_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_COND_SHIFT) & XS1_SWMEM_CTRL0_COND_MASK)) -#define XS1_SWMEM_CTRL0_EV_VALID_SHIFT 0x8 -#define XS1_SWMEM_CTRL0_EV_VALID_SIZE 0x1 -#define XS1_SWMEM_CTRL0_EV_VALID_MASK \ - (((1 << XS1_SWMEM_CTRL0_EV_VALID_SIZE) - 1) << XS1_SWMEM_CTRL0_EV_VALID_SHIFT) -#define XS1_SWMEM_CTRL0_EV_VALID(x) \ - (((x)&XS1_SWMEM_CTRL0_EV_VALID_MASK) >> XS1_SWMEM_CTRL0_EV_VALID_SHIFT) -#define XS1_SWMEM_CTRL0_EV_VALID_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_EV_VALID_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_EV_VALID_SHIFT) & XS1_SWMEM_CTRL0_EV_VALID_MASK)) -#define XS1_SWMEM_CTRL0_T_WAITING_SHIFT 0x17 -#define XS1_SWMEM_CTRL0_T_WAITING_SIZE 0x1 -#define XS1_SWMEM_CTRL0_T_WAITING_MASK \ - (((1 << XS1_SWMEM_CTRL0_T_WAITING_SIZE) - 1) \ - << XS1_SWMEM_CTRL0_T_WAITING_SHIFT) -#define XS1_SWMEM_CTRL0_T_WAITING(x) \ - (((x)&XS1_SWMEM_CTRL0_T_WAITING_MASK) >> XS1_SWMEM_CTRL0_T_WAITING_SHIFT) -#define XS1_SWMEM_CTRL0_T_WAITING_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_T_WAITING_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_T_WAITING_SHIFT) & \ - XS1_SWMEM_CTRL0_T_WAITING_MASK)) -#define XS1_SWMEM_CTRL0_T_NUM_SHIFT 0x18 -#define XS1_SWMEM_CTRL0_T_NUM_SIZE 0x8 -#define XS1_SWMEM_CTRL0_T_NUM_MASK \ - (((1 << XS1_SWMEM_CTRL0_T_NUM_SIZE) - 1) << XS1_SWMEM_CTRL0_T_NUM_SHIFT) -#define XS1_SWMEM_CTRL0_T_NUM(x) \ - (((x)&XS1_SWMEM_CTRL0_T_NUM_MASK) >> XS1_SWMEM_CTRL0_T_NUM_SHIFT) -#define XS1_SWMEM_CTRL0_T_NUM_SET(x, v) \ - (((x) & ~XS1_SWMEM_CTRL0_T_NUM_MASK) | \ - (((v) << XS1_SWMEM_CTRL0_T_NUM_SHIFT) & XS1_SWMEM_CTRL0_T_NUM_MASK)) -#define XS1_OTPA_MOSI_LSB_SHIFT 0x0 -#define XS1_OTPA_MOSI_LSB_SIZE 0x1 -#define XS1_OTPA_MOSI_LSB_MASK \ - (((1 << XS1_OTPA_MOSI_LSB_SIZE) - 1) << XS1_OTPA_MOSI_LSB_SHIFT) -#define XS1_OTPA_MOSI_LSB(x) \ - (((x)&XS1_OTPA_MOSI_LSB_MASK) >> XS1_OTPA_MOSI_LSB_SHIFT) -#define XS1_OTPA_MOSI_LSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MOSI_LSB_MASK) | \ - (((v) << XS1_OTPA_MOSI_LSB_SHIFT) & XS1_OTPA_MOSI_LSB_MASK)) -#define XS1_OTPA_MOSI_MSB_SHIFT 0x7 -#define XS1_OTPA_MOSI_MSB_SIZE 0x1 -#define XS1_OTPA_MOSI_MSB_MASK \ - (((1 << XS1_OTPA_MOSI_MSB_SIZE) - 1) << XS1_OTPA_MOSI_MSB_SHIFT) -#define XS1_OTPA_MOSI_MSB(x) \ - (((x)&XS1_OTPA_MOSI_MSB_MASK) >> XS1_OTPA_MOSI_MSB_SHIFT) -#define XS1_OTPA_MOSI_MSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MOSI_MSB_MASK) | \ - (((v) << XS1_OTPA_MOSI_MSB_SHIFT) & XS1_OTPA_MOSI_MSB_MASK)) -#define XS1_OTPA_CLK_IDX_SHIFT 0x8 -#define XS1_OTPA_CLK_IDX_SIZE 0x1 -#define XS1_OTPA_CLK_IDX_MASK \ - (((1 << XS1_OTPA_CLK_IDX_SIZE) - 1) << XS1_OTPA_CLK_IDX_SHIFT) -#define XS1_OTPA_CLK_IDX(x) \ - (((x)&XS1_OTPA_CLK_IDX_MASK) >> XS1_OTPA_CLK_IDX_SHIFT) -#define XS1_OTPA_CLK_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_CLK_IDX_MASK) | \ - (((v) << XS1_OTPA_CLK_IDX_SHIFT) & XS1_OTPA_CLK_IDX_MASK)) -#define XS1_OTPA_SP_IDX_SHIFT 0x9 -#define XS1_OTPA_SP_IDX_SIZE 0x1 -#define XS1_OTPA_SP_IDX_MASK \ - (((1 << XS1_OTPA_SP_IDX_SIZE) - 1) << XS1_OTPA_SP_IDX_SHIFT) -#define XS1_OTPA_SP_IDX(x) (((x)&XS1_OTPA_SP_IDX_MASK) >> XS1_OTPA_SP_IDX_SHIFT) -#define XS1_OTPA_SP_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_SP_IDX_MASK) | \ - (((v) << XS1_OTPA_SP_IDX_SHIFT) & XS1_OTPA_SP_IDX_MASK)) -#define XS1_OTPA_CS_IDX_SHIFT 0xa -#define XS1_OTPA_CS_IDX_SIZE 0x1 -#define XS1_OTPA_CS_IDX_MASK \ - (((1 << XS1_OTPA_CS_IDX_SIZE) - 1) << XS1_OTPA_CS_IDX_SHIFT) -#define XS1_OTPA_CS_IDX(x) (((x)&XS1_OTPA_CS_IDX_MASK) >> XS1_OTPA_CS_IDX_SHIFT) -#define XS1_OTPA_CS_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_CS_IDX_MASK) | \ - (((v) << XS1_OTPA_CS_IDX_SHIFT) & XS1_OTPA_CS_IDX_MASK)) -#define XS1_OTPA_CKE_IDX_SHIFT 0xb -#define XS1_OTPA_CKE_IDX_SIZE 0x1 -#define XS1_OTPA_CKE_IDX_MASK \ - (((1 << XS1_OTPA_CKE_IDX_SIZE) - 1) << XS1_OTPA_CKE_IDX_SHIFT) -#define XS1_OTPA_CKE_IDX(x) \ - (((x)&XS1_OTPA_CKE_IDX_MASK) >> XS1_OTPA_CKE_IDX_SHIFT) -#define XS1_OTPA_CKE_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_CKE_IDX_MASK) | \ - (((v) << XS1_OTPA_CKE_IDX_SHIFT) & XS1_OTPA_CKE_IDX_MASK)) -#define XS1_OTPA_DCTRL_IDX_SHIFT 0xc -#define XS1_OTPA_DCTRL_IDX_SIZE 0x1 -#define XS1_OTPA_DCTRL_IDX_MASK \ - (((1 << XS1_OTPA_DCTRL_IDX_SIZE) - 1) << XS1_OTPA_DCTRL_IDX_SHIFT) -#define XS1_OTPA_DCTRL_IDX(x) \ - (((x)&XS1_OTPA_DCTRL_IDX_MASK) >> XS1_OTPA_DCTRL_IDX_SHIFT) -#define XS1_OTPA_DCTRL_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_DCTRL_IDX_MASK) | \ - (((v) << XS1_OTPA_DCTRL_IDX_SHIFT) & XS1_OTPA_DCTRL_IDX_MASK)) -#define XS1_OTPA_PD_IDX_SHIFT 0xd -#define XS1_OTPA_PD_IDX_SIZE 0x1 -#define XS1_OTPA_PD_IDX_MASK \ - (((1 << XS1_OTPA_PD_IDX_SIZE) - 1) << XS1_OTPA_PD_IDX_SHIFT) -#define XS1_OTPA_PD_IDX(x) (((x)&XS1_OTPA_PD_IDX_MASK) >> XS1_OTPA_PD_IDX_SHIFT) -#define XS1_OTPA_PD_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_PD_IDX_MASK) | \ - (((v) << XS1_OTPA_PD_IDX_SHIFT) & XS1_OTPA_PD_IDX_MASK)) -#define XS1_OTPA_SEL_IDX_SHIFT 0xe -#define XS1_OTPA_SEL_IDX_SIZE 0x1 -#define XS1_OTPA_SEL_IDX_MASK \ - (((1 << XS1_OTPA_SEL_IDX_SIZE) - 1) << XS1_OTPA_SEL_IDX_SHIFT) -#define XS1_OTPA_SEL_IDX(x) \ - (((x)&XS1_OTPA_SEL_IDX_MASK) >> XS1_OTPA_SEL_IDX_SHIFT) -#define XS1_OTPA_SEL_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_SEL_IDX_MASK) | \ - (((v) << XS1_OTPA_SEL_IDX_SHIFT) & XS1_OTPA_SEL_IDX_MASK)) -#define XS1_OTPA_CK_IDX_SHIFT 0xf -#define XS1_OTPA_CK_IDX_SIZE 0x1 -#define XS1_OTPA_CK_IDX_MASK \ - (((1 << XS1_OTPA_CK_IDX_SIZE) - 1) << XS1_OTPA_CK_IDX_SHIFT) -#define XS1_OTPA_CK_IDX(x) (((x)&XS1_OTPA_CK_IDX_MASK) >> XS1_OTPA_CK_IDX_SHIFT) -#define XS1_OTPA_CK_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_CK_IDX_MASK) | \ - (((v) << XS1_OTPA_CK_IDX_SHIFT) & XS1_OTPA_CK_IDX_MASK)) -#define XS1_OTPA_A_LSB_SHIFT 0x10 -#define XS1_OTPA_A_LSB_SIZE 0x1 -#define XS1_OTPA_A_LSB_MASK \ - (((1 << XS1_OTPA_A_LSB_SIZE) - 1) << XS1_OTPA_A_LSB_SHIFT) -#define XS1_OTPA_A_LSB(x) (((x)&XS1_OTPA_A_LSB_MASK) >> XS1_OTPA_A_LSB_SHIFT) -#define XS1_OTPA_A_LSB_SET(x, v) \ - (((x) & ~XS1_OTPA_A_LSB_MASK) | \ - (((v) << XS1_OTPA_A_LSB_SHIFT) & XS1_OTPA_A_LSB_MASK)) -#define XS1_OTPA_A_MSB_SHIFT 0x1a -#define XS1_OTPA_A_MSB_SIZE 0x1 -#define XS1_OTPA_A_MSB_MASK \ - (((1 << XS1_OTPA_A_MSB_SIZE) - 1) << XS1_OTPA_A_MSB_SHIFT) -#define XS1_OTPA_A_MSB(x) (((x)&XS1_OTPA_A_MSB_MASK) >> XS1_OTPA_A_MSB_SHIFT) -#define XS1_OTPA_A_MSB_SET(x, v) \ - (((x) & ~XS1_OTPA_A_MSB_MASK) | \ - (((v) << XS1_OTPA_A_MSB_SHIFT) & XS1_OTPA_A_MSB_MASK)) -#define XS1_OTPA_RST_IDX_SHIFT 0x1b -#define XS1_OTPA_RST_IDX_SIZE 0x1 -#define XS1_OTPA_RST_IDX_MASK \ - (((1 << XS1_OTPA_RST_IDX_SIZE) - 1) << XS1_OTPA_RST_IDX_SHIFT) -#define XS1_OTPA_RST_IDX(x) \ - (((x)&XS1_OTPA_RST_IDX_MASK) >> XS1_OTPA_RST_IDX_SHIFT) -#define XS1_OTPA_RST_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_RST_IDX_MASK) | \ - (((v) << XS1_OTPA_RST_IDX_SHIFT) & XS1_OTPA_RST_IDX_MASK)) -#define XS1_OTPA_ARB_REQ_IDX_SHIFT 0x1c -#define XS1_OTPA_ARB_REQ_IDX_SIZE 0x1 -#define XS1_OTPA_ARB_REQ_IDX_MASK \ - (((1 << XS1_OTPA_ARB_REQ_IDX_SIZE) - 1) << XS1_OTPA_ARB_REQ_IDX_SHIFT) -#define XS1_OTPA_ARB_REQ_IDX(x) \ - (((x)&XS1_OTPA_ARB_REQ_IDX_MASK) >> XS1_OTPA_ARB_REQ_IDX_SHIFT) -#define XS1_OTPA_ARB_REQ_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_ARB_REQ_IDX_MASK) | \ - (((v) << XS1_OTPA_ARB_REQ_IDX_SHIFT) & XS1_OTPA_ARB_REQ_IDX_MASK)) -#define XS1_OTPA_MUXSEL_LSB_SHIFT 0x1d -#define XS1_OTPA_MUXSEL_LSB_SIZE 0x1 -#define XS1_OTPA_MUXSEL_LSB_MASK \ - (((1 << XS1_OTPA_MUXSEL_LSB_SIZE) - 1) << XS1_OTPA_MUXSEL_LSB_SHIFT) -#define XS1_OTPA_MUXSEL_LSB(x) \ - (((x)&XS1_OTPA_MUXSEL_LSB_MASK) >> XS1_OTPA_MUXSEL_LSB_SHIFT) -#define XS1_OTPA_MUXSEL_LSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MUXSEL_LSB_MASK) | \ - (((v) << XS1_OTPA_MUXSEL_LSB_SHIFT) & XS1_OTPA_MUXSEL_LSB_MASK)) -#define XS1_OTPA_MUXSEL_MSB_SHIFT 0x1f -#define XS1_OTPA_MUXSEL_MSB_SIZE 0x1 -#define XS1_OTPA_MUXSEL_MSB_MASK \ - (((1 << XS1_OTPA_MUXSEL_MSB_SIZE) - 1) << XS1_OTPA_MUXSEL_MSB_SHIFT) -#define XS1_OTPA_MUXSEL_MSB(x) \ - (((x)&XS1_OTPA_MUXSEL_MSB_MASK) >> XS1_OTPA_MUXSEL_MSB_SHIFT) -#define XS1_OTPA_MUXSEL_MSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MUXSEL_MSB_MASK) | \ - (((v) << XS1_OTPA_MUXSEL_MSB_SHIFT) & XS1_OTPA_MUXSEL_MSB_MASK)) -#define XS1_OTPA_MISO_LSB_SHIFT 0x0 -#define XS1_OTPA_MISO_LSB_SIZE 0x1 -#define XS1_OTPA_MISO_LSB_MASK \ - (((1 << XS1_OTPA_MISO_LSB_SIZE) - 1) << XS1_OTPA_MISO_LSB_SHIFT) -#define XS1_OTPA_MISO_LSB(x) \ - (((x)&XS1_OTPA_MISO_LSB_MASK) >> XS1_OTPA_MISO_LSB_SHIFT) -#define XS1_OTPA_MISO_LSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MISO_LSB_MASK) | \ - (((v) << XS1_OTPA_MISO_LSB_SHIFT) & XS1_OTPA_MISO_LSB_MASK)) -#define XS1_OTPA_MISO_MSB_SHIFT 0x7 -#define XS1_OTPA_MISO_MSB_SIZE 0x1 -#define XS1_OTPA_MISO_MSB_MASK \ - (((1 << XS1_OTPA_MISO_MSB_SIZE) - 1) << XS1_OTPA_MISO_MSB_SHIFT) -#define XS1_OTPA_MISO_MSB(x) \ - (((x)&XS1_OTPA_MISO_MSB_MASK) >> XS1_OTPA_MISO_MSB_SHIFT) -#define XS1_OTPA_MISO_MSB_SET(x, v) \ - (((x) & ~XS1_OTPA_MISO_MSB_MASK) | \ - (((v) << XS1_OTPA_MISO_MSB_SHIFT) & XS1_OTPA_MISO_MSB_MASK)) -#define XS1_OTPA_FLAG_IDX_SHIFT 0x8 -#define XS1_OTPA_FLAG_IDX_SIZE 0x1 -#define XS1_OTPA_FLAG_IDX_MASK \ - (((1 << XS1_OTPA_FLAG_IDX_SIZE) - 1) << XS1_OTPA_FLAG_IDX_SHIFT) -#define XS1_OTPA_FLAG_IDX(x) \ - (((x)&XS1_OTPA_FLAG_IDX_MASK) >> XS1_OTPA_FLAG_IDX_SHIFT) -#define XS1_OTPA_FLAG_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_FLAG_IDX_MASK) | \ - (((v) << XS1_OTPA_FLAG_IDX_SHIFT) & XS1_OTPA_FLAG_IDX_MASK)) -#define XS1_OTPA_ARB_GNT_IDX_SHIFT 0x9 -#define XS1_OTPA_ARB_GNT_IDX_SIZE 0x1 -#define XS1_OTPA_ARB_GNT_IDX_MASK \ - (((1 << XS1_OTPA_ARB_GNT_IDX_SIZE) - 1) << XS1_OTPA_ARB_GNT_IDX_SHIFT) -#define XS1_OTPA_ARB_GNT_IDX(x) \ - (((x)&XS1_OTPA_ARB_GNT_IDX_MASK) >> XS1_OTPA_ARB_GNT_IDX_SHIFT) -#define XS1_OTPA_ARB_GNT_IDX_SET(x, v) \ - (((x) & ~XS1_OTPA_ARB_GNT_IDX_MASK) | \ - (((v) << XS1_OTPA_ARB_GNT_IDX_SHIFT) & XS1_OTPA_ARB_GNT_IDX_MASK)) -#define XS1_SYNC_CTRL0_INUSE_SHIFT 0x0 -#define XS1_SYNC_CTRL0_INUSE_SIZE 0x1 -#define XS1_SYNC_CTRL0_INUSE_MASK \ - (((1 << XS1_SYNC_CTRL0_INUSE_SIZE) - 1) << XS1_SYNC_CTRL0_INUSE_SHIFT) -#define XS1_SYNC_CTRL0_INUSE(x) \ - (((x)&XS1_SYNC_CTRL0_INUSE_MASK) >> XS1_SYNC_CTRL0_INUSE_SHIFT) -#define XS1_SYNC_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_SYNC_CTRL0_INUSE_MASK) | \ - (((v) << XS1_SYNC_CTRL0_INUSE_SHIFT) & XS1_SYNC_CTRL0_INUSE_MASK)) -#define XS1_SYNC_CTRL0_MSYNCED_SHIFT 0x1 -#define XS1_SYNC_CTRL0_MSYNCED_SIZE 0x1 -#define XS1_SYNC_CTRL0_MSYNCED_MASK \ - (((1 << XS1_SYNC_CTRL0_MSYNCED_SIZE) - 1) << XS1_SYNC_CTRL0_MSYNCED_SHIFT) -#define XS1_SYNC_CTRL0_MSYNCED(x) \ - (((x)&XS1_SYNC_CTRL0_MSYNCED_MASK) >> XS1_SYNC_CTRL0_MSYNCED_SHIFT) -#define XS1_SYNC_CTRL0_MSYNCED_SET(x, v) \ - (((x) & ~XS1_SYNC_CTRL0_MSYNCED_MASK) | \ - (((v) << XS1_SYNC_CTRL0_MSYNCED_SHIFT) & XS1_SYNC_CTRL0_MSYNCED_MASK)) -#define XS1_SYNC_CTRL0_JOIN_SHIFT 0x2 -#define XS1_SYNC_CTRL0_JOIN_SIZE 0x1 -#define XS1_SYNC_CTRL0_JOIN_MASK \ - (((1 << XS1_SYNC_CTRL0_JOIN_SIZE) - 1) << XS1_SYNC_CTRL0_JOIN_SHIFT) -#define XS1_SYNC_CTRL0_JOIN(x) \ - (((x)&XS1_SYNC_CTRL0_JOIN_MASK) >> XS1_SYNC_CTRL0_JOIN_SHIFT) -#define XS1_SYNC_CTRL0_JOIN_SET(x, v) \ - (((x) & ~XS1_SYNC_CTRL0_JOIN_MASK) | \ - (((v) << XS1_SYNC_CTRL0_JOIN_SHIFT) & XS1_SYNC_CTRL0_JOIN_MASK)) -#define XS1_SYNC_CTRL0_MASTER_SHIFT 0x8 -#define XS1_SYNC_CTRL0_MASTER_SIZE 0x8 -#define XS1_SYNC_CTRL0_MASTER_MASK \ - (((1 << XS1_SYNC_CTRL0_MASTER_SIZE) - 1) << XS1_SYNC_CTRL0_MASTER_SHIFT) -#define XS1_SYNC_CTRL0_MASTER(x) \ - (((x)&XS1_SYNC_CTRL0_MASTER_MASK) >> XS1_SYNC_CTRL0_MASTER_SHIFT) -#define XS1_SYNC_CTRL0_MASTER_SET(x, v) \ - (((x) & ~XS1_SYNC_CTRL0_MASTER_MASK) | \ - (((v) << XS1_SYNC_CTRL0_MASTER_SHIFT) & XS1_SYNC_CTRL0_MASTER_MASK)) -#define XS1_SYNC_TBV0_SLAVES_SHIFT 0x0 -#define XS1_SYNC_TBV0_SLAVES_SIZE 0x8 -#define XS1_SYNC_TBV0_SLAVES_MASK \ - (((1 << XS1_SYNC_TBV0_SLAVES_SIZE) - 1) << XS1_SYNC_TBV0_SLAVES_SHIFT) -#define XS1_SYNC_TBV0_SLAVES(x) \ - (((x)&XS1_SYNC_TBV0_SLAVES_MASK) >> XS1_SYNC_TBV0_SLAVES_SHIFT) -#define XS1_SYNC_TBV0_SLAVES_SET(x, v) \ - (((x) & ~XS1_SYNC_TBV0_SLAVES_MASK) | \ - (((v) << XS1_SYNC_TBV0_SLAVES_SHIFT) & XS1_SYNC_TBV0_SLAVES_MASK)) -#define XS1_LOCK_CTRL0_INUSE_SHIFT 0x0 -#define XS1_LOCK_CTRL0_INUSE_SIZE 0x1 -#define XS1_LOCK_CTRL0_INUSE_MASK \ - (((1 << XS1_LOCK_CTRL0_INUSE_SIZE) - 1) << XS1_LOCK_CTRL0_INUSE_SHIFT) -#define XS1_LOCK_CTRL0_INUSE(x) \ - (((x)&XS1_LOCK_CTRL0_INUSE_MASK) >> XS1_LOCK_CTRL0_INUSE_SHIFT) -#define XS1_LOCK_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_LOCK_CTRL0_INUSE_MASK) | \ - (((v) << XS1_LOCK_CTRL0_INUSE_SHIFT) & XS1_LOCK_CTRL0_INUSE_MASK)) -#define XS1_LOCK_CTRL0_OWNT_V_SHIFT 0x1 -#define XS1_LOCK_CTRL0_OWNT_V_SIZE 0x1 -#define XS1_LOCK_CTRL0_OWNT_V_MASK \ - (((1 << XS1_LOCK_CTRL0_OWNT_V_SIZE) - 1) << XS1_LOCK_CTRL0_OWNT_V_SHIFT) -#define XS1_LOCK_CTRL0_OWNT_V(x) \ - (((x)&XS1_LOCK_CTRL0_OWNT_V_MASK) >> XS1_LOCK_CTRL0_OWNT_V_SHIFT) -#define XS1_LOCK_CTRL0_OWNT_V_SET(x, v) \ - (((x) & ~XS1_LOCK_CTRL0_OWNT_V_MASK) | \ - (((v) << XS1_LOCK_CTRL0_OWNT_V_SHIFT) & XS1_LOCK_CTRL0_OWNT_V_MASK)) -#define XS1_LOCK_CTRL0_OWNT_SHIFT 0x8 -#define XS1_LOCK_CTRL0_OWNT_SIZE 0x8 -#define XS1_LOCK_CTRL0_OWNT_MASK \ - (((1 << XS1_LOCK_CTRL0_OWNT_SIZE) - 1) << XS1_LOCK_CTRL0_OWNT_SHIFT) -#define XS1_LOCK_CTRL0_OWNT(x) \ - (((x)&XS1_LOCK_CTRL0_OWNT_MASK) >> XS1_LOCK_CTRL0_OWNT_SHIFT) -#define XS1_LOCK_CTRL0_OWNT_SET(x, v) \ - (((x) & ~XS1_LOCK_CTRL0_OWNT_MASK) | \ - (((v) << XS1_LOCK_CTRL0_OWNT_SHIFT) & XS1_LOCK_CTRL0_OWNT_MASK)) -#define XS1_LOCK_TBV0_WAITING_SHIFT 0x0 -#define XS1_LOCK_TBV0_WAITING_SIZE 0x8 -#define XS1_LOCK_TBV0_WAITING_MASK \ - (((1 << XS1_LOCK_TBV0_WAITING_SIZE) - 1) << XS1_LOCK_TBV0_WAITING_SHIFT) -#define XS1_LOCK_TBV0_WAITING(x) \ - (((x)&XS1_LOCK_TBV0_WAITING_MASK) >> XS1_LOCK_TBV0_WAITING_SHIFT) -#define XS1_LOCK_TBV0_WAITING_SET(x, v) \ - (((x) & ~XS1_LOCK_TBV0_WAITING_MASK) | \ - (((v) << XS1_LOCK_TBV0_WAITING_SHIFT) & XS1_LOCK_TBV0_WAITING_MASK)) -#define XS1_MMAP_CTRL0_INUSE_SHIFT 0x0 -#define XS1_MMAP_CTRL0_INUSE_SIZE 0x1 -#define XS1_MMAP_CTRL0_INUSE_MASK \ - (((1 << XS1_MMAP_CTRL0_INUSE_SIZE) - 1) << XS1_MMAP_CTRL0_INUSE_SHIFT) -#define XS1_MMAP_CTRL0_INUSE(x) \ - (((x)&XS1_MMAP_CTRL0_INUSE_MASK) >> XS1_MMAP_CTRL0_INUSE_SHIFT) -#define XS1_MMAP_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_INUSE_MASK) | \ - (((v) << XS1_MMAP_CTRL0_INUSE_SHIFT) & XS1_MMAP_CTRL0_INUSE_MASK)) -#define XS1_MMAP_CTRL0_RO_SHIFT 0x1 -#define XS1_MMAP_CTRL0_RO_SIZE 0x1 -#define XS1_MMAP_CTRL0_RO_MASK \ - (((1 << XS1_MMAP_CTRL0_RO_SIZE) - 1) << XS1_MMAP_CTRL0_RO_SHIFT) -#define XS1_MMAP_CTRL0_RO(x) \ - (((x)&XS1_MMAP_CTRL0_RO_MASK) >> XS1_MMAP_CTRL0_RO_SHIFT) -#define XS1_MMAP_CTRL0_RO_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_RO_MASK) | \ - (((v) << XS1_MMAP_CTRL0_RO_SHIFT) & XS1_MMAP_CTRL0_RO_MASK)) -#define XS1_MMAP_CTRL0_LOCK_SHIFT 0x2 -#define XS1_MMAP_CTRL0_LOCK_SIZE 0x1 -#define XS1_MMAP_CTRL0_LOCK_MASK \ - (((1 << XS1_MMAP_CTRL0_LOCK_SIZE) - 1) << XS1_MMAP_CTRL0_LOCK_SHIFT) -#define XS1_MMAP_CTRL0_LOCK(x) \ - (((x)&XS1_MMAP_CTRL0_LOCK_MASK) >> XS1_MMAP_CTRL0_LOCK_SHIFT) -#define XS1_MMAP_CTRL0_LOCK_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_LOCK_MASK) | \ - (((v) << XS1_MMAP_CTRL0_LOCK_SHIFT) & XS1_MMAP_CTRL0_LOCK_MASK)) -#define XS1_MMAP_CTRL0_GLOBAL_SHIFT 0x3 -#define XS1_MMAP_CTRL0_GLOBAL_SIZE 0x1 -#define XS1_MMAP_CTRL0_GLOBAL_MASK \ - (((1 << XS1_MMAP_CTRL0_GLOBAL_SIZE) - 1) << XS1_MMAP_CTRL0_GLOBAL_SHIFT) -#define XS1_MMAP_CTRL0_GLOBAL(x) \ - (((x)&XS1_MMAP_CTRL0_GLOBAL_MASK) >> XS1_MMAP_CTRL0_GLOBAL_SHIFT) -#define XS1_MMAP_CTRL0_GLOBAL_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_GLOBAL_MASK) | \ - (((v) << XS1_MMAP_CTRL0_GLOBAL_SHIFT) & XS1_MMAP_CTRL0_GLOBAL_MASK)) -#define XS1_MMAP_CTRL0_ASID_SHIFT 0x4 -#define XS1_MMAP_CTRL0_ASID_SIZE 0x2 -#define XS1_MMAP_CTRL0_ASID_MASK \ - (((1 << XS1_MMAP_CTRL0_ASID_SIZE) - 1) << XS1_MMAP_CTRL0_ASID_SHIFT) -#define XS1_MMAP_CTRL0_ASID(x) \ - (((x)&XS1_MMAP_CTRL0_ASID_MASK) >> XS1_MMAP_CTRL0_ASID_SHIFT) -#define XS1_MMAP_CTRL0_ASID_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_ASID_MASK) | \ - (((v) << XS1_MMAP_CTRL0_ASID_SHIFT) & XS1_MMAP_CTRL0_ASID_MASK)) -#define XS1_MMAP_CTRL0_LENGTH_SHIFT 0x6 -#define XS1_MMAP_CTRL0_LENGTH_SIZE 0x2 -#define XS1_MMAP_CTRL0_LENGTH_MASK \ - (((1 << XS1_MMAP_CTRL0_LENGTH_SIZE) - 1) << XS1_MMAP_CTRL0_LENGTH_SHIFT) -#define XS1_MMAP_CTRL0_LENGTH(x) \ - (((x)&XS1_MMAP_CTRL0_LENGTH_MASK) >> XS1_MMAP_CTRL0_LENGTH_SHIFT) -#define XS1_MMAP_CTRL0_LENGTH_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_LENGTH_MASK) | \ - (((v) << XS1_MMAP_CTRL0_LENGTH_SHIFT) & XS1_MMAP_CTRL0_LENGTH_MASK)) -#define XS1_MMAP_CTRL0_PHY_ADDR_SHIFT 0x8 -#define XS1_MMAP_CTRL0_PHY_ADDR_SIZE 0x18 -#define XS1_MMAP_CTRL0_PHY_ADDR_MASK \ - (((1 << XS1_MMAP_CTRL0_PHY_ADDR_SIZE) - 1) << XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) -#define XS1_MMAP_CTRL0_PHY_ADDR(x) \ - (((x)&XS1_MMAP_CTRL0_PHY_ADDR_MASK) >> XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) -#define XS1_MMAP_CTRL0_PHY_ADDR_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL0_PHY_ADDR_MASK) | \ - (((v) << XS1_MMAP_CTRL0_PHY_ADDR_SHIFT) & XS1_MMAP_CTRL0_PHY_ADDR_MASK)) -#define XS1_MMAP_CTRL1_THREADS_EN_SHIFT 0x0 -#define XS1_MMAP_CTRL1_THREADS_EN_SIZE 0x8 -#define XS1_MMAP_CTRL1_THREADS_EN_MASK \ - (((1 << XS1_MMAP_CTRL1_THREADS_EN_SIZE) - 1) \ - << XS1_MMAP_CTRL1_THREADS_EN_SHIFT) -#define XS1_MMAP_CTRL1_THREADS_EN(x) \ - (((x)&XS1_MMAP_CTRL1_THREADS_EN_MASK) >> XS1_MMAP_CTRL1_THREADS_EN_SHIFT) -#define XS1_MMAP_CTRL1_THREADS_EN_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL1_THREADS_EN_MASK) | \ - (((v) << XS1_MMAP_CTRL1_THREADS_EN_SHIFT) & \ - XS1_MMAP_CTRL1_THREADS_EN_MASK)) -#define XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT 0x8 -#define XS1_MMAP_CTRL1_VIRT_ADDR_SIZE 0x18 -#define XS1_MMAP_CTRL1_VIRT_ADDR_MASK \ - (((1 << XS1_MMAP_CTRL1_VIRT_ADDR_SIZE) - 1) << XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) -#define XS1_MMAP_CTRL1_VIRT_ADDR(x) \ - (((x)&XS1_MMAP_CTRL1_VIRT_ADDR_MASK) >> XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) -#define XS1_MMAP_CTRL1_VIRT_ADDR_SET(x, v) \ - (((x) & ~XS1_MMAP_CTRL1_VIRT_ADDR_MASK) | \ - (((v) << XS1_MMAP_CTRL1_VIRT_ADDR_SHIFT) & XS1_MMAP_CTRL1_VIRT_ADDR_MASK)) -#define XS1_CHANEND_CTRL0_INUSE_SHIFT 0x0 -#define XS1_CHANEND_CTRL0_INUSE_SIZE 0x1 -#define XS1_CHANEND_CTRL0_INUSE_MASK \ - (((1 << XS1_CHANEND_CTRL0_INUSE_SIZE) - 1) << XS1_CHANEND_CTRL0_INUSE_SHIFT) -#define XS1_CHANEND_CTRL0_INUSE(x) \ - (((x)&XS1_CHANEND_CTRL0_INUSE_MASK) >> XS1_CHANEND_CTRL0_INUSE_SHIFT) -#define XS1_CHANEND_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_INUSE_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_INUSE_SHIFT) & XS1_CHANEND_CTRL0_INUSE_MASK)) -#define XS1_CHANEND_CTRL0_IE_MODE_SHIFT 0x1 -#define XS1_CHANEND_CTRL0_IE_MODE_SIZE 0x1 -#define XS1_CHANEND_CTRL0_IE_MODE_MASK \ - (((1 << XS1_CHANEND_CTRL0_IE_MODE_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_IE_MODE_SHIFT) -#define XS1_CHANEND_CTRL0_IE_MODE(x) \ - (((x)&XS1_CHANEND_CTRL0_IE_MODE_MASK) >> XS1_CHANEND_CTRL0_IE_MODE_SHIFT) -#define XS1_CHANEND_CTRL0_IE_MODE_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_IE_MODE_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_IE_MODE_SHIFT) & \ - XS1_CHANEND_CTRL0_IE_MODE_MASK)) -#define XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT 0x2 -#define XS1_CHANEND_CTRL0_IE_ENABLED_SIZE 0x1 -#define XS1_CHANEND_CTRL0_IE_ENABLED_MASK \ - (((1 << XS1_CHANEND_CTRL0_IE_ENABLED_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) -#define XS1_CHANEND_CTRL0_IE_ENABLED(x) \ - (((x)&XS1_CHANEND_CTRL0_IE_ENABLED_MASK) >> \ - XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) -#define XS1_CHANEND_CTRL0_IE_ENABLED_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_IE_ENABLED_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_IE_ENABLED_SHIFT) & \ - XS1_CHANEND_CTRL0_IE_ENABLED_MASK)) -#define XS1_CHANEND_CTRL0_IN_READY_SHIFT 0x4 -#define XS1_CHANEND_CTRL0_IN_READY_SIZE 0x1 -#define XS1_CHANEND_CTRL0_IN_READY_MASK \ - (((1 << XS1_CHANEND_CTRL0_IN_READY_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_IN_READY_SHIFT) -#define XS1_CHANEND_CTRL0_IN_READY(x) \ - (((x)&XS1_CHANEND_CTRL0_IN_READY_MASK) >> XS1_CHANEND_CTRL0_IN_READY_SHIFT) -#define XS1_CHANEND_CTRL0_IN_READY_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_IN_READY_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_IN_READY_SHIFT) & \ - XS1_CHANEND_CTRL0_IN_READY_MASK)) -#define XS1_CHANEND_CTRL0_IN_WAITING_SHIFT 0x5 -#define XS1_CHANEND_CTRL0_IN_WAITING_SIZE 0x1 -#define XS1_CHANEND_CTRL0_IN_WAITING_MASK \ - (((1 << XS1_CHANEND_CTRL0_IN_WAITING_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) -#define XS1_CHANEND_CTRL0_IN_WAITING(x) \ - (((x)&XS1_CHANEND_CTRL0_IN_WAITING_MASK) >> \ - XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) -#define XS1_CHANEND_CTRL0_IN_WAITING_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_IN_WAITING_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_IN_WAITING_SHIFT) & \ - XS1_CHANEND_CTRL0_IN_WAITING_MASK)) -#define XS1_CHANEND_CTRL0_OUT_READY_SHIFT 0x6 -#define XS1_CHANEND_CTRL0_OUT_READY_SIZE 0x1 -#define XS1_CHANEND_CTRL0_OUT_READY_MASK \ - (((1 << XS1_CHANEND_CTRL0_OUT_READY_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_OUT_READY_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_READY(x) \ - (((x)&XS1_CHANEND_CTRL0_OUT_READY_MASK) >> XS1_CHANEND_CTRL0_OUT_READY_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_READY_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_OUT_READY_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_OUT_READY_SHIFT) & \ - XS1_CHANEND_CTRL0_OUT_READY_MASK)) -#define XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT 0x7 -#define XS1_CHANEND_CTRL0_OUT_WAITING_SIZE 0x1 -#define XS1_CHANEND_CTRL0_OUT_WAITING_MASK \ - (((1 << XS1_CHANEND_CTRL0_OUT_WAITING_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_WAITING(x) \ - (((x)&XS1_CHANEND_CTRL0_OUT_WAITING_MASK) >> \ - XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_WAITING_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_OUT_WAITING_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_OUT_WAITING_SHIFT) & \ - XS1_CHANEND_CTRL0_OUT_WAITING_MASK)) -#define XS1_CHANEND_CTRL0_EV_VALID_SHIFT 0x8 -#define XS1_CHANEND_CTRL0_EV_VALID_SIZE 0x1 -#define XS1_CHANEND_CTRL0_EV_VALID_MASK \ - (((1 << XS1_CHANEND_CTRL0_EV_VALID_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_EV_VALID_SHIFT) -#define XS1_CHANEND_CTRL0_EV_VALID(x) \ - (((x)&XS1_CHANEND_CTRL0_EV_VALID_MASK) >> XS1_CHANEND_CTRL0_EV_VALID_SHIFT) -#define XS1_CHANEND_CTRL0_EV_VALID_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_EV_VALID_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_EV_VALID_SHIFT) & \ - XS1_CHANEND_CTRL0_EV_VALID_MASK)) -#define XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT 0x10 -#define XS1_CHANEND_CTRL0_IN_T_NUM_SIZE 0x8 -#define XS1_CHANEND_CTRL0_IN_T_NUM_MASK \ - (((1 << XS1_CHANEND_CTRL0_IN_T_NUM_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) -#define XS1_CHANEND_CTRL0_IN_T_NUM(x) \ - (((x)&XS1_CHANEND_CTRL0_IN_T_NUM_MASK) >> XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) -#define XS1_CHANEND_CTRL0_IN_T_NUM_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_IN_T_NUM_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_IN_T_NUM_SHIFT) & \ - XS1_CHANEND_CTRL0_IN_T_NUM_MASK)) -#define XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT 0x18 -#define XS1_CHANEND_CTRL0_OUT_T_NUM_SIZE 0x8 -#define XS1_CHANEND_CTRL0_OUT_T_NUM_MASK \ - (((1 << XS1_CHANEND_CTRL0_OUT_T_NUM_SIZE) - 1) \ - << XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_T_NUM(x) \ - (((x)&XS1_CHANEND_CTRL0_OUT_T_NUM_MASK) >> XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) -#define XS1_CHANEND_CTRL0_OUT_T_NUM_SET(x, v) \ - (((x) & ~XS1_CHANEND_CTRL0_OUT_T_NUM_MASK) | \ - (((v) << XS1_CHANEND_CTRL0_OUT_T_NUM_SHIFT) & \ - XS1_CHANEND_CTRL0_OUT_T_NUM_MASK)) -#define XS1_CLKBLK_CTRL0_INUSE_SHIFT 0x0 -#define XS1_CLKBLK_CTRL0_INUSE_SIZE 0x1 -#define XS1_CLKBLK_CTRL0_INUSE_MASK \ - (((1 << XS1_CLKBLK_CTRL0_INUSE_SIZE) - 1) << XS1_CLKBLK_CTRL0_INUSE_SHIFT) -#define XS1_CLKBLK_CTRL0_INUSE(x) \ - (((x)&XS1_CLKBLK_CTRL0_INUSE_MASK) >> XS1_CLKBLK_CTRL0_INUSE_SHIFT) -#define XS1_CLKBLK_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL0_INUSE_MASK) | \ - (((v) << XS1_CLKBLK_CTRL0_INUSE_SHIFT) & XS1_CLKBLK_CTRL0_INUSE_MASK)) -#define XS1_CLKBLK_CTRL0_STARTED_SHIFT 0x1 -#define XS1_CLKBLK_CTRL0_STARTED_SIZE 0x1 -#define XS1_CLKBLK_CTRL0_STARTED_MASK \ - (((1 << XS1_CLKBLK_CTRL0_STARTED_SIZE) - 1) << XS1_CLKBLK_CTRL0_STARTED_SHIFT) -#define XS1_CLKBLK_CTRL0_STARTED(x) \ - (((x)&XS1_CLKBLK_CTRL0_STARTED_MASK) >> XS1_CLKBLK_CTRL0_STARTED_SHIFT) -#define XS1_CLKBLK_CTRL0_STARTED_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL0_STARTED_MASK) | \ - (((v) << XS1_CLKBLK_CTRL0_STARTED_SHIFT) & XS1_CLKBLK_CTRL0_STARTED_MASK)) -#define XS1_CLKBLK_CTRL0_STOPPING_SHIFT 0x2 -#define XS1_CLKBLK_CTRL0_STOPPING_SIZE 0x1 -#define XS1_CLKBLK_CTRL0_STOPPING_MASK \ - (((1 << XS1_CLKBLK_CTRL0_STOPPING_SIZE) - 1) \ - << XS1_CLKBLK_CTRL0_STOPPING_SHIFT) -#define XS1_CLKBLK_CTRL0_STOPPING(x) \ - (((x)&XS1_CLKBLK_CTRL0_STOPPING_MASK) >> XS1_CLKBLK_CTRL0_STOPPING_SHIFT) -#define XS1_CLKBLK_CTRL0_STOPPING_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL0_STOPPING_MASK) | \ - (((v) << XS1_CLKBLK_CTRL0_STOPPING_SHIFT) & \ - XS1_CLKBLK_CTRL0_STOPPING_MASK)) -#define XS1_CLKBLK_CTRL0_T_WAITING_SHIFT 0x17 -#define XS1_CLKBLK_CTRL0_T_WAITING_SIZE 0x1 -#define XS1_CLKBLK_CTRL0_T_WAITING_MASK \ - (((1 << XS1_CLKBLK_CTRL0_T_WAITING_SIZE) - 1) \ - << XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) -#define XS1_CLKBLK_CTRL0_T_WAITING(x) \ - (((x)&XS1_CLKBLK_CTRL0_T_WAITING_MASK) >> XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) -#define XS1_CLKBLK_CTRL0_T_WAITING_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL0_T_WAITING_MASK) | \ - (((v) << XS1_CLKBLK_CTRL0_T_WAITING_SHIFT) & \ - XS1_CLKBLK_CTRL0_T_WAITING_MASK)) -#define XS1_CLKBLK_CTRL0_T_NUM_SHIFT 0x18 -#define XS1_CLKBLK_CTRL0_T_NUM_SIZE 0x8 -#define XS1_CLKBLK_CTRL0_T_NUM_MASK \ - (((1 << XS1_CLKBLK_CTRL0_T_NUM_SIZE) - 1) << XS1_CLKBLK_CTRL0_T_NUM_SHIFT) -#define XS1_CLKBLK_CTRL0_T_NUM(x) \ - (((x)&XS1_CLKBLK_CTRL0_T_NUM_MASK) >> XS1_CLKBLK_CTRL0_T_NUM_SHIFT) -#define XS1_CLKBLK_CTRL0_T_NUM_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL0_T_NUM_MASK) | \ - (((v) << XS1_CLKBLK_CTRL0_T_NUM_SHIFT) & XS1_CLKBLK_CTRL0_T_NUM_MASK)) -#define XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT 0x0 -#define XS1_CLKBLK_CTRL1_FALL_DELAY_SIZE 0x9 -#define XS1_CLKBLK_CTRL1_FALL_DELAY_MASK \ - (((1 << XS1_CLKBLK_CTRL1_FALL_DELAY_SIZE) - 1) \ - << XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) -#define XS1_CLKBLK_CTRL1_FALL_DELAY(x) \ - (((x)&XS1_CLKBLK_CTRL1_FALL_DELAY_MASK) >> XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) -#define XS1_CLKBLK_CTRL1_FALL_DELAY_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL1_FALL_DELAY_MASK) | \ - (((v) << XS1_CLKBLK_CTRL1_FALL_DELAY_SHIFT) & \ - XS1_CLKBLK_CTRL1_FALL_DELAY_MASK)) -#define XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT 0x10 -#define XS1_CLKBLK_CTRL1_RISE_DELAY_SIZE 0x9 -#define XS1_CLKBLK_CTRL1_RISE_DELAY_MASK \ - (((1 << XS1_CLKBLK_CTRL1_RISE_DELAY_SIZE) - 1) \ - << XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) -#define XS1_CLKBLK_CTRL1_RISE_DELAY(x) \ - (((x)&XS1_CLKBLK_CTRL1_RISE_DELAY_MASK) >> XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) -#define XS1_CLKBLK_CTRL1_RISE_DELAY_SET(x, v) \ - (((x) & ~XS1_CLKBLK_CTRL1_RISE_DELAY_MASK) | \ - (((v) << XS1_CLKBLK_CTRL1_RISE_DELAY_SHIFT) & \ - XS1_CLKBLK_CTRL1_RISE_DELAY_MASK)) -#define XS1_COPROC_CTRL0_INUSE_SHIFT 0x0 -#define XS1_COPROC_CTRL0_INUSE_SIZE 0x1 -#define XS1_COPROC_CTRL0_INUSE_MASK \ - (((1 << XS1_COPROC_CTRL0_INUSE_SIZE) - 1) << XS1_COPROC_CTRL0_INUSE_SHIFT) -#define XS1_COPROC_CTRL0_INUSE(x) \ - (((x)&XS1_COPROC_CTRL0_INUSE_MASK) >> XS1_COPROC_CTRL0_INUSE_SHIFT) -#define XS1_COPROC_CTRL0_INUSE_SET(x, v) \ - (((x) & ~XS1_COPROC_CTRL0_INUSE_MASK) | \ - (((v) << XS1_COPROC_CTRL0_INUSE_SHIFT) & XS1_COPROC_CTRL0_INUSE_MASK)) -#define XS1_COPROC_CTRL0_OWNT_V_SHIFT 0x1 -#define XS1_COPROC_CTRL0_OWNT_V_SIZE 0x1 -#define XS1_COPROC_CTRL0_OWNT_V_MASK \ - (((1 << XS1_COPROC_CTRL0_OWNT_V_SIZE) - 1) << XS1_COPROC_CTRL0_OWNT_V_SHIFT) -#define XS1_COPROC_CTRL0_OWNT_V(x) \ - (((x)&XS1_COPROC_CTRL0_OWNT_V_MASK) >> XS1_COPROC_CTRL0_OWNT_V_SHIFT) -#define XS1_COPROC_CTRL0_OWNT_V_SET(x, v) \ - (((x) & ~XS1_COPROC_CTRL0_OWNT_V_MASK) | \ - (((v) << XS1_COPROC_CTRL0_OWNT_V_SHIFT) & XS1_COPROC_CTRL0_OWNT_V_MASK)) -#define XS1_COPROC_CTRL0_OWNT_SHIFT 0x8 -#define XS1_COPROC_CTRL0_OWNT_SIZE 0x8 -#define XS1_COPROC_CTRL0_OWNT_MASK \ - (((1 << XS1_COPROC_CTRL0_OWNT_SIZE) - 1) << XS1_COPROC_CTRL0_OWNT_SHIFT) -#define XS1_COPROC_CTRL0_OWNT(x) \ - (((x)&XS1_COPROC_CTRL0_OWNT_MASK) >> XS1_COPROC_CTRL0_OWNT_SHIFT) -#define XS1_COPROC_CTRL0_OWNT_SET(x, v) \ - (((x) & ~XS1_COPROC_CTRL0_OWNT_MASK) | \ - (((v) << XS1_COPROC_CTRL0_OWNT_SHIFT) & XS1_COPROC_CTRL0_OWNT_MASK)) -#define XS1_COPROC_TBV0_WAITING_SHIFT 0x0 -#define XS1_COPROC_TBV0_WAITING_SIZE 0x8 -#define XS1_COPROC_TBV0_WAITING_MASK \ - (((1 << XS1_COPROC_TBV0_WAITING_SIZE) - 1) << XS1_COPROC_TBV0_WAITING_SHIFT) -#define XS1_COPROC_TBV0_WAITING(x) \ - (((x)&XS1_COPROC_TBV0_WAITING_MASK) >> XS1_COPROC_TBV0_WAITING_SHIFT) -#define XS1_COPROC_TBV0_WAITING_SET(x, v) \ - (((x) & ~XS1_COPROC_TBV0_WAITING_MASK) | \ - (((v) << XS1_COPROC_TBV0_WAITING_SHIFT) & XS1_COPROC_TBV0_WAITING_MASK)) -#define XS1_DBG_INT_REQ_DBG_SHIFT 0x0 -#define XS1_DBG_INT_REQ_DBG_SIZE 0x1 -#define XS1_DBG_INT_REQ_DBG_MASK \ - (((1 << XS1_DBG_INT_REQ_DBG_SIZE) - 1) << XS1_DBG_INT_REQ_DBG_SHIFT) -#define XS1_DBG_INT_REQ_DBG(x) \ - (((x)&XS1_DBG_INT_REQ_DBG_MASK) >> XS1_DBG_INT_REQ_DBG_SHIFT) -#define XS1_DBG_INT_REQ_DBG_SET(x, v) \ - (((x) & ~XS1_DBG_INT_REQ_DBG_MASK) | \ - (((v) << XS1_DBG_INT_REQ_DBG_SHIFT) & XS1_DBG_INT_REQ_DBG_MASK)) -#define XS1_DBG_INT_IN_DBG_SHIFT 0x1 -#define XS1_DBG_INT_IN_DBG_SIZE 0x1 -#define XS1_DBG_INT_IN_DBG_MASK \ - (((1 << XS1_DBG_INT_IN_DBG_SIZE) - 1) << XS1_DBG_INT_IN_DBG_SHIFT) -#define XS1_DBG_INT_IN_DBG(x) \ - (((x)&XS1_DBG_INT_IN_DBG_MASK) >> XS1_DBG_INT_IN_DBG_SHIFT) -#define XS1_DBG_INT_IN_DBG_SET(x, v) \ - (((x) & ~XS1_DBG_INT_IN_DBG_MASK) | \ - (((v) << XS1_DBG_INT_IN_DBG_SHIFT) & XS1_DBG_INT_IN_DBG_MASK)) -#define XS1_DBG_CTRL_PSWITCH_RO_SHIFT 0x1f -#define XS1_DBG_CTRL_PSWITCH_RO_SIZE 0x1 -#define XS1_DBG_CTRL_PSWITCH_RO_MASK \ - (((1 << XS1_DBG_CTRL_PSWITCH_RO_SIZE) - 1) << XS1_DBG_CTRL_PSWITCH_RO_SHIFT) -#define XS1_DBG_CTRL_PSWITCH_RO(x) \ - (((x)&XS1_DBG_CTRL_PSWITCH_RO_MASK) >> XS1_DBG_CTRL_PSWITCH_RO_SHIFT) -#define XS1_DBG_CTRL_PSWITCH_RO_SET(x, v) \ - (((x) & ~XS1_DBG_CTRL_PSWITCH_RO_MASK) | \ - (((v) << XS1_DBG_CTRL_PSWITCH_RO_SHIFT) & XS1_DBG_CTRL_PSWITCH_RO_MASK)) -#define XS1_DEVICE_ID0_VERSION_SHIFT 0x0 -#define XS1_DEVICE_ID0_VERSION_SIZE 0x8 -#define XS1_DEVICE_ID0_VERSION_MASK \ - (((1 << XS1_DEVICE_ID0_VERSION_SIZE) - 1) << XS1_DEVICE_ID0_VERSION_SHIFT) -#define XS1_DEVICE_ID0_VERSION(x) \ - (((x)&XS1_DEVICE_ID0_VERSION_MASK) >> XS1_DEVICE_ID0_VERSION_SHIFT) -#define XS1_DEVICE_ID0_VERSION_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID0_VERSION_MASK) | \ - (((v) << XS1_DEVICE_ID0_VERSION_SHIFT) & XS1_DEVICE_ID0_VERSION_MASK)) -#define XS1_DEVICE_ID0_REVISION_SHIFT 0x8 -#define XS1_DEVICE_ID0_REVISION_SIZE 0x8 -#define XS1_DEVICE_ID0_REVISION_MASK \ - (((1 << XS1_DEVICE_ID0_REVISION_SIZE) - 1) << XS1_DEVICE_ID0_REVISION_SHIFT) -#define XS1_DEVICE_ID0_REVISION(x) \ - (((x)&XS1_DEVICE_ID0_REVISION_MASK) >> XS1_DEVICE_ID0_REVISION_SHIFT) -#define XS1_DEVICE_ID0_REVISION_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID0_REVISION_MASK) | \ - (((v) << XS1_DEVICE_ID0_REVISION_SHIFT) & XS1_DEVICE_ID0_REVISION_MASK)) -#define XS1_DEVICE_ID0_NODE_SHIFT 0x10 -#define XS1_DEVICE_ID0_NODE_SIZE 0x8 -#define XS1_DEVICE_ID0_NODE_MASK \ - (((1 << XS1_DEVICE_ID0_NODE_SIZE) - 1) << XS1_DEVICE_ID0_NODE_SHIFT) -#define XS1_DEVICE_ID0_NODE(x) \ - (((x)&XS1_DEVICE_ID0_NODE_MASK) >> XS1_DEVICE_ID0_NODE_SHIFT) -#define XS1_DEVICE_ID0_NODE_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID0_NODE_MASK) | \ - (((v) << XS1_DEVICE_ID0_NODE_SHIFT) & XS1_DEVICE_ID0_NODE_MASK)) -#define XS1_DEVICE_ID0_PID_SHIFT 0x18 -#define XS1_DEVICE_ID0_PID_SIZE 0x8 -#define XS1_DEVICE_ID0_PID_MASK \ - (((1 << XS1_DEVICE_ID0_PID_SIZE) - 1) << XS1_DEVICE_ID0_PID_SHIFT) -#define XS1_DEVICE_ID0_PID(x) \ - (((x)&XS1_DEVICE_ID0_PID_MASK) >> XS1_DEVICE_ID0_PID_SHIFT) -#define XS1_DEVICE_ID0_PID_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID0_PID_MASK) | \ - (((v) << XS1_DEVICE_ID0_PID_SHIFT) & XS1_DEVICE_ID0_PID_MASK)) -#define XS1_DEVICE_ID1_NUM_THREADS_SHIFT 0x0 -#define XS1_DEVICE_ID1_NUM_THREADS_SIZE 0x8 -#define XS1_DEVICE_ID1_NUM_THREADS_MASK \ - (((1 << XS1_DEVICE_ID1_NUM_THREADS_SIZE) - 1) \ - << XS1_DEVICE_ID1_NUM_THREADS_SHIFT) -#define XS1_DEVICE_ID1_NUM_THREADS(x) \ - (((x)&XS1_DEVICE_ID1_NUM_THREADS_MASK) >> XS1_DEVICE_ID1_NUM_THREADS_SHIFT) -#define XS1_DEVICE_ID1_NUM_THREADS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID1_NUM_THREADS_MASK) | \ - (((v) << XS1_DEVICE_ID1_NUM_THREADS_SHIFT) & \ - XS1_DEVICE_ID1_NUM_THREADS_MASK)) -#define XS1_DEVICE_ID1_NUM_SYNCS_SHIFT 0x8 -#define XS1_DEVICE_ID1_NUM_SYNCS_SIZE 0x8 -#define XS1_DEVICE_ID1_NUM_SYNCS_MASK \ - (((1 << XS1_DEVICE_ID1_NUM_SYNCS_SIZE) - 1) << XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) -#define XS1_DEVICE_ID1_NUM_SYNCS(x) \ - (((x)&XS1_DEVICE_ID1_NUM_SYNCS_MASK) >> XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) -#define XS1_DEVICE_ID1_NUM_SYNCS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID1_NUM_SYNCS_MASK) | \ - (((v) << XS1_DEVICE_ID1_NUM_SYNCS_SHIFT) & XS1_DEVICE_ID1_NUM_SYNCS_MASK)) -#define XS1_DEVICE_ID1_NUM_LOCKS_SHIFT 0x10 -#define XS1_DEVICE_ID1_NUM_LOCKS_SIZE 0x8 -#define XS1_DEVICE_ID1_NUM_LOCKS_MASK \ - (((1 << XS1_DEVICE_ID1_NUM_LOCKS_SIZE) - 1) << XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) -#define XS1_DEVICE_ID1_NUM_LOCKS(x) \ - (((x)&XS1_DEVICE_ID1_NUM_LOCKS_MASK) >> XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) -#define XS1_DEVICE_ID1_NUM_LOCKS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID1_NUM_LOCKS_MASK) | \ - (((v) << XS1_DEVICE_ID1_NUM_LOCKS_SHIFT) & XS1_DEVICE_ID1_NUM_LOCKS_MASK)) -#define XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT 0x18 -#define XS1_DEVICE_ID1_NUM_CHANENDS_SIZE 0x8 -#define XS1_DEVICE_ID1_NUM_CHANENDS_MASK \ - (((1 << XS1_DEVICE_ID1_NUM_CHANENDS_SIZE) - 1) \ - << XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) -#define XS1_DEVICE_ID1_NUM_CHANENDS(x) \ - (((x)&XS1_DEVICE_ID1_NUM_CHANENDS_MASK) >> XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) -#define XS1_DEVICE_ID1_NUM_CHANENDS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID1_NUM_CHANENDS_MASK) | \ - (((v) << XS1_DEVICE_ID1_NUM_CHANENDS_SHIFT) & \ - XS1_DEVICE_ID1_NUM_CHANENDS_MASK)) -#define XS1_DEVICE_ID2_NUM_TIMERS_SHIFT 0x0 -#define XS1_DEVICE_ID2_NUM_TIMERS_SIZE 0x8 -#define XS1_DEVICE_ID2_NUM_TIMERS_MASK \ - (((1 << XS1_DEVICE_ID2_NUM_TIMERS_SIZE) - 1) \ - << XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) -#define XS1_DEVICE_ID2_NUM_TIMERS(x) \ - (((x)&XS1_DEVICE_ID2_NUM_TIMERS_MASK) >> XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) -#define XS1_DEVICE_ID2_NUM_TIMERS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID2_NUM_TIMERS_MASK) | \ - (((v) << XS1_DEVICE_ID2_NUM_TIMERS_SHIFT) & \ - XS1_DEVICE_ID2_NUM_TIMERS_MASK)) -#define XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT 0x8 -#define XS1_DEVICE_ID2_NUM_CLKBLKS_SIZE 0x8 -#define XS1_DEVICE_ID2_NUM_CLKBLKS_MASK \ - (((1 << XS1_DEVICE_ID2_NUM_CLKBLKS_SIZE) - 1) \ - << XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) -#define XS1_DEVICE_ID2_NUM_CLKBLKS(x) \ - (((x)&XS1_DEVICE_ID2_NUM_CLKBLKS_MASK) >> XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) -#define XS1_DEVICE_ID2_NUM_CLKBLKS_SET(x, v) \ - (((x) & ~XS1_DEVICE_ID2_NUM_CLKBLKS_MASK) | \ - (((v) << XS1_DEVICE_ID2_NUM_CLKBLKS_SHIFT) & \ - XS1_DEVICE_ID2_NUM_CLKBLKS_MASK)) -#define XS1_JUNK_SHIFT 0x2 -#define XS1_JUNK_SIZE 0x1 -#define XS1_JUNK_MASK (((1 << XS1_JUNK_SIZE) - 1) << XS1_JUNK_SHIFT) -#define XS1_JUNK(x) (((x)&XS1_JUNK_MASK) >> XS1_JUNK_SHIFT) -#define XS1_JUNK_SET(x, v) \ - (((x) & ~XS1_JUNK_MASK) | (((v) << XS1_JUNK_SHIFT) & XS1_JUNK_MASK)) -#define XS1_NETWORK_SHIFT 0x4 -#define XS1_NETWORK_SIZE 0x2 -#define XS1_NETWORK_MASK (((1 << XS1_NETWORK_SIZE) - 1) << XS1_NETWORK_SHIFT) -#define XS1_NETWORK(x) (((x)&XS1_NETWORK_MASK) >> XS1_NETWORK_SHIFT) -#define XS1_NETWORK_SET(x, v) \ - (((x) & ~XS1_NETWORK_MASK) | (((v) << XS1_NETWORK_SHIFT) & XS1_NETWORK_MASK)) -#define XS1_SRC_TARGET_ID_SHIFT 0x10 -#define XS1_SRC_TARGET_ID_SIZE 0x8 -#define XS1_SRC_TARGET_ID_MASK \ - (((1 << XS1_SRC_TARGET_ID_SIZE) - 1) << XS1_SRC_TARGET_ID_SHIFT) -#define XS1_SRC_TARGET_ID(x) \ - (((x)&XS1_SRC_TARGET_ID_MASK) >> XS1_SRC_TARGET_ID_SHIFT) -#define XS1_SRC_TARGET_ID_SET(x, v) \ - (((x) & ~XS1_SRC_TARGET_ID_MASK) | \ - (((v) << XS1_SRC_TARGET_ID_SHIFT) & XS1_SRC_TARGET_ID_MASK)) -#define XS1_SRC_TARGET_TYPE_SHIFT 0x18 -#define XS1_SRC_TARGET_TYPE_SIZE 0x2 -#define XS1_SRC_TARGET_TYPE_MASK \ - (((1 << XS1_SRC_TARGET_TYPE_SIZE) - 1) << XS1_SRC_TARGET_TYPE_SHIFT) -#define XS1_SRC_TARGET_TYPE(x) \ - (((x)&XS1_SRC_TARGET_TYPE_MASK) >> XS1_SRC_TARGET_TYPE_SHIFT) -#define XS1_SRC_TARGET_TYPE_SET(x, v) \ - (((x) & ~XS1_SRC_TARGET_TYPE_MASK) | \ - (((v) << XS1_SRC_TARGET_TYPE_SHIFT) & XS1_SRC_TARGET_TYPE_MASK)) -#define XS1_SS_DEVICE_ID0_VERSION_SHIFT 0x0 -#define XS1_SS_DEVICE_ID0_VERSION_SIZE 0x8 -#define XS1_SS_DEVICE_ID0_VERSION_MASK \ - (((1 << XS1_SS_DEVICE_ID0_VERSION_SIZE) - 1) \ - << XS1_SS_DEVICE_ID0_VERSION_SHIFT) -#define XS1_SS_DEVICE_ID0_VERSION(x) \ - (((x)&XS1_SS_DEVICE_ID0_VERSION_MASK) >> XS1_SS_DEVICE_ID0_VERSION_SHIFT) -#define XS1_SS_DEVICE_ID0_VERSION_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID0_VERSION_MASK) | \ - (((v) << XS1_SS_DEVICE_ID0_VERSION_SHIFT) & \ - XS1_SS_DEVICE_ID0_VERSION_MASK)) -#define XS1_SS_DEVICE_ID0_REVISION_SHIFT 0x8 -#define XS1_SS_DEVICE_ID0_REVISION_SIZE 0x8 -#define XS1_SS_DEVICE_ID0_REVISION_MASK \ - (((1 << XS1_SS_DEVICE_ID0_REVISION_SIZE) - 1) \ - << XS1_SS_DEVICE_ID0_REVISION_SHIFT) -#define XS1_SS_DEVICE_ID0_REVISION(x) \ - (((x)&XS1_SS_DEVICE_ID0_REVISION_MASK) >> XS1_SS_DEVICE_ID0_REVISION_SHIFT) -#define XS1_SS_DEVICE_ID0_REVISION_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID0_REVISION_MASK) | \ - (((v) << XS1_SS_DEVICE_ID0_REVISION_SHIFT) & \ - XS1_SS_DEVICE_ID0_REVISION_MASK)) -#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT 0x10 -#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SIZE 0x8 -#define XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK \ - (((1 << XS1_SS_DEVICE_ID0_BOOT_CTRL_SIZE) - 1) \ - << XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) -#define XS1_SS_DEVICE_ID0_BOOT_CTRL(x) \ - (((x)&XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK) >> XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) -#define XS1_SS_DEVICE_ID0_BOOT_CTRL_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK) | \ - (((v) << XS1_SS_DEVICE_ID0_BOOT_CTRL_SHIFT) & \ - XS1_SS_DEVICE_ID0_BOOT_CTRL_MASK)) -#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT 0x0 -#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SIZE 0x8 -#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK \ - (((1 << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SIZE) - 1) \ - << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC(x) \ - (((x)&XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK) >> \ - XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK) | \ - (((v) << XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_SHIFT) & \ - XS1_SS_DEVICE_ID1_NUM_PLINKS_PER_PROC_MASK)) -#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT 0x8 -#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SIZE 0x8 -#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK \ - (((1 << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SIZE) - 1) \ - << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS(x) \ - (((x)&XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK) >> \ - XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK) | \ - (((v) << XS1_SS_DEVICE_ID1_NUM_PROCESSORS_SHIFT) & \ - XS1_SS_DEVICE_ID1_NUM_PROCESSORS_MASK)) -#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT 0x10 -#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SIZE 0x8 -#define XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK \ - (((1 << XS1_SS_DEVICE_ID1_NUM_SLINKS_SIZE) - 1) \ - << XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_SLINKS(x) \ - (((x)&XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK) >> \ - XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) -#define XS1_SS_DEVICE_ID1_NUM_SLINKS_SET(x, v) \ - (((x) & ~XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK) | \ - (((v) << XS1_SS_DEVICE_ID1_NUM_SLINKS_SHIFT) & \ - XS1_SS_DEVICE_ID1_NUM_SLINKS_MASK)) -#define XS1_SS_NODE_CONFIG_HEADERS_SHIFT 0x0 -#define XS1_SS_NODE_CONFIG_HEADERS_SIZE 0x1 -#define XS1_SS_NODE_CONFIG_HEADERS_MASK \ - (((1 << XS1_SS_NODE_CONFIG_HEADERS_SIZE) - 1) \ - << XS1_SS_NODE_CONFIG_HEADERS_SHIFT) -#define XS1_SS_NODE_CONFIG_HEADERS(x) \ - (((x)&XS1_SS_NODE_CONFIG_HEADERS_MASK) >> XS1_SS_NODE_CONFIG_HEADERS_SHIFT) -#define XS1_SS_NODE_CONFIG_HEADERS_SET(x, v) \ - (((x) & ~XS1_SS_NODE_CONFIG_HEADERS_MASK) | \ - (((v) << XS1_SS_NODE_CONFIG_HEADERS_SHIFT) & \ - XS1_SS_NODE_CONFIG_HEADERS_MASK)) -#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT 0x8 -#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SIZE 0x1 -#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK \ - (((1 << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SIZE) - 1) \ - << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) -#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG(x) \ - (((x)&XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK) >> \ - XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) -#define XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SET(x, v) \ - (((x) & ~XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK) | \ - (((v) << XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_SHIFT) & \ - XS1_SS_NODE_CONFIG_DISABLE_PLL_CTL_REG_MASK)) -#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT 0x1f -#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SIZE 0x1 -#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK \ - (((1 << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SIZE) - 1) \ - << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) -#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE(x) \ - (((x)&XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK) >> \ - XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) -#define XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SET(x, v) \ - (((x) & ~XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK) | \ - (((v) << XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_SHIFT) & \ - XS1_SS_NODE_CONFIG_DISABLE_SSCTL_UPDATE_MASK)) -#define XS1_SS_NODE_ID_ID_SHIFT 0x0 -#define XS1_SS_NODE_ID_ID_SIZE 0x10 -#define XS1_SS_NODE_ID_ID_MASK \ - (((1 << XS1_SS_NODE_ID_ID_SIZE) - 1) << XS1_SS_NODE_ID_ID_SHIFT) -#define XS1_SS_NODE_ID_ID(x) \ - (((x)&XS1_SS_NODE_ID_ID_MASK) >> XS1_SS_NODE_ID_ID_SHIFT) -#define XS1_SS_NODE_ID_ID_SET(x, v) \ - (((x) & ~XS1_SS_NODE_ID_ID_MASK) | \ - (((v) << XS1_SS_NODE_ID_ID_SHIFT) & XS1_SS_NODE_ID_ID_MASK)) -#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT 0x0 -#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SIZE 0x7 -#define XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK \ - (((1 << XS1_SS_PLL_CTL_INPUT_DIVISOR_SIZE) - 1) \ - << XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) -#define XS1_SS_PLL_CTL_INPUT_DIVISOR(x) \ - (((x)&XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK) >> \ - XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) -#define XS1_SS_PLL_CTL_INPUT_DIVISOR_SET(x, v) \ - (((x) & ~XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK) | \ - (((v) << XS1_SS_PLL_CTL_INPUT_DIVISOR_SHIFT) & \ - XS1_SS_PLL_CTL_INPUT_DIVISOR_MASK)) -#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT 0x8 -#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SIZE 0xd -#define XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK \ - (((1 << XS1_SS_PLL_CTL_FEEDBACK_MUL_SIZE) - 1) \ - << XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) -#define XS1_SS_PLL_CTL_FEEDBACK_MUL(x) \ - (((x)&XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK) >> XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) -#define XS1_SS_PLL_CTL_FEEDBACK_MUL_SET(x, v) \ - (((x) & ~XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK) | \ - (((v) << XS1_SS_PLL_CTL_FEEDBACK_MUL_SHIFT) & \ - XS1_SS_PLL_CTL_FEEDBACK_MUL_MASK)) -#define XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT 0x17 -#define XS1_SS_PLL_CTL_POST_DIVISOR_SIZE 0x3 -#define XS1_SS_PLL_CTL_POST_DIVISOR_MASK \ - (((1 << XS1_SS_PLL_CTL_POST_DIVISOR_SIZE) - 1) \ - << XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) -#define XS1_SS_PLL_CTL_POST_DIVISOR(x) \ - (((x)&XS1_SS_PLL_CTL_POST_DIVISOR_MASK) >> XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) -#define XS1_SS_PLL_CTL_POST_DIVISOR_SET(x, v) \ - (((x) & ~XS1_SS_PLL_CTL_POST_DIVISOR_MASK) | \ - (((v) << XS1_SS_PLL_CTL_POST_DIVISOR_SHIFT) & \ - XS1_SS_PLL_CTL_POST_DIVISOR_MASK)) -#define XS1_SS_APP_PLL_ENABLE_SHIFT 0x1b -#define XS1_SS_APP_PLL_ENABLE_SIZE 0x1 -#define XS1_SS_APP_PLL_ENABLE_MASK \ - (((1 << XS1_SS_APP_PLL_ENABLE_SIZE) - 1) << XS1_SS_APP_PLL_ENABLE_SHIFT) -#define XS1_SS_APP_PLL_ENABLE(x) \ - (((x)&XS1_SS_APP_PLL_ENABLE_MASK) >> XS1_SS_APP_PLL_ENABLE_SHIFT) -#define XS1_SS_APP_PLL_ENABLE_SET(x, v) \ - (((x) & ~XS1_SS_APP_PLL_ENABLE_MASK) | \ - (((v) << XS1_SS_APP_PLL_ENABLE_SHIFT) & XS1_SS_APP_PLL_ENABLE_MASK)) -#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT 0x0 -#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SIZE 0x8 -#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK \ - (((1 << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SIZE) - 1) \ - << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) -#define XS1_SS_FRAC_N_PERIOD_CYC_CNT(x) \ - (((x)&XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK) >> \ - XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) -#define XS1_SS_FRAC_N_PERIOD_CYC_CNT_SET(x, v) \ - (((x) & ~XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK) | \ - (((v) << XS1_SS_FRAC_N_PERIOD_CYC_CNT_SHIFT) & \ - XS1_SS_FRAC_N_PERIOD_CYC_CNT_MASK)) -#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT 0x8 -#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SIZE 0x8 -#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK \ - (((1 << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SIZE) - 1) \ - << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) -#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT(x) \ - (((x)&XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK) >> \ - XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) -#define XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SET(x, v) \ - (((x) & ~XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK) | \ - (((v) << XS1_SS_FRAC_N_F_HIGH_CYC_CNT_SHIFT) & \ - XS1_SS_FRAC_N_F_HIGH_CYC_CNT_MASK)) -#define XS1_SS_FRAC_N_ENABLE_SHIFT 0x1f -#define XS1_SS_FRAC_N_ENABLE_SIZE 0x1 -#define XS1_SS_FRAC_N_ENABLE_MASK \ - (((1 << XS1_SS_FRAC_N_ENABLE_SIZE) - 1) << XS1_SS_FRAC_N_ENABLE_SHIFT) -#define XS1_SS_FRAC_N_ENABLE(x) \ - (((x)&XS1_SS_FRAC_N_ENABLE_MASK) >> XS1_SS_FRAC_N_ENABLE_SHIFT) -#define XS1_SS_FRAC_N_ENABLE_SET(x, v) \ - (((x) & ~XS1_SS_FRAC_N_ENABLE_MASK) | \ - (((v) << XS1_SS_FRAC_N_ENABLE_SHIFT) & XS1_SS_FRAC_N_ENABLE_MASK)) -#define XS1_SS_PLL_CTL_NLOCK_SHIFT 0x1e -#define XS1_SS_PLL_CTL_NRESET_SHIFT 0x1f -#define XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT 0x0 -#define XS1_SS_CLK_DIVIDER_CLK_DIV_SIZE 0x10 -#define XS1_SS_CLK_DIVIDER_CLK_DIV_MASK \ - (((1 << XS1_SS_CLK_DIVIDER_CLK_DIV_SIZE) - 1) \ - << XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) -#define XS1_SS_CLK_DIVIDER_CLK_DIV(x) \ - (((x)&XS1_SS_CLK_DIVIDER_CLK_DIV_MASK) >> XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) -#define XS1_SS_CLK_DIVIDER_CLK_DIV_SET(x, v) \ - (((x) & ~XS1_SS_CLK_DIVIDER_CLK_DIV_MASK) | \ - (((v) << XS1_SS_CLK_DIVIDER_CLK_DIV_SHIFT) & \ - XS1_SS_CLK_DIVIDER_CLK_DIV_MASK)) -#define XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT 0x0 -#define XS1_SS_SSWITCH_REF_CLK_DIV_SIZE 0x10 -#define XS1_SS_SSWITCH_REF_CLK_DIV_MASK \ - (((1 << XS1_SS_SSWITCH_REF_CLK_DIV_SIZE) - 1) \ - << XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) -#define XS1_SS_SSWITCH_REF_CLK_DIV(x) \ - (((x)&XS1_SS_SSWITCH_REF_CLK_DIV_MASK) >> XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) -#define XS1_SS_SSWITCH_REF_CLK_DIV_SET(x, v) \ - (((x) & ~XS1_SS_SSWITCH_REF_CLK_DIV_MASK) | \ - (((v) << XS1_SS_SSWITCH_REF_CLK_DIV_SHIFT) & \ - XS1_SS_SSWITCH_REF_CLK_DIV_MASK)) -#define XS1_DIM0_DIR_SHIFT 0x0 -#define XS1_DIM0_DIR_SIZE 0x4 -#define XS1_DIM0_DIR_MASK (((1 << XS1_DIM0_DIR_SIZE) - 1) << XS1_DIM0_DIR_SHIFT) -#define XS1_DIM0_DIR(x) (((x)&XS1_DIM0_DIR_MASK) >> XS1_DIM0_DIR_SHIFT) -#define XS1_DIM0_DIR_SET(x, v) \ - (((x) & ~XS1_DIM0_DIR_MASK) | \ - (((v) << XS1_DIM0_DIR_SHIFT) & XS1_DIM0_DIR_MASK)) -#define XS1_DIM1_DIR_SHIFT 0x4 -#define XS1_DIM1_DIR_SIZE 0x4 -#define XS1_DIM1_DIR_MASK (((1 << XS1_DIM1_DIR_SIZE) - 1) << XS1_DIM1_DIR_SHIFT) -#define XS1_DIM1_DIR(x) (((x)&XS1_DIM1_DIR_MASK) >> XS1_DIM1_DIR_SHIFT) -#define XS1_DIM1_DIR_SET(x, v) \ - (((x) & ~XS1_DIM1_DIR_MASK) | \ - (((v) << XS1_DIM1_DIR_SHIFT) & XS1_DIM1_DIR_MASK)) -#define XS1_DIM2_DIR_SHIFT 0x8 -#define XS1_DIM2_DIR_SIZE 0x4 -#define XS1_DIM2_DIR_MASK (((1 << XS1_DIM2_DIR_SIZE) - 1) << XS1_DIM2_DIR_SHIFT) -#define XS1_DIM2_DIR(x) (((x)&XS1_DIM2_DIR_MASK) >> XS1_DIM2_DIR_SHIFT) -#define XS1_DIM2_DIR_SET(x, v) \ - (((x) & ~XS1_DIM2_DIR_MASK) | \ - (((v) << XS1_DIM2_DIR_SHIFT) & XS1_DIM2_DIR_MASK)) -#define XS1_DIM3_DIR_SHIFT 0xc -#define XS1_DIM3_DIR_SIZE 0x4 -#define XS1_DIM3_DIR_MASK (((1 << XS1_DIM3_DIR_SIZE) - 1) << XS1_DIM3_DIR_SHIFT) -#define XS1_DIM3_DIR(x) (((x)&XS1_DIM3_DIR_MASK) >> XS1_DIM3_DIR_SHIFT) -#define XS1_DIM3_DIR_SET(x, v) \ - (((x) & ~XS1_DIM3_DIR_MASK) | \ - (((v) << XS1_DIM3_DIR_SHIFT) & XS1_DIM3_DIR_MASK)) -#define XS1_DIM4_DIR_SHIFT 0x10 -#define XS1_DIM4_DIR_SIZE 0x4 -#define XS1_DIM4_DIR_MASK (((1 << XS1_DIM4_DIR_SIZE) - 1) << XS1_DIM4_DIR_SHIFT) -#define XS1_DIM4_DIR(x) (((x)&XS1_DIM4_DIR_MASK) >> XS1_DIM4_DIR_SHIFT) -#define XS1_DIM4_DIR_SET(x, v) \ - (((x) & ~XS1_DIM4_DIR_MASK) | \ - (((v) << XS1_DIM4_DIR_SHIFT) & XS1_DIM4_DIR_MASK)) -#define XS1_DIM5_DIR_SHIFT 0x14 -#define XS1_DIM5_DIR_SIZE 0x4 -#define XS1_DIM5_DIR_MASK (((1 << XS1_DIM5_DIR_SIZE) - 1) << XS1_DIM5_DIR_SHIFT) -#define XS1_DIM5_DIR(x) (((x)&XS1_DIM5_DIR_MASK) >> XS1_DIM5_DIR_SHIFT) -#define XS1_DIM5_DIR_SET(x, v) \ - (((x) & ~XS1_DIM5_DIR_MASK) | \ - (((v) << XS1_DIM5_DIR_SHIFT) & XS1_DIM5_DIR_MASK)) -#define XS1_DIM6_DIR_SHIFT 0x18 -#define XS1_DIM6_DIR_SIZE 0x4 -#define XS1_DIM6_DIR_MASK (((1 << XS1_DIM6_DIR_SIZE) - 1) << XS1_DIM6_DIR_SHIFT) -#define XS1_DIM6_DIR(x) (((x)&XS1_DIM6_DIR_MASK) >> XS1_DIM6_DIR_SHIFT) -#define XS1_DIM6_DIR_SET(x, v) \ - (((x) & ~XS1_DIM6_DIR_MASK) | \ - (((v) << XS1_DIM6_DIR_SHIFT) & XS1_DIM6_DIR_MASK)) -#define XS1_DIM7_DIR_SHIFT 0x1c -#define XS1_DIM7_DIR_SIZE 0x4 -#define XS1_DIM7_DIR_MASK (((1 << XS1_DIM7_DIR_SIZE) - 1) << XS1_DIM7_DIR_SHIFT) -#define XS1_DIM7_DIR(x) (((x)&XS1_DIM7_DIR_MASK) >> XS1_DIM7_DIR_SHIFT) -#define XS1_DIM7_DIR_SET(x, v) \ - (((x) & ~XS1_DIM7_DIR_MASK) | \ - (((v) << XS1_DIM7_DIR_SHIFT) & XS1_DIM7_DIR_MASK)) -#define XS1_DIM8_DIR_SHIFT 0x0 -#define XS1_DIM8_DIR_SIZE 0x4 -#define XS1_DIM8_DIR_MASK (((1 << XS1_DIM8_DIR_SIZE) - 1) << XS1_DIM8_DIR_SHIFT) -#define XS1_DIM8_DIR(x) (((x)&XS1_DIM8_DIR_MASK) >> XS1_DIM8_DIR_SHIFT) -#define XS1_DIM8_DIR_SET(x, v) \ - (((x) & ~XS1_DIM8_DIR_MASK) | \ - (((v) << XS1_DIM8_DIR_SHIFT) & XS1_DIM8_DIR_MASK)) -#define XS1_DIM9_DIR_SHIFT 0x4 -#define XS1_DIM9_DIR_SIZE 0x4 -#define XS1_DIM9_DIR_MASK (((1 << XS1_DIM9_DIR_SIZE) - 1) << XS1_DIM9_DIR_SHIFT) -#define XS1_DIM9_DIR(x) (((x)&XS1_DIM9_DIR_MASK) >> XS1_DIM9_DIR_SHIFT) -#define XS1_DIM9_DIR_SET(x, v) \ - (((x) & ~XS1_DIM9_DIR_MASK) | \ - (((v) << XS1_DIM9_DIR_SHIFT) & XS1_DIM9_DIR_MASK)) -#define XS1_DIMA_DIR_SHIFT 0x8 -#define XS1_DIMA_DIR_SIZE 0x4 -#define XS1_DIMA_DIR_MASK (((1 << XS1_DIMA_DIR_SIZE) - 1) << XS1_DIMA_DIR_SHIFT) -#define XS1_DIMA_DIR(x) (((x)&XS1_DIMA_DIR_MASK) >> XS1_DIMA_DIR_SHIFT) -#define XS1_DIMA_DIR_SET(x, v) \ - (((x) & ~XS1_DIMA_DIR_MASK) | \ - (((v) << XS1_DIMA_DIR_SHIFT) & XS1_DIMA_DIR_MASK)) -#define XS1_DIMB_DIR_SHIFT 0xc -#define XS1_DIMB_DIR_SIZE 0x4 -#define XS1_DIMB_DIR_MASK (((1 << XS1_DIMB_DIR_SIZE) - 1) << XS1_DIMB_DIR_SHIFT) -#define XS1_DIMB_DIR(x) (((x)&XS1_DIMB_DIR_MASK) >> XS1_DIMB_DIR_SHIFT) -#define XS1_DIMB_DIR_SET(x, v) \ - (((x) & ~XS1_DIMB_DIR_MASK) | \ - (((v) << XS1_DIMB_DIR_SHIFT) & XS1_DIMB_DIR_MASK)) -#define XS1_DIMC_DIR_SHIFT 0x10 -#define XS1_DIMC_DIR_SIZE 0x4 -#define XS1_DIMC_DIR_MASK (((1 << XS1_DIMC_DIR_SIZE) - 1) << XS1_DIMC_DIR_SHIFT) -#define XS1_DIMC_DIR(x) (((x)&XS1_DIMC_DIR_MASK) >> XS1_DIMC_DIR_SHIFT) -#define XS1_DIMC_DIR_SET(x, v) \ - (((x) & ~XS1_DIMC_DIR_MASK) | \ - (((v) << XS1_DIMC_DIR_SHIFT) & XS1_DIMC_DIR_MASK)) -#define XS1_DIMD_DIR_SHIFT 0x14 -#define XS1_DIMD_DIR_SIZE 0x4 -#define XS1_DIMD_DIR_MASK (((1 << XS1_DIMD_DIR_SIZE) - 1) << XS1_DIMD_DIR_SHIFT) -#define XS1_DIMD_DIR(x) (((x)&XS1_DIMD_DIR_MASK) >> XS1_DIMD_DIR_SHIFT) -#define XS1_DIMD_DIR_SET(x, v) \ - (((x) & ~XS1_DIMD_DIR_MASK) | \ - (((v) << XS1_DIMD_DIR_SHIFT) & XS1_DIMD_DIR_MASK)) -#define XS1_DIME_DIR_SHIFT 0x18 -#define XS1_DIME_DIR_SIZE 0x4 -#define XS1_DIME_DIR_MASK (((1 << XS1_DIME_DIR_SIZE) - 1) << XS1_DIME_DIR_SHIFT) -#define XS1_DIME_DIR(x) (((x)&XS1_DIME_DIR_MASK) >> XS1_DIME_DIR_SHIFT) -#define XS1_DIME_DIR_SET(x, v) \ - (((x) & ~XS1_DIME_DIR_MASK) | \ - (((v) << XS1_DIME_DIR_SHIFT) & XS1_DIME_DIR_MASK)) -#define XS1_DIMF_DIR_SHIFT 0x1c -#define XS1_DIMF_DIR_SIZE 0x4 -#define XS1_DIMF_DIR_MASK (((1 << XS1_DIMF_DIR_SIZE) - 1) << XS1_DIMF_DIR_SHIFT) -#define XS1_DIMF_DIR(x) (((x)&XS1_DIMF_DIR_MASK) >> XS1_DIMF_DIR_SHIFT) -#define XS1_DIMF_DIR_SET(x, v) \ - (((x) & ~XS1_DIMF_DIR_MASK) | \ - (((v) << XS1_DIMF_DIR_SHIFT) & XS1_DIMF_DIR_MASK)) -#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT 0x0 -#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SIZE 0x1 -#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK \ - (((1 << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SIZE) - 1) \ - << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG(x) \ - (((x)&XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK) >> \ - XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SET(x, v) \ - (((x) & ~XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK) | \ - (((v) << XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_SHIFT) & \ - XS1_GLOBAL_DEBUG_ENABLE_INDEBUG_MASK)) -#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT 0x1 -#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SIZE 0x1 -#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK \ - (((1 << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SIZE) - 1) \ - << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) -#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ(x) \ - (((x)&XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK) >> \ - XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) -#define XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SET(x, v) \ - (((x) & ~XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK) | \ - (((v) << XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_SHIFT) & \ - XS1_GLOBAL_DEBUG_ENABLE_GLOBAL_DEBUG_REQ_MASK)) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT 0x0 -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SIZE 0x1 -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK \ - (((1 << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SIZE) - 1) \ - << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG(x) \ - (((x)&XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK) >> \ - XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SET(x, v) \ - (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK) | \ - (((v) << XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_SHIFT) & \ - XS1_GLOBAL_DEBUG_SOURCE_XCORE0_INDEBUG_MASK)) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT 0x1 -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SIZE 0x1 -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK \ - (((1 << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SIZE) - 1) \ - << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG(x) \ - (((x)&XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK) >> \ - XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SET(x, v) \ - (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK) | \ - (((v) << XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_SHIFT) & \ - XS1_GLOBAL_DEBUG_SOURCE_XCORE1_INDEBUG_MASK)) -#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT 0x4 -#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SIZE 0x1 -#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK \ - (((1 << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SIZE) - 1) \ - << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG(x) \ - (((x)&XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK) >> \ - XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) -#define XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SET(x, v) \ - (((x) & ~XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK) | \ - (((v) << XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_SHIFT) & \ - XS1_GLOBAL_DEBUG_SOURCE_EXTERNAL_PAD_INDEBUG_MASK)) -#define XS1_LINK_SRC_INUSE_SHIFT 0x0 -#define XS1_LINK_SRC_INUSE_SIZE 0x1 -#define XS1_LINK_SRC_INUSE_MASK \ - (((1 << XS1_LINK_SRC_INUSE_SIZE) - 1) << XS1_LINK_SRC_INUSE_SHIFT) -#define XS1_LINK_SRC_INUSE(x) \ - (((x)&XS1_LINK_SRC_INUSE_MASK) >> XS1_LINK_SRC_INUSE_SHIFT) -#define XS1_LINK_SRC_INUSE_SET(x, v) \ - (((x) & ~XS1_LINK_SRC_INUSE_MASK) | \ - (((v) << XS1_LINK_SRC_INUSE_SHIFT) & XS1_LINK_SRC_INUSE_MASK)) -#define XS1_LINK_DST_INUSE_SHIFT 0x1 -#define XS1_LINK_DST_INUSE_SIZE 0x1 -#define XS1_LINK_DST_INUSE_MASK \ - (((1 << XS1_LINK_DST_INUSE_SIZE) - 1) << XS1_LINK_DST_INUSE_SHIFT) -#define XS1_LINK_DST_INUSE(x) \ - (((x)&XS1_LINK_DST_INUSE_MASK) >> XS1_LINK_DST_INUSE_SHIFT) -#define XS1_LINK_DST_INUSE_SET(x, v) \ - (((x) & ~XS1_LINK_DST_INUSE_MASK) | \ - (((v) << XS1_LINK_DST_INUSE_SHIFT) & XS1_LINK_DST_INUSE_MASK)) -#define XS1_LINK_JUNK_SHIFT 0x2 -#define XS1_LINK_JUNK_SIZE 0x1 -#define XS1_LINK_JUNK_MASK \ - (((1 << XS1_LINK_JUNK_SIZE) - 1) << XS1_LINK_JUNK_SHIFT) -#define XS1_LINK_JUNK(x) (((x)&XS1_LINK_JUNK_MASK) >> XS1_LINK_JUNK_SHIFT) -#define XS1_LINK_JUNK_SET(x, v) \ - (((x) & ~XS1_LINK_JUNK_MASK) | \ - (((v) << XS1_LINK_JUNK_SHIFT) & XS1_LINK_JUNK_MASK)) -#define XS1_LINK_NETWORK_SHIFT 0x4 -#define XS1_LINK_NETWORK_SIZE 0x2 -#define XS1_LINK_NETWORK_MASK \ - (((1 << XS1_LINK_NETWORK_SIZE) - 1) << XS1_LINK_NETWORK_SHIFT) -#define XS1_LINK_NETWORK(x) \ - (((x)&XS1_LINK_NETWORK_MASK) >> XS1_LINK_NETWORK_SHIFT) -#define XS1_LINK_NETWORK_SET(x, v) \ - (((x) & ~XS1_LINK_NETWORK_MASK) | \ - (((v) << XS1_LINK_NETWORK_SHIFT) & XS1_LINK_NETWORK_MASK)) -#define XS1_LINK_DIRECTION_SHIFT 0x8 -#define XS1_LINK_DIRECTION_SIZE 0x4 -#define XS1_LINK_DIRECTION_MASK \ - (((1 << XS1_LINK_DIRECTION_SIZE) - 1) << XS1_LINK_DIRECTION_SHIFT) -#define XS1_LINK_DIRECTION(x) \ - (((x)&XS1_LINK_DIRECTION_MASK) >> XS1_LINK_DIRECTION_SHIFT) -#define XS1_LINK_DIRECTION_SET(x, v) \ - (((x) & ~XS1_LINK_DIRECTION_MASK) | \ - (((v) << XS1_LINK_DIRECTION_SHIFT) & XS1_LINK_DIRECTION_MASK)) -#define XS1_XLINK_INTER_TOKEN_DELAY_SHIFT 0x0 -#define XS1_XLINK_INTER_TOKEN_DELAY_SIZE 0xb -#define XS1_XLINK_INTER_TOKEN_DELAY_MASK \ - (((1 << XS1_XLINK_INTER_TOKEN_DELAY_SIZE) - 1) \ - << XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) -#define XS1_XLINK_INTER_TOKEN_DELAY(x) \ - (((x)&XS1_XLINK_INTER_TOKEN_DELAY_MASK) >> XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) -#define XS1_XLINK_INTER_TOKEN_DELAY_SET(x, v) \ - (((x) & ~XS1_XLINK_INTER_TOKEN_DELAY_MASK) | \ - (((v) << XS1_XLINK_INTER_TOKEN_DELAY_SHIFT) & \ - XS1_XLINK_INTER_TOKEN_DELAY_MASK)) -#define XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT 0xb -#define XS1_XLINK_INTRA_TOKEN_DELAY_SIZE 0xb -#define XS1_XLINK_INTRA_TOKEN_DELAY_MASK \ - (((1 << XS1_XLINK_INTRA_TOKEN_DELAY_SIZE) - 1) \ - << XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) -#define XS1_XLINK_INTRA_TOKEN_DELAY(x) \ - (((x)&XS1_XLINK_INTRA_TOKEN_DELAY_MASK) >> XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) -#define XS1_XLINK_INTRA_TOKEN_DELAY_SET(x, v) \ - (((x) & ~XS1_XLINK_INTRA_TOKEN_DELAY_MASK) | \ - (((v) << XS1_XLINK_INTRA_TOKEN_DELAY_SHIFT) & \ - XS1_XLINK_INTRA_TOKEN_DELAY_MASK)) -#define XS1_XLINK_RX_RESET_SHIFT 0x17 -#define XS1_XLINK_RX_RESET_SIZE 0x1 -#define XS1_XLINK_RX_RESET_MASK \ - (((1 << XS1_XLINK_RX_RESET_SIZE) - 1) << XS1_XLINK_RX_RESET_SHIFT) -#define XS1_XLINK_RX_RESET(x) \ - (((x)&XS1_XLINK_RX_RESET_MASK) >> XS1_XLINK_RX_RESET_SHIFT) -#define XS1_XLINK_RX_RESET_SET(x, v) \ - (((x) & ~XS1_XLINK_RX_RESET_MASK) | \ - (((v) << XS1_XLINK_RX_RESET_SHIFT) & XS1_XLINK_RX_RESET_MASK)) -#define XS1_XLINK_HELLO_SHIFT 0x18 -#define XS1_XLINK_HELLO_SIZE 0x1 -#define XS1_XLINK_HELLO_MASK \ - (((1 << XS1_XLINK_HELLO_SIZE) - 1) << XS1_XLINK_HELLO_SHIFT) -#define XS1_XLINK_HELLO(x) (((x)&XS1_XLINK_HELLO_MASK) >> XS1_XLINK_HELLO_SHIFT) -#define XS1_XLINK_HELLO_SET(x, v) \ - (((x) & ~XS1_XLINK_HELLO_MASK) | \ - (((v) << XS1_XLINK_HELLO_SHIFT) & XS1_XLINK_HELLO_MASK)) -#define XS1_TX_CREDIT_SHIFT 0x19 -#define XS1_TX_CREDIT_SIZE 0x1 -#define XS1_TX_CREDIT_MASK \ - (((1 << XS1_TX_CREDIT_SIZE) - 1) << XS1_TX_CREDIT_SHIFT) -#define XS1_TX_CREDIT(x) (((x)&XS1_TX_CREDIT_MASK) >> XS1_TX_CREDIT_SHIFT) -#define XS1_TX_CREDIT_SET(x, v) \ - (((x) & ~XS1_TX_CREDIT_MASK) | \ - (((v) << XS1_TX_CREDIT_SHIFT) & XS1_TX_CREDIT_MASK)) -#define XS1_RX_CREDIT_SHIFT 0x1a -#define XS1_RX_CREDIT_SIZE 0x1 -#define XS1_RX_CREDIT_MASK \ - (((1 << XS1_RX_CREDIT_SIZE) - 1) << XS1_RX_CREDIT_SHIFT) -#define XS1_RX_CREDIT(x) (((x)&XS1_RX_CREDIT_MASK) >> XS1_RX_CREDIT_SHIFT) -#define XS1_RX_CREDIT_SET(x, v) \ - (((x) & ~XS1_RX_CREDIT_MASK) | \ - (((v) << XS1_RX_CREDIT_SHIFT) & XS1_RX_CREDIT_MASK)) -#define XS1_XLINK_RX_ERROR_SHIFT 0x1b -#define XS1_XLINK_RX_ERROR_SIZE 0x1 -#define XS1_XLINK_RX_ERROR_MASK \ - (((1 << XS1_XLINK_RX_ERROR_SIZE) - 1) << XS1_XLINK_RX_ERROR_SHIFT) -#define XS1_XLINK_RX_ERROR(x) \ - (((x)&XS1_XLINK_RX_ERROR_MASK) >> XS1_XLINK_RX_ERROR_SHIFT) -#define XS1_XLINK_RX_ERROR_SET(x, v) \ - (((x) & ~XS1_XLINK_RX_ERROR_MASK) | \ - (((v) << XS1_XLINK_RX_ERROR_SHIFT) & XS1_XLINK_RX_ERROR_MASK)) -#define XS1_XLINK_WIDE_SHIFT 0x1e -#define XS1_XLINK_WIDE_SIZE 0x1 -#define XS1_XLINK_WIDE_MASK \ - (((1 << XS1_XLINK_WIDE_SIZE) - 1) << XS1_XLINK_WIDE_SHIFT) -#define XS1_XLINK_WIDE(x) (((x)&XS1_XLINK_WIDE_MASK) >> XS1_XLINK_WIDE_SHIFT) -#define XS1_XLINK_WIDE_SET(x, v) \ - (((x) & ~XS1_XLINK_WIDE_MASK) | \ - (((v) << XS1_XLINK_WIDE_SHIFT) & XS1_XLINK_WIDE_MASK)) -#define XS1_XLINK_ENABLE_SHIFT 0x1f -#define XS1_XLINK_ENABLE_SIZE 0x1 -#define XS1_XLINK_ENABLE_MASK \ - (((1 << XS1_XLINK_ENABLE_SIZE) - 1) << XS1_XLINK_ENABLE_SHIFT) -#define XS1_XLINK_ENABLE(x) \ - (((x)&XS1_XLINK_ENABLE_MASK) >> XS1_XLINK_ENABLE_SHIFT) -#define XS1_XLINK_ENABLE_SET(x, v) \ - (((x) & ~XS1_XLINK_ENABLE_MASK) | \ - (((v) << XS1_XLINK_ENABLE_SHIFT) & XS1_XLINK_ENABLE_MASK)) -#define XS1_XSTATIC_DEST_CHAN_END_SHIFT 0x0 -#define XS1_XSTATIC_DEST_CHAN_END_SIZE 0x5 -#define XS1_XSTATIC_DEST_CHAN_END_MASK \ - (((1 << XS1_XSTATIC_DEST_CHAN_END_SIZE) - 1) \ - << XS1_XSTATIC_DEST_CHAN_END_SHIFT) -#define XS1_XSTATIC_DEST_CHAN_END(x) \ - (((x)&XS1_XSTATIC_DEST_CHAN_END_MASK) >> XS1_XSTATIC_DEST_CHAN_END_SHIFT) -#define XS1_XSTATIC_DEST_CHAN_END_SET(x, v) \ - (((x) & ~XS1_XSTATIC_DEST_CHAN_END_MASK) | \ - (((v) << XS1_XSTATIC_DEST_CHAN_END_SHIFT) & \ - XS1_XSTATIC_DEST_CHAN_END_MASK)) -#define XS1_XSTATIC_ENABLE_SHIFT 0x1f -#define XS1_XSTATIC_ENABLE_SIZE 0x1 -#define XS1_XSTATIC_ENABLE_MASK \ - (((1 << XS1_XSTATIC_ENABLE_SIZE) - 1) << XS1_XSTATIC_ENABLE_SHIFT) -#define XS1_XSTATIC_ENABLE(x) \ - (((x)&XS1_XSTATIC_ENABLE_MASK) >> XS1_XSTATIC_ENABLE_SHIFT) -#define XS1_XSTATIC_ENABLE_SET(x, v) \ - (((x) & ~XS1_XSTATIC_ENABLE_MASK) | \ - (((v) << XS1_XSTATIC_ENABLE_SHIFT) & XS1_XSTATIC_ENABLE_MASK)) -#define XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT 0x8 -#define XS1_SSCTRL_PSCTRL_CORE_NUM_SIZE 0x8 -#define XS1_SSCTRL_PSCTRL_CORE_NUM_MASK \ - (((1 << XS1_SSCTRL_PSCTRL_CORE_NUM_SIZE) - 1) \ - << XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) -#define XS1_SSCTRL_PSCTRL_CORE_NUM(x) \ - (((x)&XS1_SSCTRL_PSCTRL_CORE_NUM_MASK) >> XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) -#define XS1_SSCTRL_PSCTRL_CORE_NUM_SET(x, v) \ - (((x) & ~XS1_SSCTRL_PSCTRL_CORE_NUM_MASK) | \ - (((v) << XS1_SSCTRL_PSCTRL_CORE_NUM_SHIFT) & \ - XS1_SSCTRL_PSCTRL_CORE_NUM_MASK)) -#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT 0x0 -#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SIZE 0x2 -#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK \ - (((1 << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT(x) \ - (((x)&XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK) >> \ - XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_SHIFT) & \ - XS1_USB_PHY_CFG0_UTMI_XCVRSELECT_MASK)) -#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT 0x2 -#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SIZE 0x1 -#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK \ - (((1 << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT(x) \ - (((x)&XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK) >> \ - XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_UTMI_TERMSELECT_SHIFT) & \ - XS1_USB_PHY_CFG0_UTMI_TERMSELECT_MASK)) -#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT 0x3 -#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SIZE 0x2 -#define XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK \ - (((1 << XS1_USB_PHY_CFG0_UTMI_OPMODE_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_OPMODE(x) \ - (((x)&XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK) >> \ - XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_OPMODE_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_UTMI_OPMODE_SHIFT) & \ - XS1_USB_PHY_CFG0_UTMI_OPMODE_MASK)) -#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT 0x5 -#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SIZE 0x1 -#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK \ - (((1 << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM(x) \ - (((x)&XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK) >> \ - XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) -#define XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_UTMI_SUSPENDM_SHIFT) & \ - XS1_USB_PHY_CFG0_UTMI_SUSPENDM_MASK)) -#define XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT 0x6 -#define XS1_USB_PHY_CFG0_DPPULLDOWN_SIZE 0x1 -#define XS1_USB_PHY_CFG0_DPPULLDOWN_MASK \ - (((1 << XS1_USB_PHY_CFG0_DPPULLDOWN_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) -#define XS1_USB_PHY_CFG0_DPPULLDOWN(x) \ - (((x)&XS1_USB_PHY_CFG0_DPPULLDOWN_MASK) >> XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) -#define XS1_USB_PHY_CFG0_DPPULLDOWN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_DPPULLDOWN_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_DPPULLDOWN_SHIFT) & \ - XS1_USB_PHY_CFG0_DPPULLDOWN_MASK)) -#define XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT 0x7 -#define XS1_USB_PHY_CFG0_DMPULLDOWN_SIZE 0x1 -#define XS1_USB_PHY_CFG0_DMPULLDOWN_MASK \ - (((1 << XS1_USB_PHY_CFG0_DMPULLDOWN_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) -#define XS1_USB_PHY_CFG0_DMPULLDOWN(x) \ - (((x)&XS1_USB_PHY_CFG0_DMPULLDOWN_MASK) >> XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) -#define XS1_USB_PHY_CFG0_DMPULLDOWN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_DMPULLDOWN_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_DMPULLDOWN_SHIFT) & \ - XS1_USB_PHY_CFG0_DMPULLDOWN_MASK)) -#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT 0x8 -#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SIZE 0x1 -#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK \ - (((1 << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) -#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN(x) \ - (((x)&XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK) >> \ - XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) -#define XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_TXBITSTUFF_EN_SHIFT) & \ - XS1_USB_PHY_CFG0_TXBITSTUFF_EN_MASK)) -#define XS1_USB_PHY_CFG0_PLL_EN_SHIFT 0x9 -#define XS1_USB_PHY_CFG0_PLL_EN_SIZE 0x1 -#define XS1_USB_PHY_CFG0_PLL_EN_MASK \ - (((1 << XS1_USB_PHY_CFG0_PLL_EN_SIZE) - 1) << XS1_USB_PHY_CFG0_PLL_EN_SHIFT) -#define XS1_USB_PHY_CFG0_PLL_EN(x) \ - (((x)&XS1_USB_PHY_CFG0_PLL_EN_MASK) >> XS1_USB_PHY_CFG0_PLL_EN_SHIFT) -#define XS1_USB_PHY_CFG0_PLL_EN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_PLL_EN_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_PLL_EN_SHIFT) & XS1_USB_PHY_CFG0_PLL_EN_MASK)) -#define XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT 0xa -#define XS1_USB_PHY_CFG0_LPM_ALIVE_SIZE 0x1 -#define XS1_USB_PHY_CFG0_LPM_ALIVE_MASK \ - (((1 << XS1_USB_PHY_CFG0_LPM_ALIVE_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) -#define XS1_USB_PHY_CFG0_LPM_ALIVE(x) \ - (((x)&XS1_USB_PHY_CFG0_LPM_ALIVE_MASK) >> XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) -#define XS1_USB_PHY_CFG0_LPM_ALIVE_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_LPM_ALIVE_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_LPM_ALIVE_SHIFT) & \ - XS1_USB_PHY_CFG0_LPM_ALIVE_MASK)) -#define XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT 0xb -#define XS1_USB_PHY_CFG0_IDPAD_EN_SIZE 0x1 -#define XS1_USB_PHY_CFG0_IDPAD_EN_MASK \ - (((1 << XS1_USB_PHY_CFG0_IDPAD_EN_SIZE) - 1) \ - << XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) -#define XS1_USB_PHY_CFG0_IDPAD_EN(x) \ - (((x)&XS1_USB_PHY_CFG0_IDPAD_EN_MASK) >> XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) -#define XS1_USB_PHY_CFG0_IDPAD_EN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_IDPAD_EN_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_IDPAD_EN_SHIFT) & \ - XS1_USB_PHY_CFG0_IDPAD_EN_MASK)) -#define XS1_USB_PHY_CFG0_XTLSEL_SHIFT 0xc -#define XS1_USB_PHY_CFG0_XTLSEL_SIZE 0x3 -#define XS1_USB_PHY_CFG0_XTLSEL_MASK \ - (((1 << XS1_USB_PHY_CFG0_XTLSEL_SIZE) - 1) << XS1_USB_PHY_CFG0_XTLSEL_SHIFT) -#define XS1_USB_PHY_CFG0_XTLSEL(x) \ - (((x)&XS1_USB_PHY_CFG0_XTLSEL_MASK) >> XS1_USB_PHY_CFG0_XTLSEL_SHIFT) -#define XS1_USB_PHY_CFG0_XTLSEL_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG0_XTLSEL_MASK) | \ - (((v) << XS1_USB_PHY_CFG0_XTLSEL_SHIFT) & XS1_USB_PHY_CFG0_XTLSEL_MASK)) -#define XS1_USB_PHY_CFG2_PONRST_SHIFT 0x0 -#define XS1_USB_PHY_CFG2_PONRST_SIZE 0x1 -#define XS1_USB_PHY_CFG2_PONRST_MASK \ - (((1 << XS1_USB_PHY_CFG2_PONRST_SIZE) - 1) << XS1_USB_PHY_CFG2_PONRST_SHIFT) -#define XS1_USB_PHY_CFG2_PONRST(x) \ - (((x)&XS1_USB_PHY_CFG2_PONRST_MASK) >> XS1_USB_PHY_CFG2_PONRST_SHIFT) -#define XS1_USB_PHY_CFG2_PONRST_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG2_PONRST_MASK) | \ - (((v) << XS1_USB_PHY_CFG2_PONRST_SHIFT) & XS1_USB_PHY_CFG2_PONRST_MASK)) -#define XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT 0x1 -#define XS1_USB_PHY_CFG2_UTMI_RESET_SIZE 0x1 -#define XS1_USB_PHY_CFG2_UTMI_RESET_MASK \ - (((1 << XS1_USB_PHY_CFG2_UTMI_RESET_SIZE) - 1) \ - << XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) -#define XS1_USB_PHY_CFG2_UTMI_RESET(x) \ - (((x)&XS1_USB_PHY_CFG2_UTMI_RESET_MASK) >> XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) -#define XS1_USB_PHY_CFG2_UTMI_RESET_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG2_UTMI_RESET_MASK) | \ - (((v) << XS1_USB_PHY_CFG2_UTMI_RESET_SHIFT) & \ - XS1_USB_PHY_CFG2_UTMI_RESET_MASK)) -#define XS1_USB_PHY_CFG3_VCONTROL_SHIFT 0x0 -#define XS1_USB_PHY_CFG3_VCONTROL_SIZE 0x4 -#define XS1_USB_PHY_CFG3_VCONTROL_MASK \ - (((1 << XS1_USB_PHY_CFG3_VCONTROL_SIZE) - 1) \ - << XS1_USB_PHY_CFG3_VCONTROL_SHIFT) -#define XS1_USB_PHY_CFG3_VCONTROL(x) \ - (((x)&XS1_USB_PHY_CFG3_VCONTROL_MASK) >> XS1_USB_PHY_CFG3_VCONTROL_SHIFT) -#define XS1_USB_PHY_CFG3_VCONTROL_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG3_VCONTROL_MASK) | \ - (((v) << XS1_USB_PHY_CFG3_VCONTROL_SHIFT) & \ - XS1_USB_PHY_CFG3_VCONTROL_MASK)) -#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT 0x4 -#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SIZE 0x1 -#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK \ - (((1 << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SIZE) - 1) \ - << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) -#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE(x) \ - (((x)&XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK) >> \ - XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) -#define XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK) | \ - (((v) << XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_SHIFT) & \ - XS1_USB_PHY_CFG3_EXTERNAL_TEST_MODE_MASK)) -#define XS1_USB_PHY_CFG3_LS_EN_SHIFT 0x5 -#define XS1_USB_PHY_CFG3_LS_EN_SIZE 0x1 -#define XS1_USB_PHY_CFG3_LS_EN_MASK \ - (((1 << XS1_USB_PHY_CFG3_LS_EN_SIZE) - 1) << XS1_USB_PHY_CFG3_LS_EN_SHIFT) -#define XS1_USB_PHY_CFG3_LS_EN(x) \ - (((x)&XS1_USB_PHY_CFG3_LS_EN_MASK) >> XS1_USB_PHY_CFG3_LS_EN_SHIFT) -#define XS1_USB_PHY_CFG3_LS_EN_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG3_LS_EN_MASK) | \ - (((v) << XS1_USB_PHY_CFG3_LS_EN_SHIFT) & XS1_USB_PHY_CFG3_LS_EN_MASK)) -#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT 0x6 -#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SIZE 0x1 -#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK \ - (((1 << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SIZE) - 1) \ - << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) -#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM(x) \ - (((x)&XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK) >> \ - XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) -#define XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK) | \ - (((v) << XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_SHIFT) & \ - XS1_USB_PHY_CFG3_UTMI_VCONTROLLOADM_MASK)) -#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT 0x7 -#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SIZE 0x1 -#define XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK \ - (((1 << XS1_USB_PHY_CFG3_HS_BIST_MODE_SIZE) - 1) \ - << XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) -#define XS1_USB_PHY_CFG3_HS_BIST_MODE(x) \ - (((x)&XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK) >> \ - XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) -#define XS1_USB_PHY_CFG3_HS_BIST_MODE_SET(x, v) \ - (((x) & ~XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK) | \ - (((v) << XS1_USB_PHY_CFG3_HS_BIST_MODE_SHIFT) & \ - XS1_USB_PHY_CFG3_HS_BIST_MODE_MASK)) -#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT 0x0 -#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SIZE 0x1 -#define XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK \ - (((1 << XS1_USB_SHIM_CFG_AND_RXV_RXA_SIZE) - 1) \ - << XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) -#define XS1_USB_SHIM_CFG_AND_RXV_RXA(x) \ - (((x)&XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK) >> \ - XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) -#define XS1_USB_SHIM_CFG_AND_RXV_RXA_SET(x, v) \ - (((x) & ~XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK) | \ - (((v) << XS1_USB_SHIM_CFG_AND_RXV_RXA_SHIFT) & \ - XS1_USB_SHIM_CFG_AND_RXV_RXA_MASK)) -#define XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT 0x1 -#define XS1_USB_SHIM_CFG_FLAG_MODE_SIZE 0x1 -#define XS1_USB_SHIM_CFG_FLAG_MODE_MASK \ - (((1 << XS1_USB_SHIM_CFG_FLAG_MODE_SIZE) - 1) \ - << XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) -#define XS1_USB_SHIM_CFG_FLAG_MODE(x) \ - (((x)&XS1_USB_SHIM_CFG_FLAG_MODE_MASK) >> XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) -#define XS1_USB_SHIM_CFG_FLAG_MODE_SET(x, v) \ - (((x) & ~XS1_USB_SHIM_CFG_FLAG_MODE_MASK) | \ - (((v) << XS1_USB_SHIM_CFG_FLAG_MODE_SHIFT) & \ - XS1_USB_SHIM_CFG_FLAG_MODE_MASK)) -#define XS1_WATCHDOG_COUNT_ENABLE_SHIFT 0x0 -#define XS1_WATCHDOG_COUNT_ENABLE_SIZE 0x1 -#define XS1_WATCHDOG_COUNT_ENABLE_MASK \ - (((1 << XS1_WATCHDOG_COUNT_ENABLE_SIZE) - 1) \ - << XS1_WATCHDOG_COUNT_ENABLE_SHIFT) -#define XS1_WATCHDOG_COUNT_ENABLE(x) \ - (((x)&XS1_WATCHDOG_COUNT_ENABLE_MASK) >> XS1_WATCHDOG_COUNT_ENABLE_SHIFT) -#define XS1_WATCHDOG_COUNT_ENABLE_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_COUNT_ENABLE_MASK) | \ - (((v) << XS1_WATCHDOG_COUNT_ENABLE_SHIFT) & \ - XS1_WATCHDOG_COUNT_ENABLE_MASK)) -#define XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT 0x1 -#define XS1_WATCHDOG_TRIGGER_ENABLE_SIZE 0x1 -#define XS1_WATCHDOG_TRIGGER_ENABLE_MASK \ - (((1 << XS1_WATCHDOG_TRIGGER_ENABLE_SIZE) - 1) \ - << XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) -#define XS1_WATCHDOG_TRIGGER_ENABLE(x) \ - (((x)&XS1_WATCHDOG_TRIGGER_ENABLE_MASK) >> XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) -#define XS1_WATCHDOG_TRIGGER_ENABLE_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_TRIGGER_ENABLE_MASK) | \ - (((v) << XS1_WATCHDOG_TRIGGER_ENABLE_SHIFT) & \ - XS1_WATCHDOG_TRIGGER_ENABLE_MASK)) -#define XS1_WATCHDOG_PRESCALER_VALUE_SHIFT 0x0 -#define XS1_WATCHDOG_PRESCALER_VALUE_SIZE 0x10 -#define XS1_WATCHDOG_PRESCALER_VALUE_MASK \ - (((1 << XS1_WATCHDOG_PRESCALER_VALUE_SIZE) - 1) \ - << XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) -#define XS1_WATCHDOG_PRESCALER_VALUE(x) \ - (((x)&XS1_WATCHDOG_PRESCALER_VALUE_MASK) >> \ - XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) -#define XS1_WATCHDOG_PRESCALER_VALUE_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_PRESCALER_VALUE_MASK) | \ - (((v) << XS1_WATCHDOG_PRESCALER_VALUE_SHIFT) & \ - XS1_WATCHDOG_PRESCALER_VALUE_MASK)) -#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT 0x0 -#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SIZE 0x10 -#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK \ - (((1 << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SIZE) - 1) \ - << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) -#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE(x) \ - (((x)&XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK) >> \ - XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) -#define XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK) | \ - (((v) << XS1_WATCHDOG_PRESCALER_WRAP_VALUE_SHIFT) & \ - XS1_WATCHDOG_PRESCALER_WRAP_VALUE_MASK)) -#define XS1_WATCHDOG_COUNT_VALUE_SHIFT 0x0 -#define XS1_WATCHDOG_COUNT_VALUE_SIZE 0xc -#define XS1_WATCHDOG_COUNT_VALUE_MASK \ - (((1 << XS1_WATCHDOG_COUNT_VALUE_SIZE) - 1) << XS1_WATCHDOG_COUNT_VALUE_SHIFT) -#define XS1_WATCHDOG_COUNT_VALUE(x) \ - (((x)&XS1_WATCHDOG_COUNT_VALUE_MASK) >> XS1_WATCHDOG_COUNT_VALUE_SHIFT) -#define XS1_WATCHDOG_COUNT_VALUE_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_COUNT_VALUE_MASK) | \ - (((v) << XS1_WATCHDOG_COUNT_VALUE_SHIFT) & XS1_WATCHDOG_COUNT_VALUE_MASK)) -#define XS1_WATCHDOG_HAS_TRIGGERED_SHIFT 0x0 -#define XS1_WATCHDOG_HAS_TRIGGERED_SIZE 0x1 -#define XS1_WATCHDOG_HAS_TRIGGERED_MASK \ - (((1 << XS1_WATCHDOG_HAS_TRIGGERED_SIZE) - 1) \ - << XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) -#define XS1_WATCHDOG_HAS_TRIGGERED(x) \ - (((x)&XS1_WATCHDOG_HAS_TRIGGERED_MASK) >> XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) -#define XS1_WATCHDOG_HAS_TRIGGERED_SET(x, v) \ - (((x) & ~XS1_WATCHDOG_HAS_TRIGGERED_MASK) | \ - (((v) << XS1_WATCHDOG_HAS_TRIGGERED_SHIFT) & \ - XS1_WATCHDOG_HAS_TRIGGERED_MASK)) -#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT 0x0 -#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SIZE 0x1 -#define XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK \ - (((1 << XS1_MIPI_STATUS0_OSC_CLK_ACT_SIZE) - 1) \ - << XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) -#define XS1_MIPI_STATUS0_OSC_CLK_ACT(x) \ - (((x)&XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK) >> \ - XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) -#define XS1_MIPI_STATUS0_OSC_CLK_ACT_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK) | \ - (((v) << XS1_MIPI_STATUS0_OSC_CLK_ACT_SHIFT) & \ - XS1_MIPI_STATUS0_OSC_CLK_ACT_MASK)) -#define XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT 0x1 -#define XS1_MIPI_STATUS0_OSC_CLK_READY_SIZE 0x1 -#define XS1_MIPI_STATUS0_OSC_CLK_READY_MASK \ - (((1 << XS1_MIPI_STATUS0_OSC_CLK_READY_SIZE) - 1) \ - << XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) -#define XS1_MIPI_STATUS0_OSC_CLK_READY(x) \ - (((x)&XS1_MIPI_STATUS0_OSC_CLK_READY_MASK) >> \ - XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) -#define XS1_MIPI_STATUS0_OSC_CLK_READY_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_OSC_CLK_READY_MASK) | \ - (((v) << XS1_MIPI_STATUS0_OSC_CLK_READY_SHIFT) & \ - XS1_MIPI_STATUS0_OSC_CLK_READY_MASK)) -#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT 0x2 -#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SIZE 0x1 -#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK \ - (((1 << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SIZE) - 1) \ - << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) -#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G(x) \ - (((x)&XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK) >> \ - XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) -#define XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK) | \ - (((v) << XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_SHIFT) & \ - XS1_MIPI_STATUS0_BIT_CLK_GREATER_THAN_2400G_MASK)) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT 0x3 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SIZE 0x1 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK \ - (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SIZE) - 1) \ - << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0(x) \ - (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK) >> \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK) | \ - (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_SHIFT) & \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN0_MASK)) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT 0x4 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SIZE 0x1 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK \ - (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SIZE) - 1) \ - << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1(x) \ - (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK) >> \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK) | \ - (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_SHIFT) & \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN1_MASK)) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT 0x5 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SIZE 0x1 -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK \ - (((1 << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SIZE) - 1) \ - << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2(x) \ - (((x)&XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK) >> \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) -#define XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK) | \ - (((v) << XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_SHIFT) & \ - XS1_MIPI_STATUS0_DATA_CORRECT_LAN2_MASK)) -#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT 0xc -#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SIZE 0x1 -#define XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK \ - (((1 << XS1_MIPI_STATUS0_STOPSTATE_CLK_SIZE) - 1) \ - << XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_CLK(x) \ - (((x)&XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK) >> \ - XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_CLK_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK) | \ - (((v) << XS1_MIPI_STATUS0_STOPSTATE_CLK_SHIFT) & \ - XS1_MIPI_STATUS0_STOPSTATE_CLK_MASK)) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT 0xd -#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SIZE 0x1 -#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK \ - (((1 << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SIZE) - 1) \ - << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN0(x) \ - (((x)&XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK) >> \ - XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN0_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK) | \ - (((v) << XS1_MIPI_STATUS0_STOPSTATE_LAN0_SHIFT) & \ - XS1_MIPI_STATUS0_STOPSTATE_LAN0_MASK)) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT 0xe -#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SIZE 0x1 -#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK \ - (((1 << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SIZE) - 1) \ - << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN1(x) \ - (((x)&XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK) >> \ - XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) -#define XS1_MIPI_STATUS0_STOPSTATE_LAN1_SET(x, v) \ - (((x) & ~XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK) | \ - (((v) << XS1_MIPI_STATUS0_STOPSTATE_LAN1_SHIFT) & \ - XS1_MIPI_STATUS0_STOPSTATE_LAN1_MASK)) -#define XS1_MIPI_SHIM_STATUS_REG_SHIFT 0x0 -#define XS1_MIPI_SHIM_STATUS_REG_SIZE 0x1 -#define XS1_MIPI_SHIM_STATUS_REG_MASK \ - (((1 << XS1_MIPI_SHIM_STATUS_REG_SIZE) - 1) << XS1_MIPI_SHIM_STATUS_REG_SHIFT) -#define XS1_MIPI_SHIM_STATUS_REG(x) \ - (((x)&XS1_MIPI_SHIM_STATUS_REG_MASK) >> XS1_MIPI_SHIM_STATUS_REG_SHIFT) -#define XS1_MIPI_SHIM_STATUS_REG_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_STATUS_REG_MASK) | \ - (((v) << XS1_MIPI_SHIM_STATUS_REG_SHIFT) & XS1_MIPI_SHIM_STATUS_REG_MASK)) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT 0x0 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SIZE 0x1 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK \ - (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SIZE) - 1) \ - << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN(x) \ - (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK) >> \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK) | \ - (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_SHIFT) & \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_EN_MASK)) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT 0x8 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SIZE 0x8 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK \ - (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SIZE) - 1) \ - << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE(x) \ - (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK) >> \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK) | \ - (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_SHIFT) & \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_DATATYPE_MASK)) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT 0x10 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SIZE 0x6 -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK \ - (((1 << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SIZE) - 1) \ - << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE(x) \ - (((x)&XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK) >> \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) -#define XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK) | \ - (((v) << XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_SHIFT) & \ - XS1_MIPI_SHIM_CFG0_PIXEL_DEMUX_MODE_MASK)) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT 0x19 -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SIZE 0x1 -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK \ - (((1 << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SIZE) - 1) \ - << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT(x) \ - (((x)&XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK) >> \ - XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK) | \ - (((v) << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_SHIFT) & \ - XS1_MIPI_SHIM_CFG0_SEL_DEBUG_OUT_MASK)) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT 0x1a -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SIZE 0x1 -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK \ - (((1 << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SIZE) - 1) \ - << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG(x) \ - (((x)&XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK) >> \ - XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) -#define XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SET(x, v) \ - (((x) & ~XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK) | \ - (((v) << XS1_MIPI_SHIM_CFG0_SEL_DEBUG_SHIFT) & \ - XS1_MIPI_SHIM_CFG0_SEL_DEBUG_MASK)) -#define XS1_LPDDR_PE_TREFI_CNT_SHIFT 0x0 -#define XS1_LPDDR_PE_TREFI_CNT_SIZE 0xb -#define XS1_LPDDR_PE_TREFI_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TREFI_CNT_SIZE) - 1) << XS1_LPDDR_PE_TREFI_CNT_SHIFT) -#define XS1_LPDDR_PE_TREFI_CNT(x) \ - (((x)&XS1_LPDDR_PE_TREFI_CNT_MASK) >> XS1_LPDDR_PE_TREFI_CNT_SHIFT) -#define XS1_LPDDR_PE_TREFI_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TREFI_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TREFI_CNT_SHIFT) & XS1_LPDDR_PE_TREFI_CNT_MASK)) -#define XS1_LPDDR_PE_TRAS_CNT_SHIFT 0xb -#define XS1_LPDDR_PE_TRAS_CNT_SIZE 0x4 -#define XS1_LPDDR_PE_TRAS_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRAS_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRAS_CNT_SHIFT) -#define XS1_LPDDR_PE_TRAS_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRAS_CNT_MASK) >> XS1_LPDDR_PE_TRAS_CNT_SHIFT) -#define XS1_LPDDR_PE_TRAS_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRAS_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRAS_CNT_SHIFT) & XS1_LPDDR_PE_TRAS_CNT_MASK)) -#define XS1_LPDDR_PE_TXSR_CNT_SHIFT 0xf -#define XS1_LPDDR_PE_TXSR_CNT_SIZE 0x6 -#define XS1_LPDDR_PE_TXSR_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TXSR_CNT_SIZE) - 1) << XS1_LPDDR_PE_TXSR_CNT_SHIFT) -#define XS1_LPDDR_PE_TXSR_CNT(x) \ - (((x)&XS1_LPDDR_PE_TXSR_CNT_MASK) >> XS1_LPDDR_PE_TXSR_CNT_SHIFT) -#define XS1_LPDDR_PE_TXSR_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TXSR_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TXSR_CNT_SHIFT) & XS1_LPDDR_PE_TXSR_CNT_MASK)) -#define XS1_LPDDR_PE_TWR_CNT_SHIFT 0x15 -#define XS1_LPDDR_PE_TWR_CNT_SIZE 0x3 -#define XS1_LPDDR_PE_TWR_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TWR_CNT_SIZE) - 1) << XS1_LPDDR_PE_TWR_CNT_SHIFT) -#define XS1_LPDDR_PE_TWR_CNT(x) \ - (((x)&XS1_LPDDR_PE_TWR_CNT_MASK) >> XS1_LPDDR_PE_TWR_CNT_SHIFT) -#define XS1_LPDDR_PE_TWR_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TWR_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TWR_CNT_SHIFT) & XS1_LPDDR_PE_TWR_CNT_MASK)) -#define XS1_LPDDR_PE_TRC_CNT_SHIFT 0x0 -#define XS1_LPDDR_PE_TRC_CNT_SIZE 0x4 -#define XS1_LPDDR_PE_TRC_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRC_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRC_CNT_SHIFT) -#define XS1_LPDDR_PE_TRC_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRC_CNT_MASK) >> XS1_LPDDR_PE_TRC_CNT_SHIFT) -#define XS1_LPDDR_PE_TRC_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRC_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRC_CNT_SHIFT) & XS1_LPDDR_PE_TRC_CNT_MASK)) -#define XS1_LPDDR_PE_TRCD_CNT_SHIFT 0x4 -#define XS1_LPDDR_PE_TRCD_CNT_SIZE 0x3 -#define XS1_LPDDR_PE_TRCD_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRCD_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRCD_CNT_SHIFT) -#define XS1_LPDDR_PE_TRCD_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRCD_CNT_MASK) >> XS1_LPDDR_PE_TRCD_CNT_SHIFT) -#define XS1_LPDDR_PE_TRCD_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRCD_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRCD_CNT_SHIFT) & XS1_LPDDR_PE_TRCD_CNT_MASK)) -#define XS1_LPDDR_PE_TRP_CNT_SHIFT 0x7 -#define XS1_LPDDR_PE_TRP_CNT_SIZE 0x3 -#define XS1_LPDDR_PE_TRP_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRP_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRP_CNT_SHIFT) -#define XS1_LPDDR_PE_TRP_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRP_CNT_MASK) >> XS1_LPDDR_PE_TRP_CNT_SHIFT) -#define XS1_LPDDR_PE_TRP_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRP_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRP_CNT_SHIFT) & XS1_LPDDR_PE_TRP_CNT_MASK)) -#define XS1_LPDDR_PE_TRFC_CNT_SHIFT 0xa -#define XS1_LPDDR_PE_TRFC_CNT_SIZE 0x5 -#define XS1_LPDDR_PE_TRFC_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRFC_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRFC_CNT_SHIFT) -#define XS1_LPDDR_PE_TRFC_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRFC_CNT_MASK) >> XS1_LPDDR_PE_TRFC_CNT_SHIFT) -#define XS1_LPDDR_PE_TRFC_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRFC_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRFC_CNT_SHIFT) & XS1_LPDDR_PE_TRFC_CNT_MASK)) -#define XS1_LPDDR_PE_TRRD_CNT_SHIFT 0xf -#define XS1_LPDDR_PE_TRRD_CNT_SIZE 0x2 -#define XS1_LPDDR_PE_TRRD_CNT_MASK \ - (((1 << XS1_LPDDR_PE_TRRD_CNT_SIZE) - 1) << XS1_LPDDR_PE_TRRD_CNT_SHIFT) -#define XS1_LPDDR_PE_TRRD_CNT(x) \ - (((x)&XS1_LPDDR_PE_TRRD_CNT_MASK) >> XS1_LPDDR_PE_TRRD_CNT_SHIFT) -#define XS1_LPDDR_PE_TRRD_CNT_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_TRRD_CNT_MASK) | \ - (((v) << XS1_LPDDR_PE_TRRD_CNT_SHIFT) & XS1_LPDDR_PE_TRRD_CNT_MASK)) -#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT 0x11 -#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SIZE 0x1 -#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK \ - (((1 << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SIZE) - 1) \ - << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) -#define XS1_LPDDR_PE_EN_256M_DEV_SIZE(x) \ - (((x)&XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK) >> \ - XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) -#define XS1_LPDDR_PE_EN_256M_DEV_SIZE_SET(x, v) \ - (((x) & ~XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK) | \ - (((v) << XS1_LPDDR_PE_EN_256M_DEV_SIZE_SHIFT) & \ - XS1_LPDDR_PE_EN_256M_DEV_SIZE_MASK)) -#define XS1_DEVICE_ID0_VERSION_VALUE 0x0 -#define XS1_DEVICE_ID0_REVISION_VALUE 0x4 -#define XS1_PS_DBG_HANDLER XS1_PS_DBG_SCRATCH_0 -#define XS1_PS_DBG_COMMAND XS1_PS_DBG_SCRATCH_1 -#define XS1_PS_DBG_ARG0_REG XS1_PS_DBG_SCRATCH_2 -#define XS1_PS_DBG_ARG1_REG XS1_PS_DBG_SCRATCH_3 -#define XS1_PS_DBG_ARG2_REG XS1_PS_DBG_SCRATCH_4 -#define XS1_PS_DBG_ARG3_REG XS1_PS_DBG_SCRATCH_5 -#define XS1_PS_DBG_ARG4_REG XS1_PS_DBG_SCRATCH_6 -#define XS1_PS_DBG_ARG5_REG XS1_PS_DBG_SCRATCH_7 -#define XS1_PSWITCH_DBG_HANDLER_NUM XS1_PSWITCH_DBG_SCRATCH_0_NUM -#define XS1_PSWITCH_DBG_COMMAND_NUM XS1_PSWITCH_DBG_SCRATCH_1_NUM -#define XS1_PSWITCH_DBG_ARG0_NUM XS1_PSWITCH_DBG_SCRATCH_2_NUM -#define XS1_PSWITCH_DBG_ARG1_NUM XS1_PSWITCH_DBG_SCRATCH_3_NUM -#define XS1_PSWITCH_DBG_ARG2_NUM XS1_PSWITCH_DBG_SCRATCH_4_NUM -#define XS1_PSWITCH_DBG_ARG3_NUM XS1_PSWITCH_DBG_SCRATCH_5_NUM -#define XS1_PSWITCH_DBG_ARG4_NUM XS1_PSWITCH_DBG_SCRATCH_6_NUM -#define XS1_PSWITCH_DBG_ARG5_NUM XS1_PSWITCH_DBG_SCRATCH_7_NUM -#define XS1_XMOS_PHY_CONF_WIDTH 0x3 -#define XS1_USB_PHY_VCONTROL_SETUP_LENGTH 0x6 -#define XS1_USB_PHY_ENTER_BIST_LENGTH 0x40 -#define XS1_USB_PHY_CLKCNT_WIDTH 0x4 -#define XS1_USB_BISTGO_CTR_WIDTH 0x4 -#define XS1_USB_TESTGO_CTR_WIDTH 0x4 -#define XS1_USB_TESTGO_PULSE_LENGTH 0x8 -#define XS1_USB_BISTGO_PULSE_LENGTH 0x8 -#define XS1_CRC5_RESULT_WIDTH 0x5 -#define XS1_MS_NIBBLE "7:4" -#define XS1_LS_NIBBLE "3:0" -#endif /* _xs3a_registers_h_ */ diff --git a/new_ai_tools/include/lib_nn/src/asm/asm_constants.h b/new_ai_tools/include/lib_nn/src/asm/asm_constants.h deleted file mode 100644 index 288e85b2..00000000 --- a/new_ai_tools/include/lib_nn/src/asm/asm_constants.h +++ /dev/null @@ -1,41 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#pragma once - -#ifndef __ASSEMBLER__ - -#include - -#include "xs3_vpu.h" - -typedef struct { - // Word offset = 0 - int16_t vec_0x007F[VPU_INT8_ACC_PERIOD]; - // Word offset = 8 - int8_t vec_0x01[VPU_INT8_ACC_PERIOD]; - // Word offset = 12 - int16_t vec_0x0002[VPU_INT8_ACC_PERIOD]; - // Word offset = 20 - int8_t vec_0x80[VPU_INT8_EPV]; - // Word offset = 28 - -} vpu_constants_t; - -extern const vpu_constants_t vpu_vects; - -extern const uint32_t vpu_vect_zero[VPU_INT32_EPV]; -extern const int16_t vpu_vect_0x007F[VPU_INT16_EPV]; -extern const int8_t vpu_vect_0x01[VPU_INT8_EPV]; -extern const int8_t vpu_vect_0x02[VPU_INT8_EPV]; -extern const int8_t vpu_vect_0x80[VPU_INT8_EPV]; - -#endif // __ASSEMBLER__ - -#define VPU_MODE_32BIT 0x0000 -#define VPU_MODE_16BIT 0x0100 -#define VPU_MODE_8BIT 0x0200 - -#define VPU_VEC_0x007F (0) -#define VPU_VEC_0x01 (8) -#define VPU_VEC_0x0002 (12) -#define VPU_VEC_0x80 (20) diff --git a/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h b/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h deleted file mode 100644 index 57c28fd8..00000000 --- a/new_ai_tools/include/lib_nn/src/asm/window_op_plan.h +++ /dev/null @@ -1,25 +0,0 @@ -// Copyright 2020-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef WINDOW_OP_PLAN_H_ -#define WINDOW_OP_PLAN_H_ - -#define WOP_OUTPUT_ROWS (0) -#define WOP_OUTPUT_COLS (1) -#define WOP_OUTPUT_CHANS (2) -#define WOP_WINDOW_ROWS (3) -#define WOP_WINDOW_COLS (4) -#define WOP_START_STRIDE_X (5) -#define WOP_START_STRIDE_Y (6) -#define WOP_INNER_STRIDE_VERT (7) -#define WOP_INNER_STRIDE_HORI (8) -#define WOP_OUTER_STRIDE_VERT_X (9) -#define WOP_OUTER_STRIDE_VERT_Y (10) -#define WOP_OUTER_STRIDE_HORI_X (11) -#define WOP_OUTER_STRIDE_HORI_Y (12) -#define WOP_CHAN_STRIDE_X (13) -#define WOP_CHAN_STRIDE_Y (14) - -#define WOP_WORDS (15) - -#endif // WINDOW_OP_PLAN_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h b/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h deleted file mode 100644 index 4556363b..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/inference_engine.h +++ /dev/null @@ -1,217 +0,0 @@ -// Copyright (c) 2020, XMOS Ltd, All rights reserved -#ifndef INFERENCE_ENGINE_H_ -#define INFERENCE_ENGINE_H_ - -#ifndef __XC__ -#define UNSAFE /**/ -#else -#define UNSAFE unsafe -#endif - -#if !defined(XTFLM_DISABLED) - -#if defined(__xtflm_conf_h_exists__) -#include "xtflm_conf.h" -#else - -#ifndef XTFLM_OPERATORS -#define XTFLM_OPERATORS 10 -#endif - -#endif - -#include "tensorflow/lite/c/c_api_types.h" -#include "xcore_config.h" - -#ifdef __cplusplus - -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" -#include "xcore_device_memory.h" -#include "xcore_error_reporter.h" -#include "xcore_interpreter.h" -#include "xcore_ops.h" -#include "xcore_profiler.h" - -/** Structure that contains all the TensorFlowLite for Micro objects that must - * be allocated to create an interpreter. One of these structures has to be - * allocated for each inference engine. This structure contains C++ objects, and - * must therefore be allocated inside a C++ source file. - */ -struct tflite_micro_objects { - tflite_micro::micro::xcore::XCoreErrorReporter error_reporter; - tflite_micro::micro::xcore::XCoreProfiler xcore_profiler; - uint64_t interpreter_buffer - [(sizeof(tflite_micro::micro::xcore::XCoreInterpreter) + sizeof(uint64_t) - 1) / - sizeof(uint64_t)]; // This needs to be aligned on a double word boundary - tflite_micro::MicroMutableOpResolver resolver; - - tflite_micro::micro::xcore::XCoreInterpreter *interpreter; - const tflite_micro::Model *model; -}; -#endif - -#endif - -// Opaque definition for the C++ struct above, used in C. -struct tflite_micro_objects; - -/** Structure that contains all the data needed to describe an inference engine - * This structure contains no C++, just standard C pointers and arrays. - */ -typedef struct inference_engine { - uint32_t *UNSAFE - tensor_arena; ///< Pointer to space for tensor arena - uint32_t *UNSAFE - external_memory; ///< Pointer to external memory - uint32_t - outputs; ///< Number of output tensors, initialised on loading a model. - uint32_t inputs; ///< Number of input tensors, initialised on loading a model. - uint32_t *UNSAFE - output_buffers[NUM_OUTPUT_TENSORS]; ///< Pointers to output tensors. - uint32_t *UNSAFE - input_buffers[NUM_INPUT_TENSORS]; ///< Pointers to input tensors. - uint32_t output_sizes[NUM_OUTPUT_TENSORS]; ///< Size of each output tensor in - ///< bytes. - uint32_t - input_sizes[NUM_INPUT_TENSORS]; ///< Size of each input tensor in bytes. - uint32_t output_size; ///< Total size of all outputs - TODO: obsolete? - uint32_t input_size; ///< Total size of all inputs - TODO: obsolete? - uint32_t - tensor_arena_size; ///< Number of bytes availabe to be used for tensor_arena - uint32_t - external_memory_size; ///< Number of bytes available in external memory - uint32_t - output_times_size; ///< Number of bytes available to store profiling data - uint32_t operators_size; ///< ??? - uint32_t *UNSAFE output_times; ///< pointer to profiling data, one per layer - struct tflite_micro_objects *UNSAFE - xtflm; ///< Pointer to C++ XTFLM object - opaque to C - // status for the engine to maintain - uint32_t haveModel; ///< if 1: we have a model - uint32_t chainToNext; ///< if 1: we are chained (could be implicit in c_push - ///< being non-null?) - uint32_t acquireMode; ///< if non zero we're acquiring data - uint32_t outputGpioEn; // TODO: should this be here? Possibly not - int8_t outputGpioThresh[AISRV_GPIO_LENGTH]; - uint8_t outputGpioMode; - uint32_t debug_log_buffer[MAX_DEBUG_LOG_LENGTH / - sizeof(uint32_t)]; ///< buffer for error messages - uint32_t arena_needed_bytes; ///< Total arena needed in bytes. - uint32_t num_threads; - struct xc_context_config_t xc_config; -} inference_engine_t; - -#ifdef __cplusplus -#ifndef XTFLM_DISABLED -/** Function that initializes the inference_engine object, given a - * tflite_micro_objects object. This function has to be called from a C++ source - * files, and it performs the initialisation of the inference engine. This - * involves setting up basic pointers inside the IE object, nothing else. - * - * The function returns the operator-resolver, which must be be used to add all - * necessary operators to the inference engine. A typical calling sequence is as - * follows:: - * - * uint32_t data_ext[100000/sizeof(int)]; - * [...] - * static struct tflite_micro_objects s0; - * auto *resolver = inference_engine_initialize(ie, - * data_ext, 100000, - * nullptr, 0, - * &s0); - * resolver->AddAdd(); - * resolver->AddConv2D(); - * resolver->AddCustom(tflite_micro::ops::micro::xcore::Conv2D_V2_OpCode, - * tflite_micro::ops::micro::xcore::Register_Conv2D_V2()); - * [...] - * - * Note that when tensorflow lite for micro is disabled this function will not - * exist as it depends on all and sundry in XTFLM. - * - * Note that the lifetime of all spaces passed to this function should be longer - * than the lifetime of the inference engine. Typically all spaces are declared - * globally. - * - * \param ie Pointer to an uninitialized inference engine - * object, allocated by the caller. \param memory_primary Pointer to the - * space to be used for the tensor arena, allocated by the caller. If the fourth - * parameter is null, then this space will be used for both model and tensor - * arena. \param n_primary Number of bytes available in primary memory - * \param memory_secondary Pointer to the space to be used for the model - * allocated by the caller. If this parameter is - * null, then the primary memory will be used for both model and tensor arena. - * \param n_secondary Number of bytes available in secondary memory - * \param xtflmo C++ structure for storing the XTFLM data - * structures. Must be allocated by the caller. - * - */ -tflite_micro::MicroMutableOpResolver *inference_engine_initialize( - inference_engine_t *UNSAFE ie, uint32_t memory_primary[], - uint32_t n_memory_primary, uint32_t memory_secondary[], - uint32_t n_secondary, struct tflite_micro_objects *UNSAFE xtflmo); -#endif -extern "C" { -#endif -/** Function that unloads a model frmo the inference engine. This function - * must be called before before overwriting the model. For example, you - * have ran a model successfully, before you store a new model over the top - * of the model (from flash or anywhere else), you must first unload the - * model, then you can overwrite the model, and finally you can call the - * inference_engine_load_model to set up the new model. - * - * It is safe to call unload model if there is no model loaded. - * - * \param ie pointer to inference engine. - */ -void inference_engine_unload_model(inference_engine_t *UNSAFE ie); - -/** Function that loads a model into the inference engine. The model must be - * stored in either of the two spaces passed to the inference_engine_initialize - * function above: either the dedicated data_model space or the space shared - * with the tensor_arena. This funciton assumes the model is in place already - * and will simply parse it, not copy it. - * - * \param ie pointer to inference engine. - * \param model_bytes Number of bytes in the model - * \param model_data Pointer to the model (one of data_model or - * data_tensor_arena passed above) \param c_flash_or_tile Optional channel to flash - * or tile server - * - * \returns non zero indicates an error - */ -#ifdef __XC__ - int inference_engine_load_model(inference_engine_t * UNSAFE ie, uint32_t model_bytes, uint32_t * UNSAFE model_data, chanend ?c_flash_or_tile); -#else -int inference_engine_load_model(inference_engine_t *UNSAFE ie, - uint32_t model_bytes, - uint32_t *UNSAFE model_data, void *weights_data_ptr); -#endif - - /** Function that invokes the inference engine. This function will create an - * extra four threads enabling a model to run in 5 threads. - * - * \param ie pointer to inference engine. - */ - int interp_invoke_par_5(inference_engine_t *ie); - int interp_invoke_par_4(inference_engine_t *ie); - int interp_invoke_par_3(inference_engine_t *ie); - int interp_invoke_par_2(inference_engine_t *ie); - int interp_invoke(inference_engine_t *ie); - TfLiteStatus interp_invoke_internal(inference_engine_t *ie); - - /** Function that resets variable tensors. - * This should be called after invoking a model with stateful ops such as LSTM. - */ - int interp_reset(inference_engine_t *ie); - - /** Function that prints a summary of the time each operator took. This - * function uses printf - you may want to avoid calling it. - * - * \param ie pointer to inference engine. - */ - void print_profiler_summary(inference_engine_t *UNSAFE ie); -#ifdef __cplusplus - }; -#endif - -#endif // INFERENCE_ENGINE_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/load_weights.h b/new_ai_tools/include/lib_tflite_micro/api/load_weights.h deleted file mode 100644 index 8ff347f4..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/load_weights.h +++ /dev/null @@ -1,64 +0,0 @@ -#ifndef _load_weights_h_ -#define _load_weights_h_ - -#include -#include "thread_call.h" - -#define LOAD_WEIGHTS_MAX_BLOCKS 2 - -/** - * Function that connects to a flash or tile-ram server and loads a series of weights. - * This function completes when the data is loaded. - * - * @param c_flash_or_tile channel-end connecting to the flash server - * - * @param data_ptr array of pointers where the loaded data should be scattered - * - * @param data_sizes_in_words number of words where for each block - * - * @param N number of blocks in data_ptr and data_sizes_in_words - * - * @param external_addr address in flash or tile ram - * - * @param model_thread_count number of threads available - * - * @param tif thread_info structure for multithreading - */ -void load_weights_synchronous(chanend_t c_flash_or_tile, int *data_ptr[], int data_sizes_in_words[], - int N, int external_addr, int model_thread_count, thread_info_t *tif); - -/** - * Function that connects to a flash server and loads a series of weights. - * This function continues loading after the call completes - * - * @param c_flash_or_tile channel-end connecting to the flash server - * - * @param data_ptr array of pointers where the loaded data should be scattered - * - * @param data_sizes_in_words number of words where for each block - * - * @param N number of blocks in data_ptr and data_sizes_in_words - * - * @param external_addr address in flash or tile ram - * - * @param model_thread_count number of threads available - */ -void load_weights_asynchronous(chanend_t c_flash_or_tile, int *data_ptr[], int data_sizes_in_words[], - int N, int external_addr); - -/** - * Function that connects to a flash server and waits for the last outstanding load to complete - * Only one asynchronous load should be outstanding at any one time. - * - * @param c_flash_or_tile channel-end connecting to the flash server - */ -void load_weights_asynchronous_wait(chanend_t c_flash_or_tile); - -/** - * Function that connects to a flash or tile ram server and kills it. - * - * @param c_flash_or_tile channel-end connecting to the flash server - */ -void load_weights_quit(chanend_t c_flash_or_tile); - -#endif diff --git a/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h b/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h deleted file mode 100644 index 853fc23d..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/memory_parallel_transport.h +++ /dev/null @@ -1,52 +0,0 @@ -#include -#include "thread_call.h" - -#ifdef __XC__ -#include -#else -#include -#endif - -#ifdef __XC__ - -extern void memory_parallel_receive(chanend c, uint32_t data[], uint32_t bytes); -extern void memory_parallel_receive_thread_call(chanend c, uint32_t data[], uint32_t bytes, thread_info_t &ptr); -extern void memory_parallel_send(chanend c, uint32_t data[], uint32_t bytes); - -#else - -/** Function that receives a block of data. - * The number of bytes must be a multiple of 4. - * This function creates three threads and three channel ends in order to - * make full use of the bandwidth of the switch. - * - * \param c channel end to the sender - * \param data pointer where data must be stored - * \param bytes number of bytes that will be received. - */ -extern void memory_parallel_receive(chanend_t c, uint32_t *data, uint32_t bytes); - -/** Function that receives a block of data. - * The number of bytes must be a multiple of 4. - * This function assumes that at least three threads have been created by the - * thread_call library and will use those together with three fresh channel - * ends in order to make full use of the bandwidth of the switch. - * - * \param c channel end to the sender - * \param data pointer where data must be stored - * \param bytes number of bytes that will be received. - */ -extern void memory_parallel_receive_thread_call(chanend_t c, uint32_t *data, uint32_t bytes, thread_info_t *ptr); - -/** Function that sends a block of data. - * The number of bytes must be a multiple of 4. - * This function creates three threads and three channel ends in order to - * make full use of the bandwidth of the switch. - * - * \param c channel end to the receiver - * \param data pointer where data must be loaded frmo - * \param bytes number of bytes that will be sent. - */ -extern void memory_parallel_send(chanend_t c, uint32_t *data, uint32_t bytes); - -#endif diff --git a/new_ai_tools/include/lib_tflite_micro/api/version.h b/new_ai_tools/include/lib_tflite_micro/api/version.h deleted file mode 100644 index b7a7fd98..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/version.h +++ /dev/null @@ -1,13 +0,0 @@ -// Copyright (c) 2020, XMOS Ltd, All rights reserved -#ifndef XCORE_VERSION_H_ -#define XCORE_VERSION_H_ - -namespace lib_tflite_micro { - -static const unsigned major_version = 0; -static const unsigned minor_version = 8; -static const unsigned patch_version = 0; - -} // namespace lib_tflite_micro - -#endif // XCORE_VERSION_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h deleted file mode 100644 index 1662ce77..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/xcore_config.h +++ /dev/null @@ -1,18 +0,0 @@ -#ifndef XCORE_CONFIG_H_ -#define XCORE_CONFIG_H_ - -#include "../src/thread_call.h" - -struct xc_context_config_t { - // This is the thread count specified in the compiler. - // It's used by lookup op, beta float ops etc to split up work - // in the Prepare phase. - // Conv ops have their own thread count as the thread work is - // calculated in the compiler. - int model_thread_count; - thread_info_t thread_info; - void *UNSAFE weights_data_ptr; // DDR ptr or channel to flash/tile server. - void *UNSAFE paging_ptr; // DDR ptr for paging in/out tensor arena. -}; - -#endif // XCORE_CONFIG_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h deleted file mode 100644 index 3e47731e..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/xcore_device_memory.h +++ /dev/null @@ -1,62 +0,0 @@ -// Copyright (c) 2020, XMOS Ltd, All rights reserved -#ifndef XCORE_DEVICE_MEMORY_H_ -#define XCORE_DEVICE_MEMORY_H_ - -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -#ifdef XCORE -#ifdef _TIME_H_ -#define _clock_defined -#endif -#include - -#define STRINGIFY(NAME) #NAME -#define GET_STACKWORDS(DEST, NAME) \ - asm("ldc %[__dest], " STRINGIFY(NAME) ".nstackwords" : [__dest] "=r"(DEST)) -#define GET_STACKSIZE(DEST, NAME) \ - { \ - size_t _stack_words; \ - asm("ldc %[__dest], " STRINGIFY(NAME) ".nstackwords" \ - : [__dest] "=r"(_stack_words)); \ - DEST = (_stack_words + 2) * 4; \ - } -#define IS_RAM(a) (((uintptr_t)a >= 0x80000) && ((uintptr_t)a <= 0x100000)) -#define IS_NOT_RAM(a) ((uintptr_t)a > 0x100000) -#define IS_EXTMEM(a) \ - (((uintptr_t)a >= 0x10000000) && (((uintptr_t)a <= 0x20000000))) -#define IS_SWMEM(a) \ - (((uintptr_t)a >= 0x40000000) && (((uintptr_t)a <= 0x80000000))) - -#ifdef USE_SWMEM -#ifndef USE_QSPI_SWMEM_DEV -void swmem_setup(); -#else -#include -void swmem_setup(chanend_t ctrl_swmem_c); -#endif // USE_QSPI_SWMEM_DEV -#endif // USE_SWMEM - -void swmem_handler(void *ignored); -void swmem_teardown(); - -#else // not XCORE - -#define GET_STACKSIZE(DEST, NAME) DEST = 0 -#define GET_STACKWORDS(DEST, NAME) DEST = 0 -#define IS_RAM(a) (1) -#define IS_NOT_RAM(a) (0) - -#endif // XCORE - -void memload(void *dest, void *src, size_t size); - -#ifdef __cplusplus -} -#endif - -#endif // XCORE_DEVICE_MEMORY_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h b/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h deleted file mode 100644 index 8663c1ed..00000000 --- a/new_ai_tools/include/lib_tflite_micro/api/xcore_shared_config.h +++ /dev/null @@ -1,48 +0,0 @@ -// Copyright (c) 2020, XMOS Ltd, All rights reserved -#ifndef XCORE_SHARED_CONFIG_H_ -#define XCORE_SHARED_CONFIG_H_ - -#include "lib_nn/api/nn_arch.h" - -namespace shared_config { - -// This string is used as a key to store the shared config -// between xformer and lib_tflite_micro in the flatbuffer -constexpr char xcoreMetadataName[] = "xcoreSharedConfig"; - -constexpr int xcoreMaxNumOfTensors = 25; - -struct tensor_info_t { - uint32_t index; - uint32_t external_address; - uint32_t size; -}; - -// The metadata struct must be aligned to 16 bytes -// We cannot use alignas(16) yet in xcore -struct xcore_metadata_t { - // Target arch can be XS3A = 0, or VX4A = 1 - nn_target_arch_t target_arch; - // Versions of libraries used to build the model - uint32_t lib_nn_major_version; - uint32_t lib_nn_minor_version; - uint32_t lib_nn_patch_version; - uint32_t lib_tflite_micro_major_version; - uint32_t lib_tflite_micro_minor_version; - uint32_t lib_tflite_micro_patch_version; - uint32_t xformer_major_version; - uint32_t xformer_minor_version; - uint32_t xformer_patch_version; - // Number of threads required from the runtime to execute the model - uint32_t required_thread_count; - // Number of input tensors loaded from external memory - uint32_t num_external_input_tensors; - // Number of output tensors loaded from external memory - uint32_t num_external_output_tensors; - tensor_info_t external_input_tensors_data[xcoreMaxNumOfTensors]; - tensor_info_t external_output_tensors_data[xcoreMaxNumOfTensors]; -}; - -} // namespace shared_config - -#endif // XCORE_SHARED_CONFIG_H_ \ No newline at end of file diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h deleted file mode 100644 index 09b9a64e..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/conv2d_float.h +++ /dev/null @@ -1,155 +0,0 @@ -#ifndef _conv2d_float_h_ -#define _conv2d_float_h_ - -#ifdef __cplusplus -extern "C" { -#endif - -/** Function that calculates a fully connected. - * - * @param outputs pointer to the output data, the output data will - * be stored as an array [out_features] - * @param inputs pointer to the input data, the input data must - * be stored as an array [input_features] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_features][input_features] - * @param out_features dimension 1 of the output array - * @param input_features dimension 1 of the input array - * @returns number of MACCs - */ -extern int xc_fc_float_ref(float *outputs, float *inputs, float *kernels, - int out_features, int input_features); - -/** Optimized function that calculates a fully connected. - * - * @param outputs pointer to the output data, the output data will - * be stored as an array [out_features] - * @param inputs pointer to the input data, the input data must - * be stored as an array [input_features] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_features][input_features] - * @param out_features dimension 1 of the output array - * @param input_features dimension 1 of the input array - * @param out_f_start output features to start at - * @param out_f_end output features to end at plus one - * @returns number of MACCs - */ -extern int xc_fc_float_opt(float *outputs, float *inputs, float *kernels, - int out_features, int input_features, int out_f_start, - int out_f_end); - -/** Function that calculates a convolution with a 5x2 filter with stride 2 - * over dimension 2 of a tensor over a tensor. - * - * @param outputs pointer to the output data, the output data will be - * stored as an array [out_w][out_h][out_depth] - * @param inputs pointer to the input data, the input data must be - * stored as an array [input_w][input_h][input_depth] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_depth][5][2][depth] - * @param bias pointer to the biases, the bias must be stored as an - * array [out_depth] - * @param out_w dimension 2 of the output array - * @param out_h dimension 1 of the output array - * @param out_depth dimension 3 of the output array - * @param input_w dimension 2 of the input array - * @param input_h dimension 1 of the input array - * @param input_depth dimension 3 of the input array - * @returns number of MACCs - */ -extern int xc_conv2d_float_kw5xh2_stride_w3_ref(float *outputs, float *inputs, - float *kernels, float *biases, - int out_w, int out_h, - int out_depth, int input_w, - int input_h, int input_depth); - -/** Optimised function that calculates a convolution with a 5x2 filter with - * stride 2 over dimension 2 of a tensor. For parallel usage, supply - * multiple invocations with different values of out_depth_start and - * out_depth_end so that the whole output depth is covered between all of - * them. - * - * @param outputs pointer to the output data, the output data will be - * stored as an array [out_w][out_h][out_depth] - * @param inputs pointer to the input data, the input data must be - * stored as an array [input_w][input_h][input_depth] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_depth][5][2][depth] - * @param bias pointer to the biases, the bias must be stored as an - * array [out_depth] - * @param out_w dimension 2 of the output array - * @param out_h dimension 1 of the output array - * @param out_depth dimension 3 of the output array - * @param input_w dimension 2 of the input array - * @param input_h dimension 1 of the input array - * @param input_depth dimension 3 of the input array - * @param out_depth_start output depth to start at - * @param out_depth_end output depth to end at plus one - */ -extern void xc_conv2d_float_kw5xh2_stride_w3_opt( - float *outputs, float *inputs, float *kernels, float *biases, int out_w, - int out_h, int out_depth, int input_w, int input_h, int input_depth, - int out_depth_start, int out_depth_end); - -/** Function that calculates a transposed convolution with a 5x2 filter with - * stride 3 in dimension 2 over a tensor. - * - * @param outputs pointer to the output data, the output data will be - * stored as an array [out_w][out_h][out_depth] - * @param inputs pointer to the input data, the input data must be - * stored as an array [input_w][input_h][input_depth] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_depth][5][2][depth] - * @param bias pointer to the biases, the bias must be stored as an - * array [out_depth] - * @param out_w dimension 2 of the output array - * @param out_h dimension 1 of the output array - * @param out_depth dimension 3 of the output array - * @param input_w dimension 2 of the input array - * @param input_h dimension 1 of the input array - * @param input_depth dimension 3 of the input array - * @returns number of MACCs - */ -extern int xc_transpose_conv2d_float_kw5xh2_stride_h3_ref( - float *outputs, float *inputs, float *kernels, float *biases, int out_w, - int out_h, int out_depth, int input_w, int input_h, int input_depth); - -/** Optimised function that calculates a transposed convolution with a 5x2 - * filter with stride 3 over dimension 2 of a tensor. For parallel usage, - * supply multiple invocations with different values of out_depth_start and - * out_depth_end so that the whole output depth is covered between all of - * them. - * - * @param outputs pointer to the output data, the output data will be - * stored as an array [out_w][out_h][out_depth] - * @param inputs pointer to the input data, the input data must be - * stored as an array [input_w][input_h][input_depth] - * @param kernels pointer to the kernels, the kernels - * must be stored as an array - * [out_depth][5][2][depth] - * @param bias pointer to the biases, the bias must be stored as an - * array [out_depth] - * @param out_w dimension 2 of the output array - * @param out_h dimension 1 of the output array - * @param out_depth dimension 3 of the output array - * @param input_w dimension 2 of the input array - * @param input_h dimension 1 of the input array - * @param input_depth dimension 3 of the input array - * @param out_depth_start output depth to start at - * @param out_depth_end output depth to end at plus one - */ -extern void xc_transpose_conv2d_float_kw5xh2_stride_h3_opt( - float *outputs, float *inputs, float *kernels, float *biases, int out_w, - int out_h, int out_depth, int input_w, int input_h, int input_depth, - int out_depth_start, int out_depth_end); - -#ifdef __cplusplus -}; -#endif - -#endif diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h deleted file mode 100644 index 27212df4..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_custom_options.h +++ /dev/null @@ -1,28 +0,0 @@ -#ifndef XCORE_CUSTOM_OPTIONS_H_ -#define XCORE_CUSTOM_OPTIONS_H_ - -#include "flatbuffers/flexbuffers.h" -#include "xcore_ops.h" - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace xcore { - -class CustomOptionParser { -private: - flexbuffers::TypedVector keys_; - flexbuffers::Vector values_; - -public: - CustomOptionParser(const flexbuffers::Map &map); - CustomOptionParser(const char *buffer, size_t buffer_length); - flexbuffers::Reference parseNamedCustomOption(const char *name) const; -}; - -} // namespace xcore -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // XCORE_CUSTOM_OPTIONS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h deleted file mode 100644 index 7804f301..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_error_reporter.h +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright (c) 2019, XMOS Ltd, All rights reserved - -#ifndef XCORE_ERROR_REPORTER_H_ -#define XCORE_ERROR_REPORTER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h" - -namespace tflite_micro { -namespace micro { -namespace xcore { - -class XCoreErrorReporter : public tflite_micro::MicroErrorReporter { -public: - explicit XCoreErrorReporter(){}; - ~XCoreErrorReporter() override = default; - void Init(char *debugBuffer, int debugBufferLength); - void Log(const char *format, va_list args); - int Report(const char *format, va_list args) override; - -private: - char *buffer; - int max_len; - int len = 0; - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace xcore -} // namespace micro -} // namespace tflite_micro - -#endif // XCORE_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h deleted file mode 100644 index c07fd9a0..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_interpreter.h +++ /dev/null @@ -1,49 +0,0 @@ -// Copyright (c) 2020, XMOS Ltd, All rights reserved -#ifndef XCORE_INTERPRETER_H_ -#define XCORE_INTERPRETER_H_ - -#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" -#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "xcore_profiler.h" - -namespace tflite_micro { -namespace micro { -namespace xcore { - -class XCoreInterpreter : public tflite_micro::MicroInterpreter { -public: - XCoreInterpreter(const tflite_micro::Model *model, - const tflite_micro::MicroOpResolver &resolver, - tflite_micro::MicroAllocator *allocator, - bool use_curent_thread = true, - XCoreProfiler *profiler = nullptr); - - static XCoreInterpreter * - Create(uint8_t interpreter_buffer[], const tflite_micro::Model *model, - const tflite_micro::MicroOpResolver &resolver, uint8_t *arena, - size_t arena_size, bool use_current_thread, XCoreProfiler *profiler); - - void PrintMemoryPlan(); - TfLiteTensor *tensor(size_t tensor_index); - const char *node_name(int sub_idx, int i); - - TfLiteStatus GetTensorDetails(size_t tensor_index, char *name, int name_len, - int *shape, int *type, float *scale, - int32_t *zero_point); - - TfLiteStatus GetTensorDetailsBufferSizes(size_t tensor_index, size_t *dims, - size_t *scales, size_t *zero_points); - - size_t input_tensor_index(size_t input_index); - size_t output_tensor_index(size_t output_index); - const Model *model__; - MicroAllocator *allocator_; -}; - -} // namespace xcore -} // namespace micro -} // namespace tflite_micro - -#endif // XCORE_INTERPRETER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h deleted file mode 100644 index 3e45f53b..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_ops.h +++ /dev/null @@ -1,89 +0,0 @@ -#ifndef XCORE_OPS_H_ -#define XCORE_OPS_H_ - -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace xcore { - -constexpr const char *XC_beta_activationf32_OpCode = "XC_beta_activationf32"; -constexpr const char *XC_beta_concatf32_OpCode = "XC_beta_concatf32"; -constexpr const char *XC_beta_convf32_OpCode = "XC_beta_convf32"; -constexpr const char *XC_beta_transposeconvf32_OpCode = - "XC_beta_transposeconvf32"; -constexpr const char *XC_beta_fcf32_OpCode = "XC_beta_fcf32"; - -constexpr const char *XC_binaryi16_OpCode = "XC_binaryi16"; -constexpr const char *XC_unaryi16_OpCode = "XC_unaryi16"; - -constexpr const char *XC_conv2d_v2_OpCode = "XC_conv2d_v2"; -constexpr const char *XC_maxpool2d_OpCode = "XC_maxpool2d"; -constexpr const char *XC_softmax_OpCode = "XC_softmax"; -constexpr const char *XC_batched_softmax_OpCode = "XC_batched_softmax"; -constexpr const char *XC_ld_weights_OpCode = "XC_ld_weights"; -constexpr const char *XC_ld_weights_wait_OpCode = "XC_ld_weights_wait"; -constexpr const char *XC_add_OpCode = "XC_add"; -constexpr const char *XC_slice_OpCode = "XC_slice"; -constexpr const char *XC_broadcast_OpCode = "XC_broadcast"; -constexpr const char *XC_lookup_OpCode = "XC_lookup"; -constexpr const char *XC_pad_OpCode = "XC_pad"; -constexpr const char *XC_concat_OpCode = "XC_concat"; -constexpr const char *XC_transpose_OpCode = "XC_transpose"; -constexpr const char *XC_pad_3_to_4_OpCode = "XC_pad_3_to_4"; -constexpr const char *XC_pad_1_to_4_OpCode = "XC_pad_1_to_4"; -constexpr const char *XC_mul_OpCode = "XC_mul"; -constexpr const char *XC_mean_OpCode = "XC_mean"; -constexpr const char *XC_meani16_OpCode = "XC_meani16"; -constexpr const char *XC_expand_8_to_16_OpCode = "XC_expand_8_to_16"; -constexpr const char *XC_no_op_OpCode = "XC_no_op"; -constexpr const char *XC_store_tensor_OpCode = "XC_store_tensor"; -constexpr const char *XC_load_tensor_OpCode = "XC_load_tensor"; - -// Binarized ops -constexpr const char *XC_bsign_8_OpCode = "XC_bsign_8"; - -TFLMRegistration *Register_XC_beta_activationf32(); -TFLMRegistration *Register_XC_beta_concatf32(); -TFLMRegistration *Register_XC_beta_convf32(); -TFLMRegistration *Register_XC_beta_transposeconvf32(); -TFLMRegistration *Register_XC_beta_fcf32(); - -TFLMRegistration *Register_XC_binaryi16(); -TFLMRegistration *Register_XC_unaryi16(); - -TFLMRegistration *Register_XC_conv2d_v2(); -TFLMRegistration *Register_XC_maxpool2d(); -TFLMRegistration *Register_XC_softmax(); -TFLMRegistration *Register_XC_batched_softmax(); -TFLMRegistration *Register_XC_ld_weights(); -TFLMRegistration *Register_XC_ld_weights_wait(); -TFLMRegistration *Register_XC_add(); -TFLMRegistration *Register_XC_slice(); -TFLMRegistration *Register_XC_broadcast(); -TFLMRegistration *Register_XC_lookup(); -TFLMRegistration *Register_XC_pad(); -TFLMRegistration *Register_XC_concat(); -TFLMRegistration *Register_XC_transpose(); -TFLMRegistration *Register_XC_pad_3_to_4(); -TFLMRegistration *Register_XC_pad_1_to_4(); -TFLMRegistration *Register_XC_mul(); -TFLMRegistration *Register_XC_mean(); -TFLMRegistration *Register_XC_meani16(); -TFLMRegistration *Register_XC_expand_8_to_16(); -TFLMRegistration *Register_XC_no_op(); -TFLMRegistration *Register_XC_store_tensor(); -TFLMRegistration *Register_XC_load_tensor(); - -// Binarized ops -TFLMRegistration *Register_XC_bsign_8(); - -void RegisterXCOps(tflite_micro::MicroOpResolver *res); - -} // namespace xcore -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // XCORE_OPS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h deleted file mode 100644 index 79532c52..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_profiler.h +++ /dev/null @@ -1,49 +0,0 @@ -// Copyright (c) 2019, XMOS Ltd, All rights reserved - -#ifndef XCORE_PROFILER_H_ -#define XCORE_PROFILER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_profiler.h" - -#if !defined(XCORE_PROFILER_DEFAULT_MAX_LEVELS) -#define XCORE_PROFILER_DEFAULT_MAX_LEVELS (64) -#endif - -namespace tflite_micro { -namespace micro { -namespace xcore { - -class XCoreProfiler : public tflite_micro::MicroProfiler { -public: - explicit XCoreProfiler(){}; - ~XCoreProfiler() override = default; - - void Init(tflite_micro::MicroAllocator *allocator, - size_t max_event_count = XCORE_PROFILER_DEFAULT_MAX_LEVELS); - - void ClearEvents(); - - uint32_t BeginEvent(const char *tag) override; - - // Event_handle is ignored since TFLu does not support concurrent events. - void EndEvent(uint32_t event_handle) override; - - uint32_t const *GetEventDurations(); - size_t GetNumEvents(); - -private: - const char *event_tag_; - uint32_t event_start_time_; - size_t event_count_ = 0; - size_t max_event_count_ = 0; - uint32_t *event_durations_; - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace xcore -} // namespace micro -} // namespace tflite_micro - -#endif // XCORE_PROFILER_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h b/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h deleted file mode 100644 index c3316001..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/tflite-xcore-kernels/xcore_utils.h +++ /dev/null @@ -1,156 +0,0 @@ -#ifndef XCORE_UTILS_H_ -#define XCORE_UTILS_H_ - -#include -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace xcore { -/* Unpack an integer data type from a byte array - * T data type to unpack - * - * Example usage: - * int32_t t0 = unpack(&my_buffer[23]); - * int32_t t1 = unpack(&my_buffer[27]); - */ -template T unpack(const uint8_t *buffer) { - T retval = 0; - for (int i = 0; i < sizeof(T); ++i) - retval |= buffer[i] << (8 * i); - return retval; -} - -template -static inline T *construct_persistent_object(TfLiteContext *context) { - return new (context->AllocatePersistentBuffer(context, sizeof(T))) T; -} - -static inline bool is_ram_address(uintptr_t a) { -#ifdef XCORE - return ((a >= 0x80000) && (a <= 0x100000)); -#else - return true; -#endif -} - -static inline TfLiteStatus request_scratch_if_needed(TfLiteContext *context, - const void *source_address, - const size_t size, - int &scratch_idx) { - if (source_address && !is_ram_address((uintptr_t)source_address)) { - return context->RequestScratchBufferInArena(context, size, &scratch_idx); - } - return kTfLiteOk; -} - -static inline TfLiteStatus request_scratch_if_needed(TfLiteContext *context, - const TfLiteTensor *tensor, - int &scratch_idx) { - return request_scratch_if_needed(context, tensor->data.data, tflite_micro::EvalTensorBytes((const TfLiteEvalTensor*)tensor), - scratch_idx); -} - -extern "C" { -static inline void memload(void *dest, void *src, size_t size) { - // printf("memload dest=%d src=%d size=%d\n", (long)dest, (long)src, - // size); - memcpy(dest, src, size); -} -} - -size_t FetchBuffer(int8_t **dest, int8_t const *src, size_t size); - -template -static inline TfLiteStatus -fetch_scratch_if_needed(TfLiteContext *context, T *&array, - const TfLiteEvalTensor *tensor, int scratch_idx) { - if (scratch_idx >= 0) { - array = - static_cast(context->GetScratchBuffer(context, scratch_idx)); - const RuntimeShape shape = tflite_micro::micro::GetTensorShape(tensor); - - size_t sizeof_tensor_type; - TfLiteTypeSizeOf(tensor->type, &sizeof_tensor_type); - FetchBuffer((int8_t **)&array, tflite_micro::micro::GetTensorData(tensor), - shape.FlatSize() * sizeof_tensor_type); - } else { - array = tflite_micro::micro::GetTensorData(tensor); - } - TF_LITE_ENSURE(context, array); - return kTfLiteOk; -} - -template class PersistentArray { -private: - size_t max_size_ = 0; - size_t size_ = 0; - T *data_ = nullptr; - -public: - // call this only in the Init phase of operators - PersistentArray &allocate(TfLiteContext *context, - size_t max_size) noexcept { - assert(data_ == nullptr); - assert(max_size > 0); - - max_size_ = max_size; - data_ = reinterpret_cast( - context->AllocatePersistentBuffer(context, sizeof(T) * max_size)); - - return *this; - }; - PersistentArray &initialize() noexcept { - assert(size_ == 0); - while (size_ < max_size_) { - this->append(T()); - } - - return *this; - }; - // TODO: begin and end would be better if returned an iterator object - inline T *begin() noexcept { - assert(size_ > 0); - return &data_[0]; - } - inline T *end() noexcept { - assert(size_ > 0); - return &data_[size_]; - } - inline T &operator[](int i) noexcept { - assert(i < size_); - return data_[i]; - } - inline void append(const T &element) noexcept { - assert(size_ < max_size_); - data_[size_++] = element; - } - inline void append(T &&element) noexcept { - assert(size_ < max_size_); - data_[size_++] = std::move(element); - } - inline size_t size() noexcept { return size_; } - inline size_t max_size() noexcept { return max_size_; } -}; - -#ifndef UNSUPPORTED_KERNEL_TYPE -#define UNSUPPORTED_KERNEL_TYPE(T) \ - { \ - DebugLog("Unsupported " #T " value"); \ - TFLITE_ABORT; \ - } -#endif /*UNSUPPORTED_KERNEL_TYPE*/ - -} // namespace xcore -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // XCORE_UTILS_H_ diff --git a/new_ai_tools/include/lib_tflite_micro/src/thread_call.h b/new_ai_tools/include/lib_tflite_micro/src/thread_call.h deleted file mode 100644 index 51d4bc51..00000000 --- a/new_ai_tools/include/lib_tflite_micro/src/thread_call.h +++ /dev/null @@ -1,95 +0,0 @@ -#if !defined(__micro_thread_library_h__) -#define __micro_thread_library_h__ - -#include -#ifdef __xcore__ -#include -#else -typedef unsigned synchronizer_t; -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -#define XCORE_MAX_NUM_THREADS 5 - -#ifdef __XC__ - #define UNSAFE unsafe -#else - #define UNSAFE /**/ -#endif - -typedef struct { // THIS STRUCT MUST BE IN SYNC WITH ASSEMBLY CODE. - union { - uint64_t id_aligned[2]; // Guarantee 64-bit alignment. - uint32_t id[4]; // Actual IDs - } thread_ids; // ids of at most 4 threads - live during invoke - uint32_t synchroniser; // synchroniser for threads - live during invoke -} thread_info_t; - - -#ifndef __XC__ - -typedef void (*thread_function_pointer_t)(void * arg0, void * arg1, void * arg2); -struct inference_engine; - -/** Function that runs the client task - */ -void thread_client(thread_info_t *ptr, int n); - -/** Function that runs the client task - */ -static inline void thread_store_sync(thread_info_t *ptr, uint32_t s) { - ptr->synchroniser = s; -} - -/** Function that sets up parameters for one of the client threads - * This particular one passes the second and third arguments to the thread. - * When the thread function is actually called (through thread_call) - * the thread function will be called with those two arguments, - * and the first shared argument provided by thread_call. - * Note - we can make versions with more or fewer parameters. - * Note - we could pass this function the thread-function itself - * - * \param arg1 Second argument for the thread function - * \param arg2 Third argument for the thread function - * \param thread_id The thread_id to initialise; one of ptr[0]..ptr[3] above - */ -#ifdef __xcore__ -static inline void thread_variable_setup(void * arg1, void * arg2, uint32_t thread_id) { -#ifdef __VX4A__ - asm volatile("xm.tsetr %0, 11, %1" :: "r" (thread_id), "r" (arg1)); - asm volatile("xm.tsetr %0, 12, %1" :: "r" (thread_id), "r" (arg2)); - asm volatile("xm.tsetr %0, 24, %1" :: "r" (thread_id), "r" (1)); -#else - asm volatile("set t[%0]:r1, %1" :: "r" (thread_id), "r" (arg1)); - asm volatile("set t[%0]:r2, %1" :: "r" (thread_id), "r" (arg2)); - asm volatile("set t[%0]:r10, %1" :: "r" (thread_id), "r" (1)); -#endif -} -#else -extern void thread_variable_setup(void * arg1, void * arg2, uint32_t thread_id); -#endif - -/** Function that starts all thread functions and runs them until completion. - * It is assumed that the variable parts have been set up per thread. - * by thread_variable_setup. - * This thread will also invoke the function with the given variable arguments. - * - * \param arg0 First argument shared among all threads (usually the output pointer) - * \param arg1 Second argument for the master thread function - * \param arg2 Third argument for the master thread function - * \param fp thread function to call on all threads. - * \param ptr Pointer to the thread info block held in the xcore - * interpreter. - */ -void thread_call(void * arg0, void * arg1, void * arg2, - thread_function_pointer_t fp, thread_info_t *ptr); -#ifdef __cplusplus -}; -#endif - -#endif // __XC__ - -#endif // __micro_thread_library_h__ diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h deleted file mode 100644 index af9f8565..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_defs.h +++ /dev/null @@ -1,4 +0,0 @@ -// Copyright 2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include "XUD_USB_Defines.h" - diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h deleted file mode 100644 index b3aafdb0..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_device.h +++ /dev/null @@ -1,4 +0,0 @@ -// Copyright 2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include "xud_device.h" - diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h deleted file mode 100644 index a1ae7002..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_descriptors.h +++ /dev/null @@ -1,4 +0,0 @@ -// Copyright 2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include "xud_std_descriptors.h" - diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h b/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h deleted file mode 100644 index 54dbae9e..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/legacy/usb_std_requests.h +++ /dev/null @@ -1,4 +0,0 @@ -// Copyright 2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include "xud_std_requests.h" - diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud.h deleted file mode 100644 index 3f399301..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/xud.h +++ /dev/null @@ -1,518 +0,0 @@ -// Copyright 2011-2023 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -/* - * \brief User defines and functions for XMOS USB Device library - */ - -#ifndef _XUD_H_ -#define _XUD_H_ - -#include - -#if !defined(__XS2A__) -#define XUD_OPT_SOFTCRC5 (1) -#else -#define XUD_OPT_SOFTCRC5 (0) -#endif - -#ifdef __xud_conf_h_exists__ -#include "xud_conf.h" -#endif - -#ifndef XUD_STARTUP_ADDRESS -#define XUD_STARTUP_ADDRESS (0) -#endif - -#ifndef __ASSEMBLER__ - -#include -#include -#include -#include - -#ifndef XUD_WEAK_API -#define XUD_WEAK_API (0) -#endif - -#if defined(__STDC__) && XUD_WEAK_API -#define ATTRIB_WEAK __attribute__((weak)); -#else -#define ATTRIB_WEAK -#endif - -#if !defined(USB_TILE) - #define USB_TILE tile[0] -#endif - -// TODO use PLATFORM_REFERENCE_MHZ from platform.h -#ifndef REF_CLK_FREQ -#define REF_CLK_FREQ (100) -#endif - -#ifndef XUD_CORE_CLOCK - #ifdef __XS2A__ - //#warning XUD_CORE_CLOCK not defined, using default (500MHz) - #define XUD_CORE_CLOCK (500) - #else - //#warning XUD_CORE_CLOCK not defined, using default (600MHz) - #define XUD_CORE_CLOCK (600) - #endif -#endif - -#if !defined(PORT_USB_CLK) - /* Ports have not been defined in the .xn file */ - #define PORT_USB_CLK on USB_TILE: XS1_PORT_1J - #define PORT_USB_TXD on USB_TILE: XS1_PORT_8A - #define PORT_USB_RXD on USB_TILE: XS1_PORT_8B - #define PORT_USB_TX_READYOUT on USB_TILE: XS1_PORT_1K - #define PORT_USB_TX_READYIN on USB_TILE: XS1_PORT_1H - #define PORT_USB_RX_READY on USB_TILE: XS1_PORT_1I - #define PORT_USB_FLAG0 on USB_TILE: XS1_PORT_1E - #define PORT_USB_FLAG1 on USB_TILE: XS1_PORT_1F - #ifdef __XS2A__ - /* XS2A has an additional flag port */ - #define PORT_USB_FLAG2 on USB_TILE: XS1_PORT_1G - #endif -#endif // PORT_USB_CLK - -/** - * \var typedef XUD_EpTransferType - * \brief Typedef for endpoint data transfer types. Note: it is important that ISO is 0 - */ -typedef enum XUD_EpTransferType -{ - XUD_EPTYPE_ISO = 0, /**< Isoc */ - XUD_EPTYPE_INT, /**< Interrupt */ - XUD_EPTYPE_BUL, /**< Bulk */ - XUD_EPTYPE_CTL, /**< Control */ - XUD_EPTYPE_DIS, /**< Disabled */ -} XUD_EpTransferType; - -/** - * \var typedef XUD_EpType - * \brief Typedef for endpoint type - */ -typedef unsigned int XUD_EpType; - -/** - * \var typedef XUD_ep - * \brief Typedef for endpoint identifiers - */ -typedef unsigned int XUD_ep; - -/* Value to be or'ed in with EpTransferType to enable bus state notifications */ -#define XUD_STATUS_ENABLE 0x80000000 - -typedef enum XUD_BusSpeed -{ - XUD_SPEED_FS = 1, - XUD_SPEED_HS = 2, - XUD_SPEED_KILL = 3 -} XUD_BusSpeed_t; - -typedef enum XUD_PwrConfig -{ - XUD_PWR_BUS, - XUD_PWR_SELF -} XUD_PwrConfig; - -typedef enum XUD_Result -{ - XUD_RES_RST = -1, - XUD_RES_OKAY = 0, - //XUD_RES_CTL = 1, /* Received a control trans */ - XUD_RES_ERR = 2, -} XUD_Result_t; - -/** This performs the low-level USB I/O operations. Note that this - * needs to run in a thread with at least 80 MIPS worst case execution - * speed. - * - * \param c_epOut An array of channel ends, one channel end per - * output endpoint (USB OUT transaction); this includes - * a channel to obtain requests on Endpoint 0. - * \param noEpOut The number of output endpoints, should be at least 1 (for Endpoint 0). - * \param c_epIn An array of channel ends, one channel end per input endpoint (USB IN transaction); - * this includes a channel to respond to requests on Endpoint 0. - * \param noEpIn The number of input endpoints, should be at least 1 (for Endpoint 0). - * \param c_sof A channel to receive SOF tokens on. This channel must be connected to a process that - * can receive a token once every 125 ms. If tokens are not read, the USB layer will lock up. - * If no SOF tokens are required ``null`` should be used for this parameter. - * - * \param epTypeTableOut See ``epTypeTableIn``. - * \param epTypeTableIn This and ``epTypeTableOut`` are two arrays - * indicating the type of the endpoint. - * Legal types include: - * ``XUD_EPTYPE_CTL`` (Endpoint 0), - * ``XUD_EPTYPE_BUL`` (Bulk endpoint), - * ``XUD_EPTYPE_ISO`` (Isochronous endpoint), - * ``XUD_EPTYPE_INT`` (Interrupt endpoint), - * ``XUD_EPTYPE_DIS`` (Endpoint not used). - * The first array contains the - * endpoint types for each of the OUT - * endpoints, the second array contains the - * endpoint types for each of the IN - * endpoints. - * \param desiredSpeed This parameter specifies what speed the device will attempt to run at - * i.e. full-speed (ie 12Mbps) or high-speed (480Mbps) if supported - * by the host. Pass ``XUD_SPEED_HS`` if high-speed is desired or ``XUD_SPEED_FS`` - * if not. Low speed USB is not supported by XUD. - * \param pwrConfig Specifies whether the device is bus or self-powered. When self-powered the XUD - * will monitor the VBUS line for host disconnections. This is required for compliance reasons. - * Valid values are XUD_PWR_SELF and XUD_PWR_BUS. - * - */ -int XUD_Main(/*tileref * unsafe usbtileXUD_res_t &xudres, */ - chanend c_epOut[], int noEpOut, - chanend c_epIn[], int noEpIn, - NULLABLE_RESOURCE(chanend, c_sof), - XUD_EpType epTypeTableOut[], XUD_EpType epTypeTableIn[], - XUD_BusSpeed_t desiredSpeed, - XUD_PwrConfig pwrConfig); - -/* Legacy API support */ -int XUD_Manager(chanend c_epOut[], int noEpOut, - chanend c_epIn[], int noEpIn, - NULLABLE_RESOURCE(chanend, c_sof), - XUD_EpType epTypeTableOut[], XUD_EpType epTypeTableIn[], - NULLABLE_RESOURCE(port, p_usb_rst), - NULLABLE_RESOURCE(xcore_clock_t, clk), - unsigned rstMask, - XUD_BusSpeed_t desiredSpeed, - XUD_PwrConfig pwrConfig); - -/** - * \brief This function must be called by a thread that deals with an OUT endpoint. - * When the host sends data, the low-level driver will fill the buffer. It - * pauses until data is available. - * \param ep_out The OUT endpoint identifier (created by ``XUD_InitEP``). - * \param buffer The buffer in which to store data received from the host. - * The buffer is assumed to be word aligned. - * \param length The number of bytes written to the buffer - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. - **/ -XUD_Result_t XUD_GetBuffer(XUD_ep ep_out, unsigned char buffer[], REFERENCE_PARAM(unsigned, length)) ATTRIB_WEAK; - -/** - * \brief Request setup data from usb buffer for a specific endpoint, pauses until data is available. - * \param ep_out The OUT endpoint identifier (created by ``XUD_InitEP``). - * \param buffer A char buffer passed by ref into which data is returned. - * \param length Length of the buffer received (expect 8 bytes) - * \return XUD_RES_OKAY on success, for errors see ``Status Reporting``_. - **/ -XUD_Result_t XUD_GetSetupBuffer(XUD_ep ep_out, unsigned char buffer[], REFERENCE_PARAM(unsigned, length)) ATTRIB_WEAK; - -/** - * \brief This function must be called by a thread that deals with an IN endpoint. - * When the host asks for data, the low-level driver will transmit the buffer - * to the host. - * \param ep_in The endpoint identifier (created by ``XUD_InitEp``). - * \param buffer The buffer of data to transmit to the host. - * \param datalength The number of bytes in the buffer. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. - */ -XUD_Result_t XUD_SetBuffer(XUD_ep ep_in, unsigned char buffer[], unsigned datalength) ATTRIB_WEAK; - -/** - * \brief Similar to XUD_SetBuffer but breaks up data transfers into smaller packets. - * This function must be called by a thread that deals with an IN endpoint. - * When the host asks for data, the low-level driver will transmit the buffer - * to the host. - * \param ep_in The IN endpoint identifier (created by ``XUD_InitEp``). - * \param buffer The buffer of data to transmit to the host. - * \param datalength The number of bytes in the buffer. - * \param epMax The maximum packet size in bytes. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. - */ -XUD_Result_t XUD_SetBuffer_EpMax(XUD_ep ep_in, unsigned char buffer[], unsigned datalength, unsigned epMax) ATTRIB_WEAK; - -/** - * \brief Performs a combined ``XUD_SetBuffer`` and ``XUD_GetBuffer``. - * It transmits the buffer of the given length over the ``ep_in`` endpoint to - * answer an IN request, and then waits for a 0 length Status OUT transaction on ``ep_out``. - * This function is normally called to handle Get control requests to Endpoint 0. - * - * \param ep_out The endpoint identifier that handles Endpoint 0 OUT data in the XUD manager. - * \param ep_in The endpoint identifier that handles Endpoint 0 IN data in the XUD manager. - * \param buffer The data to send in response to the IN transaction. Note that this data - * is chopped up in fragments of at most 64 bytes. - * \param length Length of data to be sent. - * \param requested The length that the host requested, (Typically pass the value ``wLength``). - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_ - **/ -XUD_Result_t XUD_DoGetRequest(XUD_ep ep_out, XUD_ep ep_in, unsigned char buffer[], unsigned length, unsigned requested) ATTRIB_WEAK; - -/** - * \brief This function sends an empty packet back on the next IN request with - * PID1. It is normally used by Endpoint 0 to acknowledge success of a control transfer. - * \param ep_in The Endpoint 0 IN identifier to the XUD manager. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`_. - **/ -XUD_Result_t XUD_DoSetRequestStatus(XUD_ep ep_in) ATTRIB_WEAK; - -/** - * \brief This function will complete a reset on an endpoint. Can take - * one or two ``XUD_ep`` as parameters (the second parameter can be set to ``null``). - * The return value should be inspected to find the new bus-speed. - * In Endpoint 0 typically two endpoints are reset (IN and OUT). - * In other endpoints ``null`` can be passed as the second parameter. - * \param one IN or OUT endpoint identifier to perform the reset on. - * \param two Optional second IN or OUT endpoint structure to perform a reset on. - * \return Either ``XUD_SPEED_HS`` - the host has accepted that this device can execute - * at high speed, ``XUD_SPEED_FS`` - the device is running at full speed, - * or ``XUD_SPEED_KILL`` to indicate that the USB stack has been shut down - * by another part of the user code (using XUD_Kill). If the last value is - * returned, the endpoint code should call XUD_CloseEndpoint and then - * terminate. - */ -XUD_BusSpeed_t XUD_ResetEndpoint(XUD_ep one, NULLABLE_REFERENCE_PARAM(XUD_ep, two)); - -/** - * \brief This function closes an endpoint. It should be called when the USB stack - * is shutting down. It should be called on all endpoints, either in parallel - * or in numerical order, first all OUT and then all IN endpoints - * \param one endpoint to close. - */ -void XUD_CloseEndpoint(XUD_ep one); - -/** - * \brief Initialises an XUD_ep - * \param c_ep Endpoint channel to be connected to the XUD library. - * \return Endpoint identifier - */ -XUD_ep XUD_InitEp(chanend c_ep); - -/** - * \brief Mark an endpoint as STALL based on its EP address. Cleared automatically if a SETUP received on the endpoint. - * Note: the IN bit of the endpoint address is used. - * \param epNum Endpoint number. - * \warning Must be run on same tile as XUD core - */ -void XUD_SetStallByAddr(int epNum); - -/** - * \brief Mark an endpoint as NOT STALLed based on its EP address. - * Note: the IN bit of the endpoint address is used. - * \param epNum Endpoint number. - * \warning Must be run on same tile as XUD core - */ -void XUD_ClearStallByAddr(int epNum); - -/** - * \brief Mark an endpoint as STALLed. It is cleared automatically if a SETUP received on the endpoint. - * \param ep XUD_ep type. - * \warning Must be run on same tile as XUD core - */ -void XUD_SetStall(XUD_ep ep); - -/** - * \brief Mark an endpoint as NOT STALLed - * \param ep XUD_ep type. - * \warning Must be run on same tile as XUD core - */ -void XUD_ClearStall(XUD_ep ep); - -/* USB 2.0 Spec 9.1.1.5 states that configuring a device should cause all - * the status and configuration values associated with the endpoints in the - * affected interfaces to be set to their default values. This includes setting - * the data toggle of any endpoint using data toggles to the value DATA0 */ -/** - * \brief Reset an Endpoints state including data PID toggle - * Note: the IN bit of the endpoint address is used. - * \param epNum Endpoint number (including IN bit) - * \warning Must be run on same tile as XUD core - */ -void XUD_ResetEpStateByAddr(unsigned epNum); - -/** - * \brief Enable a specific USB test mode in XUD - * \param ep XUD_ep type (must be endpoint 0 in or out) - * \param testMode The desired test-mode - * \warning Must be run on same tile as XUD core - */ -void XUD_SetTestMode(XUD_ep ep, unsigned testMode); - -/** - * \brief Terminate XUD core - * \param ep XUD_ep type (must be endpoint 0 in or out) - * \warning Must be run on same tile as XUD core - */ -void XUD_Kill(XUD_ep ep); - -/***********************************************************************************************/ - -/* - * Advanced functions for supporting multple Endpoints in a single core - */ - - -/** - * \brief Marks an OUT endpoint as ready to receive data - * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). - * \param addr The address of the buffer in which to store data received from the host. - * The buffer is assumed to be word aligned. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -static inline int XUD_SetReady_OutPtr(XUD_ep ep, unsigned addr) -{ - int chan_array_ptr; - int reset; - - /* Firstly check if we have missed a USB reset - endpoint may would not want receive after a reset */ - asm volatile("ldw %0, %1[9]":"=r"(reset):"r"(ep)); - if(reset) - { - return XUD_RES_RST; - } - asm volatile("ldw %0, %1[0]":"=r"(chan_array_ptr):"r"(ep)); - asm volatile("stw %0, %1[3]"::"r"(addr),"r"(ep)); // Store buffer - asm volatile("stw %0, %1[0]"::"r"(ep),"r"(chan_array_ptr)); - - return XUD_RES_OKAY; -} - -/** - * \brief Marks an OUT endpoint as ready to receive data - * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). - * \param buffer The buffer in which to store data received from the host. - * The buffer is assumed to be word aligned. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -int XUD_SetReady_Out(XUD_ep ep, unsigned char buffer[]); - - -/** - * \brief Marks an IN endpoint as ready to transmit data - * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). - * \param addr The address of the buffer to transmit to the host. - * The buffer is assumed be word aligned. - * \param len The length of the data to transmit. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -static inline XUD_Result_t XUD_SetReady_InPtr(XUD_ep ep, unsigned addr, int len) -{ - int chan_array_ptr; - int tmp, tmp2; - int wordLength; - int tailLength; - - int reset; - - /* Firstly check if we have missed a USB reset - endpoint may not want to send out old data after a reset */ - asm volatile("ldw %0, %1[9]":"=r"(reset):"r"(ep)); - if(reset) - { - return XUD_RES_RST; - } - - /* Tail length bytes to bits */ -#ifdef __XC__ - tailLength = zext((len << 3),5); -#else - tailLength = (len << 3) & 0x1F; -#endif - - /* Datalength (bytes) --> datalength (words) */ - wordLength = len >> 2; - - /* If tail-length is 0 and word-length not 0. Make tail-length 32 and word-length-- */ - if ((tailLength == 0) && (wordLength != 0)) - { - wordLength = wordLength - 1; - tailLength = 32; - } - - /* Get end off buffer address */ - asm volatile("add %0, %1, %2":"=r"(tmp):"r"(addr),"r"(wordLength << 2)); - - /* Produce negative offset from end of buffer */ - asm volatile("neg %0, %1":"=r"(tmp2):"r"(wordLength)); - - /* Store neg index */ - asm volatile("stw %0, %1[6]"::"r"(tmp2),"r"(ep)); - - /* Store buffer pointer */ - asm volatile("stw %0, %1[3]"::"r"(tmp),"r"(ep)); - - /* Store tail len */ - asm volatile("stw %0, %1[7]"::"r"(tailLength),"r"(ep)); - - /* Finally, mark ready */ - asm volatile("ldw %0, %1[0]":"=r"(chan_array_ptr):"r"(ep)); - asm volatile("stw %0, %1[0]"::"r"(ep),"r"(chan_array_ptr)); - - return XUD_RES_OKAY; -} - -/** - * \brief Marks an IN endpoint as ready to transmit data - * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). - * \param buffer The buffer to transmit to the host. - * The buffer is assumed be word aligned. - * \param len The length of the data to transmit. - * \return XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -static inline XUD_Result_t XUD_SetReady_In(XUD_ep ep, unsigned char buffer[], int len) -{ - unsigned addr; - - asm volatile("mov %0, %1":"=r"(addr):"r"(buffer)); - - return XUD_SetReady_InPtr(ep, addr, len); -} - -/** - * \brief Select handler function for receiving OUT endpoint data in a select. - * \param c The chanend related to the endpoint - * \param ep The OUT endpoint identifier (created by ``XUD_InitEp``). - * \param length Passed by reference. The number of bytes written to the buffer (that was passed into - * XUD_SetReady_Out()) - * \param result XUD_Result_t passed by reference. XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -#ifdef __XC__ -#pragma select handler -#endif -void XUD_GetData_Select(chanend c, XUD_ep ep, REFERENCE_PARAM(unsigned, length), REFERENCE_PARAM(XUD_Result_t, result)); - - -/** - * \brief Select handler function for transmitting IN endpoint data in a select. - * \param c The chanend related to the endpoint - * \param ep The IN endpoint identifier (created by ``XUD_InitEp``). - * \param result Passed by reference. XUD_RES_OKAY on success, for errors see `Status Reporting`. - */ -#ifdef __XC__ -#pragma select handler -#endif -void XUD_SetData_Select(chanend c, XUD_ep ep, REFERENCE_PARAM(XUD_Result_t, result)); - -/* Control token defines - used to inform EPs of bus-state types */ -#define USB_RESET_TOKEN 8 /* Control token value that signals RESET */ - -#ifndef XUD_OSC_MHZ -#define XUD_OSC_MHZ (24) -#endif - -/* TODO pack this to save mem - * TODO size of this hardcoded in ResetEpStateByAddr_ - */ -typedef struct XUD_ep_info -{ - unsigned int array_ptr; // 0 - unsigned int xud_chanend; // 1 - unsigned int client_chanend; // 2 - unsigned int buffer; // 3 Pointer to buffer - unsigned int pid; // 4 Expected out PID - unsigned int epType; // 5 Data - unsigned int actualPid; // 6 Actual OUT PID received for OUT, Length (words) for IN. - unsigned int tailLength; // 7 "tail" length for IN (bytes) - unsigned int epAddress; // 8 EP address assigned by XUD (Used for marking stall etc) - unsigned int resetting; // 9 Flag to indicate to EP a bus-reset occured. - unsigned int halted; // 10 NAK or STALL - unsigned int saved_array_ptr; // 11 - unsigned int array_ptr_setup; // 12 -} XUD_ep_info; - -#endif -#endif // _XUD_H_ diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h deleted file mode 100644 index 76617f33..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/xud_conf_default.h +++ /dev/null @@ -1,11 +0,0 @@ -// Copyright 2017-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef __XUD_CONF_DEFAULT_H__ -#define __XUD_CONF_DEFAULT_H__ - -#ifdef __xud_conf_h_exists__ - #include "xud_conf.h" -#endif - -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h deleted file mode 100644 index d68b8dca..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/xud_device.h +++ /dev/null @@ -1,87 +0,0 @@ -// Copyright 2015-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -/* - * @brief USB Device helper functions - */ - -#ifndef _USB_DEVICE_H_ -#define _USB_DEVICE_H_ - -#include - -/* Low level XUD API for USB Device */ -#include "xud.h" - -/* Generic USB defines */ -#include "XUD_USB_Defines.h" - -/* Generic USB descriptor defines */ -#include "xud_std_descriptors.h" - -/* Generic USB descriptor defines */ -#include "xud_std_requests.h" - -/** - * \brief This function deals with common requests This includes Standard Device Requests listed - * in table 9-3 of the USB 2.0 Spec all devices must respond to these requests, in some - * cases a bare minimum implementation is provided and should be extended in the devices EP0 code - * It handles the following standard requests appropriately using values passed to it: - * - * Get Device Descriptor (using devDesc_hs/devDesc_fs arguments) - * - * Get Configuration Descriptor (using cfgDesc_hs/cfgDesc_fs arguments) - * - * String requests (using strDesc argument) - * - * Get Device_Qualifier Descriptor - * - * Get Other-Speed Configuration Descriptor - * - * Set/Clear Feature (Endpoint Halt) - * - * Get/Set Interface - * - * Set Configuration - * - * If the request is not recognised the endpoint is marked STALLED - * - * - * \param ep_out Endpoint from XUD (ep 0) - * \param ep_in Endpoint from XUD (ep 0) - * \param devDesc_hs The Device descriptor to use, encoded according to the USB standard - * \param devDescLength_hs Length of device descriptor in bytes - * \param cfgDesc_hs Configuration descriptor - * \param cfgDescLength_hs Length of config descriptor in bytes - * \param devDesc_fs The Device descriptor to use, encoded according to the USB standard - * \param devDescLength_fs Length of device descriptor in bytes. If 0 the HS device descriptor is used. - * \param cfgDesc_fs Configuration descriptor - * \param cfgDescLength_fs Length of config descriptor in bytes. If 0 the HS config descriptor is used. - * \param strDescs - * \param strDescsLength - * \param sp ``USB_SetupPacket_t`` (passed by ref) in which the setup data is returned - * \param usbBusSpeed The current bus speed (XUD_SPEED_HS or XUD_SPEED_FS) - * - * \return Returns XUD_RES_OKAY on success. - */ - -XUD_Result_t USB_StandardRequests(XUD_ep ep_out, XUD_ep ep_in, - NULLABLE_ARRAY_OF(unsigned char, devDesc_hs), int devDescLength_hs, - NULLABLE_ARRAY_OF(unsigned char, cfgDesc_hs), int cfgDescLength_hs, - NULLABLE_ARRAY_OF(unsigned char, devDesc_fs), int devDescLength_fs, - NULLABLE_ARRAY_OF(unsigned char, cfgDesc_fs), int cfgDescLength_fs, -#ifdef __XC__ - char * unsafe strDescs[], -#else - char * strDescs[], -#endif - int strDescsLength, REFERENCE_PARAM(USB_SetupPacket_t, sp), XUD_BusSpeed_t usbBusSpeed); -/** - * \brief Receives a Setup data packet and parses it into the passed USB_SetupPacket_t structure. - * \param ep_out OUT endpint from XUD - * \param ep_in IN endpoint to XUD - * \param sp SetupPacket structure to be filled in (passed by ref) - * \return Returns XUD_RES_OKAY on success, XUD_RES_RST on bus reset - */ -XUD_Result_t USB_GetSetupPacket(XUD_ep ep_out, XUD_ep ep_in, REFERENCE_PARAM(USB_SetupPacket_t, sp)); - -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h deleted file mode 100644 index 40e937ce..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_descriptors.h +++ /dev/null @@ -1,191 +0,0 @@ -// Copyright 2015-2022 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef _USB_DESCRIPTORS_H_ -#define _USB_DESCRIPTORS_H_ - -#if !defined (__ASSEMBLER__) -/* USB Class Codes (from usb.org) */ -typedef enum -{ - USB_CLASS_USE_CLASS = 0x00, /* Use class information in the interface descriptors */ - USB_CLASS_AUDIO = 0x01, - USB_CLASS_COMMUNICATIONS = 0x02, - USB_CLASS_HID = 0x03, - USB_CLASS_PHYSICAL = 0x05, - USB_CLASS_IMAGE = 0x06, - USB_CLASS_PRINTER = 0x07, - USB_CLASS_MASS_STORAGE = 0x08, - USB_CLASS_HUB = 0x09, - USB_CLASS_CDC_DATA = 0x0A, - USB_CLASS_SMART_CARD = 0x0B, - USB_CLASS_RESERVED = 0x0C, - USB_CLASS_CONTENT_SECURITY = 0x0D, - USB_CLASS_VIDEO = 0x0E, - USB_CLASS_PERSONAL_HEALTHCARE = 0x0F, - USB_CLASS_AUDIO_VIDEO = 0x10, -//... - USB_CLASS_MAPPED_INDEX_END = 17, - USB_CLASS_VENDOR_SPECIFIC = 0xFF -} USB_ClassCode_t; - -#endif - -/* USB Standard Descriptor types (Section 9.4, table 9-5) */ -enum USB_DescriptorTypes_t -{ - USB_DESCTYPE_DEVICE = 0x01, /* Device descriptor */ - USB_DESCTYPE_CONFIGURATION = 0x02, /* Configuration descriptor */ - USB_DESCTYPE_STRING = 0x03, /* String descriptor */ - USB_DESCTYPE_INTERFACE = 0x04, /* Interface descriptor */ - USB_DESCTYPE_ENDPOINT = 0x05, /* Endpoint descriptor */ - USB_DESCTYPE_DEVICE_QUALIFIER = 0x06, /* Device qualifier descriptor */ - USB_DESCTYPE_OTHER_SPEED = 0x07, - USB_DESCTYPE_INTERFACE_POWER = 0x08, /* Interface power descriptor */ - USB_DESCTYPE_OTG = 0x09, - USB_DESCTYPE_DEBUG = 0x0A, - USB_DESCTYPE_INTERFACE_ASSOCIATION = 0x0B, /* Interface association descriptor */ -}; - -#ifdef __STDC__ - -/* No current support for __attribute((packed)) in XC */ - -/* Generic USB Descriptor Header */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Descriptor type, either a value. See \ref USB_DescriptorTypes_t or - * a value given by the specific class */ -} __attribute__((packed)) USB_Descriptor_Header_t; - -/* USB Standard Device Descriptor (section 9.6.1, table 9-8) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t - * or a value given by the specific class */ - unsigned short bcdUSB; /* Supported USB version */ - unsigned char bDeviceClass; /* USB device class code */ - unsigned char bDeviceSubClass; /* USB device subclass code */ - unsigned char bDeviceProtocol; /* USB device protocol code */ - unsigned char bMaxPacketSize0; /* Maximum packet size for endpoint 0 (bytes) */ - unsigned short idVendor; /* Vendor ID */ - unsigned short idProduct; /* Product ID */ - unsigned short bcdDevice; /* Device release number in binary-coded decimal */ - unsigned char iManufacturer; /* Index of string descriptor describing manufacturer */ - unsigned char iProduct; /* Index of string descriptor describing product */ - unsigned char iSerialNumber; /* Index of String descriptor describing the devices serial number */ - unsigned char bNumConfigurations; /* Total number of configurations supported by the device */ -} __attribute__((packed)) USB_Descriptor_Device_t; - -/* USB Interface Association Descriptor (See IAD Engineering Change Notice) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t - or a value given by the specific class */ - unsigned char bFirstInterface; /* Index of the first associated interface */ - unsigned char bInterfaceCount; /* Total number of associated interfaces */ - unsigned char bFunctionClass; /* Interface class ID */ - unsigned char bFunctionSubClass; /* Interface subclass ID */ - unsigned char bFunctionProtocol; /* Interface protocol ID */ - unsigned char iFunction; /* Index of the string descriptor describing the - * interface association */ -} __attribute__((packed)) USB_Descriptor_Interface_Association_t; - -/* USB Standard Interface Descriptor (section 9.6.1 table 9-12) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Type of the descriptor, either a value in \ref USB_DescriptorTypes_t - * or a value given by the specific class */ - unsigned char bInterfaceNumber; /* Index of the interface in the current config */ - unsigned char bAlternateSetting; /* Alternate setting for this interface number. Multiple alternatives - * are supported per interface (with different EP configs) */ - unsigned char bNumEndpoints; /* Total endpoint count in this interface */ - unsigned char bInterfaceClass; /* Interface class code */ - - unsigned char bInterfaceSubClass; /* Interface subclass code */ - unsigned char bInterfaceProtocol; /* Interface protocol code */ - unsigned char iInterface; /* Index of the string descriptor in the string table */ -} __attribute__((packed)) USB_Descriptor_Interface_t; - -/* USB Standard Configuration Descriptor (section 9.6.1 table 9-10) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Type of the descriptor, either a value in \ref USB_DescriptorTypes_t or a value - * given by the specific class */ - unsigned short wTotalLength; /* Size of the configuration descriptor header and all sub descriptors inside - * the configuration */ - unsigned char bNumInterfaces; /* Total interface count in the configuration */ - unsigned char bConfigurationValue; /* Value to use as an argument to the SetConfiguration() request to select this - * configuration */ - unsigned char iConfiguration; /* Index of string descriptor describing this configuration */ - unsigned char bmAttributes; /* Configuration characteristics - * D7: Reserved (set to one) - * D6: Self-powered - * D5: Remote Wakeup - * D4...0: Reserved (reset to zero) - */ - unsigned char bMaxPower; /* Maximum power consumption of the USB device from the bus in this specific - * configuration when the device is fully operational. Expressed in 2 mA units - * (i.e., 50 = 100 mA) */ -} __attribute__((packed)) USB_Descriptor_Configuration_Header_t; - -/* USB Standard Endpoint Descriptor (section 9.6.1 table 9-13) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Descriptor type, either a value. See \ref USB_DescriptorTypes_t or - * a value given by the specific class */ - unsigned char bEndpointAddress; /* Address of the endpoint, includes a direction mask */ - unsigned char bmAttributes; /* Endpoint attributes, comprised of a mask of the endpoint type - * See EP_TYPE_ ad EP_ADDR) */ - unsigned short wMaxPacketSize; /* Maximum packet size (bytes) that the endpoint can receive */ - unsigned char bInterval; /* Polling interval in milliseconds for the endpoint. - * Relevant to Isochronous and Interrupt endpoints only */ -} __attribute__((packed)) USB_Descriptor_Endpoint_t; - -/* USB Standard Endpoint Descriptor (Section 9.6.6, table 9-13) */ -enum USB_Endpoint_TransType_t -{ - USB_ENDPOINT_TRANSTYPE_CTRL = 0x0, - USB_ENDPOINT_TRANSTYPE_ISO = 0x1, - USB_ENDPOINT_TRANSTYPE_BULK = 0x2, - USB_ENDPOINT_TRANSTYPE_INT = 0x3 -}; -#define USB_ENDPOINT_TRANSTYPE_SHIFT (0) - -enum USB_Endpoint_SyncType_t -{ - USB_ENDPOINT_SYNCTYPE_NONE = 0x0, - USB_ENDPOINT_SYNCTYPE_ASYNC = 0x1, - USB_ENDPOINT_SYNCTYPE_ADAPT = 0x2, - USB_ENDPOINT_SYNCTYPE_SYNC = 0x3 -}; -#define USB_ENDPOINT_SYNCTYPE_SHIFT (2) - -enum USB_Endpoint_UsageType_t -{ - USB_ENDPOINT_USAGETYPE_DATA = 0x0, - USB_ENDPOINT_USAGETYPE_FEEDBACK = 0x1, - USB_ENDPOINT_USAGETYPE_IMPLICIT = 0x2, - USB_ENDPOINT_USAGETYPE_RESERVED = 0x3 -}; -#define USB_ENDPOINT_USAGETYPE_SHIFT (4) - - - -/* USB String Descriptor (Section 9.6.7 table 9-15) */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; /* Descriptor type, either a value in \ref USB_DescriptorTypes_t - * or a value given by the specific class */ - unsigned short bString[]; /* String data, (as unicode characters) - use array of chars instead of string. - * In GCC prefix string with "L" */ -} __attribute__((packed)) USB_Descriptor_String_t; -#endif -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h b/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h deleted file mode 100644 index 5fafbb21..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/api/xud_std_requests.h +++ /dev/null @@ -1,120 +0,0 @@ -// Copyright 2015-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#ifndef _USB_STD_REQUESTS_H_ -#define _USB_STD_REQUESTS_H_ - -#include -#include "XUD_USB_Defines.h" - -/* 9.3 USB Device Requests: Table 9-2 Format of Setup Data */ -/* bmRequestType: */ -#define USB_BM_REQTYPE_DIRECTION_H2D 0 /* Host to device */ -#define USB_BM_REQTYPE_DIRECTION_D2H 1 /* Device to host */ - -#define USB_BM_REQTYPE_TYPE_STANDARD 0x00 -#define USB_BM_REQTYPE_TYPE_CLASS 0x01 -#define USB_BM_REQTYPE_TYPE_VENDOR 0x02 - -#define USB_BM_REQTYPE_RECIP_DEV 0x00 -#define USB_BM_REQTYPE_RECIP_INTER 0x01 -#define USB_BM_REQTYPE_RECIP_EP 0x02 -#define USB_BM_REQTYPE_RECIP_OTHER 0x03 - -#define USB_BMREQ_H2D_STANDARD_DEV ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_DEV)) -#define USB_BMREQ_D2H_STANDARD_DEV ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_DEV)) -#define USB_BMREQ_H2D_STANDARD_INT ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_INTER)) -#define USB_BMREQ_D2H_STANDARD_INT ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_INTER)) -#define USB_BMREQ_H2D_STANDARD_EP ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_EP)) -#define USB_BMREQ_D2H_STANDARD_EP ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_STANDARD << 5) | \ - (USB_BM_REQTYPE_RECIP_EP)) - - -#define USB_BMREQ_H2D_CLASS_INT ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ - (USB_BM_REQTYPE_RECIP_INTER)) -#define USB_BMREQ_D2H_CLASS_INT ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ - (USB_BM_REQTYPE_RECIP_INTER)) -#define USB_BMREQ_H2D_CLASS_EP ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ - (USB_BM_REQTYPE_RECIP_EP)) -#define USB_BMREQ_D2H_CLASS_EP ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_CLASS << 5) | \ - (USB_BM_REQTYPE_RECIP_EP)) - -#define USB_BMREQ_H2D_VENDOR_DEV ((USB_BM_REQTYPE_DIRECTION_H2D << 7) | \ - (USB_BM_REQTYPE_TYPE_VENDOR << 5) | \ - (USB_BM_REQTYPE_RECIP_DEV)) -#define USB_BMREQ_D2H_VENDOR_DEV ((USB_BM_REQTYPE_DIRECTION_D2H << 7) | \ - (USB_BM_REQTYPE_TYPE_VENDOR << 5) | \ - (USB_BM_REQTYPE_RECIP_DEV)) - -/* Table 9-4. Standard Request Codes */ -/* bRequest */ -#define USB_GET_STATUS 0x00 -#define USB_CLEAR_FEATURE 0x01 -#define USB_SET_FEATURE 0x03 -#define USB_SET_ADDRESS 0x05 -#define USB_GET_DESCRIPTOR 0x06 -#define USB_SET_DESCRIPTOR 0x07 -#define USB_GET_CONFIGURATION 0x08 -#define USB_SET_CONFIGURATION 0x09 -#define USB_GET_INTERFACE 0x0A -#define USB_SET_INTERFACE 0x0B -#define USB_SYNCH_FRAME 0x0C - -/** - * \var typedef USB_BmRequestType_t - * \brief Defines the Recepient, Type and Direction of a USB request. - */ -typedef struct USB_BmRequestType -{ - unsigned char Recipient; // [4..0] Request directed to: - // 0b00000: Device - // 0b00001: Specific interface - // 0b00010: Specific endpoint - // 0b00011: Other element in device - unsigned char Type; // [6..5] 0b00: Standard request - // 0b01: Class specific request - // 0b10: Request by vendor specific driver - unsigned char Direction; // [7] 0 (Host->Dev) - // 1 (Dev->Host) -} USB_BmRequestType_t; - -/** - * \var typedef USB_SetupPacket_t - * \brief Typedef for setup packet structure */ -typedef struct USB_SetupPacket -{ - USB_BmRequestType_t bmRequestType; /* (1 byte) Specifies direction of dataflow, - type of rquest and recipient */ - unsigned char bRequest; /* Specifies the request */ - unsigned short wValue; /* Host can use this to pass info to the - device in its own way */ - unsigned short wIndex; /* Typically used to pass index/offset such - as interface or EP no */ - unsigned short wLength; /* Number of data bytes in the data stage - (for Host -> Device this this is exact - count, for Dev->Host is a max. */ -} USB_SetupPacket_t; - -/** - * \brief Prints out passed ``USB_SetupPacket_t`` struct using debug IO - */ -void USB_PrintSetupPacket(USB_SetupPacket_t sp); - -void USB_ComposeSetupBuffer(USB_SetupPacket_t sp, unsigned char buffer[]); - -void USB_ParseSetupPacket(unsigned char b[], REFERENCE_PARAM(USB_SetupPacket_t, p)); -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h deleted file mode 100644 index 6f0082ef..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/src/user/XUD_USB_Defines.h +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright 2015-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -/** - * @brief Defines from the Universal Serial Bus Specification Revision 2.0 - **/ - -#ifndef _USB_DEFS_H_ -#define _USB_DEFS_H_ - -/* Table 8-1. PID Types */ -#define USB_PID_OUT 0x1 /* Tokens */ -#define USB_PID_IN 0x9 -#define USB_PID_SOF 0x5 -#define USB_PID_SETUP 0xD -#define USB_PID_DATA0 0x3 /* Data packet PID even */ -#define USB_PID_DATA1 0xB /* Data packet PID odd */ -#define USB_PID_DATA2 0x7 /* Data packet PID high-speed, high bandwidth isoc transaction in a microframe */ -#define USB_PID_MDATA 0xF /* Data packet PID high-speed and high bandwidth isoc transactions */ -#define USB_PID_ACK 0x2 /* Receiver accepts error-free data packet */ -#define USB_PID_NAK 0xA /* Receiving device cannot accept data of transmitting device cannot send data */ -#define USB_PID_STALL 0xE /* Endpoint is halted or a control pipe request is not supported */ -#define USB_PID_PRE 0xC -#define USB_PID_ERR 0xC -#define USB_PID_SPLIT 0x8 -#define USB_PID_PING 0x4 /* Hign-speed flow control probe for bulk/control endpoint */ - -/* PID with error check */ -#define USB_PID_NEGATE(PID) ((PID) | (((~PID) & 0xf) << 4)) -#define USB_PIDn_OUT 0xe1 -#define USB_PIDn_IN 0x69 -#define USB_PIDn_SOF 0xa5 -#define USB_PIDn_SETUP 0x2d -#define USB_PIDn_DATA0 0xc3 -#define USB_PIDn_DATA1 USB_PID_NEGATE(USB_PID_DATA1) -#define USB_PIDn_DATA2 USB_PID_NEGATE(USB_PID_DATA2) -#define USB_PIDn_ACK 0xd2 -#define USB_PIDn_NAK 0x5a -#define USB_PIDn_STALL 0x1e - -/* Table 9-6. Standard Feature Selectors (wValue) */ -#define USB_DEVICE_REMOTE_WAKEUP 0x01 /* Recipient: Device */ -#define USB_ENDPOINT_HALT 0x00 /* Recipient: Endpoint */ -#define USB_TEST_MODE 0x02 /* Recipient: Device */ - -#define USB_STANDARD_DEVICE_REQUEST 0x00 -#define USB_STANDARD_INTERFACE_REQUEST 0x01 -#define USB_STANDARD_ENDPOINT_REQUEST 0x02 -#define USB_VENDOR_DEVICE_REQUEST 0x40 -#define USB_VENDOR_ENDPOINT_REQUEST 0x42 -#define USB_CLASS_INTERFACE_REQUEST 0x21 -#define USB_CLASS_ENDPOINT_REQUEST 0x22 - -#define USB_WVAL_EP_HALT 0 - -// Low byte values: -//#define USB_WVALUE_GETDESC_STRING_LANGIDS 0x0 -//#define USB_WVALUE_GETDESC_STRING_IPRODUCT 0x2 - -// Test selector defines for Test mode -#define USB_WINDEX_TEST_J (0x1<<8) -#define USB_WINDEX_TEST_K (0x2<<8) -#define USB_WINDEX_TEST_SE0_NAK (0x3<<8) -#define USB_WINDEX_TEST_PACKET (0x4<<8) -#define USB_WINDEX_TEST_FORCE_ENABLE (0x5<<8) - -#define USB_MAX_NUM_EP_OUT (16) -#define USB_MAX_NUM_EP_IN (16) -#define USB_MAX_NUM_EP (32) - -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h deleted file mode 100644 index a6df0f11..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/hid.h +++ /dev/null @@ -1,23 +0,0 @@ -// Copyright 2015-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -/* - * @brief Defines from USB Device Class Definition for Human Interface Devices Specification - */ - -/* 7. Requests */ - -/* 7.1 Standard Requests - Class Descriptor Types - High byte of wValue - * The following defines valid types of Class descriptors */ -#define HID_HID 0x2100 -#define HID_REPORT 0x2200 -#define HID_PHYSICAL_DESCRIPTOR 0x2300 -/*0x24 - 0x2F: Reserved */ - -/* 7.2 Class-Specific Requests - bRequest values */ -#define HID_GET_REPORT 0x01 /* Mandatory */ -#define HID_GET_IDLE 0x02 -#define HID_GET_PROTOCOL 0x03 /* Required only for boot devices */ -/* Ox04 - 0x08 reserved */ -#define HID_SET_REPORT 0x09 -#define HID_SET_IDLE 0x0A -#define HID_SET_PROTOCOL 0x0B /* Required only for boot devices */ diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h deleted file mode 100644 index 712de4ab..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio10.h +++ /dev/null @@ -1,30 +0,0 @@ -// Copyright 2017-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef _AUDIO10_H_ -#define _AUDIO10_H_ 1 -/** - * @brief Defines from the USB Audio 1.0 Specifications - * @author Ross Owen, XMOS Limited - */ - -/* A.9. Audio Class-Specific Request Codes */ -#define UAC_B_REQ_SET_CUR 0x01 -#define UAC_B_REQ_GET_CUR 0x81 -#define UAC_B_REQ_SET_MIN 0x02 -#define UAC_B_REQ_GET_MIN 0x82 -#define UAC_B_REQ_SET_MAX 0x03 -#define UAC_B_REQ_GET_MAX 0x83 -#define UAC_B_REQ_SET_RES 0x04 -#define UAC_B_REQ_GET_RES 0x84 -#define UAC_B_REQ_SET_MEM 0x05 -#define UAC_B_REQ_GET_MEM 0x85 -#define UAC_B_REQ_GET_STAT 0xFF - -/* A.10.5 Endpoint Control Selectors: Table A-19 */ -#define EP_CONTROL_UNDEFINED 0x00 -#define SAMPLING_FREQ_CONTROL 0x01 -#define PITCH_CONTROL 0x02 - -#endif - diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h deleted file mode 100644 index ed9fec95..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudio20.h +++ /dev/null @@ -1,357 +0,0 @@ -// Copyright 2017-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -/** - * @brief Defines from the USB Audio 2.0 Specifications - * @author Ross Owen, XMOS Limited - */ -#ifndef _USBAUDIO20_H_ -#define _USBAUDIO20_H_ - -#include "usbaudiocommon.h" - -/***********************************************************************/ -/* USB Audio 2.0 Class Specification */ - -/* A.1 Audio Function Class Code */ -#define AUDIO_FUNCTION AUDIO - -/* A.2 Audio Function Subclass Codes */ -#define FUNCTION_SUBCLASS_UNDEFINED 0x00 - -/* A.3 Audio Function Protocol Codes */ -enum USB_Audio_FuncProtocolCodes_t -{ - UAC_FUNC_PROTOCOL_UNDEFINED = 0x00, - UAC_FUNC_PROTOCOL_AF_VERSION_02_00 = 0x20 -}; - -/* A.4 Audio Interface Class Code */ -#define AUDIO 0x01 - -/* A.5 Audio Interface Subclass Codes */ -enum UAC_IntSubclassCodes_t -{ - UAC_INT_SUBCLASS_AUDIOCONTROL = 0x01, - UAC_INT_SUBCLASS_AUDIOSTREAMING = 0x02, - UAC_INT_SUBCLASS_MIDISTREAMING = 0x03 -}; - -/* A.6 Audio Interface Protocol Codes */ -enum UAC_IntProtocolCodes_t -{ - UAC_INT_PROTOCOL_UNDEFINED = 0x00, - UAC_INT_PROTOCOL_IP_VERSION_02_00 = 0x20 -}; - -/* A.7 Audio Function Category Codes */ -enum UAC_AudioFunctionCategory_t -{ - UAC_FUNCTION_SUBCLASS_UNDEFINED = 0x00, - UAC_FUNCTION_DESKTOP_SPEAKER = 0x01, - UAC_FUNCITON_HOME_THEATER = 0x02, - UAC_FUNCTION_MICROPHONE = 0x03, - UAC_FUNCITON_HEADSET = 0x04, - UAC_FUNCTION_TELEPHONE = 0x05, - UAC_FUNCTION_CONVERTER = 0x06, - UAC_FUNCTION_VOICE_SOUND_RECORDER = 0x07, - UAC_FUNCTION_IO_BOX = 0x08, - UAC_FUNCTION_MUSICAL_INTRUMENT = 0x09, - UAC_FUNCTION_PRO_AUDIO = 0x0A, - UAC_FUNCTION_AUDIO_VIDEO = 0x0B, - UAC_FUNCTION_CONTROL_PANEL = 0x0C, - UAC_FUNCITON_OTHER = 0xFF -}; - -/* A.8 Audio Class-Specific Descriptor Types */ -/* Shared with Audio Class 1.0 */ -enum UAC_CSDescriptorTypes_t -{ - UAC_CS_DESCTYPE_UNDEFINED = 0x20, - UAC_CS_DESCTYPE_DEVICE = 0x21, - UAC_CS_DESCTYPE_CONFIGURATION = 0x22, - UAC_CS_DESCTYPE_STRING = 0x23, - UAC_CS_DESCTYPE_INTERFACE = 0x24, - UAC_CS_DESCTYPE_ENDPOINT = 0x25, -}; - -/* A.9 Audio Class-Specific AC Interface Descriptor Subtypes */ -enum UAC_CS_AC_InterfaceDescriptorSubtype_t -{ - UAC_CS_AC_INTERFACE_SUBTYPE_AC_DESCRIPTOR_UNDEFINED = 0x00, - UAC_CS_AC_INTERFACE_SUBTYPE_HEADER = 0x01, - UAC_CS_AC_INTERFACE_SUBTYPE_INPUT_TERMINAL = 0x02, - UAC_CS_AC_INTERFACE_SUBTYPE_OUTPUT_TERMINAL = 0x03, - UAC_CS_AC_INTERFACE_SUBTYPE_MIXER_UNIT = 0x04, - UAC_CS_AC_INTERFACE_SUBTYPE_SELECTOR_UNIT = 0x05, - UAC_CS_AC_INTERFACE_SUBTYPE_FEATURE_UNIT = 0x06, - UAC_CS_AC_INTERFACE_SUBTYPE_EFFECT_UNIT = 0x07, - UAC_CS_AC_INTERFACE_SUBTYPE_PROCESSING_UNIT = 0x08, - UAC_CS_AC_INTERFACE_SUBTYPE_EXTENSION_UNIT = 0x09, - UAC_CS_AC_INTERFACE_SUBTYPE_CLOCK_SOURCE = 0x0A, - UAC_CS_AC_INTERFACE_SUBTYPE_CLOCK_SELECTOR = 0x0B, - UAC_CS_AC_INTERFACE_SUBSYPE_CLOCK_MULTIPLIER = 0x0C, - UAC_CS_AC_INTERFACE_SUBTYPE_SAMPLE_RATE_CONVERTER = 0x0D -}; - -/* A.10 Audio Class Specific AS Interface Descriptor Subtypes */ -enum UAC_CS_AS_InterfaceDescriptorSubtype_t -{ - UAC_CS_AS_INTERFACE_SUBTYPE_UNDEFINED = 0x00, - UAC_CS_AS_INTERFACE_SUBTYPE_AS_GENERAL = 0x01, - UAC_CS_AS_INTERFACE_SUBTYPE_FORMAT_TYPE = 0x02, - UAC_CS_AS_INTERFACE_SUBTYPE_ENCODER = 0x03, - UAC_CS_AS_INTERFACE_SUBTYPE_DECODER = 0x04 -}; - -/* A.11 Effect Unit Effect Types */ -#define EFFECT_UNDEFINED 0x00 -#define PARAM_EQ_SECTION_EFFECT 0x01 -#define REVERBERATION_EFFECT 0x02 -#define MOD_DELAY_EFFECT 0x03 -#define DYN_RANGE_COMP_EFFECT 0x04 - -/* A.12 Processing Unit Process Types */ -#define PROCESS_UNDEFINED 0x00 -#define UP_DOWNMIX_PROCESS 0x01 -#define DOLBY_PROLOGIC_PROCESS 0x02 -#define STEREO_EXTENDER_PROCESS 0x03 - -/* A.13 Audio Class-Specific Endpoint Descriptor Subtypes */ -enum UAC_CS_EndpointDescriptorSubtype_t -{ - UAC_CS_ENDPOINT_SUBTYPE_UNDEFINED = 0x00, - UAC_CS_ENDPOINT_SUBTYPE_EP_GENERAL = 0x01 -}; - -/* A.14 Audio Class-Specific Request Codes */ -#define REQUEST_CODE_UNDEFINED 0x00 -#define CUR 0x01 -#define RANGE 0x02 -#define MEM 0x03 - -/* A.15 Encoder Type Codes */ -#define ENCODER_UNDEFINED 0x00 -#define OTHER_ENCODER 0x01 -#define MPEG_ENCODER 0x02 -#define AC_3_ENCODER 0x03 -#define WMA_ENCODER 0x04 -#define DTS_ENCODER 0x05 - -/* A.17 Control Selector Codes */ -/* A.17.1 Clock Source Control Selectors */ -#define CS_CONTROL_UNDEFINED 0x00 -#define CS_SAM_FREQ_CONTROL 0x01 -#define CS_CLOCK_VALID_CONTROL 0x02 - -/* A.17.2 Clock Selector Control Selectors */ -#define CX_CONTROL_UNDEFINED 0x00 -#define CX_CLOCK_SELECTOR_CONTROL 0x01 - -/* A.17.7 Feature Unit Control Selectors */ -#define FU_CONTROL_UNDEFINED 0x00 -#define FU_MUTE_CONTROL 0x01 -#define FU_VOLUME_CONTROL 0x02 - -/* A.17.11 Audio Streaming Interface Control Selectors */ -#define AS_CONTROL_UNDEFINED 0x00 -#define AS_ACT_ALT_SETTING_CONTROL 0x01 -#define AS_VAL_ALT_SETTINGS_CONTROL 0x02 -#define AS_AUDIO_DATA_FORMAT_CONTROL 0x03 - -#ifdef __STDC__ -/* Clock Source Descriptor (Table 4-6) */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bClockID; - unsigned char bmAttributes; - unsigned char bmControls; - unsigned char bAssocTerminal; - unsigned char iClockSource; -} __attribute__((packed)) USB_Descriptor_Audio_ClockSource_t; - -/* Clock Selector Descriptor (Table 4-7) */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bClockID; - unsigned char bNrPins; - unsigned char baCSourceId[1]; - unsigned char bmControl; /* bmControls - * D[1:0] : Clock Selector Control - * D[7:4] : Reserved (0) */ - unsigned char iClockSelector; - -} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_1_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bClockID; - unsigned char bNrPins; - unsigned char baCSourceId[2]; - unsigned char bmControl; /* bmControls - * D[1:0] : Clock Selector Control - * D[7:4] : Reserved (0) */ - unsigned char iClockSelector; - -} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_2_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bClockID; - unsigned char bNrPins; - unsigned char baCSourceId[3]; - unsigned char bmControl; /* bmControls - * D[1:0] : Clock Selector Control - * D[7:4] : Reserved (0) */ - unsigned char iClockSelector; - -} __attribute__((packed)) USB_Descriptor_Audio_ClockSelector_3_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bFormatType; /* Format of the audio stream, see Audio Device Formats specification */ - unsigned char bSubslotSize; /* Number of bytes for each channels subslot */ - unsigned char bBitResolution; /* Number of bits used in the above slot for sample */ -} __attribute__((packed)) USB_Descriptor_Audio_Format_Type1_t; - -/* Table 4-11: Mixer Unit Descriptor */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bUnitID; - unsigned char bNrInPins; - unsigned char baSourceID; - unsigned char bNrChannels; - unsigned bmChannelConfig; - unsigned char iChannelNames; - unsigned char bmMixerControls[18]; /* FIXME */ - unsigned char bmControls; - unsigned char iMixer; -} __attribute__((packed)) USB_Descriptor_Audio_MixerUnit_t; - -/* Table 4-24: Extension Unit Descriptor */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bUnitID; - unsigned short wExtensionCode; - unsigned char bNrInPins; - unsigned char baSourceID[1]; - unsigned char bNrChannels; - unsigned bmChannelConfig; - unsigned char iChannelNames; - unsigned char bmControls; - unsigned char iExtension; -} __attribute__((packed)) USB_Descriptor_Audio_ExtensionUnit_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bUnitID; - unsigned short wExtensionCode; - unsigned char bNrInPins; - unsigned char baSourceID[2]; - unsigned char bNrChannels; - unsigned bmChannelConfig; - unsigned char iChannelNames; - unsigned char bmControls; - unsigned char iExtension; -} __attribute__((packed)) USB_Descriptor_Audio_ExtensionUnit2_t; -#endif - -/***********************************************************************/ -/** USB Device Class Definition for Audio Data Formats **/ - -/* A.1 Format Type Codes */ -enum USB_audio_Fmt_FormatType_t -{ - UAC_FORMAT_TYPE_UNDEFINED = 0x00, - UAC_FORMAT_TYPE_I = 0x01, - UAC_FORMAT_TYPE_II = 0x02, - UAC_FORMAT_TYPE_III = 0x03, - UAC_FORMAT_TYPE_IV = 0x04, - UAC_EXT_FORMAT_TYPE_I = 0x81, - UAC_EXT_FORMAT_TYPE_II = 0x82, - UAC_EXT_FORMAT_TYPE_III = 0x83 -}; - -/* A.2 AudioData Format Bit Allocation in the bmFormats field */ -/* A.2.1 Audio Data Format Type I Bit Allocations */ -enum USB_Audio_Fmt_DataFormat_TypeI_t -{ - UAC_FORMAT_TYPEI_PCM = 0x00000001, - UAC_FORMAT_TYPEI_PCM8 = 0x00000002, - UAC_FORMAT_TYPEI_IEEE_FLOAT = 0x00000004, - UAC_FORMAT_TYPEI_RAW_DATA = 0x80000000, -}; - -/* A.2.2 Audio Data Format Type II Bit Allocations */ -enum USB_Audio_Fmt_DataFormat_TypeII_t -{ - UAC_FORMAT_TYPEII_MPEG = 0x00000001, - UAC_FORMAT_TYPEII_AC3 = 0x00000002, - UAC_FORMAT_TYPEII_WMA = 0x00000004, - UAC_FORMAT_TYPEII_DTS = 0x00000008, - UAC_FORMAT_TYPEII_RAW_DATA = 0x80000000 -}; - -/* A.3 Side Band Protocol Codes */ -#define PROTOCOL_UNDEFINED 0x00 -#define PRESS_TIMESTAMP_PROTOCOL 0x01 - -/***********************************************************************/ -/* Univeral Serial Bus Device Class Definition for Terminal Types */ - -/* 2.1 USB Terminal Types */ -/* Terminal Types that describe Terminals that handle signals carried over USB */ -#define USB_TERMTYPE_UNDEFINED 0x0100 -#define USB_TERMTYPE_USB_STREAMING 0x0101 -#define USB_TERMTYPE_VENDOR_SPECIFIC 0x01FF - -/* 2.2 Input Terminal Types */ -/* Terminal Types that describe Terminals that are designed to record sounds */ -enum USB_Audio_TT_InputTermType_t -{ - UAC_TT_INPUT_TERMTYPE_INPUT_UNDEFINED = 0x0200, - UAC_TT_INPUT_TERMTYPE_MICROPHONE = 0x0201, - UAC_TT_INPUT_TERMTYPE_DESKTOP_MICROPHONE = 0x0202, - UAC_TT_INPUT_TERMTYPE_PERSONAL_MICROPHONE = 0x0203, - UAC_TT_INPUT_TERMTYPE_OMNIDIRECTIONAL_MICROPHONE = 0x0204, - UAC_TT_INPUT_TERMTYPE_MICROPHONE_ARRAY = 0x0205, - UAC_TT_INPUT_TERMTYPE_PROCESSING_MICROPHONE_ARRAY = 0x0206 -}; - -/* 2.3 Output Terminal Types */ -/* These Terminal Types describe Terminals that produce audible signals that are intended to - * be heard by the user of the audio function */ -enum USB_Audio_TT_OutputTermType_t -{ - UAC_TT_OUTPUT_TERMTYPE_SPEAKER = 0x0301, - UAC_TT_OUTPUT_TERMTYPE_HEADPHONES = 0x0302, - UAC_TT_OUTPUT_TERMTYPE_HEAD_MOUNTED_DISPLAY = 0x0303, - UAC_TT_OUTPUT_TERMTYPE_DESKTOP_SPEAKER = 0x0304, - UAC_TT_OUTPUT_TERMTYPE_ROOM_SPEAKER = 0x0305, - UAC_TT_OUTPUT_TERMTYPE_COMMUNICATION_SPEAKER = 0x0306, - UAC_TT_OUTPUT_TERMTYPE_LOW_FREQ_EFFECTS_SPEAKER = 0x0307 -}; -#endif diff --git a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h b/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h deleted file mode 100644 index 87f20615..00000000 --- a/new_ai_tools/include/lib_xud/lib_xud/src/user/class/usbaudiocommon.h +++ /dev/null @@ -1,168 +0,0 @@ -// Copyright 2017-2021 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. - -#ifndef _USBAUDIOCOMMON_H_ -#define _USBAUDIOCOMMON_H_ - -#include "xud_conf_default.h" - -#ifdef __STDC__ -typedef struct -{ - unsigned char bLength; /* Size of descriptor (bytes) */ - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned short bcdADC; /* Binary coded decimal indicating the supported Audio Class version */ - unsigned char bCatagory; /* Primary use of this audio function. See Audio Function Category Codes */ - unsigned short wTotalLength; /* Total length of the Audio class-specific descriptors, including this descriptor */ - unsigned char bmControls; /* D[1:0]: Latency control. D[7:2]: Reserved. Must be set to 0 */ -} __attribute__((packed)) UAC_Descriptor_Interface_AC_t; - -/* Table 4-9: Input Terminal Descriptor */ -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bTerminalID; /* Unique ID of this terminal unit */ - unsigned short wTerminalType; - unsigned char bAssocTerminal; /* ID of associated output terminal, for physically grouped terminals - * such as the speaker and microphone of a phone handset */ - unsigned char bCSourceID; /* ID of the clock entity to which this Input Terminal is connected */ - unsigned char bNrChannels; /* Number of logicial output channels in the Terminal's - output audio channel cluster */ - unsigned bmChannelConfig; /* Channel layout supported by this terminal */ - unsigned char iChannelNames; /* Index in string table describing channels (points to first channel) */ - unsigned short bmControls; /* Bitmap */ - unsigned char iTerminal; /* Index of string descriptor describing this terminal */ -} __attribute__((packed))USB_Descriptor_Audio_InputTerminal_t; - -#if 0 -//UAC 1.0 version -typedef struct -{ - unsigned char bLength; /* Size of the descriptor (bytes) */ - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bTerminalID; /* Unique ID of this terminal unit */ - unsigned short wTerminalType; - unsigned char bAssocTerminal; /* ID of associated output terminal, for physically grouped terminals - * such as the speaker and microphone of a phone handset */ - unsigned char bNrChannels; /* Total number of separate audio channels within this interface */ - unsigned short wChannelConfig; /* Channel layout supported by this terminal */ - unsigned char iChannelNames; /* Index in string table describing channels (points to first channel */ - unsigned char iTerminal; /* Index of string descriptor describing this terminal */ -} USB_Descriptor_Audio_InputTerminal_t; -#endif - -/* Table 4-10: Output Terminal Descriptor */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bTerminalID; - unsigned short wTerminalType; - unsigned char bAssocTerminal; - unsigned char bSourceID; - unsigned char bCSourceID; - unsigned short bmControls; - unsigned char iTerminal; -} __attribute__((packed)) USB_Descriptor_Audio_OutputTerminal_t; - -#if 0 -/* UAC 1.0 Version */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bTerminalID; - unsigned short wTerminalType; - unsigned char bAssocTerminal; - unsigned char bSourceID; - unsigned char iTerminal; -} USB_Descriptor_Audio_OutputTerminal_t; -#endif - -/* Note, we need seperate _out and _in structs due to varying channel count */ -/* Table 4-13: Feature Unit Descriptor */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bUnitID; /* Unique ID for this feature unit */ - unsigned char bSourceID; /* Source ID value of the audio source input into this feature unit */ - unsigned bmaControls[NUM_USB_CHAN_OUT+1]; /* Feature masks for the control channel, and each separate audio channel */ - unsigned char iFeature; /* String table index describing this feature unit */ -} __attribute__((packed)) USB_Descriptor_Audio_FeatureUnit_Out_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bUnitID; /* Unique ID for this feature unit */ - unsigned char bSourceID; /* Source ID value of the audio source input into this feature unit */ - unsigned bmaControls[NUM_USB_CHAN_IN+1]; /* Feature masks for the control channel, and each separate audio channel */ - unsigned char iFeature; /* String table index describing this feature unit */ -} __attribute__((packed)) USB_Descriptor_Audio_FeatureUnit_In_t; - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bTerminalLink; - unsigned char bmControls; - unsigned char bFormatType; - unsigned bmFormats; - unsigned char bNrChannels; - unsigned bmChannelConfig; - unsigned char iChannelNames; -} __attribute__((packed)) USB_Descriptor_Audio_Interface_AS_t; - -#if 0 -/* UAC1 Version */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubType; - unsigned char bTerminalLink; - unsigned char bmControls; - unsigned char bFormatType; - unsigned bmFormats; - unsigned char bNrChannels; - unsigned char bmChannelConfig; -} __attribute__((packed)) USB_Descriptor_Audio_Interface_AS_t; -#endif - -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bmAttributes; - unsigned char bmControls; - unsigned char bLockDelayUnits; - unsigned short wLockDelay; -} __attribute__((packed)) USB_Descriptor_Audio_Class_AS_Endpoint_t; - -#if 0 -/* UAC1.0 Version */ -typedef struct -{ - unsigned char bLength; - unsigned char bDescriptorType; - unsigned char bDescriptorSubtype; - unsigned char bmAttributes; - unsigned char bLockDelayUnits; - unsigned short wLockDelay; -} __attribute__((packed)) USB_Descriptor_Audio_Class_AS_Endpoint_t; -#endif -#endif -#endif - - diff --git a/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h deleted file mode 100644 index c79273ea..00000000 --- a/new_ai_tools/include/signal/micro/kernels/delay_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ -#define SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ - -extern const int g_gen_data_size_3_delay; -extern const unsigned char g_gen_data_3_delay[]; - -extern const int g_gen_data_size_5_delay; -extern const unsigned char g_gen_data_5_delay[]; - -#endif // SIGNAL_MICRO_KERNELS_DELAY_FLEXBUFFERS_GENERATED_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h deleted file mode 100644 index f2840f66..00000000 --- a/new_ai_tools/include/signal/micro/kernels/energy_flexbuffers_generated_data.h +++ /dev/null @@ -1,28 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_start_index_2_end_index_4; -extern const unsigned char g_gen_data_start_index_2_end_index_4[]; - -extern const int g_gen_data_size_start_index_0_end_index_4; -extern const unsigned char g_gen_data_start_index_0_end_index_4[]; - -extern const int g_gen_data_size_start_index_4_end_index_8; -extern const unsigned char g_gen_data_start_index_4_end_index_8[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_ENERGY_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h deleted file mode 100644 index 9471b836..00000000 --- a/new_ai_tools/include/signal/micro/kernels/fft_flexbuffers_generated_data.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_fft_length_64_float; -extern const unsigned char g_gen_data_fft_length_64_float[]; - -extern const int g_gen_data_size_fft_length_64_int16; -extern const unsigned char g_gen_data_fft_length_64_int16[]; - -extern const int g_gen_data_size_fft_length_64_int32; -extern const unsigned char g_gen_data_fft_length_64_int32[]; - -extern const int g_gen_data_size_fft_length_512_float; -extern const unsigned char g_gen_data_fft_length_512_float[]; - -extern const int g_gen_data_size_fft_length_512_int16; -extern const unsigned char g_gen_data_fft_length_512_int16[]; - -extern const int g_gen_data_size_fft_length_512_int32; -extern const unsigned char g_gen_data_fft_length_512_int32[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FFT_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h deleted file mode 100644 index 59e74e7f..00000000 --- a/new_ai_tools/include/signal/micro/kernels/filter_bank_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_filter_bank_32_channel; -extern const unsigned char g_gen_data_filter_bank_32_channel[]; - -extern const int g_gen_data_size_filter_bank_16_channel; -extern const unsigned char g_gen_data_filter_bank_16_channel[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h deleted file mode 100644 index dbc3bd92..00000000 --- a/new_ai_tools/include/signal/micro/kernels/filter_bank_log_flexbuffers_generated_data.h +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_filter_bank_log_scale_1600_correction_bits_3; -extern const unsigned char - g_gen_data_filter_bank_log_scale_1600_correction_bits_3[]; - -extern const int g_gen_data_size_filter_bank_log_scale_32768_correction_bits_5; -extern const unsigned char - g_gen_data_filter_bank_log_scale_32768_correction_bits_5[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_LOG_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h deleted file mode 100644 index 175a14e2..00000000 --- a/new_ai_tools/include/signal/micro/kernels/filter_bank_spectral_subtraction_flexbuffers_generated_data.h +++ /dev/null @@ -1,26 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_filter_bank_spectral_subtraction_32_channel; -extern const unsigned char - g_gen_data_filter_bank_spectral_subtraction_32_channel[]; -extern const int g_gen_data_size_filter_bank_spectral_subtraction_16_channel; -extern const unsigned char - g_gen_data_filter_bank_spectral_subtraction_16_channel[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FILTER_BANK_SPECTRAL_SUBTRACTION_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h deleted file mode 100644 index 655bfa6a..00000000 --- a/new_ai_tools/include/signal/micro/kernels/framer_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_3_1_0_framer; -extern const unsigned char g_gen_data_3_1_0_framer[]; - -extern const int g_gen_data_size_5_2_1_framer; -extern const unsigned char g_gen_data_5_2_1_framer[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_FRAMER_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/irfft.h b/new_ai_tools/include/signal/micro/kernels/irfft.h deleted file mode 100644 index 380bc3e7..00000000 --- a/new_ai_tools/include/signal/micro/kernels/irfft.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef SIGNAL_MICRO_KERNELS_IRFFT_H_ -#define SIGNAL_MICRO_KERNELS_IRFFT_H_ - -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite { -namespace tflm_signal { - -TFLMRegistration* Register_IRFFT(); -TFLMRegistration* Register_IRFFT_FLOAT(); -TFLMRegistration* Register_IRFFT_INT16(); -TFLMRegistration* Register_IRFFT_INT32(); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_MICRO_KERNELS_IRFFT_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h deleted file mode 100644 index adb4fbab..00000000 --- a/new_ai_tools/include/signal/micro/kernels/overlap_add_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_overlap_add_float; -extern const unsigned char g_gen_data_overlap_add_float[]; - -extern const int g_gen_data_size_overlap_add_int16; -extern const unsigned char g_gen_data_overlap_add_int16[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_OVERLAP_ADD_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h deleted file mode 100644 index 32b4cd72..00000000 --- a/new_ai_tools/include/signal/micro/kernels/pcan_flexbuffers_generated_data.h +++ /dev/null @@ -1,7 +0,0 @@ -#ifndef SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ -#define SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ - -extern const int g_gen_data_size_snr_shift_6_test; -extern const unsigned char g_gen_data_snr_shift_6_test[]; - -#endif // SIGNAL_MICRO_KERNELS_PCAN_FLEXBUFFERS_GENERATED_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/rfft.h b/new_ai_tools/include/signal/micro/kernels/rfft.h deleted file mode 100644 index f732c6fd..00000000 --- a/new_ai_tools/include/signal/micro/kernels/rfft.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef SIGNAL_MICRO_KERNELS_RFFT_H_ -#define SIGNAL_MICRO_KERNELS_RFFT_H_ - -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite { -namespace tflm_signal { - -TFLMRegistration* Register_RFFT(); -TFLMRegistration* Register_RFFT_FLOAT(); -TFLMRegistration* Register_RFFT_INT16(); -TFLMRegistration* Register_RFFT_INT32(); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_MICRO_KERNELS_RFFT_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h deleted file mode 100644 index 47a38277..00000000 --- a/new_ai_tools/include/signal/micro/kernels/stacker_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_stacker_3_channels_step_1; -extern const unsigned char g_gen_data_stacker_3_channels_step_1[]; - -extern const int g_gen_data_size_stacker_10_channels_step_2; -extern const unsigned char g_gen_data_stacker_10_channels_step_2[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_STACKER_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h b/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h deleted file mode 100644 index cd26fc00..00000000 --- a/new_ai_tools/include/signal/micro/kernels/window_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ -#define SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ - -extern const int g_gen_data_size_window_shift_12; -extern const unsigned char g_gen_data_window_shift_12[]; - -extern const int g_gen_data_size_window_shift_8; -extern const unsigned char g_gen_data_window_shift_8[]; - -#endif // SIGNAL_MICRO_KERNELS_TEST_DATA_GENERATION_GENERATE_WINDOW_FLEXBUFFERS_DATA_H_ diff --git a/new_ai_tools/include/signal/src/circular_buffer.h b/new_ai_tools/include/signal/src/circular_buffer.h deleted file mode 100644 index d175a9b9..00000000 --- a/new_ai_tools/include/signal/src/circular_buffer.h +++ /dev/null @@ -1,118 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_CIRCULAR_BUFFER_H_ -#define SIGNAL_SRC_CIRCULAR_BUFFER_H_ - -#include -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above -struct CircularBuffer { - // Max number of elements, value passed-in to CircularBufferAlloc. - size_t capacity; - // Next position to read. - size_t read; - // Next position to write. - size_t write; - // Flag to indicate emptiness. - int32_t empty; - // Auto-generated size variable - int32_t buffer_size; - // Array of the circular buffer elements (integers). - int16_t* buffer; -}; - -// Returns the size of the memory that the circular buffer needs -// in order to hold `capacity` items. -size_t CircularBufferGetNeededMemory(size_t capacity); - -// Initialize an instance of the circular buffer that holds `capacity` items. -// `state` points to a memory allocation of size `state_size`. The size -// should be greater or equal to the value returned by -// CircularBufferGetNeededMemory(capacity). Fails if it isn't. -// On success, returns a pointer to the circular buffer's object. -CircularBuffer* CircularBufferInit(size_t capacity, void* state, - size_t state_size); - -// Reset a circular buffer to its initial empty state -void CircularBufferReset(CircularBuffer* cb); - -size_t CircularBufferCapacity(const CircularBuffer* cb); - -bool CircularBufferFull(const CircularBuffer* cb); - -bool CircularBufferEmpty(const CircularBuffer* cb); - -// Returns the number of elements ready to read -size_t CircularBufferAvailable(const CircularBuffer* cb); - -// Returns the number of elements available to write. -size_t CircularBufferCanWrite(const CircularBuffer* cb); - -// Adds a single `value` to the buffer and advances the write pointer. -void CircularBufferAdd(CircularBuffer* cb, int16_t value); - -// Writes `n` `values` into the buffer and advances the write pointer. -void CircularBufferWrite(CircularBuffer* cb, const int16_t* values, size_t n); - -// Writes `n` zeros into the buffer and advances the write pointer. -void CircularBufferWriteZeros(CircularBuffer* cb, size_t n); - -// Returns a pointer to a buffer where elements can be written, and -// advances the write pointer as though they have already been written. -// Fails if `n` elements are not available contiguously at the current -// write position. -int16_t* CircularBufferReserveForWrite(CircularBuffer* cb, size_t n); - -// Copies the final region (`count` elements) of the buffer `n` times, to -// the end of the buffer. -void CircularBufferExtend(CircularBuffer* cb, size_t count, int32_t n); - -// Reads a single value from the buffer and advances the read pointer -int16_t CircularBufferRemove(CircularBuffer* cb); - -// Reads the value at the given `index`, does not modify the read pointer. -int16_t CircularBufferPeek(const CircularBuffer* cb, size_t index); - -// Rewinds to restore the previous `n` values read -void CircularBufferRewind(CircularBuffer* cb, size_t n); - -// Returns a pointer directly into the circular buffer at the given `index`. -// Caller is responsible for not reading past the end. -const int16_t* CircularBufferPeekDirect(const CircularBuffer* cb, size_t index); - -// Returns a pointer into the circular buffer at the current read pointer, -// setting `n` to the number of values available to be read from here. -const int16_t* CircularBufferPeekMax(const CircularBuffer* cb, size_t* n); - -// Copies `n` `values` from the buffer and does not advance the read -// pointer and does not update the empty flag. -void CircularBufferGet(CircularBuffer* cb, size_t n, int16_t* values); - -// Discards the next `n` values by advancing the read index. -// Valid for n > 0. -void CircularBufferDiscard(CircularBuffer* cb, size_t n); - -// Shifts the buffer with `n` values (`n` can be negative) by moving -// the read index. -void CircularBufferShift(CircularBuffer* cb, int n); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_CIRCULAR_BUFFER_H_ diff --git a/new_ai_tools/include/signal/src/complex.h b/new_ai_tools/include/signal/src/complex.h deleted file mode 100644 index 6f203034..00000000 --- a/new_ai_tools/include/signal/src/complex.h +++ /dev/null @@ -1,29 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_COMPLEX_H_ -#define SIGNAL_SRC_COMPLEX_H_ - -#include - -// We would use the standard complex type in complex.h, but there's -// no guarantee that all architectures will support it. -template -struct Complex { - T real; - T imag; -}; - -#endif // SIGNAL_SRC_COMPLEX_H_ diff --git a/new_ai_tools/include/signal/src/energy.h b/new_ai_tools/include/signal/src/energy.h deleted file mode 100644 index 5a1cf37e..00000000 --- a/new_ai_tools/include/signal/src/energy.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_ENERGY_H_ -#define SIGNAL_ENERGY_H_ - -#include - -#include "signal/src/complex.h" - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Calculates the power spectrum from a DFT output between start and end indices -// -// * `start_index` and `end_index` must valid indices into `input` -// * `output` must be the same size as `input`. Only the values at indices -// `start_index` and `end_index` inclusive should be considered valid. -void SpectrumToEnergy(const Complex* input, int start_index, - int end_index, uint32_t* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_ENERGY_H_ diff --git a/new_ai_tools/include/signal/src/fft_auto_scale.h b/new_ai_tools/include/signal/src/fft_auto_scale.h deleted file mode 100644 index c566a0e9..00000000 --- a/new_ai_tools/include/signal/src/fft_auto_scale.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_FFT_AUTO_SCALE_H_ -#define SIGNAL_SRC_FFT_AUTO_SCALE_H_ - -#include -#include - -// TODO(b/286250473): remove namespace once de-duped libraries -namespace tflite { -namespace tflm_signal { - -// Auto scales `input` and write the result to `output` -// Elements in `input` are left shifted to maximize the amplitude without -// clipping, -// * both `input` and `output` must be of size `size` -int FftAutoScale(const int16_t* input, int size, int16_t* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_FFT_AUTO_SCALE_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank.h b/new_ai_tools/include/signal/src/filter_bank.h deleted file mode 100644 index 95b2168a..00000000 --- a/new_ai_tools/include/signal/src/filter_bank.h +++ /dev/null @@ -1,69 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_FILTER_BANK_H_ -#define SIGNAL_SRC_FILTER_BANK_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -struct FilterbankConfig { - // Number of filterbank channels - int32_t num_channels; - - // Each of the following three arrays is of size num_channels + 1 - // An extra channel is needed for scratch. See implementation of - // FilterbankAccumulateChannels() for more details - - // For each channel, the index in the input (spectrum) where its band starts - const int16_t* channel_frequency_starts; - // For each channel, the index in the weights/unweights arrays where - // it filter weights start - const int16_t* channel_weight_starts; - // For each channel, the number of bins in the input (spectrum) that span - // its band - const int16_t* channel_widths; - - // The weights array holds the triangular filter weights of all the filters - // in the bank. The output of each filter in the bank is caluclated by - // multiplying the elements in the input spectrum that are in its band - // (see above: channel_frequency_starts, channel_widths) by the filter weights - // then accumulating. Each element in the unweights array holds the 1 minus - // corresponding elements in the weights array and is used to make this - // operation more efficient. For more details, see documnetation in - // FilterbankAccumulateChannels() - const int16_t* weights; - const int16_t* unweights; - int32_t output_scale; - - int32_t input_correction_bits; -}; - -// Accumulate the energy spectrum bins in `input` into filter bank channels -// contained in `output`. -// * `input` - Spectral energy array -// * `output` - of size `config.num_channels` + 1. -// Elements [1:num_channels] contain the filter bank channels. -// Element 0 is used as scratch and should be ignored -void FilterbankAccumulateChannels(const FilterbankConfig* config, - const uint32_t* input, uint64_t* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_FILTER_BANK_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_log.h b/new_ai_tools/include/signal/src/filter_bank_log.h deleted file mode 100644 index e8514c73..00000000 --- a/new_ai_tools/include/signal/src/filter_bank_log.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_FILTER_BANK_LOG_H_ -#define SIGNAL_SRC_FILTER_BANK_LOG_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Apply natural log to each element in array `input` of size `num_channels` -// with pre-shift and post scaling. -// The operation is roughly equivalent to: -// `output` = min(Log(`input` << `correction_bits`) * `output_scale`, INT16_MAX) -// Where: -// If (input << `correction_bits`) is 1 or 0, the function returns 0 -void FilterbankLog(const uint32_t* input, int num_channels, - int32_t output_scale, uint32_t correction_bits, - int16_t* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_FILTER_BANK_LOG_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h b/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h deleted file mode 100644 index e862d773..00000000 --- a/new_ai_tools/include/signal/src/filter_bank_spectral_subtraction.h +++ /dev/null @@ -1,73 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ -#define SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -struct SpectralSubtractionConfig { - // Number of filterbank channels in input and output - int32_t num_channels; - // The constant used for the lowpass filter for finding the noise. - // Higher values correspond to more aggressively adapting estimates - // of the noise. - // Scale is 1 << spectral_subtraction_bits - uint32_t smoothing; - // One minus smoothing constant for low pass filter. - // Scale is 1 << spectral_subtraction_bits - uint32_t one_minus_smoothing; - // The maximum cap to subtract away from the signal (ie, if this is - // 0.2, then the result of spectral subtraction will not go below - // 0.2 * signal). - // Scale is 1 << spectral_subtraction_bits - uint32_t min_signal_remaining; - // If positive, specifies the filter coefficient for odd-index - // channels, while 'smoothing' is used as the coefficient for even- - // index channels. Otherwise, the same filter coefficient is - // used on all channels. - // Scale is 1 << spectral_subtraction_bits - uint32_t alternate_smoothing; - // Alternate One minus smoothing constant for low pass filter. - // Scale is 1 << spectral_subtraction_bits - uint32_t alternate_one_minus_smoothing; - // Extra fractional bits for the noise_estimate smoothing filter. - uint32_t smoothing_bits; - // Scaling bits for some members of this struct - uint32_t spectral_subtraction_bits; - // If true, when the filterbank level drops below the output, - // the noise estimate will be forced down to the new noise level. - // If false, the noise estimate will remain above the current - // filterbank output (but the subtraction will still keep the - // output non negative). - bool clamping; -}; - -// Apply spectral subtraction to each element in `input`, then write the result -// to `output` and `noise_estimate`. `input`, `output` and `noise estimate` -// must all be of size `config.num_channels`. `config` holds the -// parameters of the spectral subtraction algorithm. -void FilterbankSpectralSubtraction(const SpectralSubtractionConfig* config, - const uint32_t* input, uint32_t* output, - uint32_t* noise_estimate); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_FILTER_BANK_SPECTRAL_SUBTRACTION_H_ diff --git a/new_ai_tools/include/signal/src/filter_bank_square_root.h b/new_ai_tools/include/signal/src/filter_bank_square_root.h deleted file mode 100644 index 7d484b9f..00000000 --- a/new_ai_tools/include/signal/src/filter_bank_square_root.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ -#define SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Apply square root to each element in `input`, then shift right by -// `scale_down_bits` before writing the result to `output`, -// `input` and `output` must both be of size `num_channels` -void FilterbankSqrt(const uint64_t* input, int num_channels, - int scale_down_bits, uint32_t* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_FILTER_BANK_SQUARE_ROOT_H_ diff --git a/new_ai_tools/include/signal/src/irfft.h b/new_ai_tools/include/signal/src/irfft.h deleted file mode 100644 index c2b54d78..00000000 --- a/new_ai_tools/include/signal/src/irfft.h +++ /dev/null @@ -1,84 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_IRFFT_H_ -#define SIGNAL_SRC_IRFFT_H_ - -#include -#include - -#include "signal/src/complex.h" - -// TODO(b/286250473): remove namespace once de-duped libraries -namespace tflite { -namespace tflm_signal { - -// IRFFT (Inverse Real Fast Fourier Transform) -// IFFT for real valued time domain outputs. - -// 16-bit Integer input/output - -// Returns the size of the memory that an IRFFT of `fft_length` needs -size_t IrfftInt16GetNeededMemory(int32_t fft_length); - -// Initialize the state of an IRFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// IrfftGetNeededMemory(fft_length). Fails if it isn't. -void* IrfftInt16Init(int32_t fft_length, void* state, size_t state_size); - -// Applies IRFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see IRfftInit) -// * `output` must be of size output -void IrfftInt16Apply(void* state, const Complex* input, - int16_t* output); - -// 32-bit Integer input/output - -// Returns the size of the memory that an IRFFT of `fft_length` needs -size_t IrfftInt32GetNeededMemory(int32_t fft_length); - -// Initialize the state of an IRFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// IrfftGetNeededMemory(fft_length). Fails if it isn't. -void* IrfftInt32Init(int32_t fft_length, void* state, size_t state_size); - -// Applies IRFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see IRfftInit) -// * `output` must be of size output -void IrfftInt32Apply(void* state, const Complex* input, - int32_t* output); - -// Floating point input/output - -// Returns the size of the memory that an IRFFT of `fft_length` needs -size_t IrfftFloatGetNeededMemory(int32_t fft_length); - -// Initialize the state of an IRFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// IrfftGetNeededMemory(fft_length). Fails if it isn't. -void* IrfftFloatInit(int32_t fft_length, void* state, size_t state_size); - -// Applies IRFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see IRfftInit) -// * `output` must be of size output -void IrfftFloatApply(void* state, const Complex* input, float* output); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_IRFFT_H_ \ No newline at end of file diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h deleted file mode 100644 index 75f3dcda..00000000 --- a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_common.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ -#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ - -// This header file should be included in all variants of kiss_fft_$type.{h,cc} -// so that their sub-included source files do not mistakenly wrap libc header -// files within their kissfft_$type namespaces. -// E.g., This header avoids kissfft_int16.h containing: -// namespace kiss_fft_int16 { -// #include "kiss_fft.h" -// } -// where kiss_fft_.h contains: -// #include -// -// TRICK: By including the following header files here, their preprocessor -// header guards prevent them being re-defined inside of the kiss_fft_$type -// namespaces declared within the kiss_fft_$type.{h,cc} sources. -// Note that the original kiss_fft*.h files are untouched since they -// may be used in libraries that include them directly. - -#include -#include -#include -#include -#include - -#ifdef FIXED_POINT -#include -#endif - -#ifdef USE_SIMD -#include -#endif - -#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_COMMON_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h deleted file mode 100644 index c795f463..00000000 --- a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_float.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ -#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ - -#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" - -// Wrap floating point kiss fft in its own namespace. Enables us to link an -// application with different kiss fft resolutions -// (16/32 bit integer, float, double) without getting a linker error. -#undef FIXED_POINT -namespace kiss_fft_float { -#include "kiss_fft.h" -#include "tools/kiss_fftr.h" -} // namespace kiss_fft_float -#undef FIXED_POINT - -#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_FLOAT_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h deleted file mode 100644 index 7e0c9bae..00000000 --- a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int16.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ -#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ - -#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" -// Wrap floating point kiss fft in its own namespace. Enables us to link an -// application with different kiss fft resolutions -// (16/32 bit integer, float, double) without getting a linker error. -#define FIXED_POINT 16 -namespace kiss_fft_fixed16 { -#include "kiss_fft.h" -#include "tools/kiss_fftr.h" -} // namespace kiss_fft_fixed16 -#undef FIXED_POINT - -#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT16_H_ diff --git a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h b/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h deleted file mode 100644 index 47c7103d..00000000 --- a/new_ai_tools/include/signal/src/kiss_fft_wrappers/kiss_fft_int32.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ -#define SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ - -#include "signal/src/kiss_fft_wrappers/kiss_fft_common.h" - -// Wrap floating point kiss fft in its own namespace. Enables us to link an -// application with different kiss fft resolutions -// (16/32 bit integer, float, double) without getting a linker error. -#define FIXED_POINT 32 -namespace kiss_fft_fixed32 { -#include "kiss_fft.h" -#include "tools/kiss_fftr.h" -} // namespace kiss_fft_fixed32 -#undef FIXED_POINT - -#endif // SIGNAL_SRC_KISS_FFT_WRAPPERS_KISS_FFT_INT32_H_ diff --git a/new_ai_tools/include/signal/src/log.h b/new_ai_tools/include/signal/src/log.h deleted file mode 100644 index 13045f46..00000000 --- a/new_ai_tools/include/signal/src/log.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_LOG_H_ -#define SIGNAL_SRC_LOG_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Natural logarithm of an integer. The result is multiplied by out_scale -uint32_t Log32(uint32_t x, uint32_t out_scale); - -} // namespace tflm_signal -} // namespace tflite -#endif // SIGNAL_SRC_LOG_H_ diff --git a/new_ai_tools/include/signal/src/max_abs.h b/new_ai_tools/include/signal/src/max_abs.h deleted file mode 100644 index 538f7965..00000000 --- a/new_ai_tools/include/signal/src/max_abs.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_MAX_ABS_H_ -#define SIGNAL_SRC_MAX_ABS_H_ - -#include - -// TODO(b/286250473): remove namespace once de-duped libraries -namespace tflite { -namespace tflm_signal { - -// Returns the maximum absolute value of the `size` elements in `input` -int16_t MaxAbs16(const int16_t* input, int size); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_MAX_ABS_H_ diff --git a/new_ai_tools/include/signal/src/msb.h b/new_ai_tools/include/signal/src/msb.h deleted file mode 100644 index 2bdcb47b..00000000 --- a/new_ai_tools/include/signal/src/msb.h +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_MSB_H_ -#define SIGNAL_SRC_MSB_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Index of the most significant bit -uint32_t MostSignificantBit32(uint32_t x); -uint32_t MostSignificantBit64(uint64_t x); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_MSB_H_ diff --git a/new_ai_tools/include/signal/src/overlap_add.h b/new_ai_tools/include/signal/src/overlap_add.h deleted file mode 100644 index 61898094..00000000 --- a/new_ai_tools/include/signal/src/overlap_add.h +++ /dev/null @@ -1,46 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef SIGNAL_SRC_OVERLAP_ADD_H_ -#define SIGNAL_SRC_OVERLAP_ADD_H_ - -#include -#include - -namespace tflm_signal { -// Adds (with saturation) the contents of `input` to the contents of `buffer`, -// both of size `input_size`, then copies the first `output_size` elements of -// `buffer` to `output`, shifts the last `input_size`-`output_size` elements of -// `buffer` to the beginning of `buffer` and fills the trailing `output_size` -// samples in `buffer` with zeros. -// input: {input[0] ... input[input_size-1]} -// buffer: {buffer[0] ... buffer[input_size-1]} -// After invocation: -// output: {saturate(input[0] + buffer[0]), -// ... , -// saturate(input[output_size-1] + buffer[output_size-1])} -// buffer: {saturate(input[output_size] + buffer[output_size]), -// ... -// saturate( input[input_size-output_size-1] -// + buffer[input_size-output_size-1]), -// zeros(output_size)} -void OverlapAdd(const int16_t* input, int16_t* buffer, int input_size, - int16_t* output, int output_size); - -// The same as the int16_t variant above, but without saturation -void OverlapAdd(const float* input, float* buffer, int input_size, - float* output, int output_size); - -} // namespace tflm_signal -#endif // SIGNAL_SRC_OVERLAP_ADD_H_ diff --git a/new_ai_tools/include/signal/src/pcan_argc_fixed.h b/new_ai_tools/include/signal/src/pcan_argc_fixed.h deleted file mode 100644 index 36eaf3d4..00000000 --- a/new_ai_tools/include/signal/src/pcan_argc_fixed.h +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H -#define SIGNAL_MICRO_KERNELS__SRC_PCAN_AGC_FIXED_H -#include - -#include "msb.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite { -namespace tflm_signal { - -#define kPcanSnrBits 12 -#define kPcanOutputBits 6 - -int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut); - -uint32_t PcanShrink(const uint32_t x); - -void ApplyPcanAutoGainControlFixed(const int16_t* gain_lut, int32_t snr_shift, - const uint32_t* noise_estimate, - uint32_t* filterbank_output, - int num_channels); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_MICRO_KERNELS__PCAN_AGC_FIXED_H diff --git a/new_ai_tools/include/signal/src/rfft.h b/new_ai_tools/include/signal/src/rfft.h deleted file mode 100644 index 3305af60..00000000 --- a/new_ai_tools/include/signal/src/rfft.h +++ /dev/null @@ -1,85 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_RFFT_H_ -#define SIGNAL_SRC_RFFT_H_ - -#include -#include - -#include "signal/src/complex.h" - -// TODO(b/286250473): remove namespace once de-duped libraries -namespace tflm_signal { - -// RFFT (Real Fast Fourier Transform) -// FFT for real valued time domain inputs. - -// 16-bit Integer input/output - -// Returns the size of the memory that an RFFT of `fft_length` needs -size_t RfftInt16GetNeededMemory(int32_t fft_length); - -// Initialize the state of an RFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// RfftGetNeededMemory(fft_length). -// Return the value of `state` on success or nullptr on failure -void* RfftInt16Init(int32_t fft_length, void* state, size_t state_size); - -// Applies RFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see RfftInit) -// * `output` must be of size (`fft_length` * 2) + 1 elements -void RfftInt16Apply(void* state, const int16_t* input, - Complex* output); - -// 32-bit Integer input/output - -// Returns the size of the memory that an RFFT of `fft_length` needs -size_t RfftInt32GetNeededMemory(int32_t fft_length); - -// Initialize the state of an RFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// RfftGetNeededMemory(fft_length). -// Return the value of `state` on success or nullptr on failure -void* RfftInt32Init(int32_t fft_length, void* state, size_t state_size); - -// Applies RFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see RfftInit) -// * `output` must be of size (`fft_length` * 2) + 1 elements -void RfftInt32Apply(void* state, const int32_t* input, - Complex* output); - -// Floating point input/output - -// Returns the size of the memory that an RFFT of `fft_length` needs -size_t RfftFloatGetNeededMemory(int32_t fft_length); - -// Initialize the state of an RFFT of `fft_length` -// `state` points to an opaque state of size `state_size`, which -// must be greater or equal to the value returned by -// RfftGetNeededMemory(fft_length). -// Return the value of `state` on success or nullptr on failure -void* RfftFloatInit(int32_t fft_length, void* state, size_t state_size); - -// Applies RFFT to `input` and writes the result to `output` -// * `input` must be of size `fft_length` elements (see RfftInit) -// * `output` must be of size (`fft_length` * 2) + 1 elements -void RfftFloatApply(void* state, const float* input, Complex* output); - -} // namespace tflm_signal - -#endif // SIGNAL_SRC_RFFT_H_ diff --git a/new_ai_tools/include/signal/src/square_root.h b/new_ai_tools/include/signal/src/square_root.h deleted file mode 100644 index b32855c5..00000000 --- a/new_ai_tools/include/signal/src/square_root.h +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_SQUARE_ROOT_H_ -#define SIGNAL_SRC_SQUARE_ROOT_H_ - -#include - -namespace tflite { -namespace tflm_signal { -// TODO(b/286250473): remove namespace once de-duped libraries above - -// Square root -uint16_t Sqrt32(uint32_t num); -uint32_t Sqrt64(uint64_t num); - -} // namespace tflm_signal -} // namespace tflite - -#endif // SIGNAL_SRC_SQUARE_ROOT_H_ diff --git a/new_ai_tools/include/signal/src/window.h b/new_ai_tools/include/signal/src/window.h deleted file mode 100644 index 88e8d119..00000000 --- a/new_ai_tools/include/signal/src/window.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef SIGNAL_SRC_WINDOW_H_ -#define SIGNAL_SRC_WINDOW_H_ - -#include - -namespace tflm_signal { - -// Applies a window function to an input signal -// -// * `input` and `window` must be both of size `size` elements and are -// multiplied element-by element. -// * `shift` is a right shift to apply before writing the result to `output`. -void ApplyWindow(const int16_t* input, const int16_t* window, int size, - int shift, int16_t* output); -} // namespace tflm_signal -#endif // SIGNAL_SRC_WINDOW_H_ diff --git a/new_ai_tools/include/signal/testdata/fft_test_data.h b/new_ai_tools/include/signal/testdata/fft_test_data.h deleted file mode 100644 index 35a0b46a..00000000 --- a/new_ai_tools/include/signal/testdata/fft_test_data.h +++ /dev/null @@ -1,48 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef SIGNAL_TESTDATA_FFT_TEST_DATA_H_ -#define SIGNAL_TESTDATA_FFT_TEST_DATA_H_ - -#include - -namespace tflite { - -/* These arrays are generated using random data. They serve to detect changes - * in the kernels. They do not test correctness. - */ -extern const int16_t kRfftInt16Length512Input[]; -extern const int16_t kRfftInt16Length512Golden[]; - -extern const int32_t kRfftInt32Length512Input[]; -extern const int32_t kRfftInt32Length512Golden[]; - -extern const float kRfftFloatLength512Input[]; -extern const float kRfftFloatLength512Golden[]; - -extern const int16_t kIrfftInt16Length512Input[]; -extern const int16_t kIrfftInt16Length512Golden[]; - -extern const int32_t kIrfftInt32Length512Input[]; -extern const int32_t kIrfftInt32Length512Golden[]; - -extern const float kIrfftFloatLength512Input[]; -extern const float kIrfftFloatLength512Golden[]; - -extern const int16_t kFftAutoScaleLength512Input[]; -extern const int16_t kFftAutoScaleLength512Golden[]; - -} // namespace tflite - -#endif // SIGNAL_TESTDATA_FFT_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/array.h b/new_ai_tools/include/tensorflow/lite/array.h deleted file mode 100644 index 4b4390d7..00000000 --- a/new_ai_tools/include/tensorflow/lite/array.h +++ /dev/null @@ -1,156 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_ARRAY_H_ -#define TENSORFLOW_LITE_ARRAY_H_ - -#include -#include -#include -#include -#include - -#include "tensorflow/lite/core/c/common.h" - -namespace tflite_micro { - -/// TfLite*Array helpers - -namespace array_internal { - -// Function object used as a deleter for unique_ptr holding TFLite*Array -// objects. -struct TfLiteArrayDeleter { - void operator()(TfLiteIntArray* a); - void operator()(TfLiteFloatArray* a); -}; - -// Maps T to the corresponding TfLiteArray type. -template -struct TfLiteArrayInfo; - -template <> -struct TfLiteArrayInfo { - using Type = TfLiteIntArray; -}; - -template <> -struct TfLiteArrayInfo { - using Type = TfLiteFloatArray; -}; - -} // namespace array_internal - -template -using TfLiteArrayUniquePtr = - std::unique_ptr::Type, - array_internal::TfLiteArrayDeleter>; - -// `unique_ptr` wrapper for `TfLiteIntArray`s. -using IntArrayUniquePtr = TfLiteArrayUniquePtr; - -// `unique_ptr` wrapper for `TfLiteFloatArray`s. -using FloatArrayUniquePtr = TfLiteArrayUniquePtr; - -// Allocates a TfLiteArray of given size using malloc. -// -// This builds an int array by default as this is the overwhelming part of the -// use cases. -template -TfLiteArrayUniquePtr BuildTfLiteArray(int size); - -// Allocates a TfLiteIntArray of given size using malloc. -template <> -inline IntArrayUniquePtr BuildTfLiteArray(const int size) { - return IntArrayUniquePtr(TfLiteMicroIntArrayCreate(size)); -} - -// Allocates a TfLiteFloatArray of given size using malloc. -template <> -inline FloatArrayUniquePtr BuildTfLiteArray(const int size) { - return FloatArrayUniquePtr(TfLiteMicroFloatArrayCreate(size)); -} - -// Allocates a TFLiteArray of given size and initializes it with the given -// values. -// -// `values` is expected to holds `size` elements. -// -// If T is explicitely specified and the type of values is not the same as T, -// then a static_cast is performed. -template ::value, U, T>> -TfLiteArrayUniquePtr BuildTfLiteArray(const int size, - const U* const values) { - TfLiteArrayUniquePtr array = BuildTfLiteArray(size); - // If size is 0, the array pointer may be null. - if (array && values) { - if (std::is_same::value) { - memcpy(array->data, values, size * sizeof(Type)); - } else { - for (int i = 0; i < size; ++i) { - array->data[i] = static_cast(values[i]); - } - } - } - return array; -} - -// Allocates a TFLiteArray and initializes it with the given array. -// -// `values` is expected to holds `size` elements. -template -TfLiteArrayUniquePtr BuildTfLiteArray(const T (&values)[N]) { - return BuildTfLiteArray(static_cast(N), values); -} - -// Allocates a TFLiteArray and initializes it with the given values. -// -// This uses SFINAE to only be picked up by for types that implement `data()` -// and `size()` member functions. We cannot reuse detection facilities provided -// by Abseil in this code. -// -// To conform with the other overloads, we allow specifying the type of the -// array as well as deducing it from the container. -template < - class T = void, class Container, - class ElementType = - std::decay_t().data())>, - class SizeType = std::decay_t().size())>, - class Type = - std::conditional_t::value, ElementType, T>> -TfLiteArrayUniquePtr BuildTfLiteArray(const Container& values) { - return BuildTfLiteArray(static_cast(values.size()), values.data()); -} - -// Allocates a TFLiteArray and initializes it with the given values. -template -TfLiteArrayUniquePtr BuildTfLiteArray( - const std::initializer_list& values) { - return BuildTfLiteArray(static_cast(values.size()), values.begin()); -} - -// Allocates a TFLiteArray and initializes it with the given array. -inline IntArrayUniquePtr BuildTfLiteArray(const TfLiteIntArray& other) { - return BuildTfLiteArray(other.size, other.data); -} - -// Allocates a TFLiteArray and initializes it with the given array. -inline FloatArrayUniquePtr BuildTfLiteArray(const TfLiteFloatArray& other) { - return BuildTfLiteArray(other.size, other.data); -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_ARRAY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/builtin_op_data.h deleted file mode 100644 index 161801cb..00000000 --- a/new_ai_tools/include/tensorflow/lite/builtin_op_data.h +++ /dev/null @@ -1,22 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -// Compatibility shim for new location of interface definitions. - -#ifndef TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ -#define TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ - -#include "tensorflow/lite/core/c/builtin_op_data.h" - -#endif // TENSORFLOW_LITE_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/builtin_ops.h b/new_ai_tools/include/tensorflow/lite/builtin_ops.h deleted file mode 100644 index a8d68368..00000000 --- a/new_ai_tools/include/tensorflow/lite/builtin_ops.h +++ /dev/null @@ -1,241 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_BUILTIN_OPS_H_ -#define TENSORFLOW_LITE_BUILTIN_OPS_H_ - -// DO NOT EDIT MANUALLY: This file is automatically generated by -// `schema/builtin_ops_header/generator.cc`. - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -// The enum for builtin operators. -// Note: CUSTOM, DELEGATE, and PLACEHOLDER_FOR_GREATER_OP_CODES are 3 special -// ops which are not real built-in ops. -typedef enum { - kTfLiteBuiltinAdd = 0, - kTfLiteBuiltinAveragePool2d = 1, - kTfLiteBuiltinConcatenation = 2, - kTfLiteBuiltinConv2d = 3, - kTfLiteBuiltinDepthwiseConv2d = 4, - kTfLiteBuiltinDepthToSpace = 5, - kTfLiteBuiltinDequantize = 6, - kTfLiteBuiltinEmbeddingLookup = 7, - kTfLiteBuiltinFloor = 8, - kTfLiteBuiltinFullyConnected = 9, - kTfLiteBuiltinHashtableLookup = 10, - kTfLiteBuiltinL2Normalization = 11, - kTfLiteBuiltinL2Pool2d = 12, - kTfLiteBuiltinLocalResponseNormalization = 13, - kTfLiteBuiltinLogistic = 14, - kTfLiteBuiltinLshProjection = 15, - kTfLiteBuiltinLstm = 16, - kTfLiteBuiltinMaxPool2d = 17, - kTfLiteBuiltinMul = 18, - kTfLiteBuiltinRelu = 19, - kTfLiteBuiltinReluN1To1 = 20, - kTfLiteBuiltinRelu6 = 21, - kTfLiteBuiltinReshape = 22, - kTfLiteBuiltinResizeBilinear = 23, - kTfLiteBuiltinRnn = 24, - kTfLiteBuiltinSoftmax = 25, - kTfLiteBuiltinSpaceToDepth = 26, - kTfLiteBuiltinSvdf = 27, - kTfLiteBuiltinTanh = 28, - kTfLiteBuiltinConcatEmbeddings = 29, - kTfLiteBuiltinSkipGram = 30, - kTfLiteBuiltinCall = 31, - kTfLiteBuiltinCustom = 32, - kTfLiteBuiltinEmbeddingLookupSparse = 33, - kTfLiteBuiltinPad = 34, - kTfLiteBuiltinUnidirectionalSequenceRnn = 35, - kTfLiteBuiltinGather = 36, - kTfLiteBuiltinBatchToSpaceNd = 37, - kTfLiteBuiltinSpaceToBatchNd = 38, - kTfLiteBuiltinTranspose = 39, - kTfLiteBuiltinMean = 40, - kTfLiteBuiltinSub = 41, - kTfLiteBuiltinDiv = 42, - kTfLiteBuiltinSqueeze = 43, - kTfLiteBuiltinUnidirectionalSequenceLstm = 44, - kTfLiteBuiltinStridedSlice = 45, - kTfLiteBuiltinBidirectionalSequenceRnn = 46, - kTfLiteBuiltinExp = 47, - kTfLiteBuiltinTopkV2 = 48, - kTfLiteBuiltinSplit = 49, - kTfLiteBuiltinLogSoftmax = 50, - kTfLiteBuiltinDelegate = 51, - kTfLiteBuiltinBidirectionalSequenceLstm = 52, - kTfLiteBuiltinCast = 53, - kTfLiteBuiltinPrelu = 54, - kTfLiteBuiltinMaximum = 55, - kTfLiteBuiltinArgMax = 56, - kTfLiteBuiltinMinimum = 57, - kTfLiteBuiltinLess = 58, - kTfLiteBuiltinNeg = 59, - kTfLiteBuiltinPadv2 = 60, - kTfLiteBuiltinGreater = 61, - kTfLiteBuiltinGreaterEqual = 62, - kTfLiteBuiltinLessEqual = 63, - kTfLiteBuiltinSelect = 64, - kTfLiteBuiltinSlice = 65, - kTfLiteBuiltinSin = 66, - kTfLiteBuiltinTransposeConv = 67, - kTfLiteBuiltinSparseToDense = 68, - kTfLiteBuiltinTile = 69, - kTfLiteBuiltinExpandDims = 70, - kTfLiteBuiltinEqual = 71, - kTfLiteBuiltinNotEqual = 72, - kTfLiteBuiltinLog = 73, - kTfLiteBuiltinSum = 74, - kTfLiteBuiltinSqrt = 75, - kTfLiteBuiltinRsqrt = 76, - kTfLiteBuiltinShape = 77, - kTfLiteBuiltinPow = 78, - kTfLiteBuiltinArgMin = 79, - kTfLiteBuiltinFakeQuant = 80, - kTfLiteBuiltinReduceProd = 81, - kTfLiteBuiltinReduceMax = 82, - kTfLiteBuiltinPack = 83, - kTfLiteBuiltinLogicalOr = 84, - kTfLiteBuiltinOneHot = 85, - kTfLiteBuiltinLogicalAnd = 86, - kTfLiteBuiltinLogicalNot = 87, - kTfLiteBuiltinUnpack = 88, - kTfLiteBuiltinReduceMin = 89, - kTfLiteBuiltinFloorDiv = 90, - kTfLiteBuiltinReduceAny = 91, - kTfLiteBuiltinSquare = 92, - kTfLiteBuiltinZerosLike = 93, - kTfLiteBuiltinFill = 94, - kTfLiteBuiltinFloorMod = 95, - kTfLiteBuiltinRange = 96, - kTfLiteBuiltinResizeNearestNeighbor = 97, - kTfLiteBuiltinLeakyRelu = 98, - kTfLiteBuiltinSquaredDifference = 99, - kTfLiteBuiltinMirrorPad = 100, - kTfLiteBuiltinAbs = 101, - kTfLiteBuiltinSplitV = 102, - kTfLiteBuiltinUnique = 103, - kTfLiteBuiltinCeil = 104, - kTfLiteBuiltinReverseV2 = 105, - kTfLiteBuiltinAddN = 106, - kTfLiteBuiltinGatherNd = 107, - kTfLiteBuiltinCos = 108, - kTfLiteBuiltinWhere = 109, - kTfLiteBuiltinRank = 110, - kTfLiteBuiltinElu = 111, - kTfLiteBuiltinReverseSequence = 112, - kTfLiteBuiltinMatrixDiag = 113, - kTfLiteBuiltinQuantize = 114, - kTfLiteBuiltinMatrixSetDiag = 115, - kTfLiteBuiltinRound = 116, - kTfLiteBuiltinHardSwish = 117, - kTfLiteBuiltinIf = 118, - kTfLiteBuiltinWhile = 119, - kTfLiteBuiltinNonMaxSuppressionV4 = 120, - kTfLiteBuiltinNonMaxSuppressionV5 = 121, - kTfLiteBuiltinScatterNd = 122, - kTfLiteBuiltinSelectV2 = 123, - kTfLiteBuiltinDensify = 124, - kTfLiteBuiltinSegmentSum = 125, - kTfLiteBuiltinBatchMatmul = 126, - kTfLiteBuiltinPlaceholderForGreaterOpCodes = 127, - kTfLiteBuiltinCumsum = 128, - kTfLiteBuiltinCallOnce = 129, - kTfLiteBuiltinBroadcastTo = 130, - kTfLiteBuiltinRfft2d = 131, - kTfLiteBuiltinConv3d = 132, - kTfLiteBuiltinImag = 133, - kTfLiteBuiltinReal = 134, - kTfLiteBuiltinComplexAbs = 135, - kTfLiteBuiltinHashtable = 136, - kTfLiteBuiltinHashtableFind = 137, - kTfLiteBuiltinHashtableImport = 138, - kTfLiteBuiltinHashtableSize = 139, - kTfLiteBuiltinReduceAll = 140, - kTfLiteBuiltinConv3dTranspose = 141, - kTfLiteBuiltinVarHandle = 142, - kTfLiteBuiltinReadVariable = 143, - kTfLiteBuiltinAssignVariable = 144, - kTfLiteBuiltinBroadcastArgs = 145, - kTfLiteBuiltinRandomStandardNormal = 146, - kTfLiteBuiltinBucketize = 147, - kTfLiteBuiltinRandomUniform = 148, - kTfLiteBuiltinMultinomial = 149, - kTfLiteBuiltinGelu = 150, - kTfLiteBuiltinDynamicUpdateSlice = 151, - kTfLiteBuiltinRelu0To1 = 152, - kTfLiteBuiltinUnsortedSegmentProd = 153, - kTfLiteBuiltinUnsortedSegmentMax = 154, - kTfLiteBuiltinUnsortedSegmentSum = 155, - kTfLiteBuiltinAtan2 = 156, - kTfLiteBuiltinUnsortedSegmentMin = 157, - kTfLiteBuiltinSign = 158, - kTfLiteBuiltinBitcast = 159, - kTfLiteBuiltinBitwiseXor = 160, - kTfLiteBuiltinRightShift = 161, - kTfLiteBuiltinStablehloLogistic = 162, - kTfLiteBuiltinStablehloAdd = 163, - kTfLiteBuiltinStablehloDivide = 164, - kTfLiteBuiltinStablehloMultiply = 165, - kTfLiteBuiltinStablehloMaximum = 166, - kTfLiteBuiltinStablehloReshape = 167, - kTfLiteBuiltinStablehloClamp = 168, - kTfLiteBuiltinStablehloConcatenate = 169, - kTfLiteBuiltinStablehloBroadcastInDim = 170, - kTfLiteBuiltinStablehloConvolution = 171, - kTfLiteBuiltinStablehloSlice = 172, - kTfLiteBuiltinStablehloCustomCall = 173, - kTfLiteBuiltinStablehloReduce = 174, - kTfLiteBuiltinStablehloAbs = 175, - kTfLiteBuiltinStablehloAnd = 176, - kTfLiteBuiltinStablehloCosine = 177, - kTfLiteBuiltinStablehloExponential = 178, - kTfLiteBuiltinStablehloFloor = 179, - kTfLiteBuiltinStablehloLog = 180, - kTfLiteBuiltinStablehloMinimum = 181, - kTfLiteBuiltinStablehloNegate = 182, - kTfLiteBuiltinStablehloOr = 183, - kTfLiteBuiltinStablehloPower = 184, - kTfLiteBuiltinStablehloRemainder = 185, - kTfLiteBuiltinStablehloRsqrt = 186, - kTfLiteBuiltinStablehloSelect = 187, - kTfLiteBuiltinStablehloSubtract = 188, - kTfLiteBuiltinStablehloTanh = 189, - kTfLiteBuiltinStablehloScatter = 190, - kTfLiteBuiltinStablehloCompare = 191, - kTfLiteBuiltinStablehloConvert = 192, - kTfLiteBuiltinStablehloDynamicSlice = 193, - kTfLiteBuiltinStablehloDynamicUpdateSlice = 194, - kTfLiteBuiltinStablehloPad = 195, - kTfLiteBuiltinStablehloIota = 196, - kTfLiteBuiltinStablehloDotGeneral = 197, - kTfLiteBuiltinStablehloReduceWindow = 198, - kTfLiteBuiltinStablehloSort = 199, - kTfLiteBuiltinStablehloWhile = 200, - kTfLiteBuiltinStablehloGather = 201, - kTfLiteBuiltinStablehloTranspose = 202, - kTfLiteBuiltinDilate = 203, - kTfLiteBuiltinStablehloRngBitGenerator = 204, - kTfLiteBuiltinReduceWindow = 205, -} TfLiteBuiltinOperator; - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus -#endif // TENSORFLOW_LITE_BUILTIN_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h deleted file mode 100644 index 7628e5ad..00000000 --- a/new_ai_tools/include/tensorflow/lite/c/builtin_op_data.h +++ /dev/null @@ -1,20 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ -#define TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ - -#include "tensorflow/lite/core/c/builtin_op_data.h" - -#endif // TENSORFLOW_LITE_C_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/c_api_types.h b/new_ai_tools/include/tensorflow/lite/c/c_api_types.h deleted file mode 100644 index 05cda07e..00000000 --- a/new_ai_tools/include/tensorflow/lite/c/c_api_types.h +++ /dev/null @@ -1,26 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_C_C_API_TYPES_H_ -#define TENSORFLOW_LITE_C_C_API_TYPES_H_ - -/// \file -/// -/// C API types for TensorFlow Lite. -/// -/// For documentation, see tensorflow/lite/core/c/c_api_types.h - -#include "tensorflow/lite/core/c/c_api_types.h" - -#endif // TENSORFLOW_LITE_C_C_API_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/c/common.h b/new_ai_tools/include/tensorflow/lite/c/common.h deleted file mode 100644 index f5a31d3f..00000000 --- a/new_ai_tools/include/tensorflow/lite/c/common.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -/// \file -/// -/// This file defines common C types and APIs for implementing operations, -/// delegates and other constructs in TensorFlow Lite. The actual operations and -/// delegates can be defined using C++, but the interface between the -/// interpreter and the operations are C. -/// -/// For documentation, see tensorflow/lite/core/c/common.h. - -#ifndef TENSORFLOW_LITE_C_COMMON_H_ -#define TENSORFLOW_LITE_C_COMMON_H_ - -#include "tensorflow/lite/core/c/common.h" - -#endif // TENSORFLOW_LITE_C_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/context_util.h b/new_ai_tools/include/tensorflow/lite/context_util.h deleted file mode 100644 index 1698f281..00000000 --- a/new_ai_tools/include/tensorflow/lite/context_util.h +++ /dev/null @@ -1,54 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -/// \file -/// -/// This provides a few C++ helpers that are useful for manipulating C -/// structures in C++. -#ifndef TENSORFLOW_LITE_CONTEXT_UTIL_H_ -#define TENSORFLOW_LITE_CONTEXT_UTIL_H_ - -#include - -#include "tensorflow/lite/core/c/common.h" - -namespace tflite_micro { - -/// Provides a range iterable wrapper for TfLiteIntArray* (C lists) that TfLite -/// C api uses. -// Can't use the google array_view, since we can't depend on even -// absl for embedded device reasons. -class TfLiteIntArrayView { - public: - /// Construct a view of a TfLiteIntArray*. Note, `int_array` should be - /// non-null and this view does not take ownership of it. - explicit TfLiteIntArrayView(const TfLiteIntArray* int_array) - : int_array_(int_array) {} - - TfLiteIntArrayView(const TfLiteIntArrayView&) = default; - TfLiteIntArrayView& operator=(const TfLiteIntArrayView& rhs) = default; - - typedef const int* const_iterator; - const_iterator begin() const { return int_array_->data; } - const_iterator end() const { return &int_array_->data[int_array_->size]; } - size_t size() const { return end() - begin(); } - int operator[](size_t pos) const { return int_array_->data[pos]; } - - private: - const TfLiteIntArray* int_array_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_CONTEXT_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h b/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h deleted file mode 100644 index 7a71995e..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/api/error_reporter.h +++ /dev/null @@ -1,72 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ -#define TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ - -#include - -namespace tflite_micro { - -/// A functor that reports error to supporting system. Invoked similar to -/// printf. -/// -/// Usage: -/// ErrorReporter foo; -/// foo.Report("test %d", 5); -/// or -/// va_list args; -/// foo.Report("test %d", args); // where args is va_list -/// -/// Subclass ErrorReporter to provide another reporting destination. -/// For example, if you have a GUI program, you might redirect to a buffer -/// that drives a GUI error log box. -class ErrorReporter { - public: - virtual ~ErrorReporter() = default; - /// Converts `args` to character equivalents according to `format` string, - /// constructs the error string and report it. - /// Returns number of characters written or zero on success, and negative - /// number on error. - virtual int Report(const char* format, va_list args) = 0; - - /// Converts arguments to character equivalents according to `format` string, - /// constructs the error string and report it. - /// Returns number of characters written or zero on success, and negative - /// number on error. - int Report(const char* format, ...); - - /// Equivalent to `Report` above. The additional `void*` parameter is unused. - /// This method is for compatibility with macros that takes `TfLiteContext`, - /// like TF_LITE_ENSURE and related macros. - int ReportError(void*, const char* format, ...); -}; - -} // namespace tflite_micro - -// You should not make bare calls to the error reporter, instead use the -// TF_LITE_REPORT_ERROR macro, since this allows message strings to be -// stripped when the binary size has to be optimized. If you are looking to -// reduce binary size, define TF_LITE_STRIP_ERROR_STRINGS when compiling and -// every call will be stubbed out, taking no memory. -#ifndef TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_REPORT_ERROR(reporter, ...) \ - do { \ - static_cast<::tflite_micro::ErrorReporter*>(reporter)->Report(__VA_ARGS__); \ - } while (false) -#else // TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_REPORT_ERROR(reporter, ...) -#endif // TF_LITE_STRIP_ERROR_STRINGS - -#endif // TENSORFLOW_LITE_CORE_API_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h b/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h deleted file mode 100644 index cee68ccd..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/api/flatbuffer_conversions.h +++ /dev/null @@ -1,440 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ -#define TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ - -// These functions transform codes and data structures that are defined in the -// flatbuffer serialization format into in-memory values that are used by the -// runtime API and interpreter. - -#include -#include -#include - -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/c/common.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// Interface class for builtin data allocations. -class BuiltinDataAllocator { - public: - virtual void* Allocate(size_t size, size_t alignment_hint) = 0; - virtual void Deallocate(void* data) = 0; - - // Allocate a structure, but make sure it is a POD structure that doesn't - // require constructors to run. The reason we do this, is that Interpreter's C - // extension part will take ownership so destructors will not be run during - // deallocation. - template - T* AllocatePOD() { - // TODO(b/154346074): Change this to is_trivially_destructible when all - // platform targets support that properly. - static_assert(std::is_pod::value, "Builtin data structure must be POD."); - void* allocated_memory = this->Allocate(sizeof(T), alignof(T)); - return new (allocated_memory) T(); - } - - virtual ~BuiltinDataAllocator() {} -}; - -// Parse the appropriate data out of the op. -// -// This handles builtin data explicitly as there are flatbuffer schemas. -// If it returns kTfLiteOk, it passes the data out with `builtin_data`. The -// calling function has to pass in an allocator object, and this allocator -// will be called to reserve space for the output data. If the calling -// function's allocator reserves memory on the heap, then it's the calling -// function's responsibility to free it. -// If it returns kTfLiteError, `builtin_data` will be `nullptr`. -TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -// Converts the tensor data type used in the flat buffer to the representation -// used by the runtime. -TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type, - ErrorReporter* error_reporter); - -TfLiteStatus ParseAbs(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseAddN(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseAssignVariable(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseBatchMatMul(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseBatchToSpaceNd(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseBroadcastArgs(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseBroadcastTo(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseCallOnce(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseCeil(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseCast(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseConcatenation(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseCos(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseCumsum(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseDepthToSpace(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseDepthwiseConv2D(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseDequantize(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseDiv(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseElu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseEmbeddingLookup(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseExp(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseExpandDims(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseFill(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseFloor(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseFloorDiv(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseFloorMod(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseFullyConnected(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseGather(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseGatherNd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseGreater(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseGreaterEqual(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseHardSwish(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseIf(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseL2Normalization(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLeakyRelu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLess(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseLessEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLog(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseLogicalAnd(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogicalNot(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogicalOr(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogistic(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLogSoftmax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseLSTM(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseMaximum(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseMinimum(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseMirrorPad(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseNeg(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseNotEqual(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePad(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePadV2(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePow(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParsePrelu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseQuantize(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseReadVariable(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRelu(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRelu6(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseResizeBilinear(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseResizeNearestNeighbor(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseRound(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseRsqrt(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSelectV2(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseShape(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSin(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSlice(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSpaceToBatchNd(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseSpaceToDepth(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSplitV(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSqueeze(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSquaredDifference(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseStridedSlice(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseTanh(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseTranspose(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseTransposeConv(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseUnidirectionalSequenceLSTM(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseVarHandle(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseWhile(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -TfLiteStatus ParseZerosLike(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseBitwiseXor(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseRightShift(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseStablehloScatter(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseStablehloRngBitGenerator(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -TfLiteStatus ParseStablehloGather(const Operator* op, - ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, - void** builtin_data); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h b/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h deleted file mode 100644 index 7c49135a..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/api/tensor_utils.h +++ /dev/null @@ -1,28 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ - -#include "tensorflow/lite/core/c/common.h" - -namespace tflite_micro { - -// Resets a variable tensor to the default value. -TfLiteStatus ResetVariableTensor(TfLiteContext* context, TfLiteTensor* tensor); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_CORE_API_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h b/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h deleted file mode 100644 index 8464a26b..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/c/builtin_op_data.h +++ /dev/null @@ -1,626 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -/// WARNING: Users of TensorFlow Lite should not include this file directly, -/// but should instead include -/// "third_party/tensorflow/lite/c/builtin_op_data.h". -/// Only the TensorFlow Lite implementation itself should include this -/// file directly. -#ifndef TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ -#define TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ - -#include -#include - -#include "tensorflow/lite/core/c/common.h" - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -// TfLiteReshapeParams can't have dynamic data so we fix the maximum possible -// number of dimensions. -#define TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT 8 -#define TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT 8 -#define TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT 8 - -// TODO(aselle): Consider using "if this then that" for testing. - -// Useful placeholder to put in otherwise empty structs to avoid size warnings. -typedef struct { - char dummy; -} EmptyStructPlaceholder; - -// IMPORTANT: All new members of structs must be added at the end to ensure -// backwards compatibility. - -// Possible padding types (for convolutions) -typedef enum { - kTfLitePaddingUnknown = 0, - kTfLitePaddingSame, - kTfLitePaddingValid, -} TfLitePadding; - -typedef enum { - kTfLiteMirrorPaddingUnknown = 0, - kTfLiteMirrorPaddingReflect, - kTfLiteMirrorPaddingSymmetric, -} TfLiteMirrorPaddingMode; - -// TODO(b/130259536): We should move this out of builtin_op_data. -typedef struct { - int width; - int height; - int width_offset; - int height_offset; -} TfLitePaddingValues; - -typedef struct { - TfLiteMirrorPaddingMode mode; -} TfLiteMirrorPaddingParams; - -// Possible fused activation functions. -typedef enum { - kTfLiteActNone = 0, - kTfLiteActRelu, - kTfLiteActReluN1To1, // min(max(-1, x), 1) - kTfLiteActRelu6, // min(max(0, x), 6) - kTfLiteActTanh, - kTfLiteActSignBit, - kTfLiteActSigmoid, -} TfLiteFusedActivation; - -typedef struct { - // Parameters for CONV_2D version 1. - TfLitePadding padding; - int stride_width; - int stride_height; - TfLiteFusedActivation activation; - - // Parameters for CONV_2D version 2. - // Note: Version 2 supports dilation values not equal to 1. - int dilation_width_factor; - int dilation_height_factor; - - // Parameters for CONV_2D version 7 or above. - // Used to determine the default value for the quantized bias. - TfLiteType quantized_bias_type; -} TfLiteConvParams; - -typedef struct { - TfLitePadding padding; - int stride_width; - int stride_height; - int stride_depth; - int dilation_width_factor; - int dilation_height_factor; - int dilation_depth_factor; - TfLiteFusedActivation activation; -} TfLiteConv3DParams; - -typedef TfLiteConv3DParams TfLiteConv3DTransposeParams; - -typedef struct { - TfLitePadding padding; - int stride_width; - int stride_height; - int filter_width; - int filter_height; - TfLiteFusedActivation activation; - struct { - TfLitePaddingValues padding; - } computed; -} TfLitePoolParams; - -typedef struct { - // Parameters for DepthwiseConv version 1 or above. - TfLitePadding padding; - int stride_width; - int stride_height; - // `depth_multiplier` is redundant. It's used by CPU kernels in - // TensorFlow 2.0 or below, but ignored in versions above. - // - // The information can be deduced from the shape of input and the shape of - // weights. Since the TFLiteConverter toolchain doesn't support partially - // specified shapes, relying on `depth_multiplier` stops us from supporting - // graphs with dynamic shape tensors. - // - // Note: Some of the delegates (e.g. NNAPI, GPU) are still relying on this - // field. - int depth_multiplier; - TfLiteFusedActivation activation; - // Parameters for DepthwiseConv version 2 or above. - int dilation_width_factor; - int dilation_height_factor; -} TfLiteDepthwiseConvParams; - -typedef struct { - int rank; - TfLiteFusedActivation activation; - - // Parameter for SVDF version 4. - bool asymmetric_quantize_inputs; -} TfLiteSVDFParams; - -typedef struct { - TfLiteFusedActivation activation; - - // Parameter for RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteRNNParams; - -typedef struct { - bool time_major; - TfLiteFusedActivation activation; - - // Parameter for Sequence RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteSequenceRNNParams; - -typedef struct { - bool time_major; - TfLiteFusedActivation activation; - bool merge_outputs; - - // Parameter for Bidirectional RNN version 3. - bool asymmetric_quantize_inputs; -} TfLiteBidirectionalSequenceRNNParams; - -typedef enum { - kTfLiteFullyConnectedWeightsFormatDefault = 0, - kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8 = 1, -} TfLiteFullyConnectedWeightsFormat; - -typedef struct { - // Parameters for FullyConnected version 1 or above. - TfLiteFusedActivation activation; - - // Parameters for FullyConnected version 2 or above. - TfLiteFullyConnectedWeightsFormat weights_format; - - // Parameters for FullyConnected version 5 or above. - // If set to true, then the number of dimensions in the input and the output - // tensors are the same. Furthermore, all but the last dimension of the input - // and output shapes will be equal. - bool keep_num_dims; - - // Parameters for FullyConnected version 7 or above. - // If set to true and the weights are quantized, then non constant inputs - // are quantized at evaluation time with asymmetric quantization. - bool asymmetric_quantize_inputs; - - // Parameters for FullyConnected version 10 or above. - // Used to determine the default value for the quantized bias. - TfLiteType quantized_bias_type; -} TfLiteFullyConnectedParams; - -typedef enum { - kTfLiteLshProjectionUnknown = 0, - kTfLiteLshProjectionSparse = 1, - kTfLiteLshProjectionDense = 2, -} TfLiteLSHProjectionType; - -typedef struct { - TfLiteLSHProjectionType type; -} TfLiteLSHProjectionParams; - -typedef struct { - float beta; -} TfLiteSoftmaxParams; - -typedef struct { - int axis; - TfLiteFusedActivation activation; -} TfLiteConcatenationParams; - -typedef struct { - TfLiteFusedActivation activation; - // Parameter added for the version 4. - bool pot_scale_int16; -} TfLiteAddParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteSpaceToBatchNDParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteBatchToSpaceNDParams; - -typedef struct { - bool adj_x; - bool adj_y; - // Parameters for BatchMatMul version 4 or above. - // If set to true and the weights are quantized, then non constant inputs - // are quantized at evaluation time with asymmetric quantization. - bool asymmetric_quantize_inputs; -} TfLiteBatchMatMulParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteMulParams; - -typedef struct { - TfLiteFusedActivation activation; - // Parameter added for the version 5. - bool pot_scale_int16; -} TfLiteSubParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteDivParams; - -typedef struct { - TfLiteFusedActivation activation; -} TfLiteL2NormParams; - -typedef struct { - int radius; - float bias; - float alpha; - float beta; -} TfLiteLocalResponseNormParams; - -typedef enum { - kTfLiteLSTMFullKernel = 0, - kTfLiteLSTMBasicKernel -} TfLiteLSTMKernelType; - -typedef struct { - // Parameters for LSTM version 1. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // Parameters for LSTM version 2. - // kTfLiteLSTMBasicKernel is only supported in version 2 or above. - TfLiteLSTMKernelType kernel_type; - - // Parameters for LSTM version 4. - bool asymmetric_quantize_inputs; -} TfLiteLSTMParams; - -typedef struct { - // Parameters needed for the underlying LSTM. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // If set to true then the first dimension is time, otherwise batch. - bool time_major; - - // Parameter for unidirectional sequence RNN version 3. - bool asymmetric_quantize_inputs; - - // Parameter for unidirectional sequence RNN version 4. - bool diagonal_recurrent_tensors; -} TfLiteUnidirectionalSequenceLSTMParams; - -typedef struct { - // Parameters supported by version 1: - // Parameters inherited for the LSTM kernel. - TfLiteFusedActivation activation; - float cell_clip; - float proj_clip; - - // If true, store the outputs of both directions in the first output. - bool merge_outputs; - - // Parameters supported by version 2: - // If set to true then the first dimension is time, otherwise batch. - bool time_major; - - // Parameters supported by version 3: - // If set to true, then hybrid ops use asymmetric quantization for inputs. - bool asymmetric_quantize_inputs; -} TfLiteBidirectionalSequenceLSTMParams; - -typedef struct { - bool align_corners; - // half_pixel_centers assumes pixels are of half the actual dimensions, and - // yields more accurate resizes. Corresponds to the same argument for the - // original TensorFlow op in TF2.0. - bool half_pixel_centers; -} TfLiteResizeBilinearParams; - -typedef struct { - bool align_corners; - bool half_pixel_centers; -} TfLiteResizeNearestNeighborParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLitePadParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLitePadV2Params; - -typedef struct { - // These fields are only used in old models for backward compatibility. - // In the current implementation, we use the 2nd input of the op as the shape, - // and these fields are unused. - int32_t shape[TFLITE_RESHAPE_PARAMS_MAX_DIMENSION_COUNT]; - int num_dimensions; -} TfLiteReshapeParams; - -typedef struct { - int ngram_size; - int max_skip_size; - bool include_all_ngrams; -} TfLiteSkipGramParams; - -typedef struct { - int block_size; -} TfLiteSpaceToDepthParams; - -typedef struct { - int block_size; -} TfLiteDepthToSpaceParams; - -typedef struct { - TfLiteType in_data_type; - TfLiteType out_data_type; -} TfLiteCastParams; - -typedef enum { - kTfLiteCombinerTypeSum = 0, - kTfLiteCombinerTypeMean = 1, - kTfLiteCombinerTypeSqrtn = 2, -} TfLiteCombinerType; - -typedef struct { - TfLiteCombinerType combiner; -} TfLiteEmbeddingLookupSparseParams; - -typedef struct { - int axis; - int batch_dims; -} TfLiteGatherParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteTransposeParams; - -typedef struct { - bool keep_dims; -} TfLiteReducerParams; - -typedef struct { - int num_splits; -} TfLiteSplitParams; - -typedef struct { - int num_splits; -} TfLiteSplitVParams; - -typedef struct { - // TODO(ahentz): We can't have dynamic data in this struct, at least not yet. - // For now we will fix the maximum possible number of dimensions. - int32_t squeeze_dims[8]; - int num_squeeze_dims; -} TfLiteSqueezeParams; - -typedef struct { - int begin_mask; - int end_mask; - int ellipsis_mask; - int new_axis_mask; - int shrink_axis_mask; - - // Parameters supported by version 8: - // If true, then the end tensor is an offset of the begin tensor. - bool offset; -} TfLiteStridedSliceParams; - -typedef struct { - TfLiteType output_type; -} TfLiteArgMaxParams; - -typedef struct { - TfLiteType output_type; -} TfLiteArgMinParams; - -typedef struct { - // Parameters supported by version 1: - TfLitePadding padding; - int stride_width; - int stride_height; - - // Parameters supported by version 4: - TfLiteFusedActivation activation; - - // Parameters for TransposeConv version 5 or above. - // Used to determine the default value for the quantized bias. - TfLiteType quantized_bias_type; -} TfLiteTransposeConvParams; - -typedef struct { - bool validate_indices; -} TfLiteSparseToDenseParams; - -typedef struct { - TfLiteType out_type; -} TfLiteShapeParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteRankParams; - -typedef struct { - // Parameters supported by version 1: - float min; - float max; - int num_bits; - - // Parameters supported by version 2: - bool narrow_range; -} TfLiteFakeQuantParams; - -typedef struct { - int values_count; - int axis; -} TfLitePackParams; - -typedef struct { - int axis; -} TfLiteOneHotParams; - -typedef struct { - int num; - int axis; -} TfLiteUnpackParams; - -typedef struct { - float alpha; -} TfLiteLeakyReluParams; - -typedef struct { - TfLiteType index_out_type; -} TfLiteUniqueParams; - -typedef struct { - int seq_dim; - int batch_dim; -} TfLiteReverseSequenceParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteMatrixDiagParams; - -typedef struct { - EmptyStructPlaceholder placeholder; -} TfLiteMatrixSetDiagParams; - -typedef struct { - int then_subgraph_index; - int else_subgraph_index; -} TfLiteIfParams; - -typedef struct { - int cond_subgraph_index; - int body_subgraph_index; -} TfLiteWhileParams; - -typedef struct { - bool exclusive; - bool reverse; -} TfLiteCumsumParams; - -typedef struct { - int init_subgraph_index; -} TfLiteCallOnceParams; - -typedef struct { - int table_id; - TfLiteType key_dtype; - TfLiteType value_dtype; -} TfLiteHashtableParams; - -typedef struct { - const char* container; - const char* shared_name; -} TfLiteVarHandleParams; - -typedef struct { - int seed; - int seed2; -} TfLiteRandomParams; - -typedef struct { - int num_boundaries; - // This points to the memory stored in the model (flatbuffer), - // and is not owned. - const float* boundaries; -} TfLiteBucketizeParams; - -typedef struct { - bool approximate; -} TfLiteGeluParams; - -typedef struct { - int64_t dimension; -} TfLiteStablehloConcatenateParams; - -typedef struct { - // See the stablehlo spec for the explanation of the attributes: - // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter - bool indices_are_sorted; - int64_t - update_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; - int num_update_window_dims; - int64_t - inserted_window_dims[TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; - int num_inserted_window_dims; - int64_t scatter_dims_to_operand_dims - [TFLITE_STABLEHLO_SCATTER_PARAMS_MAX_DIMENSION_COUNT]; - int num_scatter_dims_to_operand_dims; - int64_t index_vector_dim; - bool unique_indices; - int update_computation_subgraph_index; -} TfLiteStablehloScatterParams; - -typedef enum { - kTfLiteRngAlgorithmUnknown = 0, - // An algorithm auto-selected by the system according to device type. - kTfLiteRngAlgorithmDefault, - // The Philox algorithm, as described in paper - // ['Parallel Random Numbers: As Easy as 1, 2, 3'] - // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf) - kTfLiteRngAlgorithmPhilox, - // The ThreeFry algorithm, as described in paper - // ['Parallel Random Numbers: As Easy as 1, 2, 3'] - // (https://www.thesalmons.org/john/random123/papers/random123sc11.pdf) - kTfLiteRngAlgorithmThreefry, -} TfLiteRngAlgorithm; - -typedef struct { - TfLiteRngAlgorithm algorithm; -} TfLiteStablehloRngBitGeneratorParams; - -typedef struct { - // See the stablehlo spec for the explanation of the attributes: - // https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather - int64_t offset_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; - int num_offset_dims; - int64_t - collapsed_slice_dims[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; - int num_collapsed_slice_dims; - int64_t start_index_map[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; - int num_start_index_map; - int64_t index_vector_dim; - int64_t slice_sizes[TFLITE_STABLEHLO_GATHER_PARAMS_MAX_DIMENSION_COUNT]; - int num_slice_sizes; - bool indices_are_sorted; -} TfLiteStablehloGatherParams; - -enum TfLiteReduceWindowFunction { - TfLiteReduceWindowFunctionUnsupported, - TfLiteReduceWindowFunctionAdd, - TfLiteReduceWindowFunctionMul, - TfLiteReduceWindowFunctionMin, - TfLiteReduceWindowFunctionMax, - TfLiteReduceWindowFunctionAll, - TfLiteReduceWindowFunctionAny -}; - -typedef struct { - enum TfLiteReduceWindowFunction reduce_function; -} TfLiteReduceWindowParams; - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus - -#endif // TENSORFLOW_LITE_CORE_C_BUILTIN_OP_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h b/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h deleted file mode 100644 index a26f482d..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/c/c_api_types.h +++ /dev/null @@ -1,178 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -/// This file declares types used by the pure C inference API defined in -/// c_api.h, some of which are also used in the C++ and C kernel and interpreter -/// APIs. - -// WARNING: Users of TensorFlow Lite should not include this file directly, -// but should instead include -// "third_party/tensorflow/lite/c/c_api_types.h". -// Only the TensorFlow Lite implementation itself should include this -// file directly. - -// IWYU pragma: private, include "third_party/tensorflow/lite/c/c_api_types.h" - -#ifndef TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ -#define TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ - -#include - -#ifdef __cplusplus -extern "C" { -#endif - -/** \addtogroup c_api_types tensorflow/lite/c/c_api_types.h - * @{ - */ - -// Define TFL_CAPI_EXPORT macro to export a function properly with a shared -// library. -#ifdef SWIG -#define TFL_CAPI_EXPORT -#elif defined(TFL_STATIC_LIBRARY_BUILD) -#define TFL_CAPI_EXPORT -#else // not definded TFL_STATIC_LIBRARY_BUILD -#if defined(_WIN32) -#ifdef TFL_COMPILE_LIBRARY -#define TFL_CAPI_EXPORT __declspec(dllexport) -#else -#define TFL_CAPI_EXPORT __declspec(dllimport) -#endif // TFL_COMPILE_LIBRARY -#else -#define TFL_CAPI_EXPORT __attribute__((visibility("default"))) -#endif // _WIN32 -#endif // SWIG - -/// Note that new error status values may be added in future in order to -/// indicate more fine-grained internal states, therefore, applications should -/// not rely on status values being members of the enum. -typedef enum TfLiteStatus { - /// Success - kTfLiteOk = 0, - - /// Generally referring to an error in the runtime (i.e. interpreter) - kTfLiteError = 1, - - /// Generally referring to an error from a TfLiteDelegate itself. - kTfLiteDelegateError = 2, - - /// Generally referring to an error in applying a delegate due to - /// incompatibility between runtime and delegate, e.g., this error is returned - /// when trying to apply a TF Lite delegate onto a model graph that's already - /// immutable. - kTfLiteApplicationError = 3, - - /// Generally referring to serialized delegate data not being found. - /// See tflite_micro::delegates::Serialization. - kTfLiteDelegateDataNotFound = 4, - - /// Generally referring to data-writing issues in delegate serialization. - /// See tflite_micro::delegates::Serialization. - kTfLiteDelegateDataWriteError = 5, - - /// Generally referring to data-reading issues in delegate serialization. - /// See tflite_micro::delegates::Serialization. - kTfLiteDelegateDataReadError = 6, - - /// Generally referring to issues when the TF Lite model has ops that cannot - /// be resolved at runtime. This could happen when the specific op is not - /// registered or built with the TF Lite framework. - kTfLiteUnresolvedOps = 7, - - /// Generally referring to invocation cancelled by the user. - /// See `interpreter::Cancel`. - // TODO(b/194915839): Implement `interpreter::Cancel`. - // TODO(b/250636993): Cancellation triggered by `SetCancellationFunction` - // should also return this status code. - kTfLiteCancelled = 8, -} TfLiteStatus; - -/// Types supported by tensor -typedef enum { - kTfLiteNoType = 0, - kTfLiteFloat32 = 1, - kTfLiteInt32 = 2, - kTfLiteUInt8 = 3, - kTfLiteInt64 = 4, - kTfLiteString = 5, - kTfLiteBool = 6, - kTfLiteInt16 = 7, - kTfLiteComplex64 = 8, - kTfLiteInt8 = 9, - kTfLiteFloat16 = 10, - kTfLiteFloat64 = 11, - kTfLiteComplex128 = 12, - kTfLiteUInt64 = 13, - kTfLiteResource = 14, - kTfLiteVariant = 15, - kTfLiteUInt32 = 16, - kTfLiteUInt16 = 17, - kTfLiteInt4 = 18, -} TfLiteType; - -/// Legacy. Will be deprecated in favor of TfLiteAffineQuantization. -/// If per-layer quantization is specified this field will still be populated in -/// addition to TfLiteAffineQuantization. -/// Parameters for asymmetric quantization. Quantized values can be converted -/// back to float using: -/// real_value = scale * (quantized_value - zero_point) -typedef struct TfLiteQuantizationParams { - float scale; - int32_t zero_point; -} TfLiteQuantizationParams; - -// -------------------------------------------------------------------------- -// Opaque types used by c_api.h, c_api_opaque.h and common.h. - -/// TfLiteOpaqueContext is an opaque version of TfLiteContext; -typedef struct TfLiteOpaqueContext TfLiteOpaqueContext; - -/// TfLiteOpaqueNode is an opaque version of TfLiteNode; -typedef struct TfLiteOpaqueNode TfLiteOpaqueNode; - -/// TfLiteOpaqueTensor is an opaque version of TfLiteTensor; -typedef struct TfLiteOpaqueTensor TfLiteOpaqueTensor; - -/// TfLiteDelegate: allows delegation of nodes to alternative backends. -/// Forward declaration of concrete type declared in common.h. -typedef struct TfLiteDelegate TfLiteDelegate; - -/// TfLiteOpaqueDelegateStruct: unconditionally opaque version of -/// TfLiteDelegate; allows delegation of nodes to alternative backends. -/// -/// This is an abstract type that is intended to have the same -/// role as TfLiteDelegate, but without exposing the implementation -/// details of how delegates are implemented. -/// WARNING: This is an experimental type and subject to change. -typedef struct TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegateStruct; - -/// TfLiteOpaqueDelegate: conditionally opaque version of -/// TfLiteDelegate; allows delegation of nodes to alternative backends. -/// For TF Lite in Play Services, this is an opaque type, -/// but for regular TF Lite, this is just a typedef for TfLiteDelegate. -/// WARNING: This is an experimental type and subject to change. -#if TFLITE_WITH_STABLE_ABI || TFLITE_USE_OPAQUE_DELEGATE -typedef TfLiteOpaqueDelegateStruct TfLiteOpaqueDelegate; -#else -typedef TfLiteDelegate TfLiteOpaqueDelegate; -#endif - -/** @} */ - -#ifdef __cplusplus -} // extern C -#endif -#endif // TENSORFLOW_LITE_CORE_C_C_API_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/c/common.h b/new_ai_tools/include/tensorflow/lite/core/c/common.h deleted file mode 100644 index 9efdc6ba..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/c/common.h +++ /dev/null @@ -1,1494 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This file defines common C types and APIs for implementing operations, -// delegates and other constructs in TensorFlow Lite. The actual operations and -// delegates can be defined using C++, but the interface between the interpreter -// and the operations are C. -// -// Summary of abstractions -// TF_LITE_ENSURE - Self-sufficient error checking -// TfLiteStatus - Status reporting -// TfLiteIntArray - stores tensor shapes (dims), -// TfLiteContext - allows an op to access the tensors -// TfLiteTensor - tensor (a multidimensional array) -// TfLiteNode - a single node or operation -// TfLiteRegistration - the implementation of a conceptual operation. -// TfLiteDelegate - allows delegation of nodes to alternative backends. -// -// Some abstractions in this file are created and managed by Interpreter. -// -// NOTE: The order of values in these structs are "semi-ABI stable". New values -// should be added only to the end of structs and never reordered. - -/// WARNING: Users of TensorFlow Lite should not include this file directly, -/// but should instead include -/// "third_party/tensorflow/lite/c/common.h". -/// Only the TensorFlow Lite implementation itself should include this -/// file directly. -// IWYU pragma: private, include "third_party/tensorflow/lite/c/common.h" - -#ifndef TENSORFLOW_LITE_CORE_C_COMMON_H_ -#define TENSORFLOW_LITE_CORE_C_COMMON_H_ - -#include -#include -#include -#include -#include - -#include "tensorflow/lite/core/c/c_api_types.h" // IWYU pragma: export - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -// The list of external context types known to TF Lite. This list exists solely -// to avoid conflicts and to ensure ops can share the external contexts they -// need. Access to the external contexts is controlled by one of the -// corresponding support files. -typedef enum TfLiteExternalContextType { - kTfLiteEigenContext = 0, // include eigen_support.h to use. - kTfLiteGemmLowpContext = 1, // include gemm_support.h to use. - kTfLiteEdgeTpuContext = 2, // Placeholder for Edge TPU support. - kTfLiteCpuBackendContext = 3, // include cpu_backend_context.h to use. - kTfLiteMaxExternalContexts = 4 -} TfLiteExternalContextType; - -// Forward declare so dependent structs and methods can reference these types -// prior to the struct definitions. -struct TfLiteContext; -struct TfLiteDelegate; -struct TfLiteRegistration; -struct TfLiteOpaqueDelegateBuilder; - -// An external context is a collection of information unrelated to the TF Lite -// framework, but useful to a subset of the ops. TF Lite knows very little -// about the actual contexts, but it keeps a list of them, and is able to -// refresh them if configurations like the number of recommended threads -// change. -typedef struct TfLiteExternalContext { - TfLiteExternalContextType type; - TfLiteStatus (*Refresh)(struct TfLiteContext* context); -} TfLiteExternalContext; - -#define kTfLiteOptionalTensor (-1) - -// Fixed size list of integers. Used for dimensions and inputs/outputs tensor -// indices -typedef struct TfLiteIntArray { - int size; - -#if defined(_MSC_VER) - // Context for why this is needed is in http://b/189926408#comment21 - int data[1]; -#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ - __GNUC_MINOR__ >= 1) || \ - defined(HEXAGON) || \ - (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1) - // gcc 6.1+ have a bug where flexible members aren't properly handled - // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c - int data[0]; -#else - int data[]; -#endif -} TfLiteIntArray; - -// Given the size (number of elements) in a TfLiteIntArray, calculate its size -// in bytes. -size_t TfLiteMicroIntArrayGetSizeInBytes(int size); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a array of a given `size` (uninitialized entries). -// This returns a pointer, that you must free using TfLiteIntArrayFree(). -TfLiteIntArray* TfLiteMicroIntArrayCreate(int size); -#endif - -// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise. -int TfLiteMicroIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b); - -// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise. -int TfLiteMicroIntArrayEqualsArray(const TfLiteIntArray* a, int b_size, - const int b_data[]); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a copy of an array passed as `src`. -// You are expected to free memory with TfLiteIntArrayFree -TfLiteIntArray* TfLiteMicroIntArrayCopy(const TfLiteIntArray* src); - -// Free memory of array `a`. -void TfLiteMicroIntArrayFree(TfLiteIntArray* a); -#endif // TF_LITE_STATIC_MEMORY - -// Fixed size list of floats. Used for per-channel quantization. -typedef struct TfLiteFloatArray { - int size; -#if defined(_MSC_VER) - // Context for why this is needed is in http://b/189926408#comment21 - float data[1]; -#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \ - __GNUC_MINOR__ >= 1) || \ - defined(HEXAGON) || \ - (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1) - // gcc 6.1+ have a bug where flexible members aren't properly handled - // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c - float data[0]; -#else - float data[]; -#endif -} TfLiteFloatArray; - -// Given the size (number of elements) in a TfLiteFloatArray, calculate its size -// in bytes. -int TfLiteFloatArrayGetSizeInBytes(int size); - -#ifndef TF_LITE_STATIC_MEMORY -// Create a array of a given `size` (uninitialized entries). -// This returns a pointer, that you must free using TfLiteFloatArrayFree(). -TfLiteFloatArray* TfLiteMicroFloatArrayCreate(int size); - -// Create a copy of an array passed as `src`. -// You are expected to free memory with TfLiteFloatArrayFree. -TfLiteFloatArray* TfLiteMicroFloatArrayCopy(const TfLiteFloatArray* src); - -// Free memory of array `a`. -void TfLiteMicroFloatArrayFree(TfLiteFloatArray* a); -#endif // TF_LITE_STATIC_MEMORY - -// Since we must not depend on any libraries, define a minimal subset of -// error macros while avoiding names that have pre-conceived meanings like -// assert and check. - -// Try to make all reporting calls through TF_LITE_KERNEL_LOG rather than -// calling the context->ReportError function directly, so that message strings -// can be stripped out if the binary size needs to be severely optimized. -#ifndef TF_LITE_STRIP_ERROR_STRINGS -#define TF_LITE_KERNEL_LOG(context, ...) \ - do { \ - (context)->ReportError((context), __VA_ARGS__); \ - } while (false) - -#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) \ - do { \ - if ((context) != nullptr) { \ - (context)->ReportError((context), __VA_ARGS__); \ - } \ - } while (false) -#else // TF_LITE_STRIP_ERROR_STRINGS -#define ARGS_UNUSED(...) (void)sizeof(#__VA_ARGS__) -#define TF_LITE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) -#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) -#endif // TF_LITE_STRIP_ERROR_STRINGS - -// Check whether value is true, and if not return kTfLiteError from -// the current function (and report the error string msg). -#define TF_LITE_ENSURE_MSG(context, value, msg) \ - do { \ - if (!(value)) { \ - TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \ - return kTfLiteError; \ - } \ - } while (0) - -// Check whether the value `a` is true, and if not return kTfLiteError from -// the current function, while also reporting the location of the error. -#define TF_LITE_ENSURE(context, a) \ - do { \ - if (!(a)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s was not true.", __FILE__, \ - __LINE__, #a); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_STATUS(a) \ - do { \ - const TfLiteStatus s = (a); \ - if (s != kTfLiteOk) { \ - return s; \ - } \ - } while (0) - -// Check whether the value `a == b` is true, and if not return kTfLiteError from -// the current function, while also reporting the location of the error. -// `a` and `b` may be evaluated more than once, so no side effects or -// extremely expensive computations should be done. -// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes. -#define TF_LITE_ENSURE_EQ(context, a, b) \ - do { \ - if ((a) != (b)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%d != %d)", __FILE__, \ - __LINE__, #a, #b, (a), (b)); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_TYPES_EQ(context, a, b) \ - do { \ - if ((a) != (b)) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%s != %s)", __FILE__, \ - __LINE__, #a, #b, TfLiteMicroTypeGetName(a), \ - TfLiteMicroTypeGetName(b)); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_NEAR(context, a, b, epsilon) \ - do { \ - auto delta = ((a) > (b)) ? ((a) - (b)) : ((b) - (a)); \ - if (delta > epsilon) { \ - TF_LITE_KERNEL_LOG((context), "%s:%d %s not near %s (%f != %f)", \ - __FILE__, __LINE__, #a, #b, static_cast(a), \ - static_cast(b)); \ - return kTfLiteError; \ - } \ - } while (0) - -#define TF_LITE_ENSURE_OK(context, status) \ - do { \ - const TfLiteStatus s = (status); \ - if ((s) != kTfLiteOk) { \ - return s; \ - } \ - } while (0) - -// Single-precision complex data type compatible with the C99 definition. -typedef struct TfLiteComplex64 { - float re, im; // real and imaginary parts, respectively. -} TfLiteComplex64; - -// Double-precision complex data type compatible with the C99 definition. -typedef struct TfLiteComplex128 { - double re, im; // real and imaginary parts, respectively. -} TfLiteComplex128; - -// Half precision data type compatible with the C99 definition. -typedef struct TfLiteFloat16 { - uint16_t data; -} TfLiteFloat16; - -// Return the name of a given type, for error reporting purposes. -const char* TfLiteMicroTypeGetName(TfLiteType type); - -// SupportedQuantizationTypes. -typedef enum TfLiteQuantizationType { - // No quantization. - kTfLiteNoQuantization = 0, - // Affine quantization (with support for per-channel quantization). - // Corresponds to TfLiteAffineQuantization. - kTfLiteAffineQuantization = 1, -} TfLiteQuantizationType; - -// Structure specifying the quantization used by the tensor, if-any. -typedef struct TfLiteQuantization { - // The type of quantization held by params. - TfLiteQuantizationType type; - // Holds an optional reference to a quantization param structure. The actual - // type depends on the value of the `type` field (see the comment there for - // the values and corresponding types). - void* params; -} TfLiteQuantization; - -// Parameters for asymmetric quantization across a dimension (i.e per output -// channel quantization). -// quantized_dimension specifies which dimension the scales and zero_points -// correspond to. -// For a particular value in quantized_dimension, quantized values can be -// converted back to float using: -// real_value = scale * (quantized_value - zero_point) -typedef struct TfLiteAffineQuantization { - TfLiteFloatArray* scale; - TfLiteIntArray* zero_point; - int32_t quantized_dimension; -} TfLiteAffineQuantization; - -/* A union of pointers that points to memory for a given tensor. */ -typedef union TfLitePtrUnion { - /* Do not access these members directly, if possible, use - * GetTensorData(tensor) instead, otherwise only access .data, as other - * members are deprecated. */ - int32_t* i32; - uint32_t* u32; - int64_t* i64; - uint64_t* u64; - float* f; - TfLiteFloat16* f16; - double* f64; - char* raw; - const char* raw_const; - uint8_t* uint8; - bool* b; - int16_t* i16; - uint16_t* ui16; - TfLiteComplex64* c64; - TfLiteComplex128* c128; - int8_t* int8; - /* Only use this member. */ - void* data; -} TfLitePtrUnion; - -// Memory allocation strategies. -// * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated. -// * kTfLiteArenaRw: Arena allocated with no guarantees about persistence, -// and available during eval. -// * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and -// only available during eval. -// * kTfLiteDynamic: Allocated during eval, or for string tensors. -// * kTfLitePersistentRo: Allocated and populated during prepare. This is -// useful for tensors that can be computed during prepare and treated -// as constant inputs for downstream ops (also in prepare). -// * kTfLiteCustom: Custom memory allocation provided by the user. See -// TfLiteCustomAllocation below. -// * kTfLiteVariantObject: Allocation is an arbitrary type-erased C++ object. -// Allocation and deallocation are done through `new` and `delete`. -typedef enum TfLiteAllocationType { - kTfLiteMemNone = 0, - kTfLiteMmapRo, - kTfLiteArenaRw, - kTfLiteArenaRwPersistent, - kTfLiteDynamic, - kTfLitePersistentRo, - kTfLiteCustom, - kTfLiteVariantObject, -} TfLiteAllocationType; - -// Memory allocation strategies. -// -// TfLiteAllocationType values have been overloaded to mean more than their -// original intent. This enum should only be used to document the allocation -// strategy used by a tensor for it data. -typedef enum TfLiteAllocationStrategy { - kTfLiteAllocationStrategyUnknown, - kTfLiteAllocationStrategyNone, // No data is allocated. - kTfLiteAllocationStrategyMMap, // Data is mmaped. - kTfLiteAllocationStrategyArena, // Handled by the arena. - kTfLiteAllocationStrategyMalloc, // Uses `malloc`/`free`. - kTfLiteAllocationStrategyNew // Uses `new[]`/`delete[]`. -} TfLiteAllocationStrategy; - -// Describes how stable a tensor attribute is with regards to an interpreter -// runs. -typedef enum TfLiteRunStability { - kTfLiteRunStabilityUnknown, - kTfLiteRunStabilityUnstable, // May change at any time. - kTfLiteRunStabilitySingleRun, // Will stay the same for one run. - kTfLiteRunStabilityAcrossRuns // Will stay the same across all runs. -} TfLiteRunStability; - -// Describes the steps of a TFLite operation life cycle. -typedef enum TfLiteRunStep { - kTfLiteRunStepUnknown, - kTfLiteRunStepInit, - kTfLiteRunStepPrepare, - kTfLiteRunStepEval -} TfLiteRunStep; - -// The delegates should use zero or positive integers to represent handles. -// -1 is reserved from unallocated status. -typedef int TfLiteBufferHandle; -enum { - kTfLiteNullBufferHandle = -1, -}; - -// Storage format of each dimension in a sparse tensor. -typedef enum TfLiteDimensionType { - kTfLiteDimDense = 0, - kTfLiteDimSparseCSR, -} TfLiteDimensionType; - -// Metadata to encode each dimension in a sparse tensor. -typedef struct TfLiteDimensionMetadata { - TfLiteDimensionType format; - int dense_size; - TfLiteIntArray* array_segments; - TfLiteIntArray* array_indices; -} TfLiteDimensionMetadata; - -// Parameters used to encode a sparse tensor. For detailed explanation of each -// field please refer to lite/schema/schema.fbs. -typedef struct TfLiteSparsity { - TfLiteIntArray* traversal_order; - TfLiteIntArray* block_map; - TfLiteDimensionMetadata* dim_metadata; - int dim_metadata_size; -} TfLiteSparsity; - -// Defines a custom memory allocation not owned by the runtime. -// `data` should be aligned to kDefaultTensorAlignment defined in -// lite/util.h. (Currently 64 bytes) -// NOTE: See Interpreter.SetCustomAllocationForTensor for details on usage. -typedef struct TfLiteCustomAllocation { - void* data; - size_t bytes; -} TfLiteCustomAllocation; - -// The flags used in `Interpreter::SetCustomAllocationForTensor`. -// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc. -typedef enum TfLiteCustomAllocationFlags { - kTfLiteCustomAllocationFlagsNone = 0, - // Skips checking whether allocation.data points to an aligned buffer as - // expected by the TFLite runtime. - // NOTE: Setting this flag can cause crashes when calling Invoke(). - // Use with caution. - kTfLiteCustomAllocationFlagsSkipAlignCheck = 1, -} TfLiteCustomAllocationFlags; - -// A tensor in the interpreter system which is a wrapper around a buffer of -// data including a dimensionality (or NULL if not currently defined). -#ifndef TF_LITE_STATIC_MEMORY -typedef struct TfLiteTensor { - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. NOTE: the product of elements of `dims` - // and the element datatype size should be equal to `bytes` below. - TfLiteIntArray* dims; - // Quantization information. - TfLiteQuantizationParams params; - // How memory is mapped - // kTfLiteMmapRo: Memory mapped read only. - // i.e. weights - // kTfLiteArenaRw: Arena allocated read write memory - // (i.e. temporaries, outputs). - TfLiteAllocationType allocation_type; - // The number of bytes required to store the data of this Tensor. I.e. - // (bytes of each element) * dims[0] * ... * dims[n-1]. For example, if - // type is kTfLiteFloat32 and dims = {3, 2} then - // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24. - size_t bytes; - - // An opaque pointer to a tflite_micro::MMapAllocation - const void* allocation; - - // Null-terminated name of this tensor. - const char* name; - - // The delegate which knows how to handle `buffer_handle`. - // WARNING: This is an experimental interface that is subject to change. - struct TfLiteDelegate* delegate; - - // An integer buffer handle that can be handled by `delegate`. - // The value is valid only when delegate is not null. - // WARNING: This is an experimental interface that is subject to change. - TfLiteBufferHandle buffer_handle; - - // If the delegate uses its own buffer (e.g. GPU memory), the delegate is - // responsible to set data_is_stale to true. - // `delegate->CopyFromBufferHandle` can be called to copy the data from - // delegate buffer. - // WARNING: This is an // experimental interface that is subject to change. - bool data_is_stale; - - // True if the tensor is a variable. - bool is_variable; - - // Quantization information. Replaces params field above. - TfLiteQuantization quantization; - - // Parameters used to encode a sparse tensor. - // This is optional. The field is NULL if a tensor is dense. - // WARNING: This is an experimental interface that is subject to change. - TfLiteSparsity* sparsity; - - // Optional. Encodes shapes with unknown dimensions with -1. This field is - // only populated when unknown dimensions exist in a read-write tensor (i.e. - // an input or output tensor). (e.g. `dims` contains [1, 1, 1, 3] and - // `dims_signature` contains [1, -1, -1, 3]). If no unknown dimensions exist - // then `dims_signature` is either null, or set to an empty array. Note that - // this field only exists when TF_LITE_STATIC_MEMORY is not defined. - const TfLiteIntArray* dims_signature; -} TfLiteTensor; - -// A structure representing an instance of a node. -// This structure only exhibits the inputs, outputs, user defined data and some -// node properties (like statefulness), not other features like the type. -typedef struct TfLiteNode { - // Inputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* inputs; - - // Outputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* outputs; - - // intermediate tensors to this node expressed as indices into the simulator's - // tensors. - TfLiteIntArray* intermediates; - - // Temporary tensors uses during the computations. This usually contains no - // tensors, but ops are allowed to change that if they need scratch space of - // any sort. - TfLiteIntArray* temporaries; - - // Opaque data provided by the node implementer through `Registration.init`. - void* user_data; - - // Opaque data provided to the node if the node is a builtin. This is usually - // a structure defined in builtin_op_data.h - void* builtin_data; - - // Custom initial data. This is the opaque data provided in the flatbuffer. - // WARNING: This is an experimental interface that is subject to change. - const void* custom_initial_data; - int custom_initial_data_size; - - // The pointer to the delegate. This is non-null only when the node is - // created by calling `interpreter.ModifyGraphWithDelegate`. - // WARNING: This is an experimental interface that is subject to change. - struct TfLiteDelegate* delegate; - - // Whether this op might have side effect (e.g. stateful op). - bool might_have_side_effect; -} TfLiteNode; -#else // defined(TF_LITE_STATIC_MEMORY)? -// NOTE: This flag is opt-in only at compile time. -// -// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct -// contains only the minimum fields required to initialize and prepare a micro -// inference graph. The fields in this struct have been ordered from -// largest-to-smallest for optimal struct sizeof. -// -// This struct does not use: -// - allocation -// - buffer_handle -// - data_is_stale -// - delegate -// - dims_signature -// - name -// - sparsity -typedef struct TfLiteTensor { - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. NOTE: the product of elements of `dims` - // and the element datatype size should be equal to `bytes` below. - TfLiteIntArray* dims; - - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; - - // TODO(b/155784997): Consider consolidating these quantization fields: - // Quantization information. Replaces params field above. - TfLiteQuantization quantization; - - // Quantization information. - TfLiteQuantizationParams params; - -} TfLiteTensor; - -// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains -// only the minimum fields required to represent a node. -// -// This struct does not use: -// - delegate -// - intermediates -// - temporaries -typedef struct TfLiteNode { - // Inputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* inputs; - - // Outputs to this node expressed as indices into the simulator's tensors. - TfLiteIntArray* outputs; - - // Opaque data provided by the node implementer through `Registration.init`. - void* user_data; - - // Opaque data provided to the node if the node is a builtin. This is usually - // a structure defined in builtin_op_data.h - void* builtin_data; - - // Custom initial data. This is the opaque data provided in the flatbuffer. - // WARNING: This is an experimental interface that is subject to change. - int custom_initial_data_size; -} TfLiteNode; -#endif // TF_LITE_STATIC_MEMORY - -// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount -// of information required for a kernel to run during TfLiteRegistration::Eval. -// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM -// builds with this flag by default internally. -typedef struct TfLiteEvalTensor { - // A union of data pointers. The appropriate type should be used for a typed - // tensor based on `type`. - TfLitePtrUnion data; - - // A pointer to a structure representing the dimensionality interpretation - // that the buffer should have. - TfLiteIntArray* dims; - - // The data type specification for data stored in `data`. This affects - // what member of `data` union should be used. - TfLiteType type; -} TfLiteEvalTensor; - -#ifndef TF_LITE_STATIC_MEMORY -// Free data memory of tensor `t`. -void TfLiteTensorDataFree(TfLiteTensor* t); - -// Free quantization data. -void TfLiteMicroQuantizationFree(TfLiteQuantization* quantization); - -// Free sparsity parameters. -void TfLiteMicroSparsityFree(TfLiteSparsity* sparsity); - -// Free memory of tensor `t`. -void TfLiteTensorFree(TfLiteTensor* t); - -// Set all of a tensor's fields (and free any previously allocated data). -void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, - TfLiteQuantizationParams quantization, char* buffer, - size_t size, TfLiteAllocationType allocation_type, - const void* allocation, bool is_variable, - TfLiteTensor* tensor); - -// Copies the contents of 'src' in 'dst'. -// Function does nothing if either 'src' or 'dst' is passed as nullptr and -// return kTfLiteOk. -// Returns kTfLiteError if 'src' and 'dst' doesn't have matching data size. -// Note function copies contents, so it won't create new data pointer -// or change allocation type. -// All Tensor related properties will be copied from 'src' to 'dst' like -// quantization, sparsity, ... -TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst); - -// Change the size of the memory block owned by `tensor` to `num_bytes`. -// Tensors with allocation types other than `kTfLiteDynamic` will be ignored and -// a kTfLiteOk will be returned. -// `tensor`'s internal data buffer will be assigned a pointer -// which can safely be passed to free or realloc if `num_bytes` is zero. -// If `preserve_data` is true, tensor data will be unchanged in the range from -// the start of the region up to the minimum of the old and new sizes. In the -// case of NULL tensor, or an error allocating new memory, returns -// `kTfLiteError`. -TfLiteStatus TfLiteTensorResizeMaybeCopy(size_t num_bytes, TfLiteTensor* tensor, - bool preserve_data); - -// Change the size of the memory block owned by `tensor` to `num_bytes`. -// Tensors with allocation types other than kTfLiteDynamic will be ignored and -// a kTfLiteOk will be returned. -// `tensor`'s internal data buffer will be assigned a pointer -// which can safely be passed to free or realloc if `num_bytes` is zero. -// Tensor data will be unchanged in the range from the start of the region up to -// the minimum of the old and new sizes. In the case -// of NULL tensor, or an error allocating new memory, returns `kTfLiteError`. -TfLiteStatus TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor); -#endif // TF_LITE_STATIC_MEMORY - -// WARNING: This is an experimental interface that is subject to change. -// -// Currently, TfLiteDelegateParams has to be allocated in a way that it's -// trivially destructable. It will be stored as `builtin_data` field in -// `TfLiteNode` of the delegate node. -// -// See also the `CreateDelegateParams` function in `interpreter.cc` details. -typedef struct TfLiteDelegateParams { - struct TfLiteDelegate* delegate; - TfLiteIntArray* nodes_to_replace; - TfLiteIntArray* input_tensors; - TfLiteIntArray* output_tensors; -} TfLiteDelegateParams; - -// WARNING: This is an experimental interface that is subject to change. -// -// Currently, TfLiteOpaqueDelegateParams has to be allocated in a way that it's -// trivially destructable. It will be stored as `builtin_data` field in -// `TfLiteNode` of the delegate node. -// -// See also the `CreateOpaqueDelegateParams` function in `subgraph.cc` -// details. -typedef struct TfLiteOpaqueDelegateParams { - TfLiteOpaqueDelegate* delegate; - void* delegate_data; - TfLiteIntArray* nodes_to_replace; - TfLiteIntArray* input_tensors; - TfLiteIntArray* output_tensors; -} TfLiteOpaqueDelegateParams; - -typedef struct TfLiteContext { - // Number of tensors in the context. - size_t tensors_size; - - // The execution plan contains a list of the node indices in execution - // order. execution_plan->size is the current number of nodes. And, - // execution_plan->data[0] is the first node that needs to be run. - // TfLiteDelegates can traverse the current execution plan by iterating - // through each member of this array and using GetNodeAndRegistration() to - // access details about a node. i.e. - // - // TfLiteIntArray* execution_plan; - // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan)); - // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) { - // int node_index = execution_plan->data[exec_index]; - // TfLiteNode* node; - // TfLiteRegistration* reg; - // context->GetNodeAndRegistration(context, node_index, &node, ®); - // } - // Note: the memory pointed by '`*execution_plan` is OWNED by TfLite runtime. - // Future calls to GetExecutionPlan invalidates earlier outputs. The following - // code snippet shows the issue of such an invocation pattern. After calling - // CheckNode, subsequent access to `plan_1st` is undefined. - // - // void CheckNode(const TfLiteNode* node) { - // ... - // TfLiteIntArray* plan_2nd; - // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_2nd)); - // ... - // } - // - // TfLiteIntArray* plan_1st; - // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_1st)); - // for (int exec_index = 0; exec_index < plan_1st->size; exec_index++) { - // int node_index = plan_1st->data[exec_index]; - // TfLiteNode* node; - // TfLiteRegistration* reg; - // context->GetNodeAndRegistration(context, node_index, &node, ®); - // CheckNode(node); - // } - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context, - TfLiteIntArray** execution_plan); - - // An array of tensors in the interpreter context (of length `tensors_size`) - TfLiteTensor* tensors; - - // opaque full context ptr (an opaque c++ data structure) - void* impl_; - - // Request memory pointer be resized. Updates dimensions on the tensor. - // NOTE: ResizeTensor takes ownership of newSize. - TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor, - TfLiteIntArray* new_size); - // Request that an error be reported with format string msg. - void (*ReportError)(struct TfLiteContext*, const char* msg, ...); - - // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries. If - // non-null, the value pointed to by `first_new_tensor_index` will be set to - // the index of the first new tensor. - TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add, - int* first_new_tensor_index); - - // Get a Tensor node by node_index. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetNodeAndRegistration)( - struct TfLiteContext*, int node_index, TfLiteNode** node, - struct TfLiteRegistration** registration); - - // Replace ops with one or more stub delegate operations. This function - // does not take ownership of `nodes_to_replace`. - TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)( - struct TfLiteContext*, struct TfLiteRegistration registration, - const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate); - - // Number of threads that are recommended to subsystems like gemmlowp and - // eigen. - int recommended_num_threads; - - // Access external contexts by type. - // WARNING: This is an experimental interface that is subject to change. - TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*, - TfLiteExternalContextType); - // Set the value of a external context. Does not take ownership of the - // pointer. - // WARNING: This is an experimental interface that is subject to change. - void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType, - TfLiteExternalContext*); - - // Flag for allowing float16 precision for FP32 calculation. - // default: false. - // WARNING: This is an experimental API and subject to change. - bool allow_fp32_relax_to_fp16; - - // Pointer to the op-level profiler, if set; nullptr otherwise. - void* profiler; - - // Allocate persistent buffer which has the same life time as the interpreter. - // Returns nullptr on failure. - // The memory is allocated from heap for TFL, and from tail in TFLM. - // This method is only available in Init or Prepare stage. - // WARNING: This is an experimental interface that is subject to change. - void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes); - - // Allocate a buffer which will be deallocated right after invoke phase. - // The memory is allocated from heap in TFL, and from volatile arena in TFLM. - // This method is only available in invoke stage. - // NOTE: If possible use RequestScratchBufferInArena method to avoid memory - // allocation during inference time. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes, - void** ptr); - - // Request a scratch buffer in the arena through static memory planning. - // This method is only available in Prepare stage and the buffer is allocated - // by the interpreter between Prepare and Eval stage. In Eval stage, - // GetScratchBuffer API can be used to fetch the address. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx, - size_t bytes, int* buffer_idx); - - // Get the scratch buffer pointer. - // This method is only available in Eval stage. - // WARNING: This is an experimental interface that is subject to change. - void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx); - - // Resize the memory pointer of the `tensor`. This method behaves the same as - // `ResizeTensor`, except that it makes a copy of the shape array internally - // so the shape array could be deallocated right afterwards. - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx, - TfLiteTensor* tensor, int dims, - const int* shape); - - // This method provides a preview of post-delegation partitioning. Each - // TfLiteDelegateParams in the referenced array corresponds to one instance of - // the delegate kernel. - // Example usage: - // - // TfLiteIntArray* nodes_to_replace = ...; - // TfLiteDelegateParams* params_array; - // int num_partitions = 0; - // TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning( - // context, delegate, nodes_to_replace, ¶ms_array, &num_partitions)); - // for (int idx = 0; idx < num_partitions; idx++) { - // const auto& partition_params = params_array[idx]; - // ... - // } - // - // NOTE: The context owns the memory referenced by partition_params_array. It - // will be cleared with another call to PreviewDelegatePartitioning, or after - // TfLiteDelegateParams::Prepare returns. - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*PreviewDelegatePartitioning)( - struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace, - TfLiteDelegateParams** partition_params_array, int* num_partitions); - - // Returns a TfLiteTensor struct for a given index. - // WARNING: This is an experimental interface that is subject to change. - // WARNING: This method may not be available on all platforms. - TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context, - int tensor_idx); - - // Returns a TfLiteEvalTensor struct for a given index. - // WARNING: This is an experimental interface that is subject to change. - // WARNING: This method may not be available on all platforms. - TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context, - int tensor_idx); - - // TODO - // New interface to avoid storing an is_variable bool - bool (*IsVariableTensor)(struct TfLiteContext* context, - TfLiteTensor* tensor); - - // TODO - // New interface to avoid storing allocation type in TfLiteTensor - bool (*IsConstantTensor)(struct TfLiteContext* context, - TfLiteTensor* tensor); - - // Retrieves named metadata buffer from the TFLite model. - // Returns kTfLiteOk if metadata is successfully obtained from the flatbuffer - // Model: that is, there exists a `metadata` entry with given `name` string. - // (see TFLite's schema.fbs). - // The corresponding `buffer` information is populated in `ptr` & `bytes`. - // The data from `ptr` is valid for the lifetime of the Interpreter. - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*GetModelMetadata)(const struct TfLiteContext* context, - const char* name, const char** ptr, - size_t* bytes); - - // Retrieves the corresponding TfLiteContext of a subgraph that the given - // subgraph_index points to and switches to the delegate context for that - // subgraph. If an invalid subgraph index is given, returns kTfLiteError. - // NOTE: This function is expected to be paired with ReleaseSubgraphContext() - // once the delegate preparation is done and/or the delegate context functions - // are no longer needed. - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*AcquireSubgraphContext)( - struct TfLiteContext* context, int subgraph_index, - struct TfLiteContext** acquired_context); - // Releases the subgraph context by switching back to the TFLite kernel - // context for the subgraph that the given subgraph_index points to. - // NOTE: This function is expected to be used after AcquireSubgraphContext() - // once the delegate preparation is done and/or the delegate context functions - // are no longer needed. - // - // WARNING: This is an experimental interface that is subject to change. - TfLiteStatus (*ReleaseSubgraphContext)(struct TfLiteContext* context, - int subgraph_index); -} TfLiteContext; - -// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration` -// for C API which doesn't use internal types (such as `TfLiteContext`) but only -// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each -// field is the exactly the same as with `TfLiteRegistration`. -typedef struct TfLiteRegistrationExternal TfLiteRegistrationExternal; - -// The valid values of the `inplace_operator` field in `TfLiteRegistration`. -// This allow an op to signal to the runtime that the same data pointer -// may be passed as an input and output without impacting the result. -// This does not mean that the memory can safely be reused, it is up to the -// runtime to determine this, e.g. if another op consumes the same input or not -// or if an input tensor has sufficient memory allocated to store the output -// data. -// -// Setting these flags authorizes the runtime to set the data pointers of an -// input and output tensor to the same value. In such cases, the memory required -// by the output must be less than or equal to that required by the shared -// input, never greater. If kTfLiteInplaceOpDataUnmodified is set, then the -// runtime can share the same input tensor with multiple operator's outputs, -// provided that kTfLiteInplaceOpDataUnmodified is set for all of them. -// Otherwise, if an input tensor is consumed by multiple operators, it may only -// be shared with the operator which is the last to consume it. -// -// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc. -typedef enum { - // The default value. This indicates that the same data pointer cannot safely - // be passed as an op's input and output. - kTfLiteInplaceOpNone = 0, - // This indicates that an op's first output's data is identical to its first - // input's data, for example Reshape. - kTfLiteInplaceOpDataUnmodified = 1, - // Setting kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput means - // that InputN may be shared with OutputN instead of with the first output. - // This flag requires one or more of kTfLiteInplaceOpInputNShared to be set. - kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput = 2, - // kTfLiteInplaceOpInputNShared indicates that it is safe for an op to share - // InputN's data pointer with an output tensor. If - // kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is set then - // kTfLiteInplaceOpInputNShared indicates that InputN may be shared - // with OutputN, otherwise kTfLiteInplaceOpInputNShared indicates that InputN - // may be shared with the first output. - // - // Indicates that an op's first input may be shared with the first output - // tensor. kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput has - // no impact on the behavior allowed by this flag. - kTfLiteInplaceOpInput0Shared = 4, - // Indicates that an op's second input may be shared with the first output - // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set - // or second output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput - // is set. - kTfLiteInplaceOpInput1Shared = 8, - // Indicates that an op's third input may be shared with the first output - // if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is not set - // or third output if kTfLiteInplaceInputCanBeSharedWithCorrespondingOutput is - // set. - kTfLiteInplaceOpInput2Shared = 16, - // Placeholder to ensure that enum can hold 64 bit values to accommodate - // future fields. - kTfLiteInplaceOpMaxValue = UINT64_MAX, -} TfLiteInPlaceOp; - -// The number of shareable inputs supported. -static const int kTfLiteMaxSharableOpInputs = 3; - -typedef struct TfLiteRegistration { - // Initializes the op from serialized data. - // Called only *once* for the lifetime of the op, so any one-time allocations - // should be made here (unless they depend on tensor sizes). - // - // If a built-in op: - // `buffer` is the op's params data (TfLiteLSTMParams*). - // `length` is zero. - // If custom op: - // `buffer` is the op's `custom_options`. - // `length` is the size of the buffer. - // - // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer - // or an instance of a struct). - // - // The returned pointer will be stored with the node in the `user_data` field, - // accessible within prepare and invoke functions below. - // NOTE: if the data is already in the desired format, simply implement this - // function to return `nullptr` and implement the free function to be a no-op. - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - - // The pointer `buffer` is the data previously returned by an init invocation. - void (*free)(TfLiteContext* context, void* buffer); - - // prepare is called when the inputs this node depends on have been resized. - // context->ResizeTensor() can be called to request output tensors to be - // resized. - // Can be called multiple times for the lifetime of the op. - // - // Returns kTfLiteOk on success. - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - - // Execute the node (should read node->inputs and output to node->outputs). - // Returns kTfLiteOk on success. - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - - // profiling_string is called during summarization of profiling information - // in order to group executions together. Providing a value here will cause a - // given op to appear multiple times is the profiling report. This is - // particularly useful for custom ops that can perform significantly - // different calculations depending on their `user-data`. - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - - // Builtin codes. If this kernel refers to a builtin this is the code - // of the builtin. This is so we can do marshaling to other frameworks like - // NN API. - // Note: It is the responsibility of the registration binder to set this - // properly. - int32_t builtin_code; - - // Custom op name. If the op is a builtin, this will be null. - // Note: It is the responsibility of the registration binder to set this - // properly. - // WARNING: This is an experimental interface that is subject to change. - const char* custom_name; - - // The version of the op. - // Note: It is the responsibility of the registration binder to set this - // properly. - int version; - - // The external version of `TfLiteRegistration`. Since we can't use internal - // types (such as `TfLiteContext`) for C API to maintain ABI stability. - // C API user will provide `TfLiteRegistrationExternal` to implement custom - // ops. We keep it inside of `TfLiteRegistration` and use it to route - // callbacks properly. - TfLiteRegistrationExternal* registration_external; - - // Retrieves asynchronous kernel. - // - // If the `async_kernel` field is nullptr, it means the operation described by - // this TfLiteRegistration object does not support asynchronous execution. - // Otherwise, the function that the field points to should only be called for - // delegate kernel nodes, i.e. `node` should be a delegate kernel node created - // by applying a delegate. - // If the function returns nullptr, that means that the underlying delegate - // does not support asynchronous execution for this `node`. - struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context, - TfLiteNode* node); - - // Indicates if an operator's output may safely overwrite its inputs. - // See the comments in `TfLiteInPlaceOp`. - uint64_t inplace_operator; -} TfLiteRegistration; - -/// \private -// Old version of `TfLiteRegistration` to maintain binary backward -// compatibility. -// The legacy registration type must be a POD struct type whose field types must -// be a prefix of the field types in TfLiteRegistration, and offset of the first -// field in TfLiteRegistration that is not present in the legacy registration -// type must be greater than or equal to the size of the legacy registration -// type. -// WARNING: This structure is deprecated / not an official part of the -// API. It should be only used for binary backward compatibility. -typedef struct TfLiteRegistration_V3 { - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - void (*free)(TfLiteContext* context, void* buffer); - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - int32_t builtin_code; - const char* custom_name; - int version; - TfLiteRegistrationExternal* registration_external; - struct TfLiteAsyncKernel* (*async_kernel)(TfLiteContext* context, - TfLiteNode* node); -} TfLiteRegistration_V3; - -/// \private -// Old version of `TfLiteRegistration` to maintain binary backward -// compatibility. -// The legacy registration type must be a POD struct type whose field types must -// be a prefix of the field types in TfLiteRegistration, and offset of the first -// field in TfLiteRegistration that is not present in the legacy registration -// type must be greater than or equal to the size of the legacy registration -// type. -// WARNING: This structure is deprecated / not an official part of the -// API. It should be only used for binary backward compatibility. -typedef struct TfLiteRegistration_V2 { - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - void (*free)(TfLiteContext* context, void* buffer); - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - int32_t builtin_code; - const char* custom_name; - int version; - TfLiteRegistrationExternal* registration_external; -} TfLiteRegistration_V2; - -/// \private -// Old version of `TfLiteRegistration` to maintain binary backward -// compatibility. -// The legacy registration type must be a POD struct type whose field types must -// be a prefix of the field types in TfLiteRegistration, and offset of the first -// field in TfLiteRegistration that is not present in the legacy registration -// type must be greater than or equal to the size of the legacy registration -// type. -// WARNING: This structure is deprecated / not an official part of the -// API. It should be only used for binary backward compatibility. -typedef struct TfLiteRegistration_V1 { - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - void (*free)(TfLiteContext* context, void* buffer); - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - int32_t builtin_code; - const char* custom_name; - int version; -} TfLiteRegistration_V1; - -// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the -// values should be 1, 2, 4, 8, ...etc. -typedef enum TfLiteDelegateFlags { - kTfLiteDelegateFlagsNone = 0, - // The flag is set if the delegate can handle dynamic sized tensors. - // For example, the output shape of a `Resize` op with non-constant shape - // can only be inferred when the op is invoked. - // In this case, the Delegate is responsible for calling - // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling - // `ResizeTensor` when invoking the op. - // - // If the delegate isn't capable to handle dynamic tensors, this flag need - // to be set to false. - kTfLiteDelegateFlagsAllowDynamicTensors = 1, - - // This flag can be used by delegates (that allow dynamic tensors) to ensure - // applicable tensor shapes are automatically propagated in the case of tensor - // resizing. - // This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors - // of a delegate kernel will have correct shapes before its Prepare() method - // is called. The runtime leverages TFLite builtin ops in the original - // execution plan to propagate shapes. - // - // A few points to note: - // 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is - // false, this one is redundant since the delegate kernels are re-initialized - // every time tensors are resized. - // 2. Enabling this flag adds some overhead to AllocateTensors(), since extra - // work is required to prepare the original execution plan. - // 3. This flag requires that the original execution plan only have ops with - // valid registrations (and not 'dummy' custom ops like with Flex). - // WARNING: This feature is experimental and subject to change. - kTfLiteDelegateFlagsRequirePropagatedShapes = 2, - - // This flag can be used by delegates to request per-operator profiling. If a - // node is a delegate node, this flag will be checked before profiling. If - // set, then the node will not be profiled. The delegate will then add per - // operator information using Profiler::EventType::OPERATOR_INVOKE_EVENT and - // the results will appear in the operator-wise Profiling section and not in - // the Delegate internal section. - kTfLiteDelegateFlagsPerOperatorProfiling = 4 -} TfLiteDelegateFlags; - -// WARNING: This is an experimental interface that is subject to change. -typedef struct TfLiteDelegate { - // Data that delegate needs to identify itself. This data is owned by the - // delegate. The delegate is owned in the user code, so the delegate is - // responsible for deallocating this when it is destroyed. - void* data_; - - // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the - // delegate a view of the current graph through TfLiteContext*. It typically - // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels() - // to ask the TensorFlow lite runtime to create macro-nodes to represent - // delegated subgraphs of the original graph. - TfLiteStatus (*Prepare)(TfLiteContext* context, - struct TfLiteDelegate* delegate); - - // Copy the data from delegate buffer handle into raw memory of the given - // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as - // long as it follows the rules for kTfLiteDynamic tensors, in which case this - // cannot be null. - TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle buffer_handle, - TfLiteTensor* tensor); - - // Copy the data from raw memory of the given 'tensor' to delegate buffer - // handle. This can be null if the delegate doesn't use its own buffer. - TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle buffer_handle, - TfLiteTensor* tensor); - - // Free the Delegate Buffer Handle. Note: This only frees the handle, but - // this doesn't release the underlying resource (e.g. textures). The - // resources are either owned by application layer or the delegate. - // This can be null if the delegate doesn't use its own buffer. - void (*FreeBufferHandle)(TfLiteContext* context, - struct TfLiteDelegate* delegate, - TfLiteBufferHandle* handle); - - // Bitmask flags. See the comments in `TfLiteDelegateFlags`. - int64_t flags; - - // The opaque delegate builder associated with this object. If set then the - // TF Lite runtime will give precedence to this field. E.g. instead of - // invoking 'Prepare' via the function pointer inside the 'TfLiteDelegate' - // object, the runtime will first check if the corresponding function - // pointer inside 'opaque_delegate_builder' is set and if so invoke that. - // - // If this field is non-null, then the 'Prepare' field (of the - // 'TfLiteDelegate') should be null. - struct TfLiteOpaqueDelegateBuilder* opaque_delegate_builder; -} TfLiteDelegate; - -// Build a 'null' delegate, with all the fields properly set to their default -// values. -TfLiteDelegate TfLiteDelegateCreate(void); - -// `TfLiteOpaqueDelegateBuilder` is used for constructing -// `TfLiteOpaqueDelegate`, see `TfLiteOpaqueDelegateCreate` below. Note: -// This struct is not ABI stable. -// -// For forward source compatibility `TfLiteOpaqueDelegateBuilder` objects should -// be brace-initialized, so that all fields (including any that might be added -// in the future) get zero-initialized. The purpose of each field is exactly -// the same as with `TfLiteDelegate`. -// -// WARNING: This is an experimental interface that is subject to change. -typedef struct TfLiteOpaqueDelegateBuilder { - // Data that delegate needs to identify itself. This data is owned by the - // delegate. The delegate is owned in the user code, so the delegate is - // responsible for deallocating this when it is destroyed. - void* data; - // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the - // delegate a view of the current graph through TfLiteContext*. It typically - // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels() - // to ask the TensorFlow lite runtime to create macro-nodes to represent - // delegated subgraphs of the original graph. - TfLiteStatus (*Prepare)(TfLiteOpaqueContext* context, // NOLINT - TfLiteOpaqueDelegate* delegate, void* data); - // Copies the data from delegate buffer handle into raw memory of the given - // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as - // long as it follows the rules for kTfLiteDynamic tensors, in which case this - // cannot be null. - TfLiteStatus (*CopyFromBufferHandle)( // NOLINT - TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data, - TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor); - // Copies the data from raw memory of the given 'tensor' to delegate buffer - // handle. This can be null if the delegate doesn't use its own buffer. - TfLiteStatus (*CopyToBufferHandle)( // NOLINT - TfLiteOpaqueContext* context, TfLiteOpaqueDelegate* delegate, void* data, - TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor); - // Frees the Delegate Buffer Handle. Note: This only frees the handle, but - // this doesn't release the underlying resource (e.g. textures). The - // resources are either owned by application layer or the delegate. - // This can be null if the delegate doesn't use its own buffer. - void (*FreeBufferHandle)(TfLiteOpaqueContext* context, // NOLINT - TfLiteOpaqueDelegate* delegate, void* data, - TfLiteBufferHandle* handle); - // Bitmask flags. See the comments in `TfLiteDelegateFlags`. - int64_t flags; -} TfLiteOpaqueDelegateBuilder; - -#ifndef TF_LITE_STATIC_MEMORY -// Creates an opaque delegate and returns its address. The opaque delegate will -// behave according to the provided 'opaque_delegate_builder'. The lifetime of -// the objects pointed to by any of the fields within the -// 'opaque_delegate_builder' must outlive the returned -// 'TfLiteOpaqueDelegate' and any 'TfLiteInterpreter', -// 'TfLiteInterpreterOptions', 'tflite_micro::Interpreter', or -// 'tflite_micro::InterpreterBuilder' that the delegate is added to. The returned -// address should be passed to 'TfLiteOpaqueDelegateDelete' for deletion. If -// 'opaque_delegate_builder' is a null pointer, then a null pointer will be -// returned. -TfLiteOpaqueDelegate* TfLiteOpaqueDelegateCreate( - const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder); - -// Deletes the provided opaque 'delegate'. This function has no effect if the -// 'delegate' is a null pointer. -void TfLiteOpaqueDelegateDelete(TfLiteOpaqueDelegate* delegate); -#endif // TF_LITE_STATIC_MEMORY - -// Returns a pointer to the data associated with the provided opaque 'delegate'. -// -// A null pointer will be returned when: -// - The 'delegate' is null. -// - The 'data' field of the 'TfLiteOpaqueDelegateBuilder' used to construct the -// 'delegate' was null. -// - Or in case of any other error. -// - The 'delegate' has been constructed via a 'TfLiteOpaqueDelegateBuilder', -// but the 'data' field of the 'TfLiteOpaqueDelegateBuilder' is null. -// -// The data_ field of 'delegate' will be returned if the -// 'opaque_delegate_builder' field is null. -void* TfLiteOpaqueDelegateGetData(const TfLiteOpaqueDelegate* delegate); - -// Returns a tensor data allocation strategy. -TfLiteAllocationStrategy TfLiteTensorGetAllocationStrategy( - const TfLiteTensor* t); - -// Returns how stable a tensor data buffer address is across runs. -TfLiteRunStability TfLiteTensorGetBufferAddressStability(const TfLiteTensor* t); - -// Returns how stable a tensor data values are across runs. -TfLiteRunStability TfLiteTensorGetDataStability(const TfLiteTensor* t); - -// Returns the operation step when the data of a tensor is populated. -// -// Some operations can precompute their results before the evaluation step. This -// makes the data available earlier for subsequent operations. -TfLiteRunStep TfLiteTensorGetDataKnownStep(const TfLiteTensor* t); - -// Returns the operation steop when the shape of a tensor is computed. -// -// Some operations can precompute the shape of their results before the -// evaluation step. This makes the shape available earlier for subsequent -// operations. -TfLiteRunStep TfLiteTensorGetShapeKnownStep(const TfLiteTensor* t); - -#ifdef __cplusplus -} // extern "C" - -#include - -// --- TFLITE VARIANT TENSORS ---- -// Programming languges usually define "variant" as a type that can hold an -// unbounded set of types. See std::any -// (https://en.cppreference.com/w/cpp/utility/any) for a related standard -// library construct. In tensorflow, variant tensors have a data member which is -// an Object that is destructible and copy constructible. -// Variant tensors are commonly used to represent non trivial data -// semantics that don't fit into simple primitives, such as lists of tensors and -// datasets. Additionally, they can facilitate containers for optimizing -// memory movement of tensor data. -// -// The following set of classes define the variant tensor member for tflite. -// They implement a type-erased container intended to be used behind the -// `data.data : void*` member of `TfLiteTensor`s. Runtime functions interact -// the variant member at the level of a `VariantData`, whereas kernels -// operate with the full knowledge of the un-erased type. The `VariantData` -// class provides abstract methods for destroying and copying `VariantData`. -// Invoking these methods will dispatch to the erased type opaquely. -// The contents of any object of type derived from `AbstractVariant` can be -// written to `TfLiteTensor::data::data : void*` from kernels. If the runtime -// were to copy such a tensor through `TfLiteTensorCopy`, the destination data -// member will contain the result of invoking the erased type's copy -// constructor. Similar for the runtime releasing tensors from memory, the -// erased type's destructor will be invoked. There are a few caveats to consider -// to use these safely, which we discuss below. -// -// EXAMPLE: READING VARIANT TENSORS -// ``` -// // retrieve input with `type == kTfLiteVariant` -// TfLiteTensor* input = ... -// // must first static cast to `VariantData`, more on this below. -// VariantData* vd_input = static_cast(t->data.data); -// CustomType* typed_input = -// static_cast(vd_input); -// // do custom work on `typed_input`... -// ``` -// -// EXAMPLE: WRITING VARIANT TENSORS -// ``` -// TfLiteTensor* output = ... -// // construct a new variant object behind the target tensor -// TfLiteVariantRealloc(output, args...); -// // again must static cast to `VariantData*` before writing to `void*`. -// output->data.data = static_cast(typed_output); -// ``` -// -// WHY STATIC CAST TO `VariantData*` -// The Standard defines a `reinterpret_cast` from a derived type to its -// parents as undefined behavior when the parent is a non-standard layout. -// https://en.cppreference.com/w/cpp/language/reinterpret_cast (see bullet 5). -// Due to the `VariantData` having virtual members it is indeed non-standard -// layout, and any type derived from `VariantData` fails to be -// "transparently-replaceable". I.e. implicit cast from derived to base in this -// case may adjust the pointer and by definition `reinterpret_cast` will not -// the adjust the pointer. -// Thus, dereferencing a pointer of type `VariantData` which addresses -// the first byte of an object of said derived type is UB unless it was first -// implicitly or statically casted to a `VariantData`. Writing the object of -// derived type directly to `void*` which is dereferenced as a `VariantData` is -// then UB, and so the intermediate cast through `VariantData` must be enforced. -// A good example of this issue is ellucidate in the bottom code snippet -// here: https://en.cppreference.com/w/cpp/utility/launder. -class VariantData { - public: - // All variant objects must be able to be destroyed and copied. - virtual ~VariantData() = default; - // A "virtual copy-constructor". Often the destination tensor of a variant - // copy may have been previously allocated in a prior call to inference. We - // allow the copy to target the destinations buffer (`maybe_alloc`), - // for potential reuse and optimizations. `maybe_alloc` must be of the same - // underlying derived type. References to whatever object is at - // `maybe_alloc` may be invalidated. - virtual VariantData* CloneTo(VariantData* maybe_alloc) const = 0; -}; - -// Concrete implementations extend `AbstractVariantData` with CRPT. -template -class AbstractVariantData : public VariantData { - public: - VariantData* CloneTo(VariantData* maybe_alloc) const override { - if (maybe_alloc != nullptr) { - // If the output is still allocated, then its object may still be - // in its life time and the destructor must be called before re-using the - // buffer. - // This may actual have a non-negligible effect on performance if the - // destructor is complex. A future iteration may - // introduce copy or move assignment semantics, allowing for the - // underlying implementation to optimize for this case. - auto* derived = static_cast(maybe_alloc); - derived->~ErasedDerived(); - return new (derived) - ErasedDerived(static_cast(*this)); - } - return new ErasedDerived(static_cast(*this)); - } - - protected: - AbstractVariantData() = default; - AbstractVariantData(const AbstractVariantData&) = default; - AbstractVariantData(AbstractVariantData&&) = delete; -}; - -// Analogous to `TfLiteTensorRealloc` for allocation of tensors whose -// data member points to an arbitrary C++ object. `VariantType` refers -// to the erased type of said object and `VariantArgs` refers to -// a list of argument types with which to construct a new `VariantType`. -// `VariantArgs` must match a constructor of `VariantType`. -template -TfLiteStatus TfLiteTensorVariantRealloc(TfLiteTensor* t, - VariantArgs&&... args) { - if (t->type != kTfLiteVariant) return kTfLiteError; - VariantType* new_vd; - if (t->data.raw != nullptr) { - auto* target_vd = static_cast(t->data.data); - target_vd->~VariantData(); - // As above, we assume if `t` is already allocated then it was allocated - // with the same `VariantType` as templated. - new_vd = new (t->data.raw) VariantType(std::forward(args)...); - } else { - new_vd = new VariantType(std::forward(args)...); - } - t->data.data = static_cast(new_vd); - - assert(false && "Disabled function!"); - // t->allocation_type = kTfLiteVariantObject; - return kTfLiteOk; -} - -#endif // __cplusplus -#endif // TENSORFLOW_LITE_CORE_C_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/core/macros.h b/new_ai_tools/include/tensorflow/lite/core/macros.h deleted file mode 100644 index d329ded7..00000000 --- a/new_ai_tools/include/tensorflow/lite/core/macros.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -// This provides utility macros and functions that are inherently platform -// specific or shared across runtime & converter. -#ifndef TENSORFLOW_LITE_CORE_MACROS_H_ -#define TENSORFLOW_LITE_CORE_MACROS_H_ - -#ifdef __has_builtin -#define TFLITE_HAS_BUILTIN(x) __has_builtin(x) -#else -#define TFLITE_HAS_BUILTIN(x) 0 -#endif - -#if (!defined(__NVCC__)) && (TFLITE_HAS_BUILTIN(__builtin_expect) || \ - (defined(__GNUC__) && __GNUC__ >= 3)) -#define TFLITE_EXPECT_FALSE(cond) __builtin_expect(cond, false) -#define TFLITE_EXPECT_TRUE(cond) __builtin_expect(!!(cond), true) -#else -#define TFLITE_EXPECT_FALSE(cond) (cond) -#define TFLITE_EXPECT_TRUE(cond) (cond) -#endif - -#ifdef _WIN32 -#define TFLITE_NOINLINE __declspec(noinline) -#else -#ifdef __has_attribute -#if __has_attribute(noinline) -#define TFLITE_NOINLINE __attribute__((noinline)) -#else -#define TFLITE_NOINLINE -#endif // __has_attribute(noinline) -#else -#define TFLITE_NOINLINE -#endif // __has_attribute -#endif // _WIN32 - -// Normally we'd use ABSL_HAVE_ATTRIBUTE_WEAK and ABSL_ATTRIBUTE_WEAK, but -// we avoid the absl dependency for binary size reasons. -#ifdef __has_attribute -#define TFLITE_HAS_ATTRIBUTE(x) __has_attribute(x) -#else -#define TFLITE_HAS_ATTRIBUTE(x) 0 -#endif - -#if (TFLITE_HAS_ATTRIBUTE(weak) || \ - (defined(__GNUC__) && !defined(__clang__))) && \ - !(defined(__llvm__) && defined(_WIN32)) && !defined(__MINGW32__) -#undef TFLITE_ATTRIBUTE_WEAK -#define TFLITE_ATTRIBUTE_WEAK __attribute__((weak)) -#define TFLITE_HAS_ATTRIBUTE_WEAK 1 -#else -#define TFLITE_ATTRIBUTE_WEAK -#define TFLITE_HAS_ATTRIBUTE_WEAK 0 -#endif - -#ifndef TF_LITE_STATIC_MEMORY -// maximum size of a valid flatbuffer -inline constexpr unsigned int flatbuffer_size_max = 2147483648; -// If none zero then the buffer is stored outside of the flatbuffers, string -inline constexpr char tflite_metadata_buffer_location[] = "buffer_location"; -// field for minimum runtime version, string -inline constexpr char tflite_metadata_min_runtime_version[] = - "min_runtime_version"; -#endif - -#endif // TENSORFLOW_LITE_CORE_MACROS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h deleted file mode 100644 index 04b3ba6f..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/bits.h +++ /dev/null @@ -1,102 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ - -#ifdef __cplusplus -#include - -extern "C" { -#endif - -static inline int CountLeadingZeros32Slow(uint64_t n) { - int zeroes = 28; - if (n >> 16) zeroes -= 16, n >>= 16; - if (n >> 8) zeroes -= 8, n >>= 8; - if (n >> 4) zeroes -= 4, n >>= 4; - return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[n] + zeroes; -} - -static inline int CountLeadingZeros32(uint32_t n) { -#if defined(_MSC_VER) - unsigned long result = 0; // NOLINT(runtime/int) - if (_BitScanReverse(&result, n)) { - return 31 - result; - } - return 32; -#elif defined(__GNUC__) - - // Handle 0 as a special case because __builtin_clz(0) is undefined. - if (n == 0) { - return 32; - } - return __builtin_clz(n); -#else - return CountLeadingZeros32Slow(n); -#endif -} - -static inline int MostSignificantBit32(uint32_t n) { - return 32 - CountLeadingZeros32(n); -} - -static inline int CountLeadingZeros64Slow(uint64_t n) { - int zeroes = 60; - if (n >> 32) zeroes -= 32, n >>= 32; - if (n >> 16) zeroes -= 16, n >>= 16; - if (n >> 8) zeroes -= 8, n >>= 8; - if (n >> 4) zeroes -= 4, n >>= 4; - return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[n] + zeroes; -} - -static inline int CountLeadingZeros64(uint64_t n) { -#if defined(_MSC_VER) && defined(_M_X64) - // MSVC does not have __builtin_clzll. Use _BitScanReverse64. - unsigned long result = 0; // NOLINT(runtime/int) - if (_BitScanReverse64(&result, n)) { - return 63 - result; - } - return 64; -#elif defined(_MSC_VER) - // MSVC does not have __builtin_clzll. Compose two calls to _BitScanReverse - unsigned long result = 0; // NOLINT(runtime/int) - if ((n >> 32) && _BitScanReverse(&result, n >> 32)) { - return 31 - result; - } - if (_BitScanReverse(&result, n)) { - return 63 - result; - } - return 64; -#elif defined(__GNUC__) - - // Handle 0 as a special case because __builtin_clzll(0) is undefined. - if (n == 0) { - return 64; - } - return __builtin_clzll(n); -#else - return CountLeadingZeros64Slow(n); -#endif -} - -static inline int MostSignificantBit64(uint64_t n) { - return 64 - CountLeadingZeros64(n); -} - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_BITS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h deleted file mode 100644 index aaffa69d..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ - -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -struct complex_int16_t { - int16_t real; - int16_t imag; -}; - -struct FftState { - int16_t* input; - struct complex_int16_t* output; - size_t fft_size; - size_t input_size; - void* scratch; - size_t scratch_size; -}; - -void FftCompute(struct FftState* state, const int16_t* input, - int input_scale_shift); - -void FftInit(struct FftState* state); - -void FftReset(struct FftState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h deleted file mode 100644 index 7a59af68..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_io.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ - -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void FftWriteMemmapPreamble(FILE* fp, const struct FftState* state); -void FftWriteMemmap(FILE* fp, const struct FftState* state, - const char* variable); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h deleted file mode 100644 index 6a471301..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/fft_util.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" - -#ifdef __cplusplus -extern "C" { -#endif - -// Prepares and FFT for the given input size. -int FftPopulateState(struct FftState* state, size_t input_size); - -// Frees any allocated buffers. -void FftFreeStateContents(struct FftState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FFT_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h deleted file mode 100644 index 1e6d3885..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank.h +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ - -#include -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" - -#define kFilterbankBits 12 - -#ifdef __cplusplus -extern "C" { -#endif - -struct FilterbankState { - int num_channels; - int start_index; - int end_index; - int16_t* channel_frequency_starts; - int16_t* channel_weight_starts; - int16_t* channel_widths; - int16_t* weights; - int16_t* unweights; - uint64_t* work; -}; - -// Converts the relevant complex values of an FFT output into energy (the -// square magnitude). -void FilterbankConvertFftComplexToEnergy(struct FilterbankState* state, - struct complex_int16_t* fft_output, - int32_t* energy); - -// Computes the mel-scale filterbank on the given energy array. Output is cached -// internally - to fetch it, you need to call FilterbankSqrt. -void FilterbankAccumulateChannels(struct FilterbankState* state, - const int32_t* energy); - -// Applies an integer square root to the 64 bit intermediate values of the -// filterbank, and returns a pointer to them. Memory will be invalidated the -// next time FilterbankAccumulateChannels is called. -uint32_t* FilterbankSqrt(struct FilterbankState* state, int scale_down_shift); - -void FilterbankReset(struct FilterbankState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h deleted file mode 100644 index 5fc96845..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_io.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ - -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void FilterbankWriteMemmapPreamble(FILE* fp, - const struct FilterbankState* state); -void FilterbankWriteMemmap(FILE* fp, const struct FilterbankState* state, - const char* variable); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h deleted file mode 100644 index 781d1024..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct FilterbankConfig { - // number of frequency channel buckets for filterbank - int num_channels; - // maximum frequency to include - float upper_band_limit; - // minimum frequency to include - float lower_band_limit; - // unused - int output_scale_shift; -}; - -// Fills the frontendConfig with "sane" defaults. -void FilterbankFillConfigWithDefaults(struct FilterbankConfig* config); - -// Allocates any buffers. -int FilterbankPopulateState(const struct FilterbankConfig* config, - struct FilterbankState* state, int sample_rate, - int spectrum_size); - -// Frees any allocated buffers. -void FilterbankFreeStateContents(struct FilterbankState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FILTERBANK_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h deleted file mode 100644 index 883df5fd..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend.h +++ /dev/null @@ -1,64 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ - -#include -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/fft.h" -#include "tensorflow/lite/experimental/microfrontend/lib/filterbank.h" -#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" -#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" -#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h" -#include "tensorflow/lite/experimental/microfrontend/lib/window.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct FrontendState { - struct WindowState window; - struct FftState fft; - struct FilterbankState filterbank; - struct NoiseReductionState noise_reduction; - struct PcanGainControlState pcan_gain_control; - struct LogScaleState log_scale; -}; - -struct FrontendOutput { - const uint16_t* values; - size_t size; -}; - -// Main entry point to processing frontend samples. Updates num_samples_read to -// contain the number of samples that have been consumed from the input array. -// Returns a struct containing the generated output. If not enough samples were -// added to generate a feature vector, the returned size will be 0 and the -// values pointer will be NULL. Note that the output pointer will be invalidated -// as soon as FrontendProcessSamples is called again, so copy the contents -// elsewhere if you need to use them later. -struct FrontendOutput FrontendProcessSamples(struct FrontendState* state, - const int16_t* samples, - size_t num_samples, - size_t* num_samples_read); - -void FrontendReset(struct FrontendState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h deleted file mode 100644 index 0d59eda7..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_io.h +++ /dev/null @@ -1,31 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h" - -#ifdef __cplusplus -extern "C" { -#endif - -int WriteFrontendStateMemmap(const char* header, const char* source, - const struct FrontendState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h deleted file mode 100644 index 895ce6cd..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/frontend_util.h +++ /dev/null @@ -1,52 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/fft_util.h" -#include "tensorflow/lite/experimental/microfrontend/lib/filterbank_util.h" -#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h" -#include "tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h" -#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h" -#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h" -#include "tensorflow/lite/experimental/microfrontend/lib/window_util.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct FrontendConfig { - struct WindowConfig window; - struct FilterbankConfig filterbank; - struct NoiseReductionConfig noise_reduction; - struct PcanGainControlConfig pcan_gain_control; - struct LogScaleConfig log_scale; -}; - -// Fills the frontendConfig with "sane" defaults. -void FrontendFillConfigWithDefaults(struct FrontendConfig* config); - -// Allocates any buffers. -int FrontendPopulateState(const struct FrontendConfig* config, - struct FrontendState* state, int sample_rate); - -// Frees any allocated buffers. -void FrontendFreeStateContents(struct FrontendState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_FRONTEND_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h deleted file mode 100644 index 33556dab..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h +++ /dev/null @@ -1,48 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ - -// This header file should be included in all variants of kiss_fft_$type.{h,cc} -// so that their sub-included source files do not mistakenly wrap libc header -// files within their kissfft_$type namespaces. -// E.g, This header avoids kissfft_int16.h containing: -// namespace kiss_fft_int16 { -// #include "kiss_fft.h" -// } -// where kiss_fft_.h contains: -// #include -// -// TRICK: By including the following header files here, their preprocessor -// header guards prevent them being re-defined inside of the kiss_fft_$type -// namespaces declared within the kiss_fft_$type.{h,cc} sources. -// Note that the original kiss_fft*.h files are untouched since they -// may be used in libraries that include them directly. - -#include -#include -#include -#include -#include - -#ifdef FIXED_POINT -#include -#endif - -#ifdef USE_SIMD -#include -#endif -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h deleted file mode 100644 index beee99aa..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.h +++ /dev/null @@ -1,33 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h" - -// Wrap 16-bit kiss fft in its own namespace. Enables us to link an application -// with different kiss fft resultions (16/32 bit interger, float, double) -// without getting a linker error. -#define FIXED_POINT 16 -namespace kissfft_fixed16 { -#include "kiss_fft.h" -#include "tools/kiss_fftr.h" -} // namespace kissfft_fixed16 -#undef FIXED_POINT -#undef kiss_fft_scalar -#undef KISS_FFT_H - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_KISS_FFT_INT16_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h deleted file mode 100644 index b2448a32..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_lut.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ - -#include - -#ifdef __cplusplus -extern "C" { -#endif - -// Number of segments in the log lookup table. The table will be kLogSegments+1 -// in length (with some padding). -#define kLogSegments 128 -#define kLogSegmentsLog2 7 - -// Scale used by lookup table. -#define kLogScale 65536 -#define kLogScaleLog2 16 -#define kLogCoeff 45426 - -extern const uint16_t kLogLut[]; - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_LUT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h deleted file mode 100644 index a383f32f..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ - -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -struct LogScaleState { - int enable_log; - int scale_shift; -}; - -// Applies a fixed point logarithm to the signal and converts it to 16 bit. Note -// that the signal array will be modified. -uint16_t* LogScaleApply(struct LogScaleState* state, uint32_t* signal, - int signal_size, int correction_bits); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h deleted file mode 100644 index 9d447ac9..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_io.h +++ /dev/null @@ -1,33 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ - -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void LogScaleWriteMemmap(FILE* fp, const struct LogScaleState* state, - const char* variable); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h deleted file mode 100644 index 11f7d9ee..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/log_scale_util.h +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ - -#include -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/log_scale.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct LogScaleConfig { - // set to false (0) to disable this module - int enable_log; - // scale results by 2^(scale_shift) - int scale_shift; -}; - -// Populates the LogScaleConfig with "sane" default values. -void LogScaleFillConfigWithDefaults(struct LogScaleConfig* config); - -// Allocates any buffers. -int LogScalePopulateState(const struct LogScaleConfig* config, - struct LogScaleState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_LOG_SCALE_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h deleted file mode 100644 index 46d3f52e..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h +++ /dev/null @@ -1,46 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ - -#define kNoiseReductionBits 14 - -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -struct NoiseReductionState { - int smoothing_bits; - uint16_t even_smoothing; - uint16_t odd_smoothing; - uint16_t min_signal_remaining; - int num_channels; - uint32_t* estimate; -}; - -// Removes stationary noise from each channel of the signal using a low pass -// filter. -void NoiseReductionApply(struct NoiseReductionState* state, uint32_t* signal); - -void NoiseReductionReset(struct NoiseReductionState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h deleted file mode 100644 index ded52118..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_io.h +++ /dev/null @@ -1,36 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ - -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void NoiseReductionWriteMemmapPreamble(FILE* fp, - const struct NoiseReductionState* state); -void NoiseReductionWriteMemmap(FILE* fp, - const struct NoiseReductionState* state, - const char* variable); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h deleted file mode 100644 index fa555391..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/noise_reduction_util.h +++ /dev/null @@ -1,50 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/noise_reduction.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct NoiseReductionConfig { - // scale the signal up by 2^(smoothing_bits) before reduction - int smoothing_bits; - // smoothing coefficient for even-numbered channels - float even_smoothing; - // smoothing coefficient for odd-numbered channels - float odd_smoothing; - // fraction of signal to preserve (1.0 disables this module) - float min_signal_remaining; -}; - -// Populates the NoiseReductionConfig with "sane" default values. -void NoiseReductionFillConfigWithDefaults(struct NoiseReductionConfig* config); - -// Allocates any buffers. -int NoiseReductionPopulateState(const struct NoiseReductionConfig* config, - struct NoiseReductionState* state, - int num_channels); - -// Frees any allocated buffers. -void NoiseReductionFreeStateContents(struct NoiseReductionState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_NOISE_REDUCTION_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h deleted file mode 100644 index 3f6222be..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ - -#include -#include - -#define kPcanSnrBits 12 -#define kPcanOutputBits 6 - -#ifdef __cplusplus -extern "C" { -#endif - -// Details at https://research.google/pubs/pub45911.pdf -struct PcanGainControlState { - int enable_pcan; - uint32_t* noise_estimate; - int num_channels; - int16_t* gain_lut; - int32_t snr_shift; -}; - -int16_t WideDynamicFunction(const uint32_t x, const int16_t* lut); - -uint32_t PcanShrink(const uint32_t x); - -void PcanGainControlApply(struct PcanGainControlState* state, uint32_t* signal); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h deleted file mode 100644 index d4bfaa2e..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_util.h +++ /dev/null @@ -1,57 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control.h" - -#define kWideDynamicFunctionBits 32 -#define kWideDynamicFunctionLUTSize (4 * kWideDynamicFunctionBits - 3) - -#ifdef __cplusplus -extern "C" { -#endif - -struct PcanGainControlConfig { - // set to false (0) to disable this module - int enable_pcan; - // gain normalization exponent (0.0 disables, 1.0 full strength) - float strength; - // positive value added in the normalization denominator - float offset; - // number of fractional bits in the gain - int gain_bits; -}; - -void PcanGainControlFillConfigWithDefaults( - struct PcanGainControlConfig* config); - -int16_t PcanGainLookupFunction(const struct PcanGainControlConfig* config, - int32_t input_bits, uint32_t x); - -int PcanGainControlPopulateState(const struct PcanGainControlConfig* config, - struct PcanGainControlState* state, - uint32_t* noise_estimate, - const int num_channels, - const uint16_t smoothing_bits, - const int32_t input_correction_bits); - -void PcanGainControlFreeStateContents(struct PcanGainControlState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_PCAN_GAIN_CONTROL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h deleted file mode 100644 index bad81514..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ - -#include -#include - -#define kFrontendWindowBits 12 - -#ifdef __cplusplus -extern "C" { -#endif - -struct WindowState { - size_t size; - int16_t* coefficients; - size_t step; - - int16_t* input; - size_t input_used; - int16_t* output; - int16_t max_abs_output_value; -}; - -// Applies a window to the samples coming in, stepping forward at the given -// rate. -int WindowProcessSamples(struct WindowState* state, const int16_t* samples, - size_t num_samples, size_t* num_samples_read); - -void WindowReset(struct WindowState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h deleted file mode 100644 index a76b2dc3..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_io.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ - -#include - -#include "tensorflow/lite/experimental/microfrontend/lib/window.h" - -#ifdef __cplusplus -extern "C" { -#endif - -void WindowWriteMemmapPreamble(FILE* fp, const struct WindowState* state); -void WindowWriteMemmap(FILE* fp, const struct WindowState* state, - const char* variable); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_IO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h b/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h deleted file mode 100644 index 68e4de9e..00000000 --- a/new_ai_tools/include/tensorflow/lite/experimental/microfrontend/lib/window_util.h +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ -#define TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ - -#include "tensorflow/lite/experimental/microfrontend/lib/window.h" - -#ifdef __cplusplus -extern "C" { -#endif - -struct WindowConfig { - // length of window frame in milliseconds - size_t size_ms; - // length of step for next frame in milliseconds - size_t step_size_ms; -}; - -// Populates the WindowConfig with "sane" default values. -void WindowFillConfigWithDefaults(struct WindowConfig* config); - -// Allocates any buffers. -int WindowPopulateState(const struct WindowConfig* config, - struct WindowState* state, int sample_rate); - -// Frees any allocated buffers. -void WindowFreeStateContents(struct WindowState* state); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // TENSORFLOW_LITE_EXPERIMENTAL_MICROFRONTEND_LIB_WINDOW_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h deleted file mode 100644 index 747ae42d..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/common.h +++ /dev/null @@ -1,1358 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/runtime_shape.h" -#ifndef ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK -#ifdef GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -#define ALLOW_SLOW_GENERIC_DEPTHWISECONV_FALLBACK -#endif -#endif - -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/core/macros.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/optimized/neon_check.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -constexpr int kReverseShift = -1; - -// Reduces and compresses dimensions so that broadcast handling becomes more -// efficient. Returns true if the output shape is broadcastable; it doesn't -// contain any degenerate dimension, i.e. shape dimension = 0. False otherwise. -template -bool ReduceDimensionsForBroadcast(const RuntimeShape& input1_shape, - const RuntimeShape& input2_shape, - size_t* compressed_input1_stride, - size_t* compressed_input2_stride, - size_t* compressed_output_shape) { - size_t num_compressed_dims = 0; - size_t compressed_input1_shape[MAX_DIM]; - size_t compressed_input2_shape[MAX_DIM]; - std::fill(compressed_input1_shape, compressed_input1_shape + MAX_DIM, 1); - std::fill(compressed_input2_shape, compressed_input2_shape + MAX_DIM, 1); - std::fill(compressed_output_shape, compressed_output_shape + MAX_DIM, 1); - bool broadcast_input1 = false; - bool broadcast_input2 = false; - bool first_nonunit = true; - const size_t num_input1_dims = input1_shape.DimensionsCount(); - const size_t num_input2_dims = input2_shape.DimensionsCount(); - const int32_t* input1_dims = input1_shape.DimsData(); - const int32_t* input2_dims = input2_shape.DimsData(); - const size_t num_common_dims = std::min(num_input1_dims, num_input2_dims); - for (size_t i = 1; i <= num_common_dims; i++) { - const size_t input1_dim = input1_dims[num_input1_dims - i]; - const size_t input2_dim = input2_dims[num_input2_dims - i]; - if (input1_dim == 0 || input2_dim == 0) { - return false; - } - if (input1_dim == 1 && input2_dim == 1) { - continue; - } - assert(!broadcast_input1 || !broadcast_input2); - - if (input1_dim == 1) { - if (!broadcast_input1) { - broadcast_input1 = true; - broadcast_input2 = false; - num_compressed_dims++; - } - compressed_input2_shape[num_compressed_dims - 1] *= input2_dim; - compressed_output_shape[num_compressed_dims - 1] *= input2_dim; - } else if (input2_dim == 1) { - if (!broadcast_input2) { - broadcast_input1 = false; - broadcast_input2 = true; - num_compressed_dims++; - } - compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; - compressed_output_shape[num_compressed_dims - 1] *= input1_dim; - } else { - TFLITE_DCHECK(input1_dim == input2_dim); - if (broadcast_input1 || broadcast_input2 || first_nonunit) { - broadcast_input1 = false; - broadcast_input2 = false; - num_compressed_dims++; - } - compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; - compressed_input2_shape[num_compressed_dims - 1] *= input1_dim; - compressed_output_shape[num_compressed_dims - 1] *= input1_dim; - } - first_nonunit = false; - } - if (num_input1_dims > num_input2_dims) { - if (!broadcast_input2) { - num_compressed_dims++; - } - for (size_t i = 0; i < num_input1_dims - num_input2_dims; i++) { - const size_t input1_dim = input1_dims[i]; - if (input1_dim == 0) { - return false; - } - compressed_input1_shape[num_compressed_dims - 1] *= input1_dim; - compressed_output_shape[num_compressed_dims - 1] *= input1_dim; - } - } else if (num_input2_dims > num_input1_dims) { - if (!broadcast_input1) { - num_compressed_dims++; - } - for (size_t i = 0; i < num_input2_dims - num_input1_dims; i++) { - const size_t input2_dim = input2_dims[i]; - if (input2_dim == 0) { - return false; - } - compressed_input2_shape[num_compressed_dims - 1] *= input2_dim; - compressed_output_shape[num_compressed_dims - 1] *= input2_dim; - } - } - num_compressed_dims = (num_compressed_dims > 1) ? num_compressed_dims : 1; - - int input1_stride = 1; - int input2_stride = 1; - for (int i = 0; i < MAX_DIM; ++i) { - compressed_input1_stride[i] = input1_stride; - input1_stride *= compressed_input1_shape[i]; - compressed_input2_stride[i] = input2_stride; - input2_stride *= compressed_input2_shape[i]; - } - for (int i = 0; i < MAX_DIM; ++i) { - if (compressed_input1_shape[i] != compressed_input2_shape[i]) { - if (compressed_input1_shape[i] == 1) { - compressed_input1_stride[i] = 0; - } else { - TFLITE_DCHECK_EQ(compressed_input2_shape[i], 1); - compressed_input2_stride[i] = 0; - } - } - } - return true; -} - -inline void GetActivationMinMax(FusedActivationFunctionType ac, - float* output_activation_min, - float* output_activation_max) { - switch (ac) { - case FusedActivationFunctionType::kNone: - *output_activation_min = std::numeric_limits::lowest(); - *output_activation_max = std::numeric_limits::max(); - break; - case FusedActivationFunctionType::kRelu: - *output_activation_min = 0.f; - *output_activation_max = std::numeric_limits::max(); - break; - case FusedActivationFunctionType::kRelu1: - *output_activation_min = -1.f; - *output_activation_max = 1.f; - break; - case FusedActivationFunctionType::kRelu6: - *output_activation_min = 0.f; - *output_activation_max = 6.f; - break; - } -} - -template -inline T ActivationFunctionWithMinMax(T x, T output_activation_min, - T output_activation_max) { - using std::max; - using std::min; - return min(max(x, output_activation_min), output_activation_max); -} - -// Legacy function, left for compatibility only. -template -float ActivationFunction(float x) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - return ActivationFunctionWithMinMax(x, output_activation_min, - output_activation_max); -} - -inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size, - const float* bias_data, int array_size, - float* array_data) { - if (bias_size == 0) return; - // Note: see b/132215220: in May 2019 we thought it would be OK to replace - // this with the Eigen one-liner: - // return (array.colwise() + bias).cwiseMin(clamp_max).cwiseMin(clamp_max). - // This turned out to severely regress performance: +4ms (i.e. 8%) on - // MobileNet v2 / 1.0 / 224. So we keep custom NEON code for now. - TFLITE_DCHECK_EQ((array_size % bias_size), 0); -#ifdef USE_NEON - float* array_ptr = array_data; - float* array_end_ptr = array_ptr + array_size; - const auto clamp_min_vec = vdupq_n_f32(clamp_min); - const auto clamp_max_vec = vdupq_n_f32(clamp_max); - for (; array_ptr != array_end_ptr; array_ptr += bias_size) { - int i = 0; - for (; i <= bias_size - 16; i += 16) { - auto b0 = vld1q_f32(bias_data + i); - auto b1 = vld1q_f32(bias_data + i + 4); - auto b2 = vld1q_f32(bias_data + i + 8); - auto b3 = vld1q_f32(bias_data + i + 12); - auto a0 = vld1q_f32(array_ptr + i); - auto a1 = vld1q_f32(array_ptr + i + 4); - auto a2 = vld1q_f32(array_ptr + i + 8); - auto a3 = vld1q_f32(array_ptr + i + 12); - auto x0 = vaddq_f32(a0, b0); - auto x1 = vaddq_f32(a1, b1); - auto x2 = vaddq_f32(a2, b2); - auto x3 = vaddq_f32(a3, b3); - x0 = vmaxq_f32(clamp_min_vec, x0); - x1 = vmaxq_f32(clamp_min_vec, x1); - x2 = vmaxq_f32(clamp_min_vec, x2); - x3 = vmaxq_f32(clamp_min_vec, x3); - x0 = vminq_f32(clamp_max_vec, x0); - x1 = vminq_f32(clamp_max_vec, x1); - x2 = vminq_f32(clamp_max_vec, x2); - x3 = vminq_f32(clamp_max_vec, x3); - vst1q_f32(array_ptr + i, x0); - vst1q_f32(array_ptr + i + 4, x1); - vst1q_f32(array_ptr + i + 8, x2); - vst1q_f32(array_ptr + i + 12, x3); - } - for (; i <= bias_size - 4; i += 4) { - auto b = vld1q_f32(bias_data + i); - auto a = vld1q_f32(array_ptr + i); - auto x = vaddq_f32(a, b); - x = vmaxq_f32(clamp_min_vec, x); - x = vminq_f32(clamp_max_vec, x); - vst1q_f32(array_ptr + i, x); - } - for (; i < bias_size; i++) { - array_ptr[i] = ActivationFunctionWithMinMax(array_ptr[i] + bias_data[i], - clamp_min, clamp_max); - } - } -#else // not NEON - for (int array_offset = 0; array_offset < array_size; - array_offset += bias_size) { - for (int i = 0; i < bias_size; i++) { - array_data[array_offset + i] = ActivationFunctionWithMinMax( - array_data[array_offset + i] + bias_data[i], clamp_min, clamp_max); - } - } -#endif -} - -// Single-rounding MultiplyByQuantizedMultiplier -#if TFLITE_SINGLE_ROUNDING -inline int32_t MultiplyByQuantizedMultiplier(int32_t x, - int32_t quantized_multiplier, - int shift) { - TFLITE_DCHECK(quantized_multiplier >= 0); - TFLITE_DCHECK(shift >= -31 && shift <= 30); - - const int64_t total_shift = 31 - shift; - const int64_t round = static_cast(1) << (total_shift - 1); - int64_t result = x * static_cast(quantized_multiplier) + round; - result = result >> total_shift; - - TFLITE_DCHECK(result >= std::numeric_limits::min() && - result <= std::numeric_limits::max()); - return static_cast(result); -} - -inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp( - int32_t x, int32_t quantized_multiplier, int shift) { - TFLITE_DCHECK_LE(shift, 0); - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne( - int32_t x, int32_t quantized_multiplier, int shift) { - TFLITE_DCHECK_GE(shift, 0); - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -inline int32_t MultiplyByQuantizedMultiplier(int64_t x, - int32_t quantized_multiplier, - int shift) { - // Inputs: - // - quantized_multiplier has fixed point at bit 31 - // - shift is -31 to +7 (negative for right shift) - // - // Assumptions: The following input ranges are assumed - // - quantize_scale>=0 (the usual range is (1<<30) to (1>>31)-1) - // - scaling is chosen so final scaled result fits in int32_t - // - input x is in the range -(1<<47) <= x < (1<<47) - TFLITE_DCHECK(quantized_multiplier >= 0); - TFLITE_DCHECK(shift >= -31 && shift < 8); - TFLITE_DCHECK(x >= -(static_cast(1) << 47) && - x < (static_cast(1) << 47)); - - const int32_t reduced_multiplier = - (quantized_multiplier < 0x7FFF0000) - ? ((quantized_multiplier + (1 << 15)) >> 16) - : 0x7FFF; - const int64_t total_shift = 15 - shift; - const int64_t round = static_cast(1) << (total_shift - 1); - int64_t result = x * static_cast(reduced_multiplier) + round; - result = result >> total_shift; - - TFLITE_DCHECK(result >= std::numeric_limits::min() && - result <= std::numeric_limits::max()); - return static_cast(result); -} - -#ifdef USE_NEON -inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows( - int32x4x4_t input_val, int32_t quantized_multiplier, int shift) { - TFLITE_DCHECK(quantized_multiplier >= 0); - - const int right_shift = std::min(-1, shift); - const int left_shift = shift - right_shift; - - const int32x4_t multiplier_dup = vdupq_n_s32(quantized_multiplier); - const int32x4_t left_shift_dup = vdupq_n_s32(left_shift); - const int32x4_t right_shift_dup = vdupq_n_s32(right_shift); - - int32x4x4_t result; - result.val[0] = vrshlq_s32( - vqdmulhq_s32(vshlq_s32(input_val.val[0], left_shift_dup), multiplier_dup), - right_shift_dup); - - result.val[1] = vrshlq_s32( - vqdmulhq_s32(vshlq_s32(input_val.val[1], left_shift_dup), multiplier_dup), - right_shift_dup); - - result.val[2] = vrshlq_s32( - vqdmulhq_s32(vshlq_s32(input_val.val[2], left_shift_dup), multiplier_dup), - right_shift_dup); - - result.val[3] = vrshlq_s32( - vqdmulhq_s32(vshlq_s32(input_val.val[3], left_shift_dup), multiplier_dup), - right_shift_dup); - - return result; -} -#endif // USE_NEON -// Double-rounding MultiplyByQuantizedMultiplier -#else -inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp( - int32_t x, int32_t quantized_multiplier, int left_shift) { - using gemmlowp::RoundingDivideByPOT; - using gemmlowp::SaturatingRoundingDoublingHighMul; - return RoundingDivideByPOT( - SaturatingRoundingDoublingHighMul(x, quantized_multiplier), -left_shift); -} - -inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne( - int32_t x, int32_t quantized_multiplier, int left_shift) { - using gemmlowp::SaturatingRoundingDoublingHighMul; - return SaturatingRoundingDoublingHighMul(x * (1 << left_shift), - quantized_multiplier); -} - -TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier( - int32_t x, int32_t quantized_multiplier, int shift); - -TFLITE_NOINLINE int32_t MultiplyByQuantizedMultiplier( - int64_t x, int32_t quantized_multiplier, int shift); - -#ifdef USE_NEON -// Round uses ARM's rounding shift right. -inline int32x4x4_t MultiplyByQuantizedMultiplier4Rows( - int32x4x4_t input_val, int32_t quantized_multiplier, int shift) { - const int left_shift = std::max(shift, 0); - const int right_shift = std::min(shift, 0); - int32x4x4_t result; - - int32x4_t multiplier_dup = vdupq_n_s32(quantized_multiplier); - int32x4_t left_shift_dup = vdupq_n_s32(left_shift); - int32x4_t right_shift_dup = vdupq_n_s32(right_shift); - - result.val[0] = - vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[0], left_shift_dup), - multiplier_dup), - right_shift_dup); - - result.val[1] = - vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[1], left_shift_dup), - multiplier_dup), - right_shift_dup); - - result.val[2] = - vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[2], left_shift_dup), - multiplier_dup), - right_shift_dup); - - result.val[3] = - vrshlq_s32(vqrdmulhq_s32(vshlq_s32(input_val.val[3], left_shift_dup), - multiplier_dup), - right_shift_dup); - - return result; -} -#endif // USE_NEON -#endif // TFLITE_SINGLE_ROUNDING - -template -int CountLeadingZeros(T integer_input) { - static_assert(std::is_unsigned::value, - "Only unsigned integer types handled."); - if (integer_input == 0) { - return std::numeric_limits::digits; - } -#if defined(__GNUC__) - if (std::is_same::value) { - return __builtin_clz(integer_input); - } else if (std::is_same::value) { - return __builtin_clzll(integer_input); - } -#endif - const T one_in_leading_positive = static_cast(1) - << (std::numeric_limits::digits - 1); - int leading_zeros = 0; - while (integer_input < one_in_leading_positive) { - integer_input <<= 1; - ++leading_zeros; - } - return leading_zeros; -} - -template -inline int CountLeadingSignBits(T integer_input) { - static_assert(std::is_signed::value, "Only signed integer types handled."); -#if defined(__GNUC__) && !defined(__clang__) - return integer_input ? __builtin_clrsb(integer_input) - : std::numeric_limits::digits; -#else - using U = typename std::make_unsigned::type; - return integer_input >= 0 - ? CountLeadingZeros(static_cast(integer_input)) - 1 - : integer_input != std::numeric_limits::min() - ? CountLeadingZeros(2 * static_cast(-integer_input) - 1) - : 0; -#endif -} - -// Use "count leading zeros" helper functions to do a fast Floor(log_2(x)). -template -inline Integer FloorLog2(Integer n) { - static_assert(std::is_integral::value, ""); - static_assert(std::is_signed::value, ""); - static_assert(sizeof(Integer) == 4 || sizeof(Integer) == 8, ""); - TFLITE_CHECK_GT(n, 0); - if (sizeof(Integer) == 4) { - return 30 - CountLeadingSignBits(n); - } else { - return 62 - CountLeadingSignBits(n); - } -} - -namespace detail { - -// LUTPopulate takes an optional type-erased transform_params to allow passing -// extra parameters to the transform function pointer. const void* is used -// instead of std::function to be compatible with TFLite Micro -template -inline typename std::enable_if::value, - FloatT>::type -LUTTransform(Func transform, const void* /*transform_params*/, FloatT value) { - static_assert(std::is_floating_point::value, - "FloatT must be a floating-point type."); - return transform(value); -} - -template -inline typename std::enable_if< - std::is_same::value, FloatT>::type -LUTTransform(Func transform, const void* transform_params, FloatT value) { - static_assert(std::is_floating_point::value, - "FloatT must be a floating-point type."); - return transform(value, transform_params); -} - -// Use the same LUT generation code for both uint8_t and int8_t. Int8_t indexes -// will be directly casted to uint8_t, the int8 LUT will thus be ordered as [0, -// 1, ..., 127, -128, ..., -2, -1] instead of [-128, -127, ..., -1, 0, 1, ..., -// 126, 127]. -template -inline void LUTPopulateInt8(float input_scale, int32_t input_zero_point, - float output_scale, int32_t output_zero_point, - Func transform, const void* transform_params, - T* lut) { - static_assert( - std::is_same::value || std::is_same::value, - "T must be an uint8 or int8 type."); - uint8_t* lut_uint8 = reinterpret_cast(lut); - const float inverse_scale = 1 / output_scale; - int32_t maxval = std::numeric_limits::max(); - int32_t minval = std::numeric_limits::min(); - for (int32_t val = minval; val <= maxval; ++val) { - const float dequantized = input_scale * (val - input_zero_point); - const float transformed = - LUTTransform(transform, transform_params, dequantized); - const float rescaled = TfLiteRound(transformed * inverse_scale); - const int32_t quantized = - static_cast(rescaled + output_zero_point); - lut_uint8[static_cast(static_cast(val))] = static_cast( - static_cast(std::max(std::min(maxval, quantized), minval))); - } -} - -// Keep floating-point type configurable for backward compatibility. float -// should be used for FloatT by default. -template -inline void LUTPopulateInt16(FloatT input_scale, int32_t input_zero_point, - FloatT output_scale, int32_t output_zero_point, - Func transform, const void* transform_params, - int16_t* lut) { - static_assert(std::is_floating_point::value, - "FloatT must be a floating-point type."); - const FloatT input_min = - input_scale * (std::numeric_limits::min() - input_zero_point); - const FloatT input_max = - input_scale * (std::numeric_limits::max() - input_zero_point); - const FloatT output_min = - output_scale * (std::numeric_limits::min() - output_zero_point); - const FloatT output_max = - output_scale * (std::numeric_limits::max() - output_zero_point); - - const int nb_steps = 512; - const FloatT step = (input_max - input_min) / nb_steps; - const FloatT half_step = step / 2; - const FloatT output_scaling_inv = - static_cast(std::numeric_limits::max() - - std::numeric_limits::min() + 1) / - (output_max - output_min); - const FloatT table_min = - static_cast(std::numeric_limits::min()); - const FloatT table_max = - static_cast(std::numeric_limits::max()); - - for (int i = 0; i < nb_steps; i++) { - const FloatT val = - LUTTransform(transform, transform_params, input_min + i * step); - const FloatT val_midpoint = LUTTransform( - transform, transform_params, input_min + i * step + half_step); - const FloatT val_next = LUTTransform(transform, transform_params, - input_min + (i + 1) * step); - - const FloatT sample_val = TfLiteRound(val * output_scaling_inv); - const FloatT midpoint_interp_val = - TfLiteRound((val_next * output_scaling_inv + - TfLiteRound(val * output_scaling_inv)) / - 2); - const FloatT midpoint_val = TfLiteRound(val_midpoint * output_scaling_inv); - const FloatT midpoint_err = midpoint_interp_val - midpoint_val; - const FloatT bias = TfLiteRound(midpoint_err / 2); - - lut[i] = static_cast(std::min( - std::max(sample_val - bias, table_min), table_max)); - } - - lut[nb_steps] = static_cast(std::min( - std::max(TfLiteRound(LUTTransform( - transform, transform_params, input_max) * - output_scaling_inv), - table_min), - table_max)); -} - -} // namespace detail - -template -inline typename std::enable_if::value || - std::is_same::value, - void>::type -LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, - int32_t output_zero_point, float (*transform)(float), T* lut) { - detail::LUTPopulateInt8(input_scale, input_zero_point, output_scale, - output_zero_point, transform, nullptr, lut); -} - -template -inline typename std::enable_if::value || - std::is_same::value, - void>::type -LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, - int32_t output_zero_point, float (*transform)(float, const void*), - const void* transform_params, T* lut) { - detail::LUTPopulateInt8(input_scale, input_zero_point, output_scale, - output_zero_point, transform, transform_params, lut); -} - -template -inline typename std::enable_if::value, void>::type -LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, - int32_t output_zero_point, float (*transform)(float), T* lut) { - detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, - output_zero_point, transform, nullptr, lut); -} - -template -inline typename std::enable_if::value, void>::type -LUTPopulate(float input_scale, int32_t input_zero_point, float output_scale, - int32_t output_zero_point, float (*transform)(float, const void*), - const void* transform_params, T* lut) { - detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, - output_zero_point, transform, - transform_params, lut); -} - -// Deprecated, avoid usage and prefer the float version. Kept for -// backward-compatiblity. -template -inline typename std::enable_if::value, void>::type -LUTPopulate(double input_scale, int32_t input_zero_point, double output_scale, - int32_t output_zero_point, double (*transform)(double), T* lut) { - detail::LUTPopulateInt16(input_scale, input_zero_point, output_scale, - output_zero_point, transform, nullptr, lut); -} - -// The size of the LUT depends on the type of input. For uint8 and int8 inputs a -// simple 256 entries LUT is used. For int16 inputs the high 9 bits are used for -// indexing and the 7 remaining bits are used for interpolation. We thus use a -// 513-entries LUT for int16 cases, 512 for the 9-bit indexing and 1 extra entry -// to interpolate the last value. -template -constexpr int LUTSize() { - static_assert(std::is_same::value || - std::is_same::value || - std::is_same::value, - "Only LUTs with uint8, int8 or int16 inputs are supported."); - // As per c++11: constexpr methods cannot have more than one return statement. - return (std::is_same::value || std::is_same::value) - ? 256 - : 513; -} - -// int16_t -> int16_t table lookup with interpolation -// LUT must have 513 values -inline int16_t LUTLookup(int16_t value, const int16_t* lut) { - // 512 base values, lut[513] is only used to calculate the slope - const uint16_t index = static_cast(256 + (value >> 7)); - assert(index < 512 && "LUT index out of range."); - const int16_t offset = value & 0x7f; - - // Base and slope are Q0.x - const int16_t base = lut[index]; - const int16_t slope = lut[index + 1] - lut[index]; - - // Q0.x * Q0.7 = Q0.(x + 7) - // Round and convert from Q0.(x + 7) to Q0.x - const int delta = (slope * offset + 64) >> 7; - - // Q0.15 + Q0.15 - return static_cast(base + delta); -} - -// int8_t -> int8_t table lookup without interpolation -// LUT must have 256 values -// LUTPopulate has ordered the LUT so that indexing it with an -// int8_t is just done by casting it to an uint8_t. -inline int8_t LUTLookup(int8_t value, const int8_t* lut) { - return lut[static_cast(value)]; -} - -// uint8_t -> uint8_t table lookup without interpolation -// LUT must have 256 values -inline uint8_t LUTLookup(uint8_t value, const uint8_t* lut) { - return lut[value]; -} - -// Table of sigmoid(i/24) at 0.16 format - 256 elements. - -// We use combined sigmoid and tanh look-up table, since -// tanh(x) = 2*sigmoid(2*x) -1. -// Both functions are symmetric, so the LUT table is only needed -// for the absolute value of the input. -static const uint16_t sigmoid_table_uint16[256] = { - 32768, 33451, 34133, 34813, 35493, 36169, 36843, 37513, 38180, 38841, 39498, - 40149, 40794, 41432, 42064, 42688, 43304, 43912, 44511, 45102, 45683, 46255, - 46817, 47369, 47911, 48443, 48964, 49475, 49975, 50464, 50942, 51409, 51865, - 52311, 52745, 53169, 53581, 53983, 54374, 54755, 55125, 55485, 55834, 56174, - 56503, 56823, 57133, 57433, 57724, 58007, 58280, 58544, 58800, 59048, 59288, - 59519, 59743, 59959, 60168, 60370, 60565, 60753, 60935, 61110, 61279, 61441, - 61599, 61750, 61896, 62036, 62172, 62302, 62428, 62549, 62666, 62778, 62886, - 62990, 63090, 63186, 63279, 63368, 63454, 63536, 63615, 63691, 63765, 63835, - 63903, 63968, 64030, 64090, 64148, 64204, 64257, 64308, 64357, 64405, 64450, - 64494, 64536, 64576, 64614, 64652, 64687, 64721, 64754, 64786, 64816, 64845, - 64873, 64900, 64926, 64950, 64974, 64997, 65019, 65039, 65060, 65079, 65097, - 65115, 65132, 65149, 65164, 65179, 65194, 65208, 65221, 65234, 65246, 65258, - 65269, 65280, 65291, 65301, 65310, 65319, 65328, 65337, 65345, 65352, 65360, - 65367, 65374, 65381, 65387, 65393, 65399, 65404, 65410, 65415, 65420, 65425, - 65429, 65433, 65438, 65442, 65445, 65449, 65453, 65456, 65459, 65462, 65465, - 65468, 65471, 65474, 65476, 65479, 65481, 65483, 65485, 65488, 65489, 65491, - 65493, 65495, 65497, 65498, 65500, 65501, 65503, 65504, 65505, 65507, 65508, - 65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517, 65517, 65518, - 65519, 65520, 65520, 65521, 65522, 65522, 65523, 65523, 65524, 65524, 65525, - 65525, 65526, 65526, 65526, 65527, 65527, 65528, 65528, 65528, 65529, 65529, - 65529, 65529, 65530, 65530, 65530, 65530, 65531, 65531, 65531, 65531, 65531, - 65532, 65532, 65532, 65532, 65532, 65532, 65533, 65533, 65533, 65533, 65533, - 65533, 65533, 65533, 65534, 65534, 65534, 65534, 65534, 65534, 65534, 65534, - 65534, 65534, 65535}; - -// TODO(b/77858996): Add these to gemmlowp. -template -IntegerType SaturatingAddNonGemmlowp(IntegerType a, IntegerType b) { - static_assert(std::is_same::value, "unimplemented"); - return a; -} - -template <> -inline std::int32_t SaturatingAddNonGemmlowp(std::int32_t a, std::int32_t b) { - std::int64_t a64 = a; - std::int64_t b64 = b; - std::int64_t sum = a64 + b64; - return static_cast(std::min( - static_cast(std::numeric_limits::max()), - std::max( - static_cast(std::numeric_limits::min()), - sum))); -} - -template -gemmlowp::FixedPoint SaturatingAddNonGemmlowp( - gemmlowp::FixedPoint a, - gemmlowp::FixedPoint b) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingAddNonGemmlowp(a.raw(), b.raw())); -} - -template -IntegerType SaturatingSub(IntegerType a, IntegerType b) { - static_assert(std::is_same::value, "unimplemented"); - return a; -} - -template <> -inline std::int16_t SaturatingSub(std::int16_t a, std::int16_t b) { - std::int32_t a32 = a; - std::int32_t b32 = b; - std::int32_t diff = a32 - b32; - return static_cast( - std::min(static_cast(32767), - std::max(static_cast(-32768), diff))); -} - -template <> -inline std::int32_t SaturatingSub(std::int32_t a, std::int32_t b) { - std::int64_t a64 = a; - std::int64_t b64 = b; - std::int64_t diff = a64 - b64; - return static_cast(std::min( - static_cast(std::numeric_limits::max()), - std::max( - static_cast(std::numeric_limits::min()), - diff))); -} - -template -gemmlowp::FixedPoint SaturatingSub( - gemmlowp::FixedPoint a, - gemmlowp::FixedPoint b) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingSub(a.raw(), b.raw())); -} -// End section to be moved to gemmlowp. - -template -IntegerType SaturatingRoundingMultiplyByPOTParam(IntegerType x, int exponent) { - if (exponent == 0) { - return x; - } - using ScalarIntegerType = - typename gemmlowp::FixedPointRawTypeTraits::ScalarRawType; - const IntegerType min = - gemmlowp::Dup(std::numeric_limits::min()); - const IntegerType max = - gemmlowp::Dup(std::numeric_limits::max()); - const int ScalarIntegerTypeBits = 8 * sizeof(ScalarIntegerType); - - const std::int32_t threshold = - ((1 << (ScalarIntegerTypeBits - 1 - exponent)) - 1); - const IntegerType positive_mask = - gemmlowp::MaskIfGreaterThan(x, gemmlowp::Dup(threshold)); - const IntegerType negative_mask = - gemmlowp::MaskIfLessThan(x, gemmlowp::Dup(-threshold)); - - IntegerType result = gemmlowp::ShiftLeft(x, exponent); - result = gemmlowp::SelectUsingMask(positive_mask, max, result); - result = gemmlowp::SelectUsingMask(negative_mask, min, result); - return result; -} - -// If we want to leave IntegerBits fixed, then multiplication -// by a power of two has to be saturating/rounding, not exact anymore. -template -gemmlowp::FixedPoint -SaturatingRoundingMultiplyByPOTParam( - gemmlowp::FixedPoint a, int exponent) { - return gemmlowp::FixedPoint::FromRaw( - SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); -} - -// Convert int32_t multiplier to int16_t with rounding. -inline void DownScaleInt32ToInt16Multiplier(int32_t multiplier_int32_t, - int16_t* multiplier_int16_t) { - TFLITE_DCHECK_GE(multiplier_int32_t, 0); - static constexpr int32_t kRoundingOffset = 1 << 15; - if (multiplier_int32_t >= - std::numeric_limits::max() - kRoundingOffset) { - *multiplier_int16_t = std::numeric_limits::max(); - return; - } - const int32_t result = (multiplier_int32_t + kRoundingOffset) >> 16; - TFLITE_DCHECK_LE(result << 16, multiplier_int32_t + kRoundingOffset); - TFLITE_DCHECK_GT(result << 16, multiplier_int32_t - kRoundingOffset); - *multiplier_int16_t = result; - TFLITE_DCHECK_EQ(*multiplier_int16_t, result); -} - -// Minimum output bits to accommodate log of maximum input range. It actually -// does not matter if one considers, say, [-64,64] or [-64,64). -// -// For example, run this through Octave: -// [0:127; ... -// ceil(log(abs( log(2.^(0:127))+1 ))/log(2)); ... -// ceil(log(abs( log(2.^(0:127))+1 ))/log(2))] -constexpr int min_log_x_output_bits(int input_bits) { - return input_bits > 90 ? 7 - : input_bits > 44 ? 6 - : input_bits > 21 ? 5 - : input_bits > 10 ? 4 - : input_bits > 4 ? 3 - : input_bits > 1 ? 2 - : 1; -} - -// Although currently the name of this function says that it cannot handle -// values less than 1, in practice it can handle as low as 1/x_max, where -// x_max is the largest representable input. In other words, the output range -// is symmetric. -template -inline gemmlowp::FixedPoint -log_x_for_x_greater_than_or_equal_to_1_impl( - gemmlowp::FixedPoint input_val) { - // assert(__builtin_clz(0u) >= std::numeric_limits::digits - 1); - // assert(__builtin_clz(0u) <= std::numeric_limits::digits); - using FixedPoint0 = gemmlowp::FixedPoint; - // The reason for accumulating the result with an extra bit of headroom is - // that z_pow_2_adj * log_2 might be saturated, and adding num_scaled * - // recip_denom will otherwise introduce an error. - static constexpr int kAccumIntegerBits = OutputIntegerBits + 1; - using FixedPointAccum = gemmlowp::FixedPoint; - - const FixedPoint0 log_2 = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1488522236, std::log(2.0)); - const FixedPoint0 sqrt_sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1805811301, std::sqrt(std::sqrt(0.5))); - const FixedPoint0 sqrt_half = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1518500250, std::sqrt(0.5)); - const FixedPoint0 one_quarter = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(FixedPoint0, 536870912, 1.0 / 4.0); - - const FixedPoint0 alpha_n = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 117049297, 11.0 / 240.0 * std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_d = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 127690142, 1.0 / 20.0 * std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_i = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 1057819769, - 2.0 / std::sqrt(std::sqrt(2.0)) - std::sqrt(std::sqrt(2.0))); - const FixedPoint0 alpha_f = GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT( - FixedPoint0, 638450708, 1.0 / 4.0 * std::sqrt(std::sqrt(2.0))); - - const FixedPointAccum shifted_quarter = - gemmlowp::Rescale(one_quarter); - - // Reinterpret the input value as Q0.31, because we will figure out the - // required shift "ourselves" instead of using, say, Rescale. - FixedPoint0 z_a = FixedPoint0::FromRaw(input_val.raw()); - // z_a_pow_2 = input_integer_bits - z_a_headroom; - int z_a_headroom_plus_1 = CountLeadingZeros(static_cast(z_a.raw())); - FixedPoint0 r_a_tmp = - SaturatingRoundingMultiplyByPOTParam(z_a, (z_a_headroom_plus_1 - 1)); - const int32_t r_a_raw = - SaturatingRoundingMultiplyByPOTParam((r_a_tmp * sqrt_half).raw(), 1); - // z_pow_2_adj = max(z_pow_2_a - 0.75, z_pow_2_b - 0.25); - // z_pow_2_adj = max(InputIntegerBits - z_a_headroom_plus_1 + 0.25, - // InputIntegerBits - z_b_headroom - 0.25); - const FixedPointAccum z_a_pow_2_adj = SaturatingAddNonGemmlowp( - FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( - static_cast(InputIntegerBits - z_a_headroom_plus_1), - 31 - kAccumIntegerBits)), - shifted_quarter); - - // z_b is treated like z_a, but premultiplying by sqrt(0.5). - FixedPoint0 z_b = z_a * sqrt_half; - int z_b_headroom = CountLeadingZeros(static_cast(z_b.raw())) - 1; - const int32_t r_b_raw = - SaturatingRoundingMultiplyByPOTParam(z_a.raw(), z_b_headroom); - const FixedPointAccum z_b_pow_2_adj = SaturatingSub( - FixedPointAccum::FromRaw(SaturatingRoundingMultiplyByPOTParam( - static_cast(InputIntegerBits - z_b_headroom), - 31 - kAccumIntegerBits)), - shifted_quarter); - - const FixedPoint0 r = FixedPoint0::FromRaw(std::min(r_a_raw, r_b_raw)); - const FixedPointAccum z_pow_2_adj = FixedPointAccum::FromRaw( - std::max(z_a_pow_2_adj.raw(), z_b_pow_2_adj.raw())); - - const FixedPoint0 p = gemmlowp::RoundingHalfSum(r, sqrt_sqrt_half); - FixedPoint0 q = r - sqrt_sqrt_half; - q = q + q; - - const FixedPoint0 common_sq = q * q; - const FixedPoint0 num = q * r + q * common_sq * alpha_n; - const FixedPoint0 denom_minus_one_0 = - p * (alpha_i + q + alpha_d * common_sq) + alpha_f * q; - const FixedPoint0 recip_denom = - one_over_one_plus_x_for_x_in_0_1(denom_minus_one_0); - - const FixedPointAccum num_scaled = gemmlowp::Rescale(num); - return gemmlowp::Rescale(z_pow_2_adj * log_2 + - num_scaled * recip_denom); -} - -template -inline gemmlowp::FixedPoint -log_x_for_x_greater_than_or_equal_to_1( - gemmlowp::FixedPoint input_val) { - static_assert( - OutputIntegerBits >= min_log_x_output_bits(InputIntegerBits), - "Output integer bits must be sufficient to accommodate logs of inputs."); - return log_x_for_x_greater_than_or_equal_to_1_impl( - input_val); -} - -inline int32_t GetReciprocal(int32_t x, int x_integer_digits, - int* num_bits_over_unit) { - int headroom_plus_one = CountLeadingZeros(static_cast(x)); - // This is the number of bits to the left of the binary point above 1.0. - // Consider x=1.25. In that case shifted_scale=0.8 and - // no later adjustment will be needed. - *num_bits_over_unit = x_integer_digits - headroom_plus_one; - const int32_t shifted_sum_minus_one = - static_cast((static_cast(x) << headroom_plus_one) - - (static_cast(1) << 31)); - - gemmlowp::FixedPoint shifted_scale = - gemmlowp::one_over_one_plus_x_for_x_in_0_1( - gemmlowp::FixedPoint::FromRaw(shifted_sum_minus_one)); - return shifted_scale.raw(); -} - -inline void GetInvSqrtQuantizedMultiplierExp(int32_t input, int reverse_shift, - int32_t* output_inv_sqrt, - int* output_shift) { - TFLITE_DCHECK_GE(input, 0); - if (input <= 1) { - // Handle the input value 1 separately to avoid overflow in that case - // in the general computation below (b/143972021). Also handle 0 as if it - // were a 1. 0 is an invalid input here (divide by zero) and 1 is a valid - // but rare/unrealistic input value. We can expect both to occur in some - // incompletely trained models, but probably not in fully trained models. - *output_inv_sqrt = std::numeric_limits::max(); - *output_shift = 0; - return; - } - TFLITE_DCHECK_GT(input, 1); - *output_shift = 11; - while (input >= (1 << 29)) { - input /= 4; - ++*output_shift; - } - const unsigned max_left_shift_bits = - CountLeadingZeros(static_cast(input)) - 1; - const unsigned max_left_shift_bit_pairs = max_left_shift_bits / 2; - const unsigned left_shift_bit_pairs = max_left_shift_bit_pairs - 1; - *output_shift -= left_shift_bit_pairs; - input <<= 2 * left_shift_bit_pairs; - TFLITE_DCHECK_GE(input, (1 << 27)); - TFLITE_DCHECK_LT(input, (1 << 29)); - using gemmlowp::FixedPoint; - using gemmlowp::Rescale; - using gemmlowp::SaturatingRoundingMultiplyByPOT; - // Using 3 integer bits gives us enough room for the internal arithmetic in - // this Newton-Raphson iteration. - using F3 = FixedPoint; - using F0 = FixedPoint; - const F3 fixedpoint_input = F3::FromRaw(input >> 1); - const F3 fixedpoint_half_input = - SaturatingRoundingMultiplyByPOT<-1>(fixedpoint_input); - const F3 fixedpoint_half_three = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F3, (1 << 28) + (1 << 27), 1.5); - // Newton-Raphson iteration - // Naive unoptimized starting guess: x = 1 - F3 x = F3::One(); - // Naive unoptimized number of iterations: 5 - for (int i = 0; i < 5; i++) { - const F3 x3 = Rescale<3>(x * x * x); - x = Rescale<3>(fixedpoint_half_three * x - fixedpoint_half_input * x3); - } - const F0 fixedpoint_half_sqrt_2 = - GEMMLOWP_CHECKED_FIXEDPOINT_CONSTANT(F0, 1518500250, std::sqrt(2.) / 2.); - x = x * fixedpoint_half_sqrt_2; - *output_inv_sqrt = x.raw(); - if (*output_shift < 0) { - *output_inv_sqrt <<= -*output_shift; - *output_shift = 0; - } - // Convert right shift (right is positive) to left shift. - *output_shift *= reverse_shift; -} - -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -inline int SubscriptToIndex(const NdArrayDesc<5>& desc, int indexes[5]) { - return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] + - indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] + - indexes[4] * desc.strides[4]; -} - -inline int SubscriptToIndex(const NdArrayDesc<8>& desc, int indexes[8]) { - return indexes[0] * desc.strides[0] + indexes[1] * desc.strides[1] + - indexes[2] * desc.strides[2] + indexes[3] * desc.strides[3] + - indexes[4] * desc.strides[4] + indexes[5] * desc.strides[5] + - indexes[6] * desc.strides[6] + indexes[7] * desc.strides[7]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - -// Copies dims to desc, calculating strides. -template -TFLITE_NOINLINE void CopyDimsToDesc(const RuntimeShape& input_shape, - NdArrayDesc* desc_out) { - int desc_stride = 1; - for (int i = N - 1; i >= 0; --i) { - desc_out->extents[i] = input_shape.Dims(i); - desc_out->strides[i] = desc_stride; - desc_stride *= input_shape.Dims(i); - } -} - -template -inline void NdArrayDescsForElementwiseBroadcast( - const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, - NdArrayDesc* desc0_out, NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); - auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); - - // Copy dims to desc, calculating strides. - CopyDimsToDesc(extended_input0_shape, desc0_out); - CopyDimsToDesc(extended_input1_shape, desc1_out); - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = extended_input0_shape.Dims(i); - const int extent1 = extended_input1_shape.Dims(i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - -template -inline void NdArrayDescsForElementwiseBroadcast( - const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, - const RuntimeShape& input2_shape, NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out, NdArrayDesc* desc2_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - TFLITE_DCHECK(desc2_out != nullptr); - - auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); - auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); - auto extended_input2_shape = RuntimeShape::ExtendedShape(N, input2_shape); - - // Copy dims to desc, calculating strides. - CopyDimsToDesc(extended_input0_shape, desc0_out); - CopyDimsToDesc(extended_input1_shape, desc1_out); - CopyDimsToDesc(extended_input2_shape, desc2_out); - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = extended_input0_shape.Dims(i); - const int extent1 = extended_input1_shape.Dims(i); - const int extent2 = extended_input2_shape.Dims(i); - - int extent = extent0; - if (extent1 != 1) extent = extent1; - if (extent2 != 1) extent = extent2; - - TFLITE_DCHECK(extent0 == 1 || extent0 == extent); - TFLITE_DCHECK(extent1 == 1 || extent1 == extent); - TFLITE_DCHECK(extent2 == 1 || extent2 == extent); - - if (!(extent0 == extent1 && extent1 == extent2)) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent; - } - if (extent1 == 1) { - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent; - } - if (extent2 == 1) { - desc2_out->strides[i] = 0; - desc2_out->extents[i] = extent; - } - } - } -} - -// Detailed implementation of NDOpsHelper, the indexes must be a zero array. -// This implementation is equivalent to N nested loops. Ex, if N=4, it can be -// re-writen as: -// for (int b = 0; b < output.extents[0]; ++b) { -// for (int y = 0; y < output.extents[1]; ++y) { -// for (int x = 0; x < output.extents[2]; ++x) { -// for (int c = 0; c < output.extents[3]; ++c) { -// calc({b,y,x,c}); -// } -// } -// } -// } -template -typename std::enable_if::type NDOpsHelperImpl( - const NdArrayDesc& output, const Calc& calc, int indexes[N]) { - for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { - NDOpsHelperImpl(output, calc, indexes); - } -} - -template -typename std::enable_if::type NDOpsHelperImpl( - const NdArrayDesc& output, const Calc& calc, int indexes[N]) { - for (indexes[DIM] = 0; indexes[DIM] < output.extents[DIM]; ++indexes[DIM]) { - calc(indexes); - } -} - -// Execute the calc function in the innermost iteration based on the shape of -// the output. The calc function should take a single argument of type int[N]. -template -inline void NDOpsHelper(const NdArrayDesc& output, const Calc& calc) { - int indexes[N] = {0}; - NDOpsHelperImpl(output, calc, indexes); -} -// Copied from gemmlowp::RoundDown when we dropped direct dependency on -// gemmlowp. -// -// Returns the runtime argument rounded down to the nearest multiple of -// the fixed Modulus. -template -Integer RoundDown(Integer i) { - return i - (i % Modulus); -} - -// Copied from gemmlowp::RoundUp when we dropped direct dependency on -// gemmlowp. -// -// Returns the runtime argument rounded up to the nearest multiple of -// the fixed Modulus. -template -Integer RoundUp(Integer i) { - return RoundDown(i + Modulus - 1); -} - -// Copied from gemmlowp::CeilQuotient when we dropped direct dependency on -// gemmlowp. -// -// Returns the quotient a / b rounded up ('ceil') to the nearest integer. -template -Integer CeilQuotient(Integer a, Integer b) { - return (a + b - 1) / b; -} - -// This function is a copy of gemmlowp::HowManyThreads, copied when we dropped -// the direct dependency of internal/optimized/ on gemmlowp. -// -// It computes a reasonable number of threads to use for a GEMM of shape -// (rows, cols, depth). -// -// TODO(b/131910176): get rid of this function by switching each call site -// to its own more sensible logic for its own workload. -template -inline int LegacyHowManyThreads(int max_num_threads, int rows, int cols, - int depth) { - // Early-exit in the default case where multi-threading is disabled. - if (max_num_threads == 1) { - return 1; - } - - // Ensure that each thread has KernelRows rows to process, if at all possible. - int thread_count = std::min(max_num_threads, rows / KernelRows); - - // Limit the number of threads according to the overall size of the problem. - if (thread_count > 1) { - // Empirically determined value. - static constexpr std::uint64_t min_cubic_size_per_thread = 64 * 1024; - - // We can only multiply two out of three sizes without risking overflow - const std::uint64_t cubic_size = - std::uint64_t(rows) * std::uint64_t(cols) * std::uint64_t(depth); - - thread_count = std::min( - thread_count, static_cast(cubic_size / min_cubic_size_per_thread)); - } - - if (thread_count < 1) { - thread_count = 1; - } - - assert(thread_count > 0 && thread_count <= max_num_threads); - return thread_count; -} - -template -void optimized_ops_preload_l1_stream(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 0 means read */ 0, /* 0 means no locality */ 0); -#else - (void)ptr; -#endif -} - -template -void optimized_ops_preload_l1_keep(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 0 means read */ 0, /* 3 means high locality */ 3); -#else - (void)ptr; -#endif -} - -template -void optimized_ops_prefetch_write_l1_keep(const T* ptr) { -#ifdef __GNUC__ - // builtin offered by GCC-compatible compilers including clang - __builtin_prefetch(ptr, /* 1 means write */ 1, /* 3 means high locality */ 3); -#else - (void)ptr; -#endif -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h deleted file mode 100644 index 7ba66ed8..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/compatibility.h +++ /dev/null @@ -1,122 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ - -#include - -#include "tensorflow/lite/kernels/op_macros.h" - -#ifndef TFLITE_DCHECK -#define TFLITE_DCHECK(condition) (condition) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_EQ -#define TFLITE_DCHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_NE -#define TFLITE_DCHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_GE -#define TFLITE_DCHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_GT -#define TFLITE_DCHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_LE -#define TFLITE_DCHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -#ifndef TFLITE_DCHECK_LT -#define TFLITE_DCHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ASSERT_FALSE -#endif - -// TODO(ahentz): Clean up: We should stick to the DCHECK versions. -#ifndef TFLITE_CHECK -#define TFLITE_CHECK(condition) (condition) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_EQ -#define TFLITE_CHECK_EQ(x, y) ((x) == (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_NE -#define TFLITE_CHECK_NE(x, y) ((x) != (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_GE -#define TFLITE_CHECK_GE(x, y) ((x) >= (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_GT -#define TFLITE_CHECK_GT(x, y) ((x) > (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_LE -#define TFLITE_CHECK_LE(x, y) ((x) <= (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TFLITE_CHECK_LT -#define TFLITE_CHECK_LT(x, y) ((x) < (y)) ? (void)0 : TFLITE_ABORT -#endif - -#ifndef TF_LITE_STATIC_MEMORY -// TODO(b/162019032): Consider removing these type-aliases. -using int8 = std::int8_t; -using uint8 = std::uint8_t; -using int16 = std::int16_t; -using uint16 = std::uint16_t; -using int32 = std::int32_t; -using uint32 = std::uint32_t; -#endif // !defined(TF_LITE_STATIC_MEMORY) - -// Allow for cross-compiler usage of function signatures - currently used for -// specifying named RUY profiler regions in templated methods. -#if defined(_MSC_VER) -#define TFLITE_PRETTY_FUNCTION __FUNCSIG__ -#elif defined(__GNUC__) -#define TFLITE_PRETTY_FUNCTION __PRETTY_FUNCTION__ -#else -#define TFLITE_PRETTY_FUNCTION __func__ -#endif - -// TFLITE_DEPRECATED() -// -// Duplicated from absl/base/macros.h to avoid pulling in that library. -// Marks a deprecated class, struct, enum, function, method and variable -// declarations. The macro argument is used as a custom diagnostic message (e.g. -// suggestion of a better alternative). -// -// Example: -// -// class TFLITE_DEPRECATED("Use Bar instead") Foo {...}; -// TFLITE_DEPRECATED("Use Baz instead") void Bar() {...} -// -// Every usage of a deprecated entity will trigger a warning when compiled with -// clang's `-Wdeprecated-declarations` option. This option is turned off by -// default, but the warnings will be reported by clang-tidy. -#if defined(__clang__) && __cplusplus >= 201103L -#define TFLITE_DEPRECATED(message) __attribute__((deprecated(message))) -#endif - -#ifndef TFLITE_DEPRECATED -#define TFLITE_DEPRECATED(message) -#endif - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h deleted file mode 100644 index ee891753..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/cppmath.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ - -#include - -namespace tflite_micro { - -#if defined(TF_LITE_USE_GLOBAL_CMATH_FUNCTIONS) || \ - (defined(__ANDROID__) && !defined(__NDK_MAJOR__)) || defined(__ZEPHYR__) -#define TF_LITE_GLOBAL_STD_PREFIX -#else -#define TF_LITE_GLOBAL_STD_PREFIX std -#endif - -#define DECLARE_STD_GLOBAL_SWITCH1(tf_name, std_name) \ - template \ - inline T tf_name(const T x) { \ - return TF_LITE_GLOBAL_STD_PREFIX::std_name(x); \ - } - -DECLARE_STD_GLOBAL_SWITCH1(TfLiteRound, round) -DECLARE_STD_GLOBAL_SWITCH1(TfLiteExpm1, expm1) - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_CPPMATH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h deleted file mode 100644 index b8d8fc60..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/max.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ - -#include - -namespace tflite_micro { - -#if defined(TF_LITE_USE_GLOBAL_MAX) || defined(__ZEPHYR__) -inline float TfLiteMax(const float& x, const float& y) { - return std::max(x, y); -} -#else -template -inline T TfLiteMax(const T& x, const T& y) { - return std::fmax(x, y); -} -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h deleted file mode 100644 index 76353545..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/min.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ - -#include - -namespace tflite_micro { - -#if defined(TF_LITE_USE_GLOBAL_MIN) || defined(__ZEPHYR__) -inline float TfLiteMin(const float& x, const float& y) { - return std::min(x, y); -} -#else -template -inline T TfLiteMin(const T& x, const T& y) { - return std::fmin(x, y); -} -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_MIN_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h deleted file mode 100644 index 7df1129d..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/optimized/neon_check.h +++ /dev/null @@ -1,20 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ - -// TFLM does not need to utilize any Neon optimizations. - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_OPTIMIZED_NEON_CHECK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h deleted file mode 100644 index 12fdc732..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor.h +++ /dev/null @@ -1,141 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ - -#include -#include - -#include "tensorflow/lite/core/c/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -// A list of tensors in a format that can be used by kernels like split and -// concatenation. -template -class VectorOfTensors { - public: - // Build with the tensors in 'tensor_list'. - VectorOfTensors(const TfLiteContext& context, - const TfLiteIntArray& tensor_list) { - int num_tensors = tensor_list.size; - - all_data_.reserve(num_tensors); - all_shape_.reserve(num_tensors); - all_shape_ptr_.reserve(num_tensors); - - for (int i = 0; i < num_tensors; ++i) { - TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; - all_data_.push_back(GetTensorData(t)); - all_shape_.push_back(GetTensorShape(t)); - } - - // Taking the pointer from inside a std::vector is only OK if the vector is - // never modified, so we populate all_shape in the previous loop and then we - // are free to grab iterators here. - for (int i = 0; i < num_tensors; ++i) { - all_shape_ptr_.push_back(&all_shape_[i]); - } - } - - explicit VectorOfTensors(const std::vector& tensors) { - int num_tensors = tensors.size(); - - all_data_.reserve(num_tensors); - all_shape_.reserve(num_tensors); - all_shape_ptr_.reserve(num_tensors); - - for (auto* t : tensors) { - all_data_.push_back(GetTensorData(t)); - all_shape_.push_back(GetTensorShape(t)); - } - - // Taking the pointer from inside a std::vector is only OK if the vector is - // never modified, so we populate all_shape in the previous loop and then we - // are free to grab iterators here. - for (int i = 0; i < num_tensors; ++i) { - all_shape_ptr_.push_back(&all_shape_[i]); - } - } - // Return a pointer to the data pointers of all tensors in the list. For - // example: - // float* const* f = v.data(); - // f[0][1] is the second element of the first tensor. - T* const* data() const { return all_data_.data(); } - - // Return a pointer the shape pointers of all tensors in the list. For - // example: - // const RuntimeShape* const* d = v.dims(); - // dims[1] are the dimensions of the second tensor in the list. - const RuntimeShape* const* shapes() const { return all_shape_ptr_.data(); } - - size_t size() const { return all_data_.size(); } - - private: - std::vector all_data_; - std::vector all_shape_; - std::vector all_shape_ptr_; -}; - -// A list of quantized tensors in a format that can be used by kernels like -// split and concatenation. -class VectorOfQuantizedTensors : public VectorOfTensors { - public: - // Build with the tensors in 'tensor_list'. - VectorOfQuantizedTensors(const TfLiteContext& context, - const TfLiteIntArray& tensor_list) - : VectorOfTensors(context, tensor_list) { - for (int i = 0; i < tensor_list.size; ++i) { - TfLiteTensor* t = &context.tensors[tensor_list.data[i]]; - zero_point_.push_back(t->params.zero_point); - scale_.push_back(t->params.scale); - } - } - - const float* scale() const { return scale_.data(); } - const int32_t* zero_point() const { return zero_point_.data(); } - - private: - std::vector zero_point_; - std::vector scale_; -}; - -// Writes randomly accessed values from `input` sequentially into `output`. -template -class SequentialTensorWriter { - public: - SequentialTensorWriter(const TfLiteTensor* input, TfLiteTensor* output) { - input_data_ = GetTensorData(input); - output_ptr_ = GetTensorData(output); - } - SequentialTensorWriter(const T* input_data, T* output_data) - : input_data_(input_data), output_ptr_(output_data) {} - - void Write(int position) { *output_ptr_++ = input_data_[position]; } - void WriteN(int position, int len) { - memcpy(output_ptr_, &input_data_[position], sizeof(T) * len); - output_ptr_ += len; - } - - private: - const T* input_data_; - T* output_ptr_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h deleted file mode 100644 index 4a27c146..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/portable_tensor_utils.h +++ /dev/null @@ -1,623 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ - -#include -#include -#include - -#include "tensorflow/lite/core/c/builtin_op_data.h" -#include "tensorflow/lite/core/c/common.h" - -#if defined(_MSC_VER) -#define __restrict__ __restrict -#endif - -namespace tflite_micro { - -// Not all backends support CpuBackendContext usage, so forward declare to avoid -// pulling in its implementation. Use of CpuBackendContext in method -// implementations is purely optional. -class CpuBackendContext; - -namespace tensor_utils { - -// Multiplies a matrix with a scalar and reduce the result on each row to a -// scalar. -// Parameters: -// - matrix: matrix of size n_row * n_col -// - scalar: the scalar that is multiplied to each element in the matrix -// - n_row: the row count of the matrix -// - n_col: the column count of the matrix -// - output: the 32bit output -// Note: We do not need saturation because the int8 * int8 is safe from overflow -// in (2^31-1) / (2^14) = 131072, which is bigger than the n_row. Non-zero -// initial output value is not exceptionally large. -void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, - int32_t n_row, int32_t n_col, - int32_t* output); - -// Add another vector for each batch in the batch vector. -template -void VectorBatchVectorAdd(const T* vector, int v_size, int n_batch, - T* batch_vector) { - for (int b = 0; b < n_batch; b++) { - for (int i = 0; i < v_size; ++i) { - batch_vector[i] += vector[i]; - } - batch_vector += v_size; - } -} - -// Cwise product of two vectors. -template -inline void VectorVectorCwiseProduct(const T* vector1, const T* vector2, - int v_size, T* result) { - for (int v = 0; v < v_size; v++) { - *result++ = *vector1++ * *vector2++; - } -} - -// Cwise product of a vector and a batch-vector. -template -inline void VectorBatchVectorCwiseProduct(const T* vector, int v_size, - const T* batch_vector, int n_batch, - T* result) { - for (int b = 0; b < n_batch; b++) { - VectorVectorCwiseProduct(vector, batch_vector, v_size, result); - // Update the pointers. - result += v_size; - batch_vector += v_size; - } -} - -// Cwise product and accumulate of two vectors. Since it's a MAC operation, the -// assumption here is that result array is initialized to valid values. -template -inline void VectorVectorCwiseProductAccumulate(const T* __restrict__ vector1, - const T* __restrict__ vector2, - int v_size, - T* __restrict__ result) { - for (int v = 0; v < v_size; v++) { - *result++ += *vector1++ * *vector2++; - } -} - -// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC -// operation, the assumption here is that result array is initialized to valid -// values. -template -inline void VectorBatchVectorCwiseProductAccumulate(const T* vector, int v_size, - const T* batch_vector, - int n_batch, T* result) { - for (int b = 0; b < n_batch; b++) { - VectorVectorCwiseProductAccumulate(vector, batch_vector, v_size, result); - // Update the pointers. - result += v_size; - batch_vector += v_size; - } -} - -// Batch vector initialization with another vector. -template -void VectorBatchVectorAssign(const T* vector, int v_size, int n_batch, - T* batch_vector) { - for (int b = 0; b < n_batch; b++) { - std::copy_n(vector, v_size, batch_vector + b * v_size); - } -} - -// Checks if all entries of vector are zero for float. -bool IsZeroVector(const float* vector, int v_size); - -// Checks if all entries of vector are zero for int8. -bool IsZeroVector(const int8_t* vector, int v_size); - -// Quantizes a buffer of floating point values using a symmetric quantization -// (i.e. linear quantization without an offset) to 8-bit signed integers. -// It also outputs the range (min, max) of the floating point buffer, and the -// scaling factor used to quantize the values. -void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min_value, - float* max_value, float* scaling_factor); - -// Quantizes a buffer of floating point values using a symmetric quantization -// (i.e. linear quantization without an offset) to 8-bit signed integers. -// It uses the range (min, max) provided to the function to calculate the -// appropriate scaling factor to quantize the values. -void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor); - -void AsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* scaling_factor, - int32_t* offset); - -// Helper function to quantize floats. -// float_data_ptr input float vectors -// n_batch number of input vectors -// n_data size of a single input vector -// quantized_data_ptr (out) vector with quantized data -// scaling_factors (out) scaling factors (one per vector) -// zero_points (out) zero points (one per vector) -// do_asymmetric controls if the quantization should be asymmetric. -inline void BatchQuantizeFloats(const float* float_data_ptr, int n_batch, - int n_data, int8_t* quantized_data_ptr, - float* scaling_factors, int32_t* zero_points, - bool do_asymmetric) { - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_data; - if (do_asymmetric) { - tensor_utils::AsymmetricQuantizeFloats( - float_data_ptr + offset, n_data, quantized_data_ptr + offset, - &scaling_factors[b], &zero_points[b]); - } else { - float unused_min, unused_max; - tensor_utils::SymmetricQuantizeFloats( - float_data_ptr + offset, n_data, quantized_data_ptr + offset, - &unused_min, &unused_max, &scaling_factors[b]); - } - } -} - -// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch -// dimension composed by input vectors independent from each other). The result -// of the multiplication is accumulated to the passed result buffer. -// More specifically, for a matrix M of shape [n, i] and a batched-vector -// of shape [i, batch] it will first compute the product of shape [n, batch]. -// This product will be accumulated to the result buffer. -void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, - int m_cols, const float* vector, - int n_batch, float* result); - -// Same as the function above, but the matrix is a sparse tensor with block -// pattern 1x4. -// This function assumes that m_cols is a multiple of the block size (4 in this -// case) so that there's no incomplete block. -void SparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result); - -// Same as the function above, but the matrix is stored in block compressed -// sparse row format with block pattern 1x16 which consists of two arrays: -// 1. A matrix array stores non-zero blocks of the matrix in row major. -// 2. A ledger array stores nrows groups, one group per row. Each group starts -// with an integer representing the number of non-zero blocks for the -// corresponding row and follows with column indexes of the first element -// of each non-zero block. -// This function assumes that -// 1. m_cols is a multiple of 16 so that all blocks are full blocks. -// 2. m_cols < 254 * 16 so that block index can be represented by uint8. -void SparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result); - -// Same as the function above, but for values quantized using symmetric -// quantization (e.g. by calling SymmetricQuantizeFloats). -// The passed scaling factors is a buffer of the quantization scaling factors -// that will be used to dequentize the products into the final result buffer. -// These scaling factors are the multiplication of the matrix scaling factor -// by the vector's scaling factor, one per batch (i.e. this allows quantizing -// each batch in the batch-vector matrix independently). -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, - const float* __restrict__ scaling_factors, int n_batch, - float* __restrict__ result); - -// Same as the function above except that vector values -// are quantized with asymmetric quantization per-batch and the matrix -// is quantized per row. -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, - const float* __restrict__ scaling_factors, int n_batch, - float* __restrict__ result, const float* __restrict__ per_channel_scale, - const int32_t* __restrict__ input_offset); - -// Same as the function above, but the matrix is a sparse tensor with block -// pattern 1x16. -// This function assumes that m_cols is a multiple of the block size (16 in this -// case) so that there's no incomplete block. Also, it assumes all offsets of -// input, output and filter are zero. -void SparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - int8_t* __restrict__ result); - -// Same as the function above, but the matrix is stored in block compressed -// sparse row format with block pattern 1x16 which consists of two arrays: -// 1. A matrix array stores non-zero blocks of the matrix in row major. -// 2. A ledger array stores nrows groups, one group per row. Each group starts -// with an integer representing the number of non-zero blocks for the -// corresponding row followed by column index of the first element of -// each non-zero block. -// This function assumes that -// 1. m_cols is a multiple of 16 so that all blocks are full blocks. -// 2. m_cols < 254 * 16 so that block index can be represented by uint8. -void SparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* __restrict__ ledger, - const int m_rows, const int m_cols, const int8_t* __restrict__ vectors, - const float* __restrict__ scaling_factors, int n_batch, - float* __restrict__ result); - -// Same as the above 8, 8, 8 integer matmul except for the presence of zero -// point and non-accumulative. -// TODO(b/148688698): remove this function by folding zero point calculation in -// prepare() function. -void MatrixBatchVectorMultiply(const int8_t* input, int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, int32_t n_cell, - int8_t* gate_output, int8_t gate_output_zp); - -// Same as above but has 16 bit and 8 bit input and 8 bit output. -// Used in projection when hidden is 16bit. -void MatrixBatchVectorMultiply(const int16_t* hidden, - const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, - int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, - int32_t n_hidden, int32_t n_output, - int32_t output_zp, int8_t* proj_output); - -// Apply Layer Normalization (https://arxiv.org/abs/1607.06450) to a Quantized -// vector. -// Parameters: -// - input: batch vector of size n_batch * n_input; 16 bit. -// - layer_norm_weights: the quantized layer normalization weights. -// - bias: the bias for the layer normalization. -// - layer_norm_scale_a: multiplier for scale factor. -// - layer_norm_scale_b: shift for scale factor. -// - variance_limit: the guard to make sure the inverse does not overflow. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output); - -// Same as above but the internal calculation is done in float. -void ApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, - const int32_t* bias, int n_batch, int n_input, - int16_t* output); - -// Apply Sigmoid to a quantized vector. -// Parameters: -// - input: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -// The input is in Q3.12 format and the output is in Q0.15 format. -void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, - int16_t* output); - -// Same as above but the internal calcualtion is float. -void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input, - int16_t* output); - -// Apply Tanh to a quantized vector. -// Parameters: -// - integer_bits: the integer bits of the input. -// Currently supports 0, 1, 2, 3, 4, 5, 6. -// - input: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -// The input is in Qm.15-m format and the output is in Q0.15 format. -void ApplyTanh(int32_t intger_bits, const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output); - -// Apply Tanh to a quantized vector. Tbe internal calculation is in float. -// - Input has 2^(integer_bits) as scale. -// - Output has Q0.15 as scale. -void ApplyTanhFloat(const int16_t* input, int32_t n_batch, int32_t n_input, - int32_t integer_bits, int16_t* output); - -// Element-wise multiplication of two quantized vectors. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - shift: the shift needed to produce the output. -// - output: the 16 bit output of size n_batch * n_input. -// Output does not need to be initialized. -void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int shift, int16_t* output); - -// Element-wise multiplication of two quantized vectors. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - shift: the shift needed to produce the output. -// - output: the 8 bit output of size n_batch * n_input. -// Output does not need to be initialized. -void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int shift, int8_t* output); - -// Element-wise multiplication of two quantized vectors with rescaling. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - multiplier: the multiplier part of scale. -// - shift: the shift part of scale. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 8 bit output of size n_batch * n_input. -// - output_zp: the zero point of output. -// Output does not need to be initialized. -// Multiplier ("m") and shift ("s") are connected to scale ("s") with s = m * -// 2^(s - 31). -void CwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output); - -// Element-wise saturating addition of two quantized vectors without rescaling. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 8 bit output of size n_batch * n_input. -// Output does not need to be initialized. -void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int16_t* output); - -// Element-wise in-place clipping of a vector. Overloaded for float, int16_t, -// int8_t. Parameters: -// - vector: vector of size v_size. -// - v_size: the size of the vector. -// - clipping_value: the value used for clipping. -void CwiseClipping(float* vector, const int v_size, const float clipping_value); -void CwiseClipping(int16_t* vector, const int v_size, - const int16_t clipping_value); -void CwiseClipping(int8_t* vector, const int v_size, - const int8_t clipping_value); - -// Dot product of two vectors. -float VectorVectorDotProduct(const float* vector1, const float* vector2, - int v_size); - -// Dot product of two batch vectors of size n_batch * v_size: -// vector1 = [x_1_1, x_1_2, ..., x_1_vsize, -// x_2_1, x_2_2, ..., x_2_vsize, -// ... -// x_nbatch_1,..., x_nbatch_vsize] -// vector2 = [y_1_1, y_1_2, ..., y_1_vsize, -// y_2_1, y_2_2, ..., y_2_vsize, -// ... -// y_nbatch_1,..., y_nbatch_vsize] -// Then result will be a vector of n_batch size starting from 'result': -// [x_1_1 * y_1_1 + x_1_2 * y_1_2 + ... + x_1_vsize * y_1_vsize, -// x_2_1 * y_2_1 + x_2_2 * y_2_2 + ... + x_2_vsize * y_2_vsize, -// ... -// x_nbatch_1 * y_nbatch_1 + ... + x_nbatch_vsize * y_nbatch_vsize] -template -inline void BatchVectorBatchVectorDotProduct(const T* vector1, const T* vector2, - int v_size, int n_batch, - T* result) { - for (int b = 0; b < n_batch; b++) { - result[b] = VectorVectorDotProduct(vector1, vector2, v_size); - vector1 += v_size; - vector2 += v_size; - } -} - -// Same as above but input is 16bit and output is 32bit. -void BatchVectorBatchVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, int v_size, - int n_batch, int32_t* result); - -// Same as above, but inputs are 16bit integer and output is 16bit integer. -void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size, - const int16_t* batch_vector, - int n_batch, int32_t multiplier, - int shift, int16_t* result); - -// Compute "1.0f - elements of vector" (used in CIFG). -void Sub1Vector(const float* vector, int v_size, float* result); - -// Compute "1.0f - elements of vector" (used in CIFG) for int16 input. -// "vector" has range [0, 32767] because it is the output of sigmoid function. -void Sub1Vector(const int16_t* vector, int v_size, int16_t* result); - -// Reduce-sum on a float input vector: -// input_vector: float pointer to input vector. -// output_vector: float pointer to vector. -// output_size: output vector size. -// reduction_size: number of consecutive elements from input vector which are -// added to get one element of output. -void ReductionSumVector(const float* input_vector, float* output_vector, - int output_size, int reduction_size); - -// Same as above but input/output is 32 bit integer. -void ReductionSumVector(const int32_t* input_vector, int32_t* output_vector, - int output_size, int reduction_size); - -// Same as above but input is 8 bit integer. -void ReductionSumVector(const int8_t* input_vector, int32_t* output_vector, - int output_size, int reduction_size); - -// Multiply all elements of vector with a scalar. -void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, - float* result); - -// Layer norm for each batch. -void MeanStddevNormalization(const float* input_vector, float* output_vector, - int v_size, int n_batch); - -// Saturate Add with rescale on both inputs. -void TwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, int32_t n_batch, - int32_t n_cell, int16_t* output); - -// Same as the function above, but provide a scratch buffer for the -// int8 x int8 -> int32 and a CpuBackendContext for the accumulator -// computation. -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, - const float* __restrict__ scaling_factors, int n_batch, - int32_t* __restrict__ scratch, float* __restrict__ result, - CpuBackendContext* __restrict__ context); - -// Same as the function above except that can make use of cached row sums. -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context); - -// Same as the function above, but provides separate scaling factor for the -// matrix and the vectors. The scaling factors are multiplied in the -// scaling_factor_scratch buffer. -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float matrix_scaling_factor, - const float* vector_scaling_factors, int n_batch, - float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, float* scaling_factor_scratch, - CpuBackendContext* context) { - for (int b = 0; b < n_batch; ++b) { - scaling_factor_scratch[b] = - vector_scaling_factors[b] * matrix_scaling_factor; - } - MatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, - scaling_factor_scratch, n_batch, result, - per_channel_scale, input_offset, scratch, - row_sums, compute_row_sums, context); -} - -// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch -// dimension composed by input vectors independent from each other). The result -// of the multiplication is accumulated to the passed result buffer. -// More specifically, for a matrix M of shape [n, i] and a batched-vector -// of shape [i, batch] it will first compute the product of shape [n, batch]. -// This product will be accumulated to the result buffer, -// Parameters: -// - input: batch vector of size n_batch * n_input -// - bias: vector of size b_input -// - input_to_gate_weights: matrix of size n_input * n_output -// - multiplier: scalar -// - shift: scalar -// - n_batch: the batch size -// - n_input: the input size -// - n_output: the output size -// - output_zp: the zero point of the output. -// - scratch: batch vector of size n_batch * n_output -// - output: the 16 bit output -// Notes: -// - this is used for gate matmul: for non-cifg it is for input, forget, -// cell, output gates; for cifg, it is for forget, cell, output gates. -// - multiplier and shift combined gives the scale. -// - assumes input zero point is 0. -// - scratch is created for optimization purpose only. -// TODO(b/152066492): this can be removed if some future optimization -// work makes it unnecessary. -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context); - -// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch -// dimension composed by input vectors independent from each other). The result -// of the multiplication is accumulated to the passed result buffer. -// More specifically, for a matrix M of shape [n, i] and a batched-vector -// of shape [i, batch] it will first compute the product of shape [n, batch]. -// This product will be accumulated to the result buffer, -// Parameters: -// - input: batch vector of size n_batch * n_input -// - bias: vector of size b_input -// - input_to_gate_weights: matrix of size n_input * n_output -// - multiplier: scalar -// - shift: scalar -// - n_batch: the batch size -// - n_input: the input size -// - n_output: the output size -// - output_zp: the zero point of the output. -// - scratch: batch vector of size n_batch * n_output -// - output: the 8 bit output -// Notes: -// - this is used for projection matmul. -// - multiplier and shift combined gives the scale. -// - assumes input zero point is 0. -// - scratch is created for optimization purpose only. -// TODO(b/152066492): this can be removed if some future optimization -// work makes it unnecessary. -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context); - -// Apply Rectified Linear to elements of a vector. -void ApplyReluToVector(const float* __restrict__ vector, int v_size, - float* __restrict__ result); - -// Apply Rectified Linear 1 (cap to [-1;1]) to elements of a vector -void ApplyRelu1ToVector(const float* __restrict__ vector, int v_size, - float* __restrict__ result); - -// Apply Rectified Linear 6 (cap to [0;6]) to elements of a vector -void ApplyRelu6ToVector(const float* __restrict__ vector, int v_size, - float* __restrict__ result); - -// Apply signbit to elements of a vector -void ApplySignbitToVector(const float* __restrict__ vector, int v_size, - float* __restrict__ result); - -// Unpack or inflate `src_buffer` by taking each element and splitting it as -// two elements into `dst_buffer`. -// Parameters: -// src_buffer : Densely packed buffer containing int4 values -// num_elements : Number of elements stored in the buffer. Note that this can -// be smaller than the size of `src_buffer` by 1 if it's odd, -// in which case the last nibble in `src_buffer` is ignored. -// This should be equal to the size of `dst_buffer`. -// dst_buffer : Buffer to unpack into. Should be allocated by the caller. -// Size should be at least `num_elements`. -// Notes: -// For example, given `src_buffer = {0x12, 0x34};`, calling this function -// will return `dst_buffer = {0x02, 0x01, 0x04, 0x03}`. -void UnpackDenseInt4IntoInt8(const int8_t* src_buffer, int num_elements, - int8_t* dst_buffer); - -} // namespace tensor_utils - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_PORTABLE_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h deleted file mode 100644 index 902efbc0..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/quantization_util.h +++ /dev/null @@ -1,292 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -// Given the min and max values of a float array, return -// reasonable quantization parameters to use for this array. -template -QuantizationParams ChooseQuantizationParams(double rmin, double rmax, - bool narrow_range) { - const T qmin = std::numeric_limits::min() + (narrow_range ? 1 : 0); - const T qmax = std::numeric_limits::max(); - const double qmin_double = qmin; - const double qmax_double = qmax; - // 0 should always be a representable value. Let's assume that the initial - // min,max range contains 0. - TFLITE_CHECK_LE(rmin, 0.); - TFLITE_CHECK_GE(rmax, 0.); - if (rmin == rmax) { - // Special case where the min,max range is a point. Should be {0}. - TFLITE_CHECK_EQ(rmin, 0.); - TFLITE_CHECK_EQ(rmax, 0.); - QuantizationParams quantization_params; - quantization_params.zero_point = 0; - quantization_params.scale = 0.; - return quantization_params; - } - - // General case. - // - // First determine the scale. - const double scale = (rmax - rmin) / (qmax_double - qmin_double); - - // Zero-point computation. - // First the initial floating-point computation. The zero-point can be - // determined from solving an affine equation for any known pair - // (real value, corresponding quantized value). - // We know two such pairs: (rmin, qmin) and (rmax, qmax). - // The arithmetic error on the zero point computed from either pair - // will be roughly machine_epsilon * (sum of absolute values of terms) - // so we want to use the variant that adds the smaller terms. - const double zero_point_from_min = qmin_double - rmin / scale; - const double zero_point_from_max = qmax_double - rmax / scale; - const double zero_point_from_min_error = - std::abs(qmin_double) + std::abs(rmin / scale); - const double zero_point_from_max_error = - std::abs(qmax_double) + std::abs(rmax / scale); - - const double zero_point_double = - zero_point_from_min_error < zero_point_from_max_error - ? zero_point_from_min - : zero_point_from_max; - - // Now we need to nudge the zero point to be an integer - // (our zero points are integer, and this is motivated by the requirement - // to be able to represent the real value "0" exactly as a quantized value, - // which is required in multiple places, for example in Im2col with SAME - // padding). - T nudged_zero_point = 0; - if (zero_point_double < qmin_double) { - nudged_zero_point = qmin; - } else if (zero_point_double > qmax_double) { - nudged_zero_point = qmax; - } else { - nudged_zero_point = static_cast(round(zero_point_double)); - } - // The zero point should always be in the range of quantized value, - // [qmin, qmax]. - TFLITE_CHECK_GE(nudged_zero_point, qmin); - TFLITE_CHECK_LE(nudged_zero_point, qmax); - - // Finally, store the result nudged quantization params. - QuantizationParams quantization_params; - quantization_params.zero_point = nudged_zero_point; - quantization_params.scale = scale; - return quantization_params; -} - -template -QuantizationParams ChooseQuantizationParams(double rmin, double rmax) { - return ChooseQuantizationParams(rmin, rmax, false); -} - -// Converts a floating-point number to an integer. For all inputs x where -// static_cast(x) is legal according to the C++ standard, the result -// is identical to that cast (i.e. the result is x with its fractional part -// truncated whenever that is representable as IntOut). -// -// static_cast would cause undefined behavior for the following cases, which -// have well-defined behavior for this function: -// -// 1. If x is NaN, the result is zero. -// -// 2. If the truncated form of x is above the representable range of IntOut, -// the result is std::numeric_limits::max(). -// -// 3. If the truncated form of x is below the representable range of IntOut, -// the result is std::numeric_limits::min(). -// -// Note that cases #2 and #3 cover infinities as well as finite numbers. -// -// The range of FloatIn must include the range of IntOut, otherwise -// the results are undefined. -// TODO(sfeuz): Replace by absl::SafeCast once available. -template -IntOut SafeCast(FloatIn x) { - static_assert(!std::numeric_limits::is_integer, - "FloatIn is integer"); - static_assert(std::numeric_limits::is_integer, - "IntOut is not integer"); - static_assert(std::numeric_limits::radix == 2, "IntOut is base 2"); - - // Special case NaN, for which the logic below doesn't work. - if (std::isnan(x)) { - return 0; - } - - // Negative values all clip to zero for unsigned results. - if (!std::numeric_limits::is_signed && x < 0) { - return 0; - } - - // Handle infinities. - if (std::isinf(x)) { - return x < 0 ? std::numeric_limits::min() - : std::numeric_limits::max(); - } - - // Set exp such that x == f * 2^exp for some f with |f| in [0.5, 1.0), - // unless x is zero in which case exp == 0. Note that this implies that the - // magnitude of x is strictly less than 2^exp. - int exp = 0; - std::frexp(x, &exp); - - // Let N be the number of non-sign bits in the representation of IntOut. If - // the magnitude of x is strictly less than 2^N, the truncated version of x - // is representable as IntOut. The only representable integer for which this - // is not the case is kMin for signed types (i.e. -2^N), but that is covered - // by the fall-through below. - if (exp <= std::numeric_limits::digits) { - return x; - } - - // Handle numbers with magnitude >= 2^N. - return x < 0 ? std::numeric_limits::min() - : std::numeric_limits::max(); -} - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of NEGATIVE its exponent --- -// this is intended as a RIGHT-shift. -// -// Restricted to the case where the multiplier < 1 (and non-negative). -void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift); - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of its exponent. -// -// Restricted to the case where the multiplier > 1. -void QuantizeMultiplierGreaterThanOne(double double_multiplier, - int32_t* quantized_multiplier, - int* left_shift); - -// Decompose a double multiplier into a Q0.31 int32 representation of its -// significand, and shift representation of its exponent. -// -// Handles an arbitrary positive multiplier. The 'shift' output-value is -// basically the 'floating-point exponent' of the multiplier: -// Negative for a right-shift (when the multiplier is <1), positive for a -// left-shift (when the multiplier is >1) -void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, - int* shift); - -// Splits a double input value into a returned fraction, and a shift value from -// the exponent, using only bitwise and integer operations to support -// microcontrollers and other environments without floating-point support. -// -// This is designed to be a replacement for how std::frexp() is used within the -// QuantizeMultiplier() function, and so has a different signature than the -// standard version, returning a 64-bit integer rather than a double. This -// result has a maximum value of 1<<31, with the fraction expressed as a -// proportion of that maximum. -// -// std::frexp() returns NaNs and infinities unmodified, but since we're -// returning integers that can't represent those values, instead we return -// a shift of std::numeric_limits::max() for all bad numbers, with an int64 -// result of 0 for NaNs, std:numeric_limits::max() for +INFINITY, and -// std::numeric_limits::min() for -INFINITY. Denormalized inputs will -// result in return values that end up truncating some bits at the end, -// reflecting the loss of precision inherent in denormalization. -int64_t IntegerFrExp(double input, int* shift); - -// Converts an integer fraction in the format produced by IntegerFrExp (where -// 0x40000000 is 1.0) and an exponent shift (between -1022 and +1022) into an -// IEEE binary64 double format result. The implementation uses only integer and -// bitwise operators, so no floating point hardware support or emulation is -// needed. This is here so quantized operations can run non-time-critical -// preparation calculations on microcontrollers and other platforms without -// float support. -double DoubleFromFractionAndShift(int64_t fraction, int shift); - -// Performs a multiplication of two numbers in double format, using only integer -// and bitwise instructions. This is aimed at supporting housekeeping functions -// for quantized operations on microcontrollers without floating-point hardware. -double IntegerDoubleMultiply(double a, double b); - -// Returns -1 if a is less than b, 0 if a and b are equal, and +1 if a is -// greater than b. It is implemented using only integer and logical instructions -// so that it can be easily run on microcontrollers for quantized operations. -int IntegerDoubleCompare(double a, double b); - -// This first creates a multiplier in a double equivalent of -// Q(input_integer_bits).(31-input_integer_bits) representation, with extra -// precision in the double's fractional bits. It then splits the result into -// significand and exponent. -void PreprocessSoftmaxScaling(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, int* left_shift); -// Like PreprocessSoftmaxScaling, but inverse scaling factors also calculated. -void PreprocessLogSoftmaxScalingExp(double beta, double input_scale, - int input_integer_bits, - int32_t* quantized_multiplier, - int* left_shift, - int32_t* reverse_scaling_divisor, - int* reverse_scaling_left_shift); -// Calculate the largest input that will result in a within-bounds intermediate -// result within MultiplyByQuantizedMultiplierGreaterThanOne. In other words, -// it must not overflow before we reduce the value by multiplication by the -// input multiplier. The negative radius is used as the minimum difference in -// Softmax. -int CalculateInputRadius(int input_integer_bits, int input_left_shift, - int total_signed_bits = 31); - -// Nudges a min/max quantization range to ensure zero is zero. -// Gymnastics with nudged zero point is to ensure that real zero maps to -// an integer, which is required for e.g. zero-padding in convolutional layers. -// Outputs nudged_min, nudged_max, nudged_scale. -void NudgeQuantizationRange(const float min, const float max, - const int quant_min, const int quant_max, - float* nudged_min, float* nudged_max, - float* nudged_scale); - -// Fake quantizes (quantizes and dequantizes) input_data using the scale, -// nudged_min, and nudged_max from NudgeQuantizationRange. This matches the code -// in TensorFlow's FakeQuantizeWithMinMaxVarsFunctor. -void FakeQuantizeArray(const float nudged_scale, const float nudged_min, - const float nudged_max, const float* input_data, - float* output_data, const float size); - -// If x is approximately a power of two (with any positive or negative -// exponent), stores that exponent (i.e. log2(x)) in *log2_result, otherwise -// returns false. -bool CheckedLog2(const float x, int* log2_result); - -// Decomposes an array of double multipliers into a Q0.31 int32 representation -// of its significand, and shift representation of its exponent. -// -// Handles an arbitrary multiplier. The 'shift' output-value is -// basically the 'floating-point exponent' of the multiplier: -// Negative for a right-shift (when the multiplier is <1), positive for a -// left-shift (when the multiplier is >1) -void QuantizeMultiplierArray(const double* effective_scales, size_t size, - int32_t* effective_scale_significand, - int* effective_shift); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_QUANTIZATION_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h deleted file mode 100644 index 63a6c47b..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add.h +++ /dev/null @@ -1,561 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ - -#include -#include -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - T activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], activation_min, activation_max); - } -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. - -// This function is used for 8-bit as well as for 16-bit, but the accumulator -// is 32-bit for both cases. The overflow does not happen due to the -// choice of the shift (20 or 15, accordingly - see add.cc for more comments). -template -inline void AddElementwise(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data) { - TFLITE_DCHECK_GT(params.input1_offset, -std::numeric_limits::max()); - TFLITE_DCHECK_GT(params.input2_offset, -std::numeric_limits::max()); - TFLITE_DCHECK_LT(params.input1_offset, std::numeric_limits::max()); - TFLITE_DCHECK_LT(params.input2_offset, std::numeric_limits::max()); - - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -// Scalar-broadcast add that can be used for inner loop of more general -// broadcast add, so that, for example, scalar-broadcast with batch will still -// be fast. -inline void AddScalarBroadcast(int size, const ArithmeticParams& params, - uint8_t input1_data, const uint8_t* input2_data, - uint8_t* output_data) { - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - - const int32_t input1_val = params.input1_offset + input1_data; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - for (int i = 0; i < size; ++i) { - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void AddGeneralParamScale(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int16_t* input1_data, - const RuntimeShape& input2_shape, - const int16_t* input2_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - int max_value = std::numeric_limits::max(); - - TFLITE_DCHECK_GT(params.input1_offset, -max_value); - TFLITE_DCHECK_GT(params.input2_offset, -max_value); - TFLITE_DCHECK_LT(params.input1_offset, max_value); - TFLITE_DCHECK_LT(params.input2_offset, max_value); - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int16_t* input1_data, - const RuntimeShape& input2_shape, const int16_t* input2_data, - const RuntimeShape& output_shape, int16_t* output_data, - bool pot_scale = true) { - if (!pot_scale) { - AddGeneralParamScale(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); - return; - } - - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - - const int input1_shift = params.input1_shift; - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - const int16_t output_activation_min = params.quantized_activation_min; - const int16_t output_activation_max = params.quantized_activation_max; - - TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); - TFLITE_DCHECK_LE(input1_shift, 0); - TFLITE_DCHECK_LE(params.input2_shift, 0); - const int16_t* not_shift_input = - input1_shift == 0 ? input1_data : input2_data; - const int16_t* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_right_shift = - input1_shift == 0 ? -params.input2_shift : -input1_shift; - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - using F0 = gemmlowp::FixedPoint; - - F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = F0::FromRaw( - gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); - F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); - const int16_t raw_output = result.raw(); - const int16_t clamped_output = std::min( - output_activation_max, std::max(output_activation_min, raw_output)); - output_data[i] = clamped_output; - } -} - -template -inline void AddBroadcast(const T* input_data, const T* broadcast_data, - T* output_data, size_t size, T activation_min, - T activation_max) { - for (size_t c = 0; c < size; ++c) { - output_data[c] = ActivationFunctionWithMinMax( - input_data[c] + broadcast_data[0], activation_min, activation_max); - } -} - -template <> -inline void AddBroadcast(const int32_t* input_data, - const int32_t* broadcast_data, - int32_t* output_data, size_t size, - int32_t activation_min, - int32_t activation_max) { - size_t c = 0; -#ifdef USE_NEON - const int32x4_t vmax = vdupq_n_s32(activation_max); - const int32x4_t vmin = vdupq_n_s32(activation_min); - const int32x4_t vb = vdupq_n_s32(broadcast_data[0]); - for (; c + 4 <= size; c += 4) { - const int32x4_t va = vld1q_s32(&input_data[c]); - int32x4_t vres = vaddq_s32(va, vb); - vres = vmaxq_s32(vmin, vres); - vres = vminq_s32(vmax, vres); - vst1q_s32(&output_data[c], vres); - } -#endif - for (; c < size; ++c) { - output_data[c] = ActivationFunctionWithMinMax( - input_data[c] + broadcast_data[0], activation_min, activation_max); - } -} - -template -void AddElementwise(const T* input1_data, const T* input2_data, T* output_data, - size_t size, T activation_min, T activation_max) { - for (size_t c = 0; c < size; ++c) { - output_data[c] = ActivationFunctionWithMinMax( - input1_data[c] + input2_data[c], activation_min, activation_max); - } -} - -template <> -inline void AddElementwise(const int32_t* input1_data, - const int32_t* input2_data, - int32_t* output_data, size_t size, - int32_t activation_min, - int32_t activation_max) { - size_t c = 0; -#ifdef USE_NEON - const int32x4_t vmax = vdupq_n_s32(activation_max); - const int32x4_t vmin = vdupq_n_s32(activation_min); - for (; c + 4 <= size; c += 4) { - const int32x4_t va = vld1q_s32(&input1_data[c]); - const int32x4_t vb = vld1q_s32(&input2_data[c]); - int32x4_t vres = vaddq_s32(va, vb); - vres = vmaxq_s32(vmin, vres); - vres = vminq_s32(vmax, vres); - vst1q_s32(&output_data[c], vres); - } -#endif - for (; c < size; ++c) { - output_data[c] = ActivationFunctionWithMinMax( - input1_data[c] + input2_data[c], activation_min, activation_max); - } -} - -template -inline void BroadcastAddRecursiveDimensions( - int dimension, size_t* input1_offset_p, size_t* input2_offset_p, - size_t* output_offset, size_t* compressed_input1_stride, - size_t* compressed_input2_stride, size_t* compressed_output_shape, - T activation_min, T activation_max, const T* input1_data, - const T* input2_data, T* output_data) { - if (dimension > 0) { - for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { - size_t input1_offset_c = *input1_offset_p; - size_t input2_offset_c = *input2_offset_p; - BroadcastAddRecursiveDimensions( - dimension - 1, &input1_offset_c, &input2_offset_c, output_offset, - compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, activation_min, activation_max, input1_data, - input2_data, output_data); - *input1_offset_p += compressed_input1_stride[dimension]; - *input2_offset_p += compressed_input2_stride[dimension]; - } - } else { - TFLITE_DCHECK(dimension == 0); - bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; - bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; - TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); - const T* input1_data_ptr = input1_data + *input1_offset_p; - const T* input2_data_ptr = input2_data + *input2_offset_p; - T* output_data_ptr = output_data + *output_offset; - if (input1_is_broadcast) { - // input1 is broadcast. - AddBroadcast(input2_data_ptr, input1_data_ptr, output_data_ptr, - compressed_output_shape[dimension], activation_min, - activation_max); - *input2_offset_p += compressed_output_shape[dimension]; - } else if (input2_is_broadcast) { - // input2 is broadcast. - AddBroadcast(input1_data_ptr, input2_data_ptr, output_data_ptr, - compressed_output_shape[dimension], activation_min, - activation_max); - *input1_offset_p += compressed_output_shape[dimension]; - } else { - // Add element-wise. - AddElementwise(input1_data_ptr, input2_data_ptr, output_data_ptr, - compressed_output_shape[dimension], activation_min, - activation_max); - *input1_offset_p += compressed_output_shape[dimension]; - *input2_offset_p += compressed_output_shape[dimension]; - } - *output_offset += compressed_output_shape[dimension]; - } -} - -template -inline typename std::enable_if::value || dummy, void>::type -BroadcastAdd6DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - constexpr int kMaxBroadcastDim = 6; - T activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - size_t compressed_input1_stride[kMaxBroadcastDim]; - size_t compressed_input2_stride[kMaxBroadcastDim]; - size_t compressed_output_shape[kMaxBroadcastDim]; - bool broadcastable_shape = ReduceDimensionsForBroadcast( - input1_shape, input2_shape, compressed_input1_stride, - compressed_input2_stride, compressed_output_shape); - // Skip broadcasting for degenerate shapes. - if (!broadcastable_shape) { - return; - } - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastAddRecursiveDimensions( - kMaxBroadcastDim - 1, &input1_offset, &input2_offset, &output_offset, - compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, activation_min, activation_max, input1_data, - input2_data, output_data); -} - -// This function is used for 8-bit as well as for 16-bit, but the accumulator -// is 32-bit for both cases. The overflow does not happen due to the -// choice of the shift (20 or 15, accordingly - see add.cc for more comments). -template -inline void BroadcastAddRecursiveDimensions( - const ArithmeticParams& params, int dimension, size_t* input1_offset_p, - size_t* input2_offset_p, size_t* output_offset, - size_t* compressed_input1_stride, size_t* compressed_input2_stride, - size_t* compressed_output_shape, const T* input1_data, const T* input2_data, - T* output_data) { - for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { - if (dimension > 0) { - size_t input1_offset_c = *input1_offset_p; - size_t input2_offset_c = *input2_offset_p; - BroadcastAddRecursiveDimensions( - params, dimension - 1, &input1_offset_c, &input2_offset_c, - output_offset, compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, input1_data, input2_data, output_data); - } else { - TFLITE_DCHECK(dimension == 0); - const int32_t input1_val = - params.input1_offset + input1_data[*input1_offset_p]; - const int32_t input2_val = - params.input2_offset + input2_data[*input2_offset_p]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, - params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, - params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[*output_offset] = static_cast(clamped_output); - ++(*output_offset); - } - *input1_offset_p += compressed_input1_stride[dimension]; - *input2_offset_p += compressed_input2_stride[dimension]; - } -} - -// This function is used for 8-bit as well as for 16-bit, but the accumulator -// is 32-bit for both cases. The overflow does not happen due to the -// choice of the shift (20 or 15, accordingly - see add.cc for more comments). -template -inline typename std::enable_if::value, void>::type -BroadcastAdd6DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - constexpr int kMaxBroadcastDim = 6; - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - size_t compressed_input1_stride[kMaxBroadcastDim]; - size_t compressed_input2_stride[kMaxBroadcastDim]; - size_t compressed_output_shape[kMaxBroadcastDim]; - bool broadcastable_shape = ReduceDimensionsForBroadcast( - input1_shape, input2_shape, compressed_input1_stride, - compressed_input2_stride, compressed_output_shape); - // Skip broadcasting for degenerate shapes. - if (!broadcastable_shape) { - return; - } - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastAddRecursiveDimensions( - params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset, - &output_offset, compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, input1_data, input2_data, output_data); -} - -template -inline void BroadcastAdd4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - return BroadcastAdd6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); -} - -inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, - const RuntimeShape& unswitched_input1_shape, - const uint8_t* unswitched_input1_data, - const RuntimeShape& unswitched_input2_shape, - const uint8_t* unswitched_input2_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - ArithmeticParams switched_params = unswitched_params; - switched_params.input1_offset = unswitched_params.input2_offset; - switched_params.input1_multiplier = unswitched_params.input2_multiplier; - switched_params.input1_shift = unswitched_params.input2_shift; - switched_params.input2_offset = unswitched_params.input1_offset; - switched_params.input2_multiplier = unswitched_params.input1_multiplier; - switched_params.input2_shift = unswitched_params.input1_shift; - - const bool use_unswitched = - unswitched_params.broadcast_category == - tflite_micro::BroadcastableOpCategory::kFirstInputBroadcastsFast; - - const ArithmeticParams& params = - use_unswitched ? unswitched_params : switched_params; - const uint8_t* input1_data = - use_unswitched ? unswitched_input1_data : unswitched_input2_data; - const uint8_t* input2_data = - use_unswitched ? unswitched_input2_data : unswitched_input1_data; - - // Fivefold nested loops. The second input resets its position for each - // iteration of the second loop. The first input resets its position at the - // beginning of the fourth loop. The innermost loop is an elementwise add of - // sections of the arrays. - uint8_t* output_data_ptr = output_data; - const uint8_t* input1_data_ptr = input1_data; - const uint8_t* input2_data_reset = input2_data; - // In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared - // between input shapes. y3 for input 1 is always broadcast, and so the - // dimension there is 1, whereas optionally y1 might be broadcast for input 2. - // Put another way, - // input1.shape.FlatSize = y0 * y1 * y2 * y4, - // input2.shape.FlatSize = y0 * y2 * y3 * y4. - int y0 = params.broadcast_shape[0]; - int y1 = params.broadcast_shape[1]; - int y2 = params.broadcast_shape[2]; - int y3 = params.broadcast_shape[3]; - int y4 = params.broadcast_shape[4]; - if (y4 > 1) { - // General fivefold pattern, with y4 > 1 so there is a non-broadcast inner - // dimension. - for (int i0 = 0; i0 < y0; ++i0) { - const uint8_t* input2_data_ptr; - for (int i1 = 0; i1 < y1; ++i1) { - input2_data_ptr = input2_data_reset; - for (int i2 = 0; i2 < y2; ++i2) { - for (int i3 = 0; i3 < y3; ++i3) { - AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, - output_data_ptr); - input2_data_ptr += y4; - output_data_ptr += y4; - } - // We have broadcast y4 of input1 data y3 times, and now move on. - input1_data_ptr += y4; - } - } - // We have broadcast y2*y3*y4 of input2 data y1 times, and now move on. - input2_data_reset = input2_data_ptr; - } - } else { - // Special case of y4 == 1, in which the innermost loop is a single element - // and can be combined with the next (y3) as an inner broadcast. - // - // Note that this handles the case of pure scalar broadcast when - // y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar - // broadcast with batch (as y2 > 1). - // - // NOTE The process is the same as the above general case except simplified - // for y4 == 1 and the loop over y3 is contained within the - // AddScalarBroadcast function. - for (int i0 = 0; i0 < y0; ++i0) { - const uint8_t* input2_data_ptr; - for (int i1 = 0; i1 < y1; ++i1) { - input2_data_ptr = input2_data_reset; - for (int i2 = 0; i2 < y2; ++i2) { - AddScalarBroadcast(y3, params, *input1_data_ptr, input2_data_ptr, - output_data_ptr); - input2_data_ptr += y3; - output_data_ptr += y3; - input1_data_ptr += 1; - } - } - input2_data_reset = input2_data_ptr; - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h deleted file mode 100644 index da40d766..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/add_n.h +++ /dev/null @@ -1,86 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_ops { - -// T is expected to be either float or int. -template -inline void AddN(const RuntimeShape& input_shape, const size_t num_inputs, - const T* const* input_data, T* output_data) { - // All inputs and output should have the same shape, this is checked during - // Prepare stage. - const size_t size = input_shape.FlatSize(); - for (size_t i = 0; i < size; ++i) { - T x = 0; - for (size_t j = 0; j < num_inputs; ++j) { - x += input_data[j][i]; - } - output_data[i] = x; - } -} - -inline void AddN(const ArithmeticParams& params, - const RuntimeShape& input_shape, const size_t num_inputs, - const int8_t* const* input_data, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - // Input offset is negative input zero point. Activation tensors are - // asymmetric quantized so they span the full int8 range. - // All inputs should have same zero-point and scale, this is checked during - // Prepare stage. - TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); - TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); - - // All inputs and output should have the same shape, this is checked during - // Prepare stage. - const size_t size = input_shape.FlatSize(); - for (size_t i = 0; i < size; ++i) { - // accumulate in scaled_x before clamping to avoid overflow - const int32_t x = params.input1_offset; // x = 0 - const int32_t shifted_x = x * (1 << params.left_shift); - int32_t scaled_x = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_x, params.input1_multiplier, params.input1_shift); - - for (size_t j = 0; j < num_inputs; ++j) { - const int32_t y = params.input1_offset + input_data[j][i]; - const int32_t shifted_y = y * (1 << params.left_shift); - int32_t scaled_y = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_y, params.input1_multiplier, params.input1_shift); - scaled_x += scaled_y; - } - - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - scaled_x, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ADD_N_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h deleted file mode 100644 index beee76b4..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/arg_min_max.h +++ /dev/null @@ -1,88 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -std::function GetComparefunction(bool is_arg_max) { - if (is_arg_max) { - return std::greater(); - } else { - return std::less(); - } -} - -template -void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, - const T3* input2_data, const RuntimeShape& output_shape, - T2* output_data, const Cmp& cmp) { - TFLITE_DCHECK_GT(input1_shape.DimensionsCount(), 0); - TFLITE_DCHECK_EQ(input1_shape.DimensionsCount() - 1, - output_shape.DimensionsCount()); - int axis = input2_data[0]; - if (axis < 0) { - axis += input1_shape.DimensionsCount(); - } - const int axis_size = input1_shape.Dims(axis); - - int outer_size = 1; - for (int i = 0; i < axis; ++i) { - TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i)); - outer_size *= input1_shape.Dims(i); - } - - int inner_size = 1; - const int dims_count = input1_shape.DimensionsCount(); - for (int i = axis + 1; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(input1_shape.Dims(i), output_shape.Dims(i - 1)); - inner_size *= input1_shape.Dims(i); - } - for (int outer = 0; outer < outer_size; ++outer) { - for (int inner = 0; inner < inner_size; ++inner) { - auto min_max_value = input1_data[outer * axis_size * inner_size + inner]; - T2 min_max_index = 0; - for (int i = 1; i < axis_size; ++i) { - const auto& curr_value = - input1_data[(outer * axis_size + i) * inner_size + inner]; - if (cmp(curr_value, min_max_value)) { - min_max_value = curr_value; - min_max_index = static_cast(i); - } - } - output_data[outer * inner_size + inner] = min_max_index; - } - } -} - -template -void ArgMinMax(const RuntimeShape& input1_shape, const T1* input1_data, - const T3* input2_data, const RuntimeShape& output_shape, - T2* output_data, const bool is_arg_max) { - ArgMinMax(input1_shape, input1_data, input2_data, output_shape, output_data, - GetComparefunction(is_arg_max)); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ARG_MIN_MAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h deleted file mode 100644 index 9b19f60c..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_matmul.h +++ /dev/null @@ -1,275 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { -namespace batch_matmul { - -// Determine which dimension is the broadcast dimension. -inline int broadcast_dim(int lhs_dim, int rhs_dim) { - if (lhs_dim == rhs_dim) return lhs_dim; - if (lhs_dim == 1) return rhs_dim; - TFLITE_DCHECK_EQ(rhs_dim, 1); - return lhs_dim; -} - -// Compute the "extent" for iterating on this dimension. -// If we are broadcasting, then don't advance (i.e return 0). -inline int extent(const RuntimeShape& shape, int x) { - if (shape.Dims(x) == 1) { - return 0; - } - int prod = 1; - for (int i = x + 1; i < shape.DimensionsCount(); ++i) { - prod *= shape.Dims(i); - } - return prod; -} - -} // namespace batch_matmul - -template -inline void BatchMatMul(const RuntimeShape& lhs_shape, const Ta* lhs_data, - const RuntimeShape& rhs_shape, const Tb* rhs_data, - const RuntimeShape& output_shape, Tout* output_data) { - const RuntimeShape extended_lhs_shape = - RuntimeShape::ExtendedShape(5, lhs_shape); - const RuntimeShape extended_rhs_shape = - RuntimeShape::ExtendedShape(5, rhs_shape); - - const int batch_dim0 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); - const int batch_dim1 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); - const int batch_dim2 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); - - const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); - const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); - const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); - const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); - const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); - const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); - - // Set params for each matrix multiply. - const int lhs_rows = extended_lhs_shape.Dims(3); - const int rhs_cols = extended_rhs_shape.Dims(4); - const int accum_depth = extended_lhs_shape.Dims(4); - - for (int b0 = 0; b0 < batch_dim0; ++b0) { - const Ta* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); - const Tb* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); - for (int b1 = 0; b1 < batch_dim1; ++b1) { - const Ta* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; - const Tb* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; - for (int b2 = 0; b2 < batch_dim2; ++b2) { - const Ta* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; - const Tb* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; - Tout* out_ptr = output_data + ((b0 * batch_dim1 * batch_dim2) + - b1 * batch_dim2 + b2) * - lhs_rows * rhs_cols; - for (int j = 0; j < rhs_cols; ++j) { - for (int i = 0; i < lhs_rows; ++i) { - Tout total = 0; - for (int k = 0; k < accum_depth; ++k) { - total += static_cast(lhs_ptr2[accum_depth * i + k]) * - static_cast(rhs_ptr2[j * accum_depth + k]); - } - int idx = lhs_rows * j + i; - out_ptr[idx] = total; - } - } - } - } - } -} - -inline void BatchMatMul(const RuntimeShape& lhs_shape, const int8_t* lhs_data, - const RuntimeShape& rhs_shape, const int8_t* rhs_data, - const float* scaling_factors, - const int32_t* input_offset, int32_t* row_sums, - const RuntimeShape& output_shape, float* output_data, - bool* compute_row_sums) { - const RuntimeShape extended_lhs_shape = - RuntimeShape::ExtendedShape(5, lhs_shape); - const RuntimeShape extended_rhs_shape = - RuntimeShape::ExtendedShape(5, rhs_shape); - - const int batch_dim0 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); - const int batch_dim1 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); - const int batch_dim2 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); - - const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); - const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); - const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); - const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); - const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); - const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); - - // Set params for each matrix multiply. - const int lhs_rows = extended_lhs_shape.Dims(3); - const int rhs_cols = extended_rhs_shape.Dims(4); - const int accum_depth = extended_lhs_shape.Dims(4); - - const int ioff_ext0 = rhs_ext0 == 0 ? 0 : rhs_cols; - const int ioff_ext1 = rhs_ext1 == 0 ? 0 : rhs_cols; - const int ioff_ext2 = rhs_ext2 == 0 ? 0 : rhs_cols; - const int woff_ext0 = lhs_ext0 == 0 ? 0 : lhs_rows; - const int woff_ext1 = lhs_ext1 == 0 ? 0 : lhs_rows; - const int woff_ext2 = lhs_ext2 == 0 ? 0 : lhs_rows; - - if (!compute_row_sums || *compute_row_sums) { - int num_weights_matrices = 1; - for (int i = 1; i < extended_lhs_shape.DimensionsCount() - 2; ++i) { - num_weights_matrices *= extended_lhs_shape.Dims(i); - } - tensor_utils::ReductionSumVector( - lhs_data, row_sums, num_weights_matrices * lhs_rows, accum_depth); - if (compute_row_sums) { - *compute_row_sums = false; - } - } - - for (int b0 = 0; b0 < batch_dim0; ++b0) { - const int8_t* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); - const int8_t* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); - const int32_t* ioff_ptr0 = input_offset + (b0 * ioff_ext0); - const float* scale_ptr0 = scaling_factors + (b0 * ioff_ext0); - const int32_t* woff_ptr0 = row_sums + (b0 * woff_ext0); - for (int b1 = 0; b1 < batch_dim1; ++b1) { - const int8_t* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; - const int8_t* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; - const int32_t* ioff_ptr1 = ioff_ptr0 + (b1 * ioff_ext1); - const float* scale_ptr1 = scale_ptr0 + (b1 * ioff_ext1); - const int32_t* woff_ptr1 = woff_ptr0 + (b1 * woff_ext1); - for (int b2 = 0; b2 < batch_dim2; ++b2) { - const int8_t* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; - const int8_t* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; - const int32_t* ioff_ptr2 = ioff_ptr1 + (b2 * ioff_ext2); - const float* scale_ptr2 = scale_ptr1 + (b2 * ioff_ext2); - const int32_t* woff_ptr2 = woff_ptr1 + (b2 * woff_ext2); - float* out_ptr = output_data + ((b0 * batch_dim1 * batch_dim2) + - b1 * batch_dim2 + b2) * - lhs_rows * rhs_cols; - for (int j = 0; j < rhs_cols; ++j) { - const float batch_scaling_factor = scale_ptr2[j]; - const float batch_offset = static_cast(ioff_ptr2[j]); - for (int i = 0; i < lhs_rows; ++i) { - int32_t total = 0; - for (int k = 0; k < accum_depth; ++k) { - total += - lhs_ptr2[accum_depth * i + k] * rhs_ptr2[j * accum_depth + k]; - } - int32_t row_sum = woff_ptr2[i]; - total -= row_sum * batch_offset; - int idx = lhs_rows * j + i; - out_ptr[idx] += batch_scaling_factor * total; - } - } - } - } - } -} - -template -inline void BatchMatMul(const FullyConnectedParams& params, - const RuntimeShape& lhs_shape, const T* lhs_data, - const RuntimeShape& rhs_shape, const T* rhs_data, - const RuntimeShape& output_shape, T* output_data) { - const RuntimeShape extended_lhs_shape = - RuntimeShape::ExtendedShape(5, lhs_shape); - const RuntimeShape extended_rhs_shape = - RuntimeShape::ExtendedShape(5, rhs_shape); - - const int batch_dim0 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); - const int batch_dim1 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); - const int batch_dim2 = batch_matmul::broadcast_dim( - extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); - - const int lhs_ext0 = batch_matmul::extent(extended_lhs_shape, 0); - const int lhs_ext1 = batch_matmul::extent(extended_lhs_shape, 1); - const int lhs_ext2 = batch_matmul::extent(extended_lhs_shape, 2); - const int rhs_ext0 = batch_matmul::extent(extended_rhs_shape, 0); - const int rhs_ext1 = batch_matmul::extent(extended_rhs_shape, 1); - const int rhs_ext2 = batch_matmul::extent(extended_rhs_shape, 2); - - // Set params for each matrix multiply. - const int lhs_rows = extended_lhs_shape.Dims(3); - const int rhs_cols = extended_rhs_shape.Dims(4); - const int accum_depth = extended_lhs_shape.Dims(4); - - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - for (int b0 = 0; b0 < batch_dim0; ++b0) { - const T* lhs_ptr0 = lhs_data + (b0 * lhs_ext0); - const T* rhs_ptr0 = rhs_data + (b0 * rhs_ext0); - for (int b1 = 0; b1 < batch_dim1; ++b1) { - const T* lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; - const T* rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; - for (int b2 = 0; b2 < batch_dim2; ++b2) { - const T* lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; - const T* rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; - T* out_ptr = output_data + - ((b0 * batch_dim1 * batch_dim2) + b1 * batch_dim2 + b2) * - lhs_rows * rhs_cols; - - for (int j = 0; j < rhs_cols; ++j) { - for (int i = 0; i < lhs_rows; ++i) { - AccumT total = 0; - for (int k = 0; k < accum_depth; ++k) { - AccumT lhs_val = lhs_ptr2[accum_depth * i + k]; - AccumT rhs_val = rhs_ptr2[accum_depth * j + k]; - total += (lhs_val + filter_offset) * (rhs_val + input_offset); - } - int32_t total_scaled = MultiplyByQuantizedMultiplier( - total, output_multiplier, output_shift); - total_scaled += output_offset; - total_scaled = std::max(total_scaled, output_activation_min); - total_scaled = std::min(total_scaled, output_activation_max); - const int idx = lhs_rows * j + i; - out_ptr[idx] = static_cast(total_scaled); - } - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h deleted file mode 100644 index 0788e971..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -// TODO(b/135760455): Move this method anonymous namespace in a cc file. -inline RuntimeShape ExtendShapeBatchToSpace(const RuntimeShape& shape) { - if (shape.DimensionsCount() == 4) { - return shape; - } - RuntimeShape new_shape(4, 1); - new_shape.SetDim(0, shape.Dims(0)); - new_shape.SetDim(1, shape.Dims(1)); - new_shape.SetDim(3, shape.Dims(2)); - return new_shape; -} - -template -inline void BatchToSpaceND(const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const int32_t* block_shape_data, - const RuntimeShape& unextended_input3_shape, - const int32_t* crops_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - ruy::profiler::ScopeLabel label("BatchToSpaceND"); - TFLITE_DCHECK_GE(unextended_input1_shape.DimensionsCount(), 3); - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(unextended_input1_shape.DimensionsCount(), - unextended_output_shape.DimensionsCount()); - - const RuntimeShape input1_shape = - ExtendShapeBatchToSpace(unextended_input1_shape); - const RuntimeShape output_shape = - ExtendShapeBatchToSpace(unextended_output_shape); - - const int output_width = output_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_batch_size = output_shape.Dims(0); - - const int depth = input1_shape.Dims(3); - const int input_width = input1_shape.Dims(2); - const int input_height = input1_shape.Dims(1); - const int input_batch_size = input1_shape.Dims(0); - - const int block_shape_height = block_shape_data[0]; - const int block_shape_width = - unextended_input1_shape.DimensionsCount() == 4 ? block_shape_data[1] : 1; - const int crops_top = crops_data[0]; - const int crops_left = - unextended_input1_shape.DimensionsCount() == 4 ? crops_data[2] : 0; - for (int in_batch = 0; in_batch < input_batch_size; ++in_batch) { - const int out_batch = in_batch % output_batch_size; - const int spatial_offset = in_batch / output_batch_size; - for (int in_h = 0; in_h < input_height; ++in_h) { - const int out_h = in_h * block_shape_height + - spatial_offset / block_shape_width - crops_top; - if (out_h < 0 || out_h >= output_height) { - continue; - } - for (int in_w = 0; in_w < input_width; ++in_w) { - const int out_w = in_w * block_shape_width + - spatial_offset % block_shape_width - crops_left; - - if (out_w < 0 || out_w >= output_width) { - continue; - } - T* out = output_data + Offset(output_shape, out_batch, out_h, out_w, 0); - const T* in = - input1_data + Offset(input1_shape, in_batch, in_h, in_w, 0); - memcpy(out, in, depth * sizeof(T)); - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_TO_SPACE_ND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h deleted file mode 100644 index c66d39b8..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/binary_function.h +++ /dev/null @@ -1,91 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -// Also appears to duplicate MinimumMaximum. -// -// R: Result type. T1: Input 1 type. T2: Input 2 type. -template -inline void BroadcastBinaryFunction4DSlow( - const RuntimeShape& unextended_input1_shape, const T1* input1_data, - const RuntimeShape& unextended_input2_shape, const T2* input2_data, - const RuntimeShape& unextended_output_shape, R* output_data, - R (*func)(T1, T2)) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - - const int* dims_data = - reinterpret_cast(output_shape.DimsDataUpTo5D()); - for (int b = 0; b < output_shape.Dims(0); ++b) { - int out_idx_b = b * dims_data[1]; - int in_idx1_b = desc1.strides[0] * b; - int in_idx2_b = desc2.strides[0] * b; - for (int y = 0; y < output_shape.Dims(1); ++y) { - int out_idx_y = (out_idx_b + y) * dims_data[2]; - int in_idx1_y = in_idx1_b + desc1.strides[1] * y; - int in_idx2_y = in_idx2_b + desc2.strides[1] * y; - for (int x = 0; x < output_shape.Dims(2); ++x) { - int out_idx_x = (out_idx_y + x) * dims_data[3]; - int in1_idx = in_idx1_y + desc1.strides[2] * x; - int in2_idx = in_idx2_y + desc2.strides[2] * x; - for (int c = 0; c < output_shape.Dims(3); ++c) { - auto out_idx = out_idx_x + c; - auto in1_val = input1_data[in1_idx]; - auto in2_val = input2_data[in2_idx]; - output_data[out_idx] = func(in1_val, in2_val); - in1_idx += desc1.strides[3]; - in2_idx += desc2.strides[3]; - } - } - } - } -} - -// R: Result type. T1: Input 1 type. T2: Input 2 type. -template -inline void BinaryFunction(const RuntimeShape& input1_shape, - const T1* input1_data, - const RuntimeShape& input2_shape, - const T2* input2_data, - const RuntimeShape& output_shape, R* output_data, - R (*func)(T1, T2)) { - const int flat_size = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = func(input1_data[i], input2_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BINARY_FUNCTION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h deleted file mode 100644 index 532aaf59..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_args.h +++ /dev/null @@ -1,56 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -void BroadcastArgs(const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - // Gets data at the backward index i of the shape tensor. Returns 1 if the - // index is out of range. - auto get_shape_data = [](const RuntimeShape& shape, const T* data, - int backward_idx) -> T { - int forward_idx = shape.FlatSize() - 1 - backward_idx; - if (forward_idx < 0) return 1; - return data[forward_idx]; - }; - - int output_num_elements = output_shape.FlatSize(); - for (int i = 0; i < output_num_elements; ++i) { - int backward_i = output_num_elements - 1 - i; - int shape1_i = get_shape_data(input1_shape, input1_data, i); - int shape2_i = get_shape_data(input2_shape, input2_data, i); - if (shape1_i == 1) { - output_data[backward_i] = shape2_i; - } else if (shape2_i == 1) { - output_data[backward_i] = shape1_i; - } else { - TFLITE_CHECK_EQ(shape1_i, shape2_i); - output_data[backward_i] = shape1_i; - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_ARGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h deleted file mode 100644 index 60a4549e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/broadcast_to.h +++ /dev/null @@ -1,97 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/kernel_util.h" - -namespace tflite_micro { -namespace reference_ops { -template -void BroadcastImpl(const NdArrayDesc& input_desc, const char* input_data, - const NdArrayDesc& output_desc, char* output_data, - int indexes[N], int dim, const int last_broadcasting_dim, - const int type_size) { - // Copy data from input to output. - if (dim == last_broadcasting_dim) { - int copy_size = output_desc.strides[dim] * type_size; - const char* data_src = - input_data + SubscriptToIndex(input_desc, indexes) * type_size; - char* data_dst = - output_data + SubscriptToIndex(output_desc, indexes) * type_size; - for (int i = 0; i < output_desc.extents[dim]; ++i, data_dst += copy_size) { - memcpy(data_dst, data_src, copy_size); - } - return; - } - - // Recursive call to find the next broadcasting. - for (indexes[dim] = 0; indexes[dim] < input_desc.extents[dim]; - ++indexes[dim]) { - BroadcastImpl(input_desc, input_data, output_desc, output_data, indexes, - dim + 1, last_broadcasting_dim, type_size); - } - - // Duplicate data in output tensor. - indexes[dim] = 0; - if (input_desc.extents[dim] != output_desc.extents[dim]) { - int copy_size = output_desc.strides[dim] * type_size; - char* data_src = - output_data + SubscriptToIndex(output_desc, indexes) * type_size; - char* data_dst = data_src + copy_size; - for (int i = 1; i < output_desc.extents[dim]; ++i, data_dst += copy_size) { - memcpy(data_dst, data_src, copy_size); - } - } -} - -template -inline void BroadcastTo(const RuntimeShape& unextended_input_shape, - const char* input_data, - const RuntimeShape& unextended_output_shape, - char* output_data, TfLiteType data_type) { - NdArrayDesc input_desc; - NdArrayDesc output_desc; - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_input_shape), - &input_desc); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), - &output_desc); - - // Get the last dimension has broadcasting. At this dimension, the data is - // copied from input tensor to output tensor. - int last_broadcast_dim = -1; - for (int i = N - 1; i >= 0; --i) { - if (input_desc.extents[i] != output_desc.extents[i]) { - last_broadcast_dim = i; - break; - } - } - - // If non-broadcasting, just copy data from input to output tensor. - if (last_broadcast_dim == -1) { - memcpy(output_data, input_data, - unextended_input_shape.FlatSize() * TfLiteTypeGetSize(data_type)); - return; - } - - // Broadcasting using memcpy. - int indexes[N] = {0}; - BroadcastImpl(input_desc, input_data, output_desc, output_data, indexes, 0, - last_broadcast_dim, TfLiteTypeGetSize(data_type)); -} -} // namespace reference_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BROADCAST_TO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h deleted file mode 100644 index 565f3584..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/ceil.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void Ceil(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = std::ceil(input_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CEIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h deleted file mode 100644 index 72b53bda..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/comparisons.h +++ /dev/null @@ -1,271 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ - -#include "tensorflow/lite/core/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline bool EqualFn(T lhs, T rhs) { - return lhs == rhs; -} - -template -inline bool NotEqualFn(T lhs, T rhs) { - return lhs != rhs; -} - -template -inline bool GreaterFn(T lhs, T rhs) { - return lhs > rhs; -} -template -inline bool GreaterEqualFn(T lhs, T rhs) { - return lhs >= rhs; -} -template -inline bool LessFn(T lhs, T rhs) { - return lhs < rhs; -} -template -inline bool LessEqualFn(T lhs, T rhs) { - return lhs <= rhs; -} - -template -using ComparisonFn = bool (*)(T, T); - -template F> -inline void ComparisonImpl( - const ComparisonParams& op_params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { - const int64_t flatsize = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int64_t i = 0; i < flatsize; ++i) { - output_data[i] = F(input1_data[i], input2_data[i]); - } -} - -template F> -inline void Comparison(const ComparisonParams& op_params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, bool* output_data) { - ComparisonImpl(op_params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); -} - -template F> -inline void ComparisonWithScaling( - const ComparisonParams& op_params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, bool* output_data) { - int left_shift = op_params.left_shift; - int32_t input1_offset = op_params.input1_offset; - int32_t input1_multiplier = op_params.input1_multiplier; - int input1_shift = op_params.input1_shift; - int32_t input2_offset = op_params.input2_offset; - int32_t input2_multiplier = op_params.input2_multiplier; - int input2_shift = op_params.input2_shift; - - const int64_t flatsize = - MatchingFlatSize(input1_shape, input2_shape, output_shape); - for (int64_t i = 0; i < flatsize; ++i) { - const int32_t input1_val = input1_offset + input1_data[i]; - const int32_t input2_val = input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << left_shift); - const int32_t shifted_input2_val = input2_val * (1 << left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, input2_shift); - output_data[i] = F(scaled_input1_val, scaled_input2_val); - } -} - -struct BroadcastComparison4DSlowCommon { - const RuntimeShape output_shape; - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; -}; - -TFLITE_NOINLINE -BroadcastComparison4DSlowCommon BroadcastComparison4DSlowPreprocess( - const RuntimeShape& unextended_input1_shape, - const RuntimeShape& unextended_input2_shape, - const RuntimeShape& unextended_output_shape); - -template F> -inline void BroadcastComparison4DSlowImpl( - const ComparisonParams& op_params, - const RuntimeShape& unextended_input1_shape, const T* input1_data, - const RuntimeShape& unextended_input2_shape, const T* input2_data, - const RuntimeShape& unextended_output_shape, bool* output_data) { - const BroadcastComparison4DSlowCommon dims = - BroadcastComparison4DSlowPreprocess(unextended_input1_shape, - unextended_input2_shape, - unextended_output_shape); - - for (int b = 0; b < dims.output_shape.Dims(0); ++b) { - for (int y = 0; y < dims.output_shape.Dims(1); ++y) { - for (int x = 0; x < dims.output_shape.Dims(2); ++x) { - for (int c = 0; c < dims.output_shape.Dims(3); ++c) { - output_data[Offset(dims.output_shape, b, y, x, c)] = - F(input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)], - input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]); - } - } - } - } -} - -template F> -inline void BroadcastComparison4DSlow(const ComparisonParams& op_params, - const RuntimeShape& input1_shape, - const float* input1_data, - const RuntimeShape& input2_shape, - const float* input2_data, - const RuntimeShape& output_shape, - bool* output_data) { - BroadcastComparison4DSlowImpl(op_params, input1_shape, input1_data, - input2_shape, input2_data, - output_shape, output_data); -} - -template F> -inline void BroadcastComparison4DSlowWithScaling( - const ComparisonParams& op_params, - const RuntimeShape& unextended_input1_shape, const T* input1_data, - const RuntimeShape& unextended_input2_shape, const T* input2_data, - const RuntimeShape& unextended_output_shape, bool* output_data) { - const BroadcastComparison4DSlowCommon dims = - BroadcastComparison4DSlowPreprocess(unextended_input1_shape, - unextended_input2_shape, - unextended_output_shape); - - int left_shift = op_params.left_shift; - int32_t input1_offset = op_params.input1_offset; - int32_t input1_multiplier = op_params.input1_multiplier; - int input1_shift = op_params.input1_shift; - int32_t input2_offset = op_params.input2_offset; - int32_t input2_multiplier = op_params.input2_multiplier; - int input2_shift = op_params.input2_shift; - - for (int b = 0; b < dims.output_shape.Dims(0); ++b) { - for (int y = 0; y < dims.output_shape.Dims(1); ++y) { - for (int x = 0; x < dims.output_shape.Dims(2); ++x) { - for (int c = 0; c < dims.output_shape.Dims(3); ++c) { - const int32_t input1_val = - input1_offset + - input1_data[SubscriptToIndex(dims.desc1, b, y, x, c)]; - const int32_t input2_val = - input2_offset + - input2_data[SubscriptToIndex(dims.desc2, b, y, x, c)]; - const int32_t shifted_input1_val = input1_val * (1 << left_shift); - const int32_t shifted_input2_val = input2_val * (1 << left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, input2_shift); - output_data[Offset(dims.output_shape, b, y, x, c)] = - F(scaled_input1_val, scaled_input2_val); - } - } - } - } -} - -#define TFLITE_COMPARISON_OP(name) \ - inline void name(const ComparisonParams& op_params, \ - const RuntimeShape& input1_shape, const float* input1_data, \ - const RuntimeShape& input2_shape, const float* input2_data, \ - const RuntimeShape& output_shape, bool* output_data) { \ - Comparison(op_params, input1_shape, input1_data, input2_shape, \ - input2_data, output_shape, output_data); \ - } \ - template \ - inline void name##NoScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - ComparisonImpl(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, output_shape, \ - output_data); \ - } \ - template \ - inline void name##WithScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - ComparisonWithScaling(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name##NoScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlowImpl( \ - op_params, input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - inline void Broadcast4DSlow##name( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const float* input1_data, const RuntimeShape& input2_shape, \ - const float* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlow(op_params, input1_shape, input1_data, \ - input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name##WithScaling( \ - const ComparisonParams& op_params, const RuntimeShape& input1_shape, \ - const T* input1_data, const RuntimeShape& input2_shape, \ - const T* input2_data, const RuntimeShape& output_shape, \ - bool* output_data) { \ - BroadcastComparison4DSlowWithScaling( \ - op_params, input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ - } -TFLITE_COMPARISON_OP(Equal) -TFLITE_COMPARISON_OP(NotEqual) -TFLITE_COMPARISON_OP(Greater) -TFLITE_COMPARISON_OP(GreaterEqual) -TFLITE_COMPARISON_OP(Less) -TFLITE_COMPARISON_OP(LessEqual) -#undef TFLITE_COMPARISON_OP - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_COMPARISONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h deleted file mode 100644 index 8f062f86..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/concatenation.h +++ /dev/null @@ -1,141 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void Concatenation(const ConcatenationParams& params, - const RuntimeShape* const* input_shapes, - const Scalar* const* input_data, - const RuntimeShape& output_shape, - Scalar* output_data) { - int axis = params.axis; - int inputs_count = params.inputs_count; - const int concat_dimensions = output_shape.DimensionsCount(); - TFLITE_DCHECK_LT(axis, concat_dimensions); - - int64_t concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); - for (int j = 0; j < concat_dimensions; j++) { - if (j != axis) { - MatchingDim(*input_shapes[i], j, output_shape, j); - } - } - concat_size += input_shapes[i]->Dims(axis); - } - TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); - int64_t outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= output_shape.Dims(i); - } - // For all input arrays, - // FlatSize() = outer_size * Dims(axis) * base_inner_size; - int64_t base_inner_size = 1; - for (int i = axis + 1; i < concat_dimensions; ++i) { - base_inner_size *= output_shape.Dims(i); - } - - Scalar* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; - const Scalar* input_ptr = input_data[i] + k * copy_size; - memcpy(output_ptr, input_ptr, copy_size * sizeof(Scalar)); - output_ptr += copy_size; - } - } -} - -// TODO(b/174275780): The quantized implementation of concatentation isn't fully -// quantized as it takes scale as a floating point value. This should be fixed -// when optimizng this routine further. -inline void ConcatenationWithScaling(const ConcatenationParams& params, - const RuntimeShape* const* input_shapes, - const uint8_t* const* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - int axis = params.axis; - const int32_t* input_zeropoint = params.input_zeropoint; - const float* input_scale = params.input_scale; - int inputs_count = params.inputs_count; - const int32_t output_zeropoint = params.output_zeropoint; - const float output_scale = params.output_scale; - - const int concat_dimensions = output_shape.DimensionsCount(); - TFLITE_DCHECK_LT(axis, concat_dimensions); - - int64_t concat_size = 0; - for (int i = 0; i < inputs_count; i++) { - TFLITE_DCHECK_EQ(input_shapes[i]->DimensionsCount(), concat_dimensions); - for (int j = 0; j < concat_dimensions; j++) { - if (j != axis) { - MatchingDim(*input_shapes[i], j, output_shape, j); - } - } - concat_size += input_shapes[i]->Dims(axis); - } - TFLITE_DCHECK_EQ(concat_size, output_shape.Dims(axis)); - int64_t outer_size = 1; - for (int i = 0; i < axis; ++i) { - outer_size *= output_shape.Dims(i); - } - // For all input arrays, - // FlatSize() = outer_size * Dims(axis) * base_inner_size; - int64_t base_inner_size = 1; - for (int i = axis + 1; i < concat_dimensions; ++i) { - base_inner_size *= output_shape.Dims(i); - } - - const float inverse_output_scale = 1.f / output_scale; - uint8_t* output_ptr = output_data; - for (int k = 0; k < outer_size; k++) { - for (int i = 0; i < inputs_count; ++i) { - const int copy_size = input_shapes[i]->Dims(axis) * base_inner_size; - const uint8_t* input_ptr = input_data[i] + k * copy_size; - if (input_zeropoint[i] == output_zeropoint && - input_scale[i] == output_scale) { - memcpy(output_ptr, input_ptr, copy_size); - } else { - const float scale = input_scale[i] * inverse_output_scale; - const float bias = -input_zeropoint[i] * scale; - for (int j = 0; j < copy_size; ++j) { - const int32_t value = static_cast(tflite_micro::TfLiteRound( - input_ptr[j] * scale + bias)) + - output_zeropoint; - output_ptr[j] = static_cast( - std::max(std::min(255, value), 0)); - } - } - output_ptr += copy_size; - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONCATENATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h deleted file mode 100644 index 38443c3b..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/conv.h +++ /dev/null @@ -1,289 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& filter_shape, - const float* filter_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data, const RuntimeShape& im2col_shape, - float* im2col_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_NE(groups, 0); - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - TFLITE_DCHECK_NE(filters_per_group, 0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - const int in_y_origin = (out_y * stride_height) - pad_height; - for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - auto group = out_channel / filters_per_group; - float total = 0.f; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - const int in_y = in_y_origin + dilation_height_factor * filter_y; - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - - if (!is_point_inside_image) { - continue; - } - for (int in_channel = 0; in_channel < filter_input_depth; - ++in_channel) { - float input_value = - input_data[Offset(input_shape, batch, in_y, in_x, - in_channel + group * filter_input_depth)]; - float filter_value = filter_data[Offset( - filter_shape, out_channel, filter_y, filter_x, in_channel)]; - total += (input_value * filter_value); - } - } - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[out_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - ActivationFunctionWithMinMax(total + bias_value, - output_activation_min, - output_activation_max); - } - } - } - } -} - -inline void Conv(const ConvParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data, const RuntimeShape& im2col_shape, - uint8_t* im2col_data, void* cpu_backend_context) { - (void)cpu_backend_context; // only used in optimized code. - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - const int in_y_origin = (out_y * stride_height) - pad_height; - for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - auto group = out_channel / filters_per_group; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - const int in_y = in_y_origin + dilation_height_factor * filter_y; - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - - if (!is_point_inside_image) { - continue; - } - - for (int in_channel = 0; in_channel < filter_input_depth; - ++in_channel) { - int32_t input_val = - input_data[Offset(input_shape, batch, in_y, in_x, - in_channel + group * filter_input_depth)]; - int32_t filter_val = filter_data[Offset( - filter_shape, out_channel, filter_y, filter_x, in_channel)]; - acc += - (filter_val + filter_offset) * (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[out_channel]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, - output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(acc); - } - } - } - } -} - -inline void HybridConvPerChannel( - const ConvParams& params, float* scaling_factors_ptr, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const float* bias_data, - const RuntimeShape& output_shape, float* output_data, - const RuntimeShape& im2col_shape, int8_t* im2col_data, - const float* per_channel_scale, int32_t* input_offset) { - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - auto group = out_channel / filters_per_group; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int in_channel = 0; in_channel < filter_input_depth; - ++in_channel) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, - in_channel + group * filter_input_depth)]; - int32_t filter_val = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - acc += filter_val * (input_val - input_offset[batch]); - } - } - } - } - float acc_float = - acc * per_channel_scale[out_channel] * scaling_factors_ptr[batch]; - if (bias_data) { - acc_float += bias_data[out_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - ActivationFunctionWithMinMax(acc_float, output_activation_min, - output_activation_max); - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h deleted file mode 100644 index 880a2bc3..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/cumsum.h +++ /dev/null @@ -1,175 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void CumSum(const T* input_data, const RuntimeShape& shape, int32_t axis, - bool exclusive, bool reverse, T* output_data) { - const int32_t rank = shape.DimensionsCount(); - TFLITE_DCHECK_GE(rank, 1); - TFLITE_DCHECK_GE(axis, 0); - TFLITE_DCHECK_LT(axis, rank); - - size_t inner = 1; - size_t outer = 1; - size_t depth = 1; - for (int32_t i = 0; i < rank; i++) { - if (i < axis) - inner *= shape.Dims(i); - else if (i > axis) - outer *= shape.Dims(i); - else - depth = shape.Dims(i); - } - - for (size_t outer_index = 0; outer_index < outer; outer_index++) { - size_t outer_index_adj; - if (reverse) - outer_index_adj = (outer - 1) - outer_index; - else - outer_index_adj = outer_index; - for (size_t inner_index = 0; inner_index < inner; inner_index++) { - T accumulator = 0; - size_t inner_index_adj; - if (reverse) - inner_index_adj = (inner - 1) - inner_index; - else - inner_index_adj = inner_index; - for (size_t depth_index = 0; depth_index < depth; depth_index++) { - size_t depth_index_adj; - if (reverse) - depth_index_adj = (depth - 1) - depth_index; - else - depth_index_adj = depth_index; - - size_t index = outer_index_adj; - index += inner_index_adj * depth * outer; - index += depth_index_adj * outer; - - if (exclusive) { - output_data[index] = accumulator; - accumulator += input_data[index]; - } else { - accumulator += input_data[index]; - output_data[index] = accumulator; - } - } - } - } -} - -// -// Quantized INT8 CUMSUM -// -inline void CumSum(const ArithmeticParams& params, const int8_t* input_data, - const RuntimeShape& shape, int32_t axis, bool exclusive, - bool reverse, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - // Input offset is negative input zero point. Activation tensors are - // asymmetric quantized so they span the full int8 range. - // All inputs should have same zero-point and scale, this is checked during - // Prepare stage. - TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); - TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); - - const int32_t rank = shape.DimensionsCount(); - TFLITE_DCHECK_GE(rank, 1); - TFLITE_DCHECK_GE(axis, 0); - TFLITE_DCHECK_LT(axis, rank); - - size_t inner = 1; - size_t outer = 1; - size_t depth = 1; - for (int32_t i = 0; i < rank; i++) { - if (i < axis) - inner *= shape.Dims(i); - else if (i > axis) - outer *= shape.Dims(i); - else - depth = shape.Dims(i); - } - - for (size_t outer_index = 0; outer_index < outer; outer_index++) { - size_t outer_index_adj; - if (reverse) - outer_index_adj = (outer - 1) - outer_index; - else - outer_index_adj = outer_index; - for (size_t inner_index = 0; inner_index < inner; inner_index++) { - int32_t accumulator = params.input1_offset; // accumulator = 0 - accumulator *= (1 << params.left_shift); - accumulator = MultiplyByQuantizedMultiplierSmallerThanOneExp( - accumulator, params.input1_multiplier, params.input1_shift); - - size_t inner_index_adj; - if (reverse) - inner_index_adj = (inner - 1) - inner_index; - else - inner_index_adj = inner_index; - - for (size_t depth_index = 0; depth_index < depth; depth_index++) { - size_t depth_index_adj; - if (reverse) - depth_index_adj = (depth - 1) - depth_index; - else - depth_index_adj = depth_index; - - size_t index = outer_index_adj; - index += inner_index_adj * depth * outer; - index += depth_index_adj * outer; - - const int32_t y = params.input1_offset + input_data[index]; - const int32_t shifted_y = y * (1 << params.left_shift); - const int32_t scaled_y = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_y, params.input1_multiplier, params.input1_shift); - - int32_t scaled_output; - if (exclusive) { - scaled_output = accumulator; - accumulator += scaled_y; - } else { - accumulator += scaled_y; - scaled_output = accumulator; - } - - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - scaled_output, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[index] = static_cast(clamped_output); - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_CUMSUM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h deleted file mode 100644 index dcc9e5fa..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depth_to_space.h +++ /dev/null @@ -1,79 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void DepthToSpace(const tflite_micro::DepthToSpaceParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - const int input_depth = input_shape.Dims(3); - const int input_width = input_shape.Dims(2); - const int input_height = input_shape.Dims(1); - const int input_batch = input_shape.Dims(0); - - const int output_depth = output_shape.Dims(3); - const int output_width = output_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_batch = output_shape.Dims(0); - - const int32_t block_size = op_params.block_size; - - TFLITE_DCHECK_EQ(input_width * block_size, output_width); - TFLITE_DCHECK_EQ(input_height * block_size, output_height); - TFLITE_DCHECK_EQ(input_depth, output_depth * block_size * block_size); - TFLITE_DCHECK_EQ(input_batch, output_batch); - - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - const int in_d = - out_d + ((out_h % block_size) * block_size + out_w % block_size) * - output_depth; - - const int in_w = out_w / block_size; - const int in_h = out_h / block_size; - const int in_b = out_b; - - const int input_index = Offset(input_shape, in_b, in_h, in_w, in_d); - const int output_index = - Offset(output_shape, out_b, out_h, out_w, out_d); - - output_data[output_index] = input_data[input_index]; - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTH_TO_SPACE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h deleted file mode 100644 index b50e7bec..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h +++ /dev/null @@ -1,100 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void DepthwiseConv( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& filter_shape, - const float* filter_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int b = 0; b < batches; ++b) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int ic = 0; ic < input_depth; ++ic) { - for (int m = 0; m < depth_multiplier; m++) { - const int oc = m + ic * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - float total = 0.f; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - float input_value = - input_data[Offset(input_shape, b, in_y, in_x, ic)]; - float filter_value = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, oc)]; - total += (input_value * filter_value); - } - } - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[oc]; - } - output_data[Offset(output_shape, b, out_y, out_x, oc)] = - ActivationFunctionWithMinMax(total + bias_value, - output_activation_min, - output_activation_max); - } - } - } - } - } -} - -} // end namespace reference_ops -} // end namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_FLOAT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h deleted file mode 100644 index aef25b7b..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h +++ /dev/null @@ -1,319 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -// Used in tests and template parameters to control which version of depthwise -// convolution is called. Primarily for reference code, and specializations -// forced in tests. -enum class DepthwiseConvImplementation { - // Run all tests against kUseStandardEntry even if also testing another - // kernel, since we need to be sure that the main DepthwiseConv() function in - // optimized_ops.h dispatches to a correctly-executing kernel. - kNone = 0, // The "default" option: use the normal - // DepthwiseConv kernel (entry) function. - kUseGenericKernel, // Forced use of generic kernel. - kUseNeon3x3, // 3x3 kernel that uses NEON when available. - kUseNeon3x3DotProduct, // 3x3 kernel that uses dot-product enabled NEON - // when available. - kUseCModel3x3DotProduct, // 3x3 kernel, reference C model that is intended - // to match overall design NEON code. - kUseUnwound3x3DotProduct, // 3x3 kernel, reference C model with unwound loops - // and some arrays. - kUseIntrinsics3x3DotProduct, // 3x3 kernel using NEON intrinsics. -}; - -// Category of depthwise convolution output rounding. -enum class DepthwiseConvOutputRounding { - kNone = 0, // Invalid: specific method must be specified. - kAwayFromZero, // Original method: exact halves rounded away from zero. - kUpward, // Halves towards +infinity: adds 0.5 before truncate. - // This is where a future kNearestEven would be placed. -}; - -// Category of depthwise convolution depth multiplication. -enum class DepthwiseConvDepthMultiplication { - kNoMultiplication = 0, // Depth multiplier = 1. - kUnitInputDepth, // Input depth = 1, output depth = depth multiplier. -}; - -namespace reference_ops { -namespace depthwise_conv { - -template -inline int32_t DepthwiseConvRound(int32_t x, int32_t quantized_multiplier, - int shift) { - TFLITE_DCHECK_NE(output_rounding, DepthwiseConvOutputRounding::kNone); - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -// Single-rounding MultiplyByQuantizedMultiplier -#if TFLITE_SINGLE_ROUNDING -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - using gemmlowp::RoundingDivideByPOT; - using gemmlowp::SaturatingRoundingDoublingHighMul; - int left_shift = shift > 0 ? shift : 0; - int right_shift = shift > 0 ? 0 : -shift; - return RoundingDivideByPOT(SaturatingRoundingDoublingHighMul( - x * (1 << left_shift), quantized_multiplier), - right_shift); -} - -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} -// Double-rounding MultiplyByQuantizedMultiplier -#else -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - return MultiplyByQuantizedMultiplier(x, quantized_multiplier, shift); -} - -template <> -inline int32_t DepthwiseConvRound( - int32_t x, int32_t quantized_multiplier, int shift) { - using gemmlowp::SaturatingRoundingDoublingHighMul; - const int left_shift = shift > 0 ? shift : 0; - const int right_shift = shift > 0 ? 0 : -shift; - const int rounding_offset = right_shift > 0 ? 1 << (right_shift - 1) : 0; - return (SaturatingRoundingDoublingHighMul(x * (1 << left_shift), - quantized_multiplier) + - rounding_offset) >> - right_shift; -} -#endif // TFLITE_SINGLE_ROUNDING - -template -struct DepthwiseConvBasicKernel { - static inline void Run( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int b = 0; b < batches; ++b) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int ic = 0; ic < input_depth; ++ic) { - for (int m = 0; m < depth_multiplier; m++) { - const int oc = m + ic * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = - in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // If the location is outside the bounds of the input image, - // use zero as a default value. - if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height)) { - int32_t input_val = - input_data[Offset(input_shape, b, in_y, in_x, ic)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, oc)]; - acc += (filter_val + filter_offset) * - (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[oc]; - } - acc = DepthwiseConvRound(acc, output_multiplier, - output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, b, out_y, out_x, oc)] = - static_cast(acc); - } - } - } - } - } - } - - // TODO(b/148596273): Reconcile reference versions, perhaps with common - // MultiplyByQuantizedMultiplier or DepthwiseConvRound function. - static inline void RunPerChannel( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - const int32_t* output_multiplier = params.output_multiplier_per_channel; - const int32_t* output_shift = params.output_shift_per_channel; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = - in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we - // force real value of 0.0 be represented by a quantized - // value. This guarantees that the input_offset is a int8_t, - // even though it is represented using int32_t. int32_t += - // int8_t - // * (int8_t - int8_t) so the highest value we can get from - // each accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold - // as long as the filter size (filter_y * filter_x * - // in_channel) does not exceed 2^16, which is the case in - // all the models we have seen so far. - acc += filter_val * (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - acc = DepthwiseConvRound( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = static_cast(acc); - } - } - } - } - } - } -}; - -} // namespace depthwise_conv - -inline void DepthwiseConv( - const DepthwiseParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - return depthwise_conv::DepthwiseConvBasicKernel< - DepthwiseConvOutputRounding::kAwayFromZero>::Run(params, input_shape, - input_data, filter_shape, - filter_data, bias_shape, - bias_data, output_shape, - output_data); -} - -} // namespace reference_ops -} // end namespace tflite - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEPTHWISECONV_UINT8_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h deleted file mode 100644 index 6e1a1cab..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/dequantize.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ - -#include - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -// Dequantizes into a float without rounding. -template -inline void Dequantize(const tflite_micro::DequantizationParams& op_params, - const RuntimeShape& input_shape, - const InputT* input_data, - const RuntimeShape& output_shape, OutputT* output_data) { - int32_t zero_point = op_params.zero_point; - const double scale = op_params.scale; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const int32_t val = input_data[i]; - const OutputT result = static_cast(scale * (val - zero_point)); - output_data[i] = result; - } -} - -// Dequantizes per-channel quantized tensor to float. -template -inline void PerChannelDequantize( - const tflite_micro::PerChannelDequantizationParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, float* output_data) { - // Ensure flat size is same. - MatchingFlatSize(input_shape, output_shape); - - const int32_t* zero_point = op_params.zero_point; - const float* scale = op_params.scale; - const int32_t quantized_dimension = op_params.quantized_dimension; - const int32_t num_dims = input_shape.DimensionsCount(); - const int32_t* dims_data = input_shape.DimsData(); - std::vector current_dim(num_dims, 0); - - do { - size_t offset = - ReducedOutputOffset(num_dims, reinterpret_cast(dims_data), - current_dim.data(), 0, nullptr); - const int channel = current_dim[quantized_dimension]; - const int32_t val = input_data[offset]; - const float result = - static_cast(scale[channel] * (val - zero_point[channel])); - output_data[offset] = result; - } while (NextIndex(num_dims, reinterpret_cast(dims_data), - current_dim.data())); -} - -} // namespace reference_ops - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DEQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h deleted file mode 100644 index 330b88b5..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/div.h +++ /dev/null @@ -1,247 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void DivCheckArithmeticParams(const ArithmeticParams& params) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - // Input offset is negative input zero point. Activation tensors are - // asymmetric quantized so they span the full int8 range. - constexpr int32_t max_value = - static_cast(std::numeric_limits::max()); - TFLITE_DCHECK_GE(params.input1_offset, -max_value); - TFLITE_DCHECK_LE(params.input1_offset, max_value); - TFLITE_DCHECK_GE(params.input2_offset, -max_value); - TFLITE_DCHECK_LE(params.input2_offset, max_value); - TFLITE_DCHECK_GE(params.output_offset, -max_value); - TFLITE_DCHECK_LE(params.output_offset, max_value); -} - -// Element-wise div that can often be used for inner loop of broadcast Div as -// well as the non-broadcast Div. -template -inline void DivElementwise(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data) { - DivCheckArithmeticParams(params); - - for (int i = 0; i < size; ++i) { - int32_t input1_val = params.input1_offset + input1_data[i]; - int32_t input2_val = params.input2_offset + input2_data[i]; - TFLITE_DCHECK_NE(input2_val, 0); - if (input2_val < 0) { - // Invert signs to avoid a negative input2_val as input2_inv needs to be - // positive to be used as multiplier of MultiplyByQuantizedMultiplier. - input1_val = -input1_val; - input2_val = -input2_val; - } - int recip_shift; - const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift); - const int headroom = CountLeadingSignBits(input1_val); - const int32_t unscaled_quotient = - MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv, - headroom); - const int total_shift = params.output_shift - recip_shift - headroom; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplierSmallerThanOneExp( - unscaled_quotient, params.output_multiplier, total_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Div(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - DivElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Div(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int8_t* input1_data, - const RuntimeShape& input2_shape, const int8_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - DivElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -template -inline void BroadcastDivSlowQuantized( - const ArithmeticParams& params, const RuntimeShape& unextended_input1_shape, - const T* input1_data, const RuntimeShape& unextended_input2_shape, - const T* input2_data, const RuntimeShape& unextended_output_shape, - T* output_data) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); - - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), - &output_desc); - - DivCheckArithmeticParams(params); - - auto div_func = [&](int indexes[N]) { - int32_t input1_val = - params.input1_offset + input1_data[SubscriptToIndex(desc1, indexes)]; - int32_t input2_val = - params.input2_offset + input2_data[SubscriptToIndex(desc2, indexes)]; - TFLITE_DCHECK_NE(input2_val, 0); - if (input2_val < 0) { - // Invert signs to avoid a negative input2_val as input2_inv needs to be - // positive to be used as multiplier of MultiplyByQuantizedMultiplier. - input1_val = -input1_val; - input2_val = -input2_val; - } - int recip_shift; - const int32_t input2_inv = GetReciprocal(input2_val, 31, &recip_shift); - const int headroom = CountLeadingSignBits(input1_val); - const int32_t unscaled_quotient = - MultiplyByQuantizedMultiplierGreaterThanOne(input1_val, input2_inv, - headroom); - const int total_shift = params.output_shift - recip_shift - headroom; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplierSmallerThanOneExp( - unscaled_quotient, params.output_multiplier, total_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[SubscriptToIndex(output_desc, indexes)] = - static_cast(clamped_output); - }; - NDOpsHelper(output_desc, div_func); -} - -template -inline void BroadcastDivSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const uint8_t* input1_data, - const RuntimeShape& unextended_input2_shape, - const uint8_t* input2_data, - const RuntimeShape& unextended_output_shape, - uint8_t* output_data) { - BroadcastDivSlowQuantized( - params, unextended_input1_shape, input1_data, unextended_input2_shape, - input2_data, unextended_output_shape, output_data); -} - -template -inline void BroadcastDivSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const int8_t* input1_data, - const RuntimeShape& unextended_input2_shape, - const int8_t* input2_data, - const RuntimeShape& unextended_output_shape, - int8_t* output_data) { - BroadcastDivSlowQuantized( - params, unextended_input1_shape, input1_data, unextended_input2_shape, - input2_data, unextended_output_shape, output_data); -} - -// TODO(jiawen): We can implement BroadcastDiv on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -template -void BroadcastDivSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const T* input2_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, &output_activation_max); - - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); - - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), - &output_desc); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest - // stride, typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - - auto div_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, indexes)] / - input2_data[SubscriptToIndex(desc2, indexes)], - output_activation_min, output_activation_max); - }; - NDOpsHelper(output_desc, div_func); -} - -template -inline void Div(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, &output_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] / input2_data[i], output_activation_min, - output_activation_max); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_DIV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h deleted file mode 100644 index 20ef92ee..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/elu.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ - -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void Elu(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - output_data[i] = val < 0.0f ? TfLiteExpm1(val) : val; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h deleted file mode 100644 index bd1cadc5..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/exp.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void Exp(const T* input_data, const size_t num_elements, - T* output_data) { - ruy::profiler::ScopeLabel label("Exp"); - for (size_t idx = 0; idx < num_elements; ++idx) { - output_data[idx] = std::exp(input_data[idx]); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_EXP_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h deleted file mode 100644 index aa412959..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fill.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -void Fill(const RuntimeShape& value_shape, const T* value_data, - const RuntimeShape& output_shape, T* output_data) { - TFLITE_DCHECK_EQ(value_shape.DimensionsCount(), 0); - const int flat_size = output_shape.FlatSize(); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = *value_data; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FILL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h deleted file mode 100644 index 28fe190e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void Floor(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - int offset = i; - output_data[offset] = std::floor(input_data[offset]); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h deleted file mode 100644 index 3b56e06a..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_div.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -T FloorDiv(T input1, T input2) { - return std::floor(std::divides()(static_cast(input1), - static_cast(input2))); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_DIV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h deleted file mode 100644 index 9a4c7139..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/floor_mod.h +++ /dev/null @@ -1,44 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ - -#include -#include - -namespace tflite_micro { - -namespace reference_ops { - -template -T FloorMod(T input1, T input2) { - struct FloatMod { - float operator()(const float lhs, const float rhs) const { - return std::fmod(lhs, rhs); - } - }; - using ModFunc = typename std::conditional::value, - std::modulus, FloatMod>::type; - ModFunc mod_func; - T trunc_mod = mod_func(input1, input2); - return (trunc_mod != 0) && ((input2 < 0) != (trunc_mod < 0)) - ? (trunc_mod + input2) - : trunc_mod; -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FLOOR_MOD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h deleted file mode 100644 index 9ede8d9b..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/fully_connected.h +++ /dev/null @@ -1,323 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& weights_shape, - const float* weights_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data) { - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - // TODO(b/62193649): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dims_count = output_shape.DimensionsCount(); - const int weights_dims_count = weights_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dims_count - 1); - const int output_depth = MatchingDim(weights_shape, weights_dims_count - 2, - output_shape, output_dims_count - 1); - const int accum_depth = weights_shape.Dims(weights_dims_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - float total = 0.f; - for (int d = 0; d < accum_depth; ++d) { - total += input_data[b * accum_depth + d] * - weights_data[out_c * accum_depth + d]; - } - float bias_value = 0.0f; - if (bias_data) { - bias_value = bias_data[out_c]; - } - output_data[out_c + output_depth * b] = ActivationFunctionWithMinMax( - total + bias_value, output_activation_min, output_activation_max); - } - } -} - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - // TODO(b/62193649): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - int32_t acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += (filter_val + filter_offset) * (input_val + input_offset); - } - if (bias_data) { - acc += bias_data[out_c]; - } - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[out_c + output_depth * b] = static_cast(acc); - } - } -} - -inline void FullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(output_offset, 0); - // TODO(b/62193649): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(filter_shape, filter_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum = bias_data[out_c]; - // Accumulation loop. - for (int d = 0; d < accum_depth; ++d) { - int16_t input_val = input_data[b * accum_depth + d] + input_offset; - int16_t filter_val = - filter_data[out_c * accum_depth + d] + filter_offset; - accum += filter_val * input_val; - } - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The quantized - // multiplier and shift here have been pre-computed offline - // (e.g. by toco). - accum = - MultiplyByQuantizedMultiplier(accum, output_multiplier, output_shift); - // Saturate, cast to int16_t, and store to output array. - accum = std::max(accum, output_activation_min - output_offset); - accum = std::min(accum, output_activation_max - output_offset); - accum += output_offset; - output_data[out_c + output_depth * b] = accum; - } - } -} - -inline void ShuffledFullyConnected( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& weights_shape, - const uint8_t* shuffled_weights_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data, uint8_t* shuffled_input_workspace_data) { - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - TFLITE_DCHECK_GE(input_shape.DimensionsCount(), 1); - TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); - TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); - // TODO(b/62193649): This really should be: - // const int batches = ArraySize(output_dims, 1); - // but the current --variable_batch hack consists in overwriting the 3rd - // dimension with the runtime batch size, as we don't keep track for each - // array of which dimension is the batch dimension in it. - const int output_dim_count = output_shape.DimensionsCount(); - const int weights_dim_count = weights_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = MatchingDim(weights_shape, weights_dim_count - 2, - output_shape, output_dim_count - 1); - const int accum_depth = weights_shape.Dims(weights_dim_count - 1); - TFLITE_DCHECK((accum_depth % 16) == 0); - TFLITE_DCHECK((output_depth % 4) == 0); - - // Shuffling and xoring of input activations into the workspace buffer - uint8_t* shuffled_input_workspace_ptr = shuffled_input_workspace_data; - if (batches == 1) { - for (int i = 0; i < accum_depth; i++) { - shuffled_input_workspace_data[i] = input_data[i] ^ 0x80; - } - } else if (batches == 4) { - for (int c = 0; c < accum_depth; c += 16) { - for (int b = 0; b < 4; b++) { - const uint8_t* src_data_ptr = input_data + b * accum_depth + c; - for (int j = 0; j < 16; j++) { - uint8_t src_val = *src_data_ptr++; - // Flip the sign bit, so that the kernel will only need to - // reinterpret these uint8_t values as int8_t, getting for free the - // subtraction of the zero_point value 128. - uint8_t dst_val = src_val ^ 0x80; - *shuffled_input_workspace_ptr++ = dst_val; - } - } - } - } else { - TFLITE_DCHECK(false); - return; - } - - // Actual computation - if (batches == 1) { - int16_t* output_ptr = output_data; - // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) - // so that just reinterpreting them as int8_t values is equivalent to - // subtracting 128 from them, thus implementing for free the subtraction of - // the zero_point value 128. - const int8_t* shuffled_weights_ptr = - reinterpret_cast(shuffled_weights_data); - // Likewise, we preshuffled and pre-xored the input data above. - const int8_t* shuffled_input_data = - reinterpret_cast(shuffled_input_workspace_data); - for (int c = 0; c < output_depth; c += 4) { - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum[4] = {0}; - // Accumulation loop. - for (int d = 0; d < accum_depth; d += 16) { - for (int i = 0; i < 4; i++) { - for (int j = 0; j < 16; j++) { - int8_t input_val = shuffled_input_data[d + j]; - int8_t weights_val = *shuffled_weights_ptr++; - accum[i] += weights_val * input_val; - } - } - } - for (int i = 0; i < 4; i++) { - // Add bias value - int32_t acc = accum[i] + bias_data[c + i]; - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The quantized - // multiplier and shift here have been pre-computed offline - // (e.g. by toco). - acc = - MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - // Saturate, cast to int16_t, and store to output array. - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_ptr[c + i] = acc; - } - } - } else if (batches == 4) { - int16_t* output_ptr = output_data; - // Shuffled weights have had their sign bit (0x80) pre-flipped (xor'd) - // so that just reinterpreting them as int8_t values is equivalent to - // subtracting 128 from them, thus implementing for free the subtraction of - // the zero_point value 128. - const int8_t* shuffled_weights_ptr = - reinterpret_cast(shuffled_weights_data); - // Likewise, we preshuffled and pre-xored the input data above. - const int8_t* shuffled_input_data = - reinterpret_cast(shuffled_input_workspace_data); - for (int c = 0; c < output_depth; c += 4) { - const int8_t* shuffled_input_ptr = shuffled_input_data; - // Accumulation loop. - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum[4][4]; - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - accum[i][b] = 0; - } - } - for (int d = 0; d < accum_depth; d += 16) { - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - for (int j = 0; j < 16; j++) { - int8_t input_val = shuffled_input_ptr[16 * b + j]; - int8_t weights_val = shuffled_weights_ptr[16 * i + j]; - accum[i][b] += weights_val * input_val; - } - } - } - shuffled_input_ptr += 64; - shuffled_weights_ptr += 64; - } - for (int i = 0; i < 4; i++) { - for (int b = 0; b < 4; b++) { - // Add bias value - int32_t acc = accum[i][b] + bias_data[c + i]; - // Down-scale the final int32_t accumulator to the scale used by our - // (16-bit, typically 3 integer bits) fixed-point format. The - // quantized multiplier and shift here have been pre-computed offline - // (e.g. by toco). - acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, - output_shift); - // Saturate, cast to int16_t, and store to output array. - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_ptr[b * output_depth + c + i] = acc; - } - } - } - } else { - TFLITE_DCHECK(false); - return; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h deleted file mode 100644 index 28867479..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/hard_swish.h +++ /dev/null @@ -1,168 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline int16_t SaturatingLeftShift(int16_t value, int amount) { - int64_t result = static_cast(value) * (1 << amount); - result = std::min(result, std::numeric_limits::max()); - result = std::max(result, std::numeric_limits::min()); - return result; -} - -// Similar to ARM instruction SQDMULH. -// Similar to gemmlowp::SaturatingRoundingDoublingHighMul except -// rounding to zero instead of to nearest (SQRDMULH). -inline std::int16_t SaturatingDoublingHighMul(std::int16_t a, std::int16_t b) { - bool overflow = a == b && a == std::numeric_limits::min(); - std::int32_t a_32(a); - std::int32_t b_32(b); - std::int32_t ab_32 = a_32 * b_32; - std::int16_t ab_x2_high16 = static_cast((ab_32) / (1 << 15)); - return overflow ? std::numeric_limits::max() : ab_x2_high16; -} - -template -inline void HardSwish(const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("ReferenceHardSwish/Float"); - auto matching_size = MatchingFlatSize(input_shape, output_shape); - const T* in_end = input_data + matching_size; - for (; input_data < in_end; input_data++, output_data++) { - const float in = *input_data; - *output_data = - in * std::min(static_cast(6), std::max(static_cast(0), in + 3)) / - 6; - } -} - -template -inline void HardSwish(const HardSwishParams& params, - const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("ReferenceHardSwish/Quantized"); - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const int16_t input_value = input_data[i] - params.input_zero_point; - // Left-shift as much as we can without overflow/saturation to put - // significant bits in the high bits of our 16-bit fixedpoint values, so - // that fixed-point approximate computations below are as accurate as - // possible. - const int16_t input_value_on_hires_input_scale = input_value * (1 << 7); - // Compute the input value on essentially the output scale, just not - // right-shifted yet. This is the value that we'll use in the (x >= +3) - // case, and that in the general case we'll multiply against the "relu-ish" - // fixed-point multiplier in [0, 1]. - const int16_t input_value_on_preshift_output_scale = - gemmlowp::SaturatingRoundingDoublingHighMul( - input_value_on_hires_input_scale, - params.output_multiplier_fixedpoint_int16); - // Now compute the "relu-ish multiplier". In the (-3 <= x <= +3) case, that - // is just an affine rescaling of x from [-3, 3] to [0, 1]. In the general - // case, it is just that plus saturation at the boundaries of [-3, 3]. - // First, we rescale from [-3, 3] to [-1, 1], saturating. - // That is done by rescaling the input value with a fixed-point multiplier - // (reluish_multiplier_fixedpoint) and bit-shift such that we represent - // that input value on the scale where the real value 3.0f is represented - // by the quantized value 32768. (+32768 is actually not representable as - // int16_t, so this saturates at +32767, and that is seen empirically to be - // a negligible contribution to numerical error/bias). - // - // This code is careful to correctly implement any magnitude of multiplier, - // involving either a right shift or a left shift, with correct saturation - // behavior in the left-shift case. This forces this code to be more - // complicated, but is necessary for real applications: a partially - // trained quantized MobileNet v3-small model that motivated this code - // exhibits some large [min, max] range boundaries, of the order of - // magnitude of 10 or 100 depending on layers. - // - // The next few lines are basically just an ordinary - // MultiplyByQuantizedMultiplier, except that we are more careful here - // about the fine details of saturation when left-shifting, because here - // overflow in left-shift is a common case, not an anomaly as - // MultiplyByQuantizedMultiplier assumes. - int16_t reluish_value = input_value_on_hires_input_scale; - // Shift left, saturating, as much as we can while ensuring that this - // saturation will not contribute to the result. That is, left shift amount - // reduced by 1. - if (params.reluish_multiplier_exponent > 0) { - reluish_value = SaturatingLeftShift( - reluish_value, params.reluish_multiplier_exponent - 1); - } - // Apply the fixed-point multiplier, dividing the value by a divisor - // ranging in [1, 2]. - reluish_value = gemmlowp::SaturatingRoundingDoublingHighMul( - reluish_value, params.reluish_multiplier_fixedpoint_int16); - // Apply the last bit of left-shift. Thus, in the left-shifting case, if - // any saturation affects the result, it is happening here --- any - // saturation having occurred above is overwritten here, not affecting the - // result. - if (params.reluish_multiplier_exponent > 0) { - reluish_value = SaturatingLeftShift(reluish_value, 1); - } - // Shift right, in the right-shifting case. - if (params.reluish_multiplier_exponent < 0) { - reluish_value = gemmlowp::RoundingDivideByPOT( - reluish_value, -params.reluish_multiplier_exponent); - } - // At this point we have rescaled the value into a 16bit fixedpoint - // reluish_value in [-1, 1]. - // We now convert that to a 16bit fixedpoint value in [0, 1]. - reluish_value = (reluish_value + (1 << 15)) >> 1; - // Use of SaturatingDoublingHighMul here is important to cancel the biases - // from the above SaturatingRoundingDoublingHighMul. - // - // On a partially trained MobileNet-v3-small, - // - // | bias on | ImageNet - // | quantized | Top-1 - // Operation used here | values | accuracy (50k) - // --------------------------------------+------------+----------- - // SaturatingDoublingHighMul | -0.0024 | 58.920 - // SaturatingRoundingDoublingHighMul | -0.0067 | 58.064 - // - // In activations_test, this is covered by this testcase: - // QuantizedActivationsOpTest.HardSwishBias - // - const int16_t preshift_output_value = SaturatingDoublingHighMul( - reluish_value, input_value_on_preshift_output_scale); - // We were so far operating on the pre-shift output scale. Now we finally - // apply that output shift, arriving at the final output scale. - int16_t output_value = gemmlowp::RoundingDivideByPOT( - preshift_output_value, -params.output_multiplier_exponent); - output_value += params.output_zero_point; - output_value = - std::min(output_value, std::numeric_limits::max()); - output_value = - std::max(output_value, std::numeric_limits::min()); - output_data[i] = output_value; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h deleted file mode 100644 index 113d7941..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/add.h +++ /dev/null @@ -1,250 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -inline void CheckArithmeticParams(const ArithmeticParams& params) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - // Input offset is negative input zero point. Activation tensors are - // asymmetric quantized so they span the full int8 range. - TFLITE_DCHECK_GE(-params.input1_offset, std::numeric_limits::min()); - TFLITE_DCHECK_GE(-params.input2_offset, std::numeric_limits::min()); - TFLITE_DCHECK_LE(-params.input1_offset, std::numeric_limits::max()); - TFLITE_DCHECK_LE(-params.input2_offset, std::numeric_limits::max()); -} - -// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2) -template -void BroadcastInput1(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, T* output_data, - void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - CheckArithmeticParams(params); - for (int i = 0; i < size; ++i) { - output_data[i] = binary_func(input1_data[0], input2_data[i], params); - } -} - -template -void BroadcastInput2(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, T* output_data, - void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - CheckArithmeticParams(params); - for (int i = 0; i < size; ++i) { - output_data[i] = binary_func(input1_data[i], input2_data[0], params); - } -} - -// TODO: b/270589088 - move to a more appropriate file (b/270589088#comment2) -template -void ElementWise(int size, const ArithmeticParams& params, const T* input1_data, - const T* input2_data, T* output_data, - void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - CheckArithmeticParams(params); - for (int i = 0; i < size; ++i) { - output_data[i] = binary_func(input1_data[i], input2_data[i], params); - } -} - -template -inline void BroadcastAddRecursiveDimensions( - const ArithmeticParams& params, int dimension, size_t* input1_offset_p, - size_t* input2_offset_p, size_t* output_offset, - size_t* compressed_input1_stride, size_t* compressed_input2_stride, - size_t* compressed_output_shape, const T* input1_data, const T* input2_data, - T* output_data, void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - if (dimension > 0) { - for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { - size_t input1_offset_c = *input1_offset_p; - size_t input2_offset_c = *input2_offset_p; - BroadcastAddRecursiveDimensions( - params, dimension - 1, &input1_offset_c, &input2_offset_c, - output_offset, compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, input1_data, input2_data, output_data, - check_arithmetic_params, binary_func); - *input1_offset_p += compressed_input1_stride[dimension]; - *input2_offset_p += compressed_input2_stride[dimension]; - } - } else { - TFLITE_DCHECK(dimension == 0); - bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; - bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; - TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); - const T* input1_data_ptr = input1_data + *input1_offset_p; - const T* input2_data_ptr = input2_data + *input2_offset_p; - T* output_data_ptr = output_data + *output_offset; - if (input1_is_broadcast) { - // input1 is broadcast. - BroadcastInput1(compressed_output_shape[dimension], params, - input1_data_ptr, input2_data_ptr, output_data_ptr, - check_arithmetic_params, binary_func); - *input2_offset_p += compressed_output_shape[dimension]; - } else if (input2_is_broadcast) { - // input2 is broadcast. - BroadcastInput2(compressed_output_shape[dimension], params, - input1_data_ptr, input2_data_ptr, output_data_ptr, - check_arithmetic_params, binary_func); - *input1_offset_p += compressed_output_shape[dimension]; - } else { - // Add element-wise. - ElementWise(compressed_output_shape[dimension], params, - input1_data_ptr, input2_data_ptr, output_data_ptr, - check_arithmetic_params, binary_func); - *input1_offset_p += compressed_output_shape[dimension]; - *input2_offset_p += compressed_output_shape[dimension]; - } - *output_offset += compressed_output_shape[dimension]; - } -} - -// TODO: b/270589088 - move to a more appropriate file. (b/270589088#comment2) -template -void BroadcastBinaryFunction6DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data, - void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - constexpr int kMaxBroadcastDim = 6; - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - size_t compressed_input1_stride[kMaxBroadcastDim]; - size_t compressed_input2_stride[kMaxBroadcastDim]; - size_t compressed_output_shape[kMaxBroadcastDim]; - bool broadcastable_shape = ReduceDimensionsForBroadcast( - input1_shape, input2_shape, compressed_input1_stride, - compressed_input2_stride, compressed_output_shape); - // Skip broadcasting for degenerate shapes. - if (!broadcastable_shape) { - return; - } - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastAddRecursiveDimensions( - params, kMaxBroadcastDim - 1, &input1_offset, &input2_offset, - &output_offset, compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, input1_data, input2_data, output_data, - check_arithmetic_params, binary_func); -} - -template -void BroadcastBinaryFunction4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data, - void (*check_arithmetic_params)(const ArithmeticParams&), - T (*binary_func)(T, T, const ArithmeticParams&)) { - BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data, - check_arithmetic_params, binary_func); -} - -inline int8_t AddFunc(int8_t x, int8_t y, const ArithmeticParams& params) { - const int32_t input1_val = params.input1_offset + x; - const int32_t input2_val = params.input2_offset + y; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sum = scaled_input1_val + scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - return static_cast(clamped_output); -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. -inline void AddElementwise(int size, const ArithmeticParams& params, - const int8_t* input1_data, const int8_t* input2_data, - int8_t* output_data) { - ElementWise(size, params, input1_data, input2_data, output_data, - CheckArithmeticParams, AddFunc); -} - -inline void Add(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int8_t* input1_data, - const RuntimeShape& input2_shape, const int8_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - CheckArithmeticParams(params); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - AddElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void BroadcastAdd6DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int8_t* input1_data, - const RuntimeShape& input2_shape, - const int8_t* input2_data, - const RuntimeShape& output_shape, - int8_t* output_data) { - BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data, - CheckArithmeticParams, AddFunc); -} - -inline void BroadcastAdd4DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int8_t* input1_data, - const RuntimeShape& input2_shape, - const int8_t* input2_data, - const RuntimeShape& output_shape, - int8_t* output_data) { - BroadcastBinaryFunction6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data, - CheckArithmeticParams, AddFunc); -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h deleted file mode 100644 index 1bce235d..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/conv.h +++ /dev/null @@ -1,241 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -// Fixed-point per-channel-quantization convolution reference kernel. -inline void ConvPerChannel( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - const int32_t input_offset = params.input_offset; // r = s(q - Z) - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int32_t output_offset = params.output_offset; - - // Set min and max value of the output. - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Consistency check. - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Check dimensions of the tensors. - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_NE(groups, 0); - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - TFLITE_DCHECK_NE(filters_per_group, 0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - const int in_y_origin = (out_y * stride_height) - pad_height; - for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - auto group = out_channel / filters_per_group; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - const int in_y = in_y_origin + dilation_height_factor * filter_y; - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - - if (!is_point_inside_image) { - continue; - } - - for (int in_channel = 0; in_channel < filter_input_depth; - ++in_channel) { - int32_t input_val = - input_data[Offset(input_shape, batch, in_y, in_x, - in_channel + group * filter_input_depth)]; - int32_t filter_val = filter_data[Offset( - filter_shape, out_channel, filter_y, filter_x, in_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we force - // real value of 0.0 be represented by a quantized value. This - // guarantees that the input_offset is a int8_t, even though - // it is represented using int32_t. int32_t += int8_t * - // (int8_t - int8_t) so the highest value we can get from each - // accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold as - // long as the filter size (filter_y * filter_x * in_channel) - // does not exceed 2^16, which is the case in all the models - // we have seen so far. - // TODO(b/174275578): Add a check to make sure the - // accumulator depth is smaller than 2^16. - acc += filter_val * (input_val + input_offset); - } - } - } - - if (bias_data) { - acc += bias_data[out_channel]; - } - acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(acc); - } - } - } - } -} - - -// Fixed-point per-channel-quantization convolution reference kernel. -// 16-bit data and 8-bit filter -template -inline void ConvPerChannel( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const AccumScalar* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - // Get parameters. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - - // Set min and max value of the output. - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Consistency check. - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = input_shape.Dims(3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Check dimensions of the tensors. - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int filter_input_depth = filter_shape.Dims(3); - const int groups = input_depth / filter_input_depth; - TFLITE_DCHECK_EQ(input_depth % filter_input_depth, 0); - const int filters_per_group = output_depth / groups; - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - const int in_y_origin = (out_y * stride_height) - pad_height; - for (int out_x = 0; out_x < output_width; ++out_x) { - const int in_x_origin = (out_x * stride_width) - pad_width; - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - auto group = out_channel / filters_per_group; - AccumScalar acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - const int in_y = in_y_origin + dilation_height_factor * filter_y; - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - - if (!is_point_inside_image) { - continue; - } - - for (int in_channel = 0; in_channel < filter_input_depth; - ++in_channel) { - int32_t input_val = - input_data[Offset(input_shape, batch, in_y, in_x, - in_channel + group * filter_input_depth)]; - int32_t filter_val = filter_data[Offset( - filter_shape, out_channel, filter_y, filter_x, in_channel)]; - // Accumulate with 64 bits accumulator. - // int64_t += int8_t * int16_t so the highest value we can - // get from each accumulation is [-127, 127] * ([-32768, - // 32767] - - // [-32768, 32767]), which is [-8322945, 8322945]. - // log2(8322945) = 22.99. - acc += filter_val * input_val; - } - } - } - if (bias_data) { - acc += bias_data[out_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(scaled_acc); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h deleted file mode 100644 index b0afaeef..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h +++ /dev/null @@ -1,291 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { -inline void DepthwiseConvPerChannel( - const DepthwiseParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data) { - // Get parameters. - // TODO(b/141565753): Re-introduce ScopedProfilingLabel on Micro. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 32 bits accumulator. - // In the nudging process during model quantization, we force - // real value of 0.0 be represented by a quantized value. This - // guarantees that the input_offset is a int8_t, even though - // it is represented using int32_t. int32_t += int8_t * - // (int8_t - int8_t) so the highest value we can get from each - // accumulation is [-127, 127] * ([-128, 127] - - // [-128, 127]), which is [-32512, 32512]. log2(32512) - // = 14.98, which means we can accumulate at least 2^16 - // multiplications without overflow. The accumulator is - // applied to a filter so the accumulation logic will hold as - // long as the filter size (filter_y * filter_x * in_channel) - // does not exceed 2^16, which is the case in all the models - // we have seen so far. - // TODO(b/174275578): Add a check to make sure the - // accumulator depth is smaller than 2^16. - acc += filter_val * (input_val + input_offset); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = static_cast(acc); - } - } - } - } - } -} - -inline void DepthwiseConvPerChannel( - const DepthwiseParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const std::int64_t* bias_data, const RuntimeShape& output_shape, - int16_t* output_data) { - // Get parameters. - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - std::int64_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - // Accumulate with 64 bits accumulator. - // We assume maximum of 2^16 accumulations as with the 8-bit - // case so actually the value in the accumulator should not - // exceed 40 bits - acc += static_cast(filter_val) * - static_cast(input_val); - } - } - } - if (bias_data) { - acc += bias_data[output_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[output_channel], - output_shift[output_channel]); - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = - static_cast(scaled_acc); - } - } - } - } - } -} - -inline void DepthwiseConvHybridPerChannel( - const DepthwiseParams& params, float* scaling_factors_ptr, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const float* bias_data, - const RuntimeShape& output_shape, float* output_data, - const float* per_channel_scale, int32_t* input_offset) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int dilation_width_factor = params.dilation_width_factor; - const int dilation_height_factor = params.dilation_height_factor; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - const int depth_multiplier = params.depth_multiplier; - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - // Check dimensions of the tensors. - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int input_depth = input_shape.Dims(3); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int bias_depth = bias_shape.FlatSize(); - TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier); - TFLITE_DCHECK_EQ(bias_depth, output_depth); - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - for (int m = 0; m < depth_multiplier; ++m) { - const int output_channel = m + in_channel * depth_multiplier; - const int in_x_origin = (out_x * stride_width) - pad_width; - const int in_y_origin = (out_y * stride_height) - pad_height; - int32_t acc = 0; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - const int in_x = in_x_origin + dilation_width_factor * filter_x; - const int in_y = - in_y_origin + dilation_height_factor * filter_y; - // Zero padding by omitting the areas outside the image. - const bool is_point_inside_image = - (in_x >= 0) && (in_x < input_width) && (in_y >= 0) && - (in_y < input_height); - if (is_point_inside_image) { - int32_t input_val = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - int32_t filter_val = filter_data[Offset( - filter_shape, 0, filter_y, filter_x, output_channel)]; - acc += filter_val * (input_val - input_offset[batch]); - } - } - } - float acc_float = static_cast(acc); - acc_float *= - per_channel_scale[output_channel] * scaling_factors_ptr[batch]; - if (bias_data && output_channel < bias_depth) { - acc_float += bias_data[output_channel]; - } - output_data[Offset(output_shape, batch, out_y, out_x, - output_channel)] = - ActivationFunctionWithMinMax(acc_float, output_activation_min, - output_activation_max); - } - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h deleted file mode 100644 index 0421d531..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h +++ /dev/null @@ -1,126 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -// For per-channel functions, since it is defined in quantization spec that -// weights are symmetric -// (https://www.tensorflow.org/lite/performance/quantization_spec#symmetric_vs_asymmetric), -// zero_point (params.weights_offset) is always 0. -// However, for per-tensor functions, params.weights_offset is still applied for -// backward compatibility. -template -void FullyConnectedPerChannel( - const FullyConnectedParams& params, const int32_t* output_multiplier, - const int* output_shift, const RuntimeShape& input_shape, - const InputType* input_data, const RuntimeShape& filter_shape, - const WeightType* filter_data, const RuntimeShape& bias_shape, - const BiasType* bias_data, const RuntimeShape& output_shape, - OutputType* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 2); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int batches = output_shape.Dims(0); - const int output_depth = output_shape.Dims(1); - TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - BiasType acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += filter_val * (input_val + input_offset); - } - if (bias_data) { - acc += bias_data[out_c]; - } - int32_t acc_scaled = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_c], output_shift[out_c]); - acc_scaled += output_offset; - acc_scaled = std::max(acc_scaled, output_activation_min); - acc_scaled = std::min(acc_scaled, output_activation_max); - output_data[out_c + output_depth * b] = - static_cast(acc_scaled); - } - } -} - -template -void FullyConnected(const FullyConnectedParams& params, - const RuntimeShape& input_shape, - const InputType* input_data, - const RuntimeShape& filter_shape, - const WeightType* filter_data, - const RuntimeShape& bias_shape, const BiasType* bias_data, - const RuntimeShape& output_shape, OutputType* output_data) { - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_GE(filter_shape.DimensionsCount(), 2); - TFLITE_DCHECK_GE(output_shape.DimensionsCount(), 1); - - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - const int filter_dim_count = filter_shape.DimensionsCount(); - const int output_dim_count = output_shape.DimensionsCount(); - const int batches = FlatSizeSkipDim(output_shape, output_dim_count - 1); - const int output_depth = output_shape.Dims(output_dim_count - 1); - TFLITE_DCHECK_LE(output_depth, filter_shape.Dims(filter_dim_count - 2)); - const int accum_depth = filter_shape.Dims(filter_dim_count - 1); - for (int b = 0; b < batches; ++b) { - for (int out_c = 0; out_c < output_depth; ++out_c) { - BiasType acc = 0; - for (int d = 0; d < accum_depth; ++d) { - int32_t input_val = input_data[b * accum_depth + d]; - int32_t filter_val = filter_data[out_c * accum_depth + d]; - acc += (filter_val + filter_offset) * (input_val + input_offset); - } - if (bias_data) { - acc += bias_data[out_c]; - } - int32_t acc_scaled = - MultiplyByQuantizedMultiplier(acc, output_multiplier, output_shift); - acc_scaled += output_offset; - acc_scaled = std::max(acc_scaled, output_activation_min); - acc_scaled = std::min(acc_scaled, output_activation_max); - output_data[out_c + output_depth * b] = - static_cast(acc_scaled); - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h deleted file mode 100644 index adf2aacc..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h +++ /dev/null @@ -1,67 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -inline void L2Normalization(int32_t input_zero_point, int32_t outer_size, - int32_t depth, const int8_t* input_data, - int8_t* output_data) { - static constexpr int8_t kMinInt8 = std::numeric_limits::min(); - static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); - // The output scale must be in sync with Prepare(). - // Output is in 1/128 scale so the actual output range is nudged from [-1, 1] - // to [-1, 127/128]. - static constexpr int32_t kOutputScale = 7; - for (int outer_index = 0; outer_index < outer_size; ++outer_index) { - // int32_t = (int8_t - int8_t) ^ 2. - // ([-128, 127] - [-128, 127]) ^ 2 = [0, (2^8 - 1)^2] so the accumulator is - // safe from overflowing in at least 2^16 steps. - int32_t acc = 0; - for (int inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input = - input_data[depth * outer_index + inner_index] - input_zero_point; - acc += input * input; - } - int32_t inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplierExp(acc, kReverseShift, &inv_l2norm_multiplier, - &inv_l2norm_shift); - - for (int inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input = - input_data[depth * outer_index + inner_index] - input_zero_point; - - // Rescale and downcast. Rescale is folded into the division. - int32_t output_in_q24 = MultiplyByQuantizedMultiplier( - input, inv_l2norm_multiplier, inv_l2norm_shift + kOutputScale); - output_in_q24 = - std::min(static_cast(kMaxInt8), - std::max(static_cast(kMinInt8), output_in_q24)); - output_data[depth * outer_index + inner_index] = - static_cast(output_in_q24); - } - } -} -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_L2NORMALIZATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h deleted file mode 100644 index be5fb90e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -inline void Logistic(int32_t input_zero_point, int32_t input_range_radius, - int32_t input_multiplier, int32_t input_left_shift, - int32_t input_size, const int8_t* input_data, - int8_t* output_data) { - // Integer bits must be in sync with Prepare() function. - static constexpr int32_t kInputIntegerBits = 4; - static constexpr int32_t kOutputIntegerBits = 8; - static constexpr int8_t kMinInt8 = std::numeric_limits::min(); - static constexpr int8_t kMaxInt8 = std::numeric_limits::max(); - static constexpr int32_t kOutputZeroPoint = -128; - - for (int i = 0; i < input_size; ++i) { - const int32_t input = - static_cast(input_data[i]) - input_zero_point; - if (input <= -input_range_radius) { - output_data[i] = kMinInt8; - } else if (input >= input_range_radius) { - output_data[i] = kMaxInt8; - } else { - const int32_t input_in_q4 = MultiplyByQuantizedMultiplier( - input, input_multiplier, input_left_shift); - using FixedPoint4 = gemmlowp::FixedPoint; - const int32_t output_in_q0 = - gemmlowp::logistic(FixedPoint4::FromRaw(input_in_q4)).raw(); - - // Rescale and downcast. - using gemmlowp::RoundingDivideByPOT; - int32_t output_in_q23 = - RoundingDivideByPOT(output_in_q0, 31 - kOutputIntegerBits); - output_in_q23 = std::min(std::max(output_in_q23 + kOutputZeroPoint, - static_cast(kMinInt8)), - static_cast(kMaxInt8)); - output_data[i] = static_cast(output_in_q23); - } - } -} - -inline void Logistic(int32_t input_multiplier, int32_t input_left_shift, - int32_t input_size, const int16_t* ptr_input_data, - int16_t* ptr_output_data) { - // We use the LUT for sigmoid and take into account, that - // tanh(x) = 2*sigmoid(2*x) - 1 - - // We scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. - // In case of general parameter scale, multiplier 3 is taken into account - // in TanhPrepare function and it is included in - // input_multiplier already. - - TFLITE_DCHECK_GE(input_left_shift, 0); - if (input_multiplier == 0) { // power of two case - input_multiplier = 3 << input_left_shift; - input_left_shift = 0; - } - - int32_t round = (input_left_shift > 0) ? 1 << (input_left_shift - 1) : 0; - - for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++) { - int32_t input_data = - ((*ptr_input_data) * input_multiplier + round) >> input_left_shift; - - // We do interpolation on unsigned values. - uint32_t abs_input_data = abs(input_data); - - // We divide by 2 power of 9, because - // we need to divide by 2 in power of 7 for - // the input conversion + 1/4 from the scale above. - - // Define uh as uint32_t type not to make this function overflow. - uint32_t uh = abs_input_data >> 9; - uint32_t result; - - if (uh >= 255) { - // Saturate to maximum. - result = 0x7FFF << 10; - } else { - uint32_t ua = sigmoid_table_uint16[uh]; - uint32_t ub = sigmoid_table_uint16[uh + 1]; - uint32_t ut = abs_input_data & 0x1ff; - // Interpolation is done using the fractional bit. - result = (ua << 9) + ut * (ub - ua); - } - - result = (input_data >= 0) ? (result + (1 << 9)) - : ((1 << (16 + 9)) - result + (1 << 9) - 1); - - // Back to 16-bit. - result >>= 10; - - *ptr_output_data = result; - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h deleted file mode 100644 index 7e3f690e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h +++ /dev/null @@ -1,18 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MEAN_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h deleted file mode 100644 index 40516f58..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/mul.h +++ /dev/null @@ -1,194 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -// Maximum dimension supported by the broadcast mul operation. -constexpr int kMaxMulBroadcastDim = 6; - -template -void MulElementwise(int size, const ArithmeticParams& params, - const InputType* input1_data, const InputType* input2_data, - OutputType* output_data) { - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[i] = static_cast(clamped_output); - } -} - -template -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - ruy::profiler::ScopeLabel label("Mul/8bit"); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - MulElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -// Mul with 16 bit inputs and int8_t outputs. -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int16_t* input1_data, - const RuntimeShape& input2_shape, const int16_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - ruy::profiler::ScopeLabel label("Mul/Int16Int8"); - int32_t output_offset = params.output_offset; - int32_t output_activation_min = params.quantized_activation_min; - int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - using F0 = gemmlowp::FixedPoint; - - F0 unclamped_result = - F0::FromRaw(input1_data[i]) * F0::FromRaw(input2_data[i]); - int16_t rescaled_result = - gemmlowp::RoundingDivideByPOT(unclamped_result.raw(), 8); - int16_t clamped_result = std::min( - output_activation_max - output_offset, rescaled_result); - clamped_result = std::max(output_activation_min - output_offset, - clamped_result); - output_data[i] = output_offset + clamped_result; - } -} - -template -inline void BroadcastMul6DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("BroadcastMul6DSlow"); - - NdArrayDesc desc1; - NdArrayDesc desc2; - // The input shapes are extended as part of NdArrayDesc initialization. - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape); - // Cache output shape dimensions. - int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; - std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), - sizeof(extended_output_shape_dims)); - - size_t input1_offset_a = 0; - size_t input2_offset_a = 0; - size_t output_offset_a = 0; - for (int a = 0; a < extended_output_shape_dims[0]; ++a) { - size_t input1_offset_d = input1_offset_a; - size_t input2_offset_d = input2_offset_a; - size_t output_offset_d = output_offset_a; - for (int d = 0; d < extended_output_shape_dims[1]; ++d) { - size_t input1_offset_b = input1_offset_d; - size_t input2_offset_b = input2_offset_d; - size_t output_offset_b = output_offset_d; - for (int b = 0; b < extended_output_shape_dims[2]; ++b) { - size_t input1_offset_y = input1_offset_b; - size_t input2_offset_y = input2_offset_b; - size_t output_offset_y = output_offset_b; - for (int y = 0; y < extended_output_shape_dims[3]; ++y) { - size_t input1_offset_x = input1_offset_y; - size_t input2_offset_x = input2_offset_y; - size_t output_offset_x = output_offset_y; - for (int x = 0; x < extended_output_shape_dims[4]; ++x) { - size_t input1_offset_c = input1_offset_x; - size_t input2_offset_c = input2_offset_x; - size_t output_offset_c = output_offset_x; - for (int c = 0; c < extended_output_shape_dims[5]; ++c) { - const int32_t input1_val = - params.input1_offset + input1_data[input1_offset_c]; - const int32_t input2_val = - params.input2_offset + input2_data[input2_offset_c]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = std::min( - params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[output_offset_c] = static_cast(clamped_output); - input1_offset_c += desc1.strides[5]; - input2_offset_c += desc2.strides[5]; - ++output_offset_c; - } - input1_offset_x += desc1.strides[4]; - input2_offset_x += desc2.strides[4]; - output_offset_x += extended_output_shape_dims[5]; - } - input1_offset_y += desc1.strides[3]; - input2_offset_y += desc2.strides[3]; - output_offset_y += - extended_output_shape_dims[4] * extended_output_shape_dims[5]; - } - input1_offset_b += desc1.strides[2]; - input2_offset_b += desc2.strides[2]; - output_offset_b += extended_output_shape_dims[3] * - extended_output_shape_dims[4] * - extended_output_shape_dims[5]; - } - input1_offset_d += desc1.strides[1]; - input2_offset_d += desc2.strides[1]; - output_offset_d += - extended_output_shape_dims[2] * extended_output_shape_dims[3] * - extended_output_shape_dims[4] * extended_output_shape_dims[5]; - } - input1_offset_a += desc1.strides[0]; - input2_offset_a += desc2.strides[0]; - output_offset_a += - extended_output_shape_dims[1] * extended_output_shape_dims[2] * - extended_output_shape_dims[3] * extended_output_shape_dims[4] * - extended_output_shape_dims[5]; - } -} - -template -inline void BroadcastMul4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); -} - -} // namespace reference_integer_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h deleted file mode 100644 index b1d58176..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h +++ /dev/null @@ -1,264 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -inline bool AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const int8_t* input_data, - const RuntimeShape& output_shape, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - if (filter_count == 0) return false; - // Round to the closest integer value. - acc = acc > 0 ? (acc + filter_count / 2) / filter_count - : (acc - filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } - return true; -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& output_shape, - int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, - std::numeric_limits::min()); - TFLITE_DCHECK_LE(params.quantized_activation_max, - std::numeric_limits::max()); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int8_t max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} - -inline bool AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const int16_t* input_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - if (filter_count == 0) return false; - // Round to the closest integer value. - acc = acc > 0 ? (acc + filter_count / 2) / filter_count - : (acc - filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } - return true; -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& output_shape, - int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, - std::numeric_limits::min()); - TFLITE_DCHECK_LE(params.quantized_activation_max, - std::numeric_limits::max()); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int16_t max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h deleted file mode 100644 index 25d81fa1..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h +++ /dev/null @@ -1,117 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ - -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -inline void Tanh(int32_t input_zero_point, int32_t input_range_radius, - int32_t input_multiplier, int32_t input_shift, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& output_shape, int8_t* output_data) { - // Integer bits must be in sync with Prepare() function. - static constexpr int32_t kInputIntegerBits = 4; - static constexpr int32_t kOutputScale = 7; - static constexpr int32_t kMinInt8 = std::numeric_limits::min(); - static constexpr int32_t kMaxInt8 = std::numeric_limits::max(); - using F4 = gemmlowp::FixedPoint; - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i) { - const int32_t input = - static_cast(input_data[i]) - input_zero_point; - if (input <= -input_range_radius) { - output_data[i] = kMinInt8; - } else if (input >= input_range_radius) { - output_data[i] = kMaxInt8; - } else { - const int32_t input_in_q4 = - MultiplyByQuantizedMultiplier(input, input_multiplier, input_shift); - const int32_t output_in_q0 = - gemmlowp::tanh(F4::FromRaw(input_in_q4)).raw(); - - // Rescale and downcast. - using gemmlowp::RoundingDivideByPOT; - int32_t output_in_q24 = - RoundingDivideByPOT(output_in_q0, 31 - kOutputScale); - output_in_q24 = std::min(std::max(output_in_q24, kMinInt8), kMaxInt8); - output_data[i] = static_cast(output_in_q24); - } - } -} - -inline void Tanh(int32_t input_multiplier, int32_t input_left_shift, - const RuntimeShape& input_shape, const int16_t* ptr_input_data, - const RuntimeShape& output_shape, int16_t* ptr_output_data) { - // We use the LUT for sigmoid and take into account, that - // tanh(x) = 2*sigmoid(2*x) - 1 - - // We scale by 3/4 to expand range [-8,8]->[-10.7,10.7]. - // In case of general parameter scale, multiplier 3 is taken into account - // in TanhPrepare function and it is included in - // input_multiplier already. - - if (input_multiplier == 0) { // power of two case - input_multiplier = 3 << input_left_shift; - input_left_shift = 0; - } - - int32_t round = (input_left_shift > 0) ? 1 << (input_left_shift - 1) : 0; - - int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i, ptr_input_data++, ptr_output_data++) { - int32_t input_data = - ((*ptr_input_data) * input_multiplier + round) >> input_left_shift; - - uint32_t abs_input_data = abs(input_data); - uint32_t uh = abs_input_data >> 8; - int32_t result; - - if (uh >= 255) { - // Saturate to maximum. - result = 0xFFFF << 8; - } else { - uint32_t ua = sigmoid_table_uint16[uh]; - uint32_t ub = sigmoid_table_uint16[uh + 1]; - - uint8_t ut = abs_input_data & 0xFF; - - result = (ua << 8) + ut * (ub - ua); - } - - result = (input_data >= 0) - ? (result - (1 << (14 + 9)) + (1 << (9 - 2))) - : (-result + (1 << (14 + 9)) + (1 << (9 - 2)) - 1); - - // Convert back to 16-bit. - result >>= (9 - 1); - - *ptr_output_data = result; - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TANH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h deleted file mode 100644 index 7434b2a7..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h +++ /dev/null @@ -1,224 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_integer_ops { - -// Fixed-point per-channel-quantization transpose convolution reference kernel. -inline void TransposeConv( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data, const RuntimeShape& im2col_shape, int8_t* im2col_data, - int32_t* scratch_buffer) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int32_t input_offset = params.input_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int num_elements = output_shape.FlatSize(); - // We need to initialize scratch_buffer to all 0s, as we apply the same - // 'scatter' based trick as in float version. - memset(scratch_buffer, 0, num_elements * sizeof(int32_t)); - - // Loop through input elements one at a time. - for (int batch = 0; batch < batches; ++batch) { - for (int in_y = 0; in_y < input_height; ++in_y) { - for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence. - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location. - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds. - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - const int8_t input_value = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - const int8_t filter_value = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)] += - (input_value + input_offset) * filter_value; - } - } - } - } - } - } - } - } - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - int32_t acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)]; - if (bias_data) { - acc += bias_data[out_channel]; - } - acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - acc += output_offset; - acc = std::max(acc, output_activation_min); - acc = std::min(acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(acc); - } - } - } - } -} - -// int16_t input (zero_point=0), int8_t filter, int32 or int64 accumulator -template -inline void TransposeConv( - const ConvParams& params, const int32_t* output_multiplier, - const int32_t* output_shift, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const Scalar* bias_data, const RuntimeShape& output_shape, - int16_t* output_data, const RuntimeShape& im2col_shape, int8_t* im2col_data, - Scalar* scratch_buffer) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - - const int num_elements = output_shape.FlatSize(); - // We need to initialize scratch_buffer to all 0s, as we apply the same - // 'scatter' based trick as in float version. - memset(scratch_buffer, 0, num_elements * sizeof(Scalar)); - - // Loop through input elements one at a time. - for (int batch = 0; batch < batches; ++batch) { - for (int in_y = 0; in_y < input_height; ++in_y) { - for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence. - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location. - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds. - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - const int32_t input_value = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - const int32_t filter_value = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)] += - input_value * filter_value; - } - } - } - } - } - } - } - } - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - Scalar acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)]; - if (bias_data) { - acc += bias_data[out_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier[out_channel], output_shift[out_channel]); - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(scaled_acc); - } - } - } - } -} - -} // namespace reference_integer_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_INTEGER_OPS_TRANSPOSE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h deleted file mode 100644 index 2744366a..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/l2normalization.h +++ /dev/null @@ -1,90 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ - -#include -#include - -#include "tensorflow/lite/core/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void L2Normalization(const tflite_micro::L2NormalizationParams& op_params, - const RuntimeShape& input_shape, - const float* input_data, - const RuntimeShape& output_shape, - float* output_data, float epsilon = 1e-6) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - for (int i = 0; i < outer_size; ++i) { - float squared_l2_norm = 0; - for (int c = 0; c < depth; ++c) { - const float val = input_data[depth * i + c]; - squared_l2_norm += val * val; - } - float l2_norm = std::sqrt(squared_l2_norm); - l2_norm = std::max(l2_norm, epsilon); - for (int c = 0; c < depth; ++c) { - output_data[depth * i + c] = input_data[depth * i + c] / l2_norm; - } - } -} - -inline void L2Normalization(const tflite_micro::L2NormalizationParams& op_params, - const RuntimeShape& input_shape, - const uint8_t* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int32_t input_zero_point = op_params.input_zero_point; - - for (int i = 0; i < outer_size; ++i) { - int32_t square_l2_norm = 0; - for (int c = 0; c < depth; c++) { - int32_t diff = input_data[depth * i + c] - input_zero_point; - square_l2_norm += diff * diff; - } - int32_t inv_l2norm_multiplier; - int inv_l2norm_shift; - GetInvSqrtQuantizedMultiplierExp(square_l2_norm, kReverseShift, - &inv_l2norm_multiplier, &inv_l2norm_shift); - for (int c = 0; c < depth; c++) { - int32_t diff = input_data[depth * i + c] - input_zero_point; - int32_t rescaled_diff = MultiplyByQuantizedMultiplierSmallerThanOneExp( - 128 * diff, inv_l2norm_multiplier, inv_l2norm_shift); - int32_t unclamped_output_val = 128 + rescaled_diff; - int32_t output_val = - std::min(static_cast(255), - std::max(static_cast(0), unclamped_output_val)); - output_data[depth * i + c] = static_cast(output_val); - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_L2NORMALIZATION_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h deleted file mode 100644 index 06e3c155..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/leaky_relu.h +++ /dev/null @@ -1,69 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void LeakyRelu(const tflite_micro::LeakyReluParams& params, - const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const float val = input_data[i]; - // Note that alpha might be > 1 or < 0, so we don't use std::max here. - output_data[i] = val > 0 ? val : val * params.alpha; - } -} - -template -inline void QuantizeLeakyRelu(const LeakyReluParams& params, - const RuntimeShape& input_shape, - const T* input_data, - const RuntimeShape& output_shape, - T* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - static const int32_t quantized_min = std::numeric_limits::min(); - static const int32_t quantized_max = std::numeric_limits::max(); - for (int i = 0; i < flat_size; ++i) { - const int32_t input_value = input_data[i] - params.input_offset; - int32_t unclamped_output; - if (input_value >= 0) { - unclamped_output = params.output_offset + - MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_identity, - params.output_shift_identity); - } else { - unclamped_output = params.output_offset + - MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_alpha, - params.output_shift_alpha); - } - const T clamped_output = - std::min(quantized_max, std::max(quantized_min, unclamped_output)); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LEAKY_RELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h deleted file mode 100644 index c99f3a20..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/log_softmax.h +++ /dev/null @@ -1,256 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ - -#include -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void LogSoftmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - // Find max element value which we'll use to ensure numerical stability - // taking advantage of the following equality: - // log(exp(x[i])/sum(exp(x[i]))) == log(exp(x[i]+C)/sum(exp(x[i]+C))) - float max = std::numeric_limits::lowest(); - for (int c = 0; c < depth; ++c) { - max = std::max(max, input_data[i * depth + c]); - } - - // Compute sum. - float sum = 0.f; - for (int c = 0; c < depth; ++c) { - sum += std::exp(input_data[i * depth + c] - max); - } - - // Compute result. - const float log_sum = std::log(sum); - for (int c = 0; c < depth; ++c) { - output_data[i * depth + c] = input_data[i * depth + c] - max - log_sum; - } - } -} - -inline void LogSoftmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, - const uint8_t* input_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - const int32_t input_multiplier = params.input_multiplier; - const int32_t input_left_shift = params.input_left_shift; - const int32_t reverse_scaling_divisor = params.reverse_scaling_divisor; - const int32_t reverse_scaling_right_shift = - params.reverse_scaling_right_shift; - const int diff_min = params.diff_min; - // The representation chosen for the input to the exp() function is Q5.26. - // We need to leave extra space since values that we skip might be as large - // as -32 before multiplying by input_beta_multiplier, and therefore as - // large as -16 afterwards. Note that exp(-8) is definitely not - // insignificant to accumulation, but exp(-16) definitely is. - static constexpr int kScaledDiffIntegerBits = 5; - static constexpr int kAccumulationIntegerBits = 12; - static constexpr int kOutputIntegerBits = 4; - using FixedPointScaledDiff = - gemmlowp::FixedPoint; - using FixedPointAccum = - gemmlowp::FixedPoint; - - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - uint8_t max_in_row = 0; - for (int c = 0; c < depth; ++c) { - max_in_row = std::max(max_in_row, input_data[i * depth + c]); - } - - FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_multiplier, input_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - sum_of_exps = sum_of_exps + gemmlowp::Rescale( - exp_on_negative_values(scaled_diff_f8)); - } - } - - const int32_t fixed_log_sum_of_exps = - log_x_for_x_greater_than_or_equal_to_1( - sum_of_exps) - .raw(); - - // rescaled_diff_min is smallest representable in - // Q(kScaledDiffIntegerBits).(31-kScaledDiffIntegerBits) plus the - // log-sub-exps that will be subtracted in the loop. - // - // The thresholds diff_min, etc are negative. - const int rescaled_diff_min = - fixed_log_sum_of_exps + std::numeric_limits::lowest(); - const int adjusted_diff_min = - std::max(static_cast( - diff_min - 1), // Note use of > below instead of >= above. - MultiplyByQuantizedMultiplierSmallerThanOneExp( - rescaled_diff_min, reverse_scaling_divisor, - -reverse_scaling_right_shift)); - - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff > adjusted_diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_multiplier, input_left_shift); - int32_t unsat_output = - gemmlowp::RoundingDivideByPOT( - (input_diff_rescaled - fixed_log_sum_of_exps), - 31 - kScaledDiffIntegerBits - kOutputIntegerBits) + - 255; - - output_data[i * depth + c] = static_cast( - std::max(std::min(unsat_output, static_cast(255)), - static_cast(0))); - } else { - // Set output to smallest value. - output_data[i * depth + c] = 0; - } - } - } -} - -template -inline void LogSoftmaxQuantized(const SoftmaxParams& params, - const size_t outer_size, const size_t depth, - const RuntimeShape& input_shape, - const T* input_data, - const RuntimeShape& output_shape, - T* output_data) { - const int32_t input_multiplier = params.input_multiplier; - const int32_t input_left_shift = params.input_left_shift; - const int32_t reverse_scaling_divisor = params.reverse_scaling_divisor; - const int32_t reverse_scaling_right_shift = - params.reverse_scaling_right_shift; - const int diff_min = params.diff_min; - - static constexpr T kMinT8 = std::numeric_limits::min(); - static constexpr T kMaxT8 = std::numeric_limits::max(); - static constexpr int32_t kMinInt32 = std::numeric_limits::min(); - - // All IntegerBits must agree with Prepare function. - // Input is chosen as Q5.26 so exp(-1 * 2^5 * 2^-1) = exp(-16) is negligible. - static constexpr int kInputIntegerBits = 5; - static constexpr int kAccumulationIntegerBits = 12; - static constexpr int kOutputIntegerBits = 4; - using F5 = gemmlowp::FixedPoint; - using F12 = gemmlowp::FixedPoint; - - for (size_t outer_index = 0; outer_index < outer_size; ++outer_index) { - T max_in_row = kMinT8; - for (size_t inner_index = 0; inner_index < depth; ++inner_index) { - max_in_row = - std::max(max_in_row, input_data[outer_index * depth + inner_index]); - } - - // Accumulator "sum_of_exps_in_q12" is safe from overflowing in 2^12 steps. - F12 sum_of_exps_in_q12 = F12::FromRaw(0); - for (size_t inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input_diff = - static_cast(input_data[outer_index * depth + inner_index]) - - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_in_q5 = MultiplyByQuantizedMultiplier( - input_diff, input_multiplier, input_left_shift); - sum_of_exps_in_q12 = - sum_of_exps_in_q12 + - gemmlowp::Rescale( - exp_on_negative_values(F5::FromRaw(input_diff_in_q5))); - } - } - - const int32_t log_sum_of_exps_in_q5 = - log_x_for_x_greater_than_or_equal_to_1( - sum_of_exps_in_q12) - .raw(); - - // Potentially reduced the valid range. shifted_log_sum_of_exps_in_q5 is - // smallest representable in Q5.26 plus the log_sum_of_exps. - const int32_t shifted_log_sum_of_exps_in_q5 = - log_sum_of_exps_in_q5 + kMinInt32; - const int32_t adjusted_diff_min = - std::max(static_cast(diff_min - 1), - MultiplyByQuantizedMultiplier(shifted_log_sum_of_exps_in_q5, - reverse_scaling_divisor, - -reverse_scaling_right_shift)); - - for (size_t inner_index = 0; inner_index < depth; ++inner_index) { - int32_t input_diff = - static_cast(input_data[outer_index * depth + inner_index]) - - max_in_row; - // Note use of > below instead of >= above. - if (input_diff > adjusted_diff_min) { - const int32_t input_diff_in_q5 = MultiplyByQuantizedMultiplier( - input_diff, input_multiplier, input_left_shift); - - // Rescale and downcast. - int32_t output_in_q27 = - gemmlowp::RoundingDivideByPOT( - (input_diff_in_q5 - log_sum_of_exps_in_q5), - 31 - kInputIntegerBits - kOutputIntegerBits) + - kMaxT8; - - output_in_q27 = - std::max(std::min(output_in_q27, static_cast(kMaxT8)), - static_cast(kMinT8)); - output_data[outer_index * depth + inner_index] = - static_cast(output_in_q27); - } else { - output_data[outer_index * depth + inner_index] = kMinT8; - } - } - } -} - -inline void LogSoftmax(const SoftmaxParams& params, const size_t outer_size, - const size_t depth, const RuntimeShape& input_shape, - const int8_t* input_data, - const RuntimeShape& output_shape, int8_t* output_data) { - LogSoftmaxQuantized(params, outer_size, depth, input_shape, input_data, - output_shape, output_data); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOG_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h deleted file mode 100644 index 6b2dcf0e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/logistic.h +++ /dev/null @@ -1,132 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void Logistic(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const float cutoff_upper = 16.619047164916992188f; - const float cutoff_lower = -9.f; - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // Rational for using approximation in reference kernel. - // 0. This approximation gives enough precision for float. - // 1. This works around an issue on an embedded chipset where exp() does not - // return correctly as expected - exp(x) should return inf when overflown - // not 1.701417 IEEE 754 defines representation for inf. - // 2. This will speed up calculation and is matching the behavior in the - // optimized kernels. (check the definition of scalar_logistic_op) - - for (int i = 0; i < flat_size; i++) { - float val = input_data[i]; - float result; - if (val > cutoff_upper) { - result = 1.0f; - } else if (val < cutoff_lower) { - result = std::exp(val); - } else { - result = 1.f / (1.f + std::exp(-val)); - } - output_data[i] = result; - } -} - -// Convenience version that allows, for example, generated-code calls to be -// uniform between data types. -inline void Logistic(const LogisticParams&, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - // Drop params: not needed. - Logistic(input_shape, input_data, output_shape, output_data); -} - -inline void Logistic(const LogisticParams& params, - const RuntimeShape& input_shape, const int16_t* input_data, - const RuntimeShape& output_shape, int16_t* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - // F0 uses 0 integer bits, range [-1, 1]. - // This is the return type of math functions such as tanh, logistic, - // whose range is in [-1, 1]. - using F0 = gemmlowp::FixedPoint; - // F3 uses 3 integer bits, range [-8, 8], the input range expected here. - using F3 = gemmlowp::FixedPoint; - - const F3 input = F3::FromRaw(input_data[i]); - F0 output = gemmlowp::logistic(input); - output_data[i] = output.raw(); - } -} - -// Quantized int8_t logistic activation. Cheats by dequantizing and -// requantizing around the floating point logistic method. This implementation -// is slow on platforms without a floating point unit. - -// TODO(b/141211002): Delete this int8_t implementation once we can reuse the -// approach used in TFLite for int8_t Logistic. -inline void Logistic(const RuntimeShape& input_shape, const int8_t* input_data, - float input_scale, int input_zero_point, - const RuntimeShape& output_shape, int8_t* output_data, - float output_scale, int output_zero_point) { - const float cutoff_upper = 16.619047164916992188f; - const float cutoff_lower = -9.f; - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // Rational for using approximation in reference kernel. - // 0. This approximation gives enough precision for float. - // 1. This works around an issue on an embedded chipset where exp() does not - // return correctly as expected - exp(x) should return inf when overflown - // not 1.701417 IEEE 754 defines representation for inf. - // 2. This will speed up calculation and is matching the behavior in the - // optimized kernels. (check the definition of scalar_logistic_op) - - for (int i = 0; i < flat_size; i++) { - // Dequantize. - float val = - static_cast((input_data[i] - input_zero_point) * input_scale); - float result; - if (val > cutoff_upper) { - result = 1.0f; - } else if (val < cutoff_lower) { - result = std::exp(val); - } else { - result = 1.f / (1.f + std::exp(-val)); - } - // Requantize - int8_t output = - static_cast(result / output_scale + output_zero_point); - output_data[i] = output; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h deleted file mode 100644 index b580eb2d..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/lstm_cell.h +++ /dev/null @@ -1,422 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/reference/concatenation.h" -#include "tensorflow/lite/kernels/internal/reference/fully_connected.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void LstmCell( - const LstmCellParams& params, const RuntimeShape& unextended_input_shape, - const float* input_data, const RuntimeShape& unextended_prev_activ_shape, - const float* prev_activ_data, const RuntimeShape& weights_shape, - const float* weights_data, const RuntimeShape& unextended_bias_shape, - const float* bias_data, const RuntimeShape& unextended_prev_state_shape, - const float* prev_state_data, - const RuntimeShape& unextended_output_state_shape, float* output_state_data, - const RuntimeShape& unextended_output_activ_shape, float* output_activ_data, - const RuntimeShape& unextended_concat_temp_shape, float* concat_temp_data, - const RuntimeShape& unextended_activ_temp_shape, float* activ_temp_data) { - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape prev_activ_shape = - RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); - const RuntimeShape bias_shape = - RuntimeShape::ExtendedShape(4, unextended_bias_shape); - const RuntimeShape prev_state_shape = - RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); - const RuntimeShape output_state_shape = - RuntimeShape::ExtendedShape(4, unextended_output_state_shape); - const RuntimeShape output_activ_shape = - RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); - const RuntimeShape concat_temp_shape = - RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); - const RuntimeShape activ_temp_shape = - RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); - TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); - - const int weights_dim_count = weights_shape.DimensionsCount(); - const int batches = - MatchingDim(input_shape, 0, prev_activ_shape, 0, prev_state_shape, 0, - output_state_shape, 0, output_activ_shape, 0); - const int height = - MatchingDim(input_shape, 1, prev_activ_shape, 1, prev_state_shape, 1, - output_state_shape, 1, output_activ_shape, 1); - const int width = - MatchingDim(input_shape, 2, prev_activ_shape, 2, prev_state_shape, 2, - output_state_shape, 2, output_activ_shape, 2); - const int input_depth = input_shape.Dims(3); - const int prev_activ_depth = prev_activ_shape.Dims(3); - const int total_input_depth = prev_activ_depth + input_depth; - TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), - total_input_depth); - TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); - const int intern_activ_depth = - MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); - TFLITE_DCHECK_EQ(weights_shape.FlatSize(), - intern_activ_depth * total_input_depth); - TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); - const int output_depth = - MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, - 3, output_activ_shape, 3); - TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); - - // Concatenate prev_activ and input data together - float const* concat_input_arrays_data[2] = {input_data, prev_activ_data}; - const RuntimeShape* concat_input_arrays_shapes[2] = {&input_shape, - &prev_activ_shape}; - tflite_micro::ConcatenationParams concat_params; - concat_params.axis = 3; - concat_params.inputs_count = 2; - Concatenation(concat_params, concat_input_arrays_shapes, - concat_input_arrays_data, concat_temp_shape, concat_temp_data); - - // Fully connected - tflite_micro::FullyConnectedParams fc_params; - fc_params.float_activation_min = std::numeric_limits::lowest(); - fc_params.float_activation_max = std::numeric_limits::max(); - FullyConnected(fc_params, concat_temp_shape, concat_temp_data, weights_shape, - weights_data, bias_shape, bias_data, activ_temp_shape, - activ_temp_data); - - // Memory state update (the LSTM "guts") - for (int b = 0; b < batches; ++b) { - for (int w = 0; w < width; ++w) { - for (int h = 0; h < height; ++h) { - for (int c = 0; c < output_depth; ++c) { - const float input_gate = - 1.f / - (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, - 0 * output_depth + c)])); - const float new_input = std::tanh(activ_temp_data[Offset( - activ_temp_shape, b, h, w, 1 * output_depth + c)]); - const float forget_gate = - 1.f / - (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, - 2 * output_depth + c)])); - const float output_gate = - 1.f / - (1.f + std::exp(-activ_temp_data[Offset(activ_temp_shape, b, h, w, - 3 * output_depth + c)])); - const float new_state = - input_gate * new_input + - forget_gate * - prev_state_data[Offset(prev_state_shape, b, h, w, c)]; - output_state_data[Offset(output_state_shape, b, h, w, c)] = new_state; - output_activ_data[Offset(output_activ_shape, b, h, w, c)] = - output_gate * std::tanh(new_state); - } - } - } - } -} - -// Quantized LSTM cell implementation. -// The quantization of the input, output arrays is as follows: -// - The input activations are quantized as uint8 on the interval -// [-1, 127/128]. -// The rationale for that is that is the natural interval for output -// activations (see next point) and these need to be concatenated together. -// We could accommodate different ranges by re-scaling, but we empirically -// found that setting the input activations range to be [-1, 127/128] in the -// first place, removing the need for re-scaling, greatly improves accuracy. -// - The output activations are quantized as uint8 on the interval -// [-1, 127/128]. -// The rationale for that is that the definition of a LSTM cell makes them -// intrinsically constrained in [-1, 1]; tweaking that to [-1, 127/128] -// makes for simpler, more accurate fixed-point arithmetic. -// - The output-at-previous-timestep state array is obviously quantized as -// the output activations. -// - The internal LSTM memory (not the output-at-previous-timestep, the other -// internal state array) is int16-quantized and may use any power-of-two, -// symmetric range i.e. [-2^N, 2^N * 32767/32768] for any N, which we call -// StateIntegerBits below, see the below discussion of that template -// parameter ("The StateIntegerBits template parameter"). -// - The output of the internal fully-connected node is int16-quantized -// on the interval [-8, 8 * 32767/32768], the rationale for which is -// explained just below ("Why [-8, 8] for fully-connected output?"). -// -// -// === The StateIntegerBits template parameter === -// -// The StateIntegerBits template parameter controls the fixed-point format used -// to represent the internal memory of the LSTM cell (not the -// output-at-previous-timestep, the other internal state array). It's currently -// a template parameter so that the model can control that. The most typical -// value for StateIntegerBits is 4. Other plausible values are anywhere between -// 3 and 5. We might eventually standardize on a single supported value, e.g. 4, -// and drop that template parameter. The reason why it can't be a runtime -// parameter is that this controls the fixed-point format used, i.e. we need to -// generate actually different code based on it. In particular, we generate code -// for a fixed-point tanh() implementation for that format, which internally -// uses a fixed-point exp() implementation, which internally uses a -// barrel-shifter with a number of steps that depends on StateIntegerBits. -// Another consequence of that is that a higher value of StateIntegerBits -// results in a more expensive implementation (more barrel shifter steps -// needed). -// -// -// === Why [-8, 8] for fully-connected output? === -// -// This array is only fed to Logistic and Tanh functions, for which -// the quantized implementation will want to use fixed-point arithmetic, -// requiring a power-of-two representation interval. Thus, we should right -// away quantize this array to a power-of-two interval; otherwise, -// implementation will need to rescale that, losing any benefit that a tighter -// representation interval might otherwise yield, while introducing some -// numerical error and computational overhead. -// -// Now, Logistic and Tanh -// are nearly constant (nearly equal to their horizontal asymptotes) -// outside of a small bounded interval around 0: -// -// Logistic(4) = 1 - 1.8e-2 Tanh(4) = 1 - 6.7e-4 -// Logistic(8) = 1 - 3.4e-4 Tanh(8) = 1 - 2.3e-7 -// Logistic(16) = 1 - 1.1e-7 Tanh(16) = 1 - 2.5e-14 -// -// From this, we see that clamping to [-4, 4] would be too inaccurate -// (the error of 1.8e-2 on Logistic would be felt even in 8bit precision) -// while clamping to [-16, 16] would make no difference even in float32. -// However, for a fixed-point implementation in 16-bit integers, using 5 -// integer bits to represent the [-16, 16] range would leave only 11 -// fractional bits, giving an increment of 2^-11 = 4.9e-4 between consecutive -// representable values. Notice that is higher than the -// worst-case clamping error with clamping to [-8, 8]: 3.4e-4 for Logistic. -// Using [-8, 8] thus seems like the better compromise overall, enjoying -// an increment of 2.4e-4 between representable values and a worst-case -// clamping error of 3.4e-4, both better than the increment of 4.9e-4 with -// [-16, 16]. -// -// Moreover, all other things being equal, it is nice to choose the narrower -// representation range, as that makes the implementation of fixed-point -// math functions a little cheaper (each integer bit requires an additional -// barrel-shifter atep in the implementation of exp(-x)). That is further -// reason to prefer [-8, 8] over [-16, 16]. The choice of [-16, 16] would make -// sense for 32-bit float or 32-bit fixed-point quantization, but we are -// aiming for 16-bit fixed-point quantization of these internal nodes here. -// -template -inline void LstmCell(const LstmCellParams& params, - const RuntimeShape& unextended_input_shape, - const uint8_t* input_data_uint8, - const RuntimeShape& unextended_prev_activ_shape, - const uint8_t* prev_activ_data_uint8, - const RuntimeShape& weights_shape, - const uint8_t* weights_data_uint8, - const RuntimeShape& unextended_bias_shape, - const int32_t* bias_data_int32, - const RuntimeShape& unextended_prev_state_shape, - const int16_t* prev_state_data_int16, - const RuntimeShape& unextended_output_state_shape, - int16_t* output_state_data_int16, - const RuntimeShape& unextended_output_activ_shape, - uint8_t* output_activ_data_uint8, - const RuntimeShape& unextended_concat_temp_shape, - uint8_t* concat_temp_data_uint8, - const RuntimeShape& unextended_activ_temp_shape, - int16_t* activ_temp_data_int16, void* gemmlowp_context) { - (void)gemmlowp_context; // only used in optimized code. - int32_t weights_zero_point = params.weights_zero_point; - int32_t accum_multiplier = params.accum_multiplier; - int accum_shift = params.accum_shift; - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_prev_activ_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_bias_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_prev_state_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_state_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_activ_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_concat_temp_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_activ_temp_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape prev_activ_shape = - RuntimeShape::ExtendedShape(4, unextended_prev_activ_shape); - const RuntimeShape bias_shape = - RuntimeShape::ExtendedShape(4, unextended_bias_shape); - const RuntimeShape prev_state_shape = - RuntimeShape::ExtendedShape(4, unextended_prev_state_shape); - const RuntimeShape output_state_shape = - RuntimeShape::ExtendedShape(4, unextended_output_state_shape); - const RuntimeShape output_activ_shape = - RuntimeShape::ExtendedShape(4, unextended_output_activ_shape); - const RuntimeShape concat_temp_shape = - RuntimeShape::ExtendedShape(4, unextended_concat_temp_shape); - const RuntimeShape activ_temp_shape = - RuntimeShape::ExtendedShape(4, unextended_activ_temp_shape); - TFLITE_DCHECK_GE(weights_shape.DimensionsCount(), 2); - - // Gather dimensions information, and perform consistency checks. - const int weights_dim_count = weights_shape.DimensionsCount(); - const int outer_size = MatchingFlatSizeSkipDim( - input_shape, 3, prev_activ_shape, prev_state_shape, output_state_shape, - output_activ_shape); - const int input_depth = input_shape.Dims(3); - const int prev_activ_depth = prev_activ_shape.Dims(3); - const int total_input_depth = prev_activ_depth + input_depth; - TFLITE_DCHECK_EQ(weights_shape.Dims(weights_dim_count - 1), - total_input_depth); - const int intern_activ_depth = - MatchingDim(weights_shape, weights_dim_count - 2, bias_shape, 3); - TFLITE_DCHECK_EQ(weights_shape.FlatSize(), - intern_activ_depth * total_input_depth); - TFLITE_DCHECK_EQ(FlatSizeSkipDim(bias_shape, 3), 1); - TFLITE_DCHECK_EQ(intern_activ_depth % 4, 0); - const int output_depth = - MatchingDim(prev_state_shape, 3, prev_activ_shape, 3, output_state_shape, - 3, output_activ_shape, 3); - TFLITE_DCHECK_EQ(output_depth, intern_activ_depth / 4); - const int fc_batches = FlatSizeSkipDim(activ_temp_shape, 3); - const int fc_output_depth = - MatchingDim(weights_shape, weights_dim_count - 2, activ_temp_shape, 3); - const int fc_accum_depth = total_input_depth; - TFLITE_DCHECK_EQ(fc_output_depth, 4 * output_depth); - - // Depth-concatenate prev_activ and input data together. - uint8_t const* concat_input_arrays_data[2] = {input_data_uint8, - prev_activ_data_uint8}; - const RuntimeShape* concat_input_arrays_shapes[2] = {&input_shape, - &prev_activ_shape}; - tflite_micro::ConcatenationParams concat_params; - concat_params.axis = 3; - concat_params.inputs_count = 2; - Concatenation(concat_params, concat_input_arrays_shapes, - concat_input_arrays_data, concat_temp_shape, - concat_temp_data_uint8); - - // Implementation of the fully connected node inside the LSTM cell. - // The operands are 8-bit integers, the accumulators are internally 32bit - // integers, and the output is 16-bit fixed-point with 3 integer bits so - // the output range is [-2^3, 2^3] == [-8, 8]. The rationale for that - // is explained in the function comment above. - for (int b = 0; b < fc_batches; ++b) { - for (int out_c = 0; out_c < fc_output_depth; ++out_c) { - // Internal accumulation. - // Initialize accumulator with the bias-value. - int32_t accum = bias_data_int32[out_c]; - // Accumulation loop. - for (int d = 0; d < fc_accum_depth; ++d) { - int16_t input_val = - concat_temp_data_uint8[b * fc_accum_depth + d] - 128; - int16_t weights_val = - weights_data_uint8[out_c * fc_accum_depth + d] - weights_zero_point; - accum += input_val * weights_val; - } - // Down-scale the final int32 accumulator to the scale used by our - // (16-bit, using 3 integer bits) fixed-point format. The quantized - // multiplier and shift here have been pre-computed offline - // (e.g. by toco). - accum = - MultiplyByQuantizedMultiplier(accum, accum_multiplier, accum_shift); - // Saturate, cast to int16, and store to the temporary activations array. - accum = std::max(-32768, std::min(32767, accum)); - activ_temp_data_int16[out_c + fc_output_depth * b] = accum; - } - } - - // Rest of the LSTM cell: tanh and logistic math functions, and some adds - // and muls, all done in 16-bit fixed-point. - for (int b = 0; b < outer_size; ++b) { - for (int c = 0; c < output_depth; ++c) { - // Define the fixed-point data types that we will use here. All use - // int16 as the underlying integer type i.e. all are 16-bit fixed-point. - // They only differ by the number of integral vs. fractional bits, - // determining the range of values that they can represent. - // - // F0 uses 0 integer bits, range [-1, 1]. - // This is the return type of math functions such as tanh, logistic, - // whose range is in [-1, 1]. - using F0 = gemmlowp::FixedPoint; - // F3 uses 3 integer bits, range [-8, 8]. - // This is the range of the previous fully-connected node's output, - // which is our input here. - using F3 = gemmlowp::FixedPoint; - // FS uses StateIntegerBits integer bits, range [-2^StateIntegerBits, - // 2^StateIntegerBits]. It's used to represent the internal state, whose - // number of integer bits is currently dictated by the model. See comment - // on the StateIntegerBits template parameter above. - using FS = gemmlowp::FixedPoint; - // Implementation of input gate, using fixed-point logistic function. - F3 input_gate_input = F3::FromRaw( - activ_temp_data_int16[b * fc_output_depth + 0 * output_depth + c]); - F0 input_gate_output = gemmlowp::logistic(input_gate_input); - // Implementation of input modulation gate, using fixed-point tanh - // function. - F3 input_modulation_gate_input = F3::FromRaw( - activ_temp_data_int16[b * fc_output_depth + 1 * output_depth + c]); - F0 input_modulation_gate_output = - gemmlowp::tanh(input_modulation_gate_input); - // Implementation of forget gate, using fixed-point logistic function. - F3 forget_gate_input = F3::FromRaw( - activ_temp_data_int16[b * fc_output_depth + 2 * output_depth + c]); - F0 forget_gate_output = gemmlowp::logistic(forget_gate_input); - // Implementation of output gate, using fixed-point logistic function. - F3 output_gate_input = F3::FromRaw( - activ_temp_data_int16[b * fc_output_depth + 3 * output_depth + c]); - F0 output_gate_output = gemmlowp::logistic(output_gate_input); - // Implementation of internal multiplication nodes, still in fixed-point. - F0 input_times_input_modulation = - input_gate_output * input_modulation_gate_output; - FS prev_state = FS::FromRaw(prev_state_data_int16[b * output_depth + c]); - FS prev_state_times_forget_state = forget_gate_output * prev_state; - // Implementation of internal addition node, saturating. - FS new_state = gemmlowp::SaturatingAdd( - gemmlowp::Rescale(input_times_input_modulation), - prev_state_times_forget_state); - // Implementation of last internal Tanh node, still in fixed-point. - // Since a Tanh fixed-point implementation is specialized for a given - // number or integer bits, and each specialization can have a substantial - // code size, and we already used above a Tanh on an input with 3 integer - // bits, and per the table in the above function comment there is no - // significant accuracy to be lost by clamping to [-8, +8] for a - // 3-integer-bits representation, let us just do that. This helps people - // porting this to targets where code footprint must be minimized. - F3 new_state_f3 = gemmlowp::Rescale<3>(new_state); - F0 output_activ_int16 = output_gate_output * gemmlowp::tanh(new_state_f3); - // Store the new internal state back to memory, as 16-bit integers. - // Note: here we store the original value with StateIntegerBits, not - // the rescaled 3-integer-bits value fed to tanh. - output_state_data_int16[b * output_depth + c] = new_state.raw(); - // Down-scale the output activations to 8-bit integers, saturating, - // and store back to memory. - int16_t rescaled_output_activ = - gemmlowp::RoundingDivideByPOT(output_activ_int16.raw(), 8); - int16_t clamped_output_activ = std::max( - -128, std::min(127, rescaled_output_activ)); - output_activ_data_uint8[b * output_depth + c] = - 128 + clamped_output_activ; - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_LSTM_CELL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h deleted file mode 100644 index 52d17105..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/maximum_minimum.h +++ /dev/null @@ -1,64 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -void MaximumMinimumBroadcastSlow(const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const T* input2_data, - const RuntimeShape& unextended_output_shape, - T* output_data, Op op) { - // Uses element-wise calculation if broadcast is not required. - if (unextended_input1_shape == unextended_input2_shape) { - const int flat_size = - MatchingElementsSize(unextended_input1_shape, unextended_input2_shape, - unextended_output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = op(input1_data[i], input2_data[i]); - } - } else { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), N); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), N); - - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDesc output_desc; - NdArrayDescsForElementwiseBroadcast( - unextended_input1_shape, unextended_input2_shape, &desc1, &desc2); - CopyDimsToDesc(RuntimeShape::ExtendedShape(N, unextended_output_shape), - &output_desc); - - auto maxmin_func = [&](int indexes[N]) { - output_data[SubscriptToIndex(output_desc, indexes)] = - op(input1_data[SubscriptToIndex(desc1, indexes)], - input2_data[SubscriptToIndex(desc2, indexes)]); - }; - NDOpsHelper(output_desc, maxmin_func); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MAXIMUM_MINIMUM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h deleted file mode 100644 index a53edf66..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/mul.h +++ /dev/null @@ -1,267 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" - -namespace tflite_micro { - -namespace reference_ops { - -// Maximum dimension supported by the broadcast mul operation. -constexpr int kMaxMulBroadcastDim = 6; - -// Element-wise mul that can often be used for inner loop of broadcast Mul as -// well as the non-broadcast Mul. -inline void MulElementwise(int size, const ArithmeticParams& params, - const uint8_t* input1_data, - const uint8_t* input2_data, uint8_t* output_data) { - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier(input1_val * input2_val, - params.output_multiplier, - params.output_shift); - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - output_data[i] = static_cast(clamped_output); - } -} - -template -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, &output_activation_max); - - const int flat_size = - MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] * input2_data[i], output_activation_min, - output_activation_max); - } -} - -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const std::complex* input1_data, - const RuntimeShape& input2_shape, - const std::complex* input2_data, - const RuntimeShape& output_shape, - std::complex* output_data) { - const int flat_size = - MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = input1_data[i] * input2_data[i]; - } -} - -inline void Mul(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape); - - MulElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -template -void BroadcastMulRecursiveDimensions( - const ArithmeticParams& params, int dimension, const T* input1_data, - const T* input2_data, T* output_data, size_t* input1_offset_p, - size_t* input2_offset_p, size_t* output_offset, - const NdArrayDesc& desc1, - const NdArrayDesc& desc2, - const int32_t extended_output_shape_dims[kMaxMulBroadcastDim], - F binary_func) { - if (dimension == kMaxMulBroadcastDim - 1) { - for (int c = 0; c < extended_output_shape_dims[dimension]; ++c) { - const T input1_val = input1_data[*input1_offset_p]; - const T input2_val = input2_data[*input2_offset_p]; - output_data[*output_offset] = binary_func(params, input1_val, input2_val); - *input1_offset_p += desc1.strides[dimension]; - *input2_offset_p += desc2.strides[dimension]; - ++(*output_offset); - } - } else { - for (int a = 0; a < extended_output_shape_dims[dimension]; ++a) { - size_t input1_offset_c = *input1_offset_p; - size_t input2_offset_c = *input2_offset_p; - BroadcastMulRecursiveDimensions( - params, dimension + 1, input1_data, input2_data, output_data, - &input1_offset_c, &input2_offset_c, output_offset, desc1, desc2, - extended_output_shape_dims, binary_func); - *input1_offset_p += desc1.strides[dimension]; - *input2_offset_p += desc2.strides[dimension]; - } - } -} - -inline void BroadcastMul6DSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const uint8_t* input1_data, - const RuntimeShape& input2_shape, - const uint8_t* input2_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, - &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, output_shape); - // Cache output shape dimensions. - int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; - std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), - sizeof(extended_output_shape_dims)); - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastMulRecursiveDimensions( - params, 0, input1_data, input2_data, output_data, &input1_offset, - &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, - [](const ArithmeticParams& params, const uint8_t input1_val, - const uint8_t input2_val) { - const int32_t offsetted_input1_val = params.input1_offset + input1_val; - const int32_t offsetted_input2_val = params.input2_offset + input2_val; - const int32_t unclamped_result = - params.output_offset + - MultiplyByQuantizedMultiplier( - offsetted_input1_val * offsetted_input2_val, - params.output_multiplier, params.output_shift); - const int32_t clamped_output = std::min( - params.quantized_activation_max, - std::max(params.quantized_activation_min, unclamped_result)); - return static_cast(clamped_output); - }); -} - -template -inline typename std::enable_if< - !is_small_integer::value || enable_for_short_integers, void>::type -BroadcastMul6DSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const T* input2_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6); - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape); - // Cache output shape dimensions. - int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; - std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), - sizeof(extended_output_shape_dims)); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest - // stride, typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for - // the best cache behavior. - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastMulRecursiveDimensions( - params, 0, input1_data, input2_data, output_data, &input1_offset, - &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, - [](const ArithmeticParams& params, const T input1_val, - const T input2_val) { - T output_activation_min; - T output_activation_max; - GetActivationParams(params, &output_activation_min, - &output_activation_max); - return ActivationFunctionWithMinMax(input1_val * input2_val, - output_activation_min, - output_activation_max); - }); -} - -inline void BroadcastMul6DSlow(const ArithmeticParams& params, - const RuntimeShape& unextended_input1_shape, - const std::complex* input1_data, - const RuntimeShape& unextended_input2_shape, - const std::complex* input2_data, - const RuntimeShape& unextended_output_shape, - std::complex* output_data) { - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 6); - TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 6); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 6); - - NdArrayDesc desc1; - NdArrayDesc desc2; - NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, - unextended_input2_shape, &desc1, &desc2); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(kMaxMulBroadcastDim, unextended_output_shape); - // Cache output shape dimensions. - int32_t extended_output_shape_dims[kMaxMulBroadcastDim]; - std::memcpy(extended_output_shape_dims, extended_output_shape.DimsData(), - sizeof(extended_output_shape_dims)); - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastMulRecursiveDimensions( - params, 0, input1_data, input2_data, output_data, &input1_offset, - &input2_offset, &output_offset, desc1, desc2, extended_output_shape_dims, - [](const ArithmeticParams& params, const std::complex input1_val, - const std::complex input2_val) { - return input1_val * input2_val; - }); -} - -template -inline void BroadcastMul4DSlow( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - return BroadcastMul6DSlow(params, input1_shape, input1_data, input2_shape, - input2_data, output_shape, output_data); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h deleted file mode 100644 index 18c291c4..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/neg.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void Negate(const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = -input_data[i]; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_NEG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h deleted file mode 100644 index feb3f3a7..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pad.h +++ /dev/null @@ -1,169 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -// TFLite Pad supports activation tensors with up to 5 dimensions. -constexpr int PadKernelMaxDimensionCount() { return 5; } - -// There are two versions of pad: Pad and PadV2. In PadV2 there is a second -// scalar input that provides the padding value. Therefore pad_value_ptr can be -// equivalent to a simple input1_data. For Pad, it should point to a zero -// value. -// -// Note that two typenames are required, so that T=P=int32_t is considered a -// specialization distinct from P=int32_t. -template -inline void PadImpl(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - const RuntimeShape ext_input_shape = - RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), input_shape); - const RuntimeShape ext_output_shape = - RuntimeShape::ExtendedShape(PadKernelMaxDimensionCount(), output_shape); - TFLITE_DCHECK_LE(op_params.left_padding_count, PadKernelMaxDimensionCount()); - TFLITE_DCHECK_LE(op_params.right_padding_count, PadKernelMaxDimensionCount()); - - // Runtime calls are currently fixed at 5 dimensions. Copy inputs so we can - // pad them to 5 dims (yes, we are "padding the padding"). - int left_padding_copy[PadKernelMaxDimensionCount()]; - for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { - left_padding_copy[i] = 0; - } - for (int i = 0; i < op_params.left_padding_count; ++i) { - left_padding_copy[i + PadKernelMaxDimensionCount() - - op_params.left_padding_count] = op_params.left_padding[i]; - } - int right_padding_copy[PadKernelMaxDimensionCount()]; - for (int i = 0; i < PadKernelMaxDimensionCount(); i++) { - right_padding_copy[i] = 0; - } - for (int i = 0; i < op_params.right_padding_count; ++i) { - right_padding_copy[i + PadKernelMaxDimensionCount() - - op_params.right_padding_count] = - op_params.right_padding[i]; - } - - const int output_batch = ext_output_shape.Dims(0); - const int output_plane = ext_output_shape.Dims(1); - const int output_height = ext_output_shape.Dims(2); - const int output_width = ext_output_shape.Dims(3); - const int output_depth = ext_output_shape.Dims(4); - - const int left_b_padding = left_padding_copy[0]; - const int left_p_padding = left_padding_copy[1]; - const int left_h_padding = left_padding_copy[2]; - const int left_w_padding = left_padding_copy[3]; - const int left_d_padding = left_padding_copy[4]; - - const int right_b_padding = right_padding_copy[0]; - const int right_p_padding = right_padding_copy[1]; - const int right_h_padding = right_padding_copy[2]; - const int right_w_padding = right_padding_copy[3]; - const int right_d_padding = right_padding_copy[4]; - - const T pad_value = *pad_value_ptr; - - const T* in_ptr = input_data; - T* out_ptr = output_data; - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_p = 0; out_p < output_plane; ++out_p) { - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - if (out_b < left_b_padding || - out_b >= output_batch - right_b_padding || - out_p < left_p_padding || - out_p >= output_plane - right_p_padding || - out_h < left_h_padding || - out_h >= output_height - right_h_padding || - out_w < left_w_padding || - out_w >= output_width - right_w_padding || - out_d < left_d_padding || - out_d >= output_depth - right_d_padding) { - *out_ptr++ = pad_value; - } else { - *out_ptr++ = *in_ptr++; - } - } - } - } - } - } -} - -template -inline void Pad(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -// The second (pad-value) input can be int32_t when, say, the first is uint8_t. -template -inline void Pad(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const int32_t* pad_value_ptr, const RuntimeShape& output_shape, - T* output_data) { - const T converted_pad_value = static_cast(*pad_value_ptr); - PadImpl(op_params, input_shape, input_data, &converted_pad_value, - output_shape, output_data); -} - -// This version avoids conflicting template matching. -template <> -inline void Pad(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, const int32_t* input_data, - const int32_t* pad_value_ptr, const RuntimeShape& output_shape, - int32_t* output_data) { - PadImpl(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -template -inline void PadImageStyle(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const P* pad_value_ptr, - const RuntimeShape& output_shape, T* output_data) { - Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -template -inline void PadImageStyle(const tflite_micro::PadParams& op_params, - const RuntimeShape& input_shape, - const float* input_data, const P* pad_value_ptr, - const RuntimeShape& output_shape, - float* output_data) { - Pad(op_params, input_shape, input_data, pad_value_ptr, output_shape, - output_data); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h deleted file mode 100644 index c4294803..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/pooling.h +++ /dev/null @@ -1,303 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline bool AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float total = 0.f; - float filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - total += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - if (filter_count == 0) return false; - const float average = total / filter_count; - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(average, params.float_activation_min, - params.float_activation_max); - } - } - } - } - return true; -} - -inline bool AveragePool(const PoolParams& params, - const RuntimeShape& input_shape, - const uint8_t* input_data, - const RuntimeShape& output_shape, - uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - int32_t acc = 0; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - acc += - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - filter_count++; - } - } - if (filter_count == 0) return false; - acc = (acc + filter_count / 2) / filter_count; - acc = std::max(acc, params.quantized_activation_min); - acc = std::min(acc, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(acc); - } - } - } - } - return true; -} - -inline void L2Pool(const PoolParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float sum_squares = 0.f; - int filter_count = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - const float val = - input_data[Offset(input_shape, batch, in_y, in_x, channel)]; - sum_squares += val * val; - filter_count++; - } - } - const float l2pool_result = std::sqrt(sum_squares / filter_count); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(l2pool_result, - params.float_activation_min, - params.float_activation_max); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - float max = std::numeric_limits::lowest(); - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - ActivationFunctionWithMinMax(max, params.float_activation_min, - params.float_activation_max); - } - } - } - } -} - -inline void MaxPool(const PoolParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - TFLITE_DCHECK_GE(params.quantized_activation_min, 0); - TFLITE_DCHECK_LE(params.quantized_activation_max, 255); - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int depth = MatchingDim(input_shape, 3, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int stride_height = params.stride_height; - const int stride_width = params.stride_width; - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int channel = 0; channel < depth; ++channel) { - const int in_x_origin = - (out_x * stride_width) - params.padding_values.width; - const int in_y_origin = - (out_y * stride_height) - params.padding_values.height; - // Compute the boundaries of the filter region clamped so as to - // ensure that the filter window fits in the input array. - const int filter_x_start = std::max(0, -in_x_origin); - const int filter_x_end = - std::min(params.filter_width, input_width - in_x_origin); - const int filter_y_start = std::max(0, -in_y_origin); - const int filter_y_end = - std::min(params.filter_height, input_height - in_y_origin); - uint8_t max = 0; - for (int filter_y = filter_y_start; filter_y < filter_y_end; - ++filter_y) { - for (int filter_x = filter_x_start; filter_x < filter_x_end; - ++filter_x) { - const int in_x = in_x_origin + filter_x; - const int in_y = in_y_origin + filter_y; - max = std::max( - max, - input_data[Offset(input_shape, batch, in_y, in_x, channel)]); - } - } - max = std::max(max, params.quantized_activation_min); - max = std::min(max, params.quantized_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, channel)] = - static_cast(max); - } - } - } - } -} -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h deleted file mode 100644 index edcaf642..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h +++ /dev/null @@ -1,333 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ - -#include "tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h" - -#if defined(_MSC_VER) -#define __restrict__ __restrict -#endif - -namespace tflite_micro { -namespace tensor_utils { - -// Check if all entries of a vector are zero for float. -bool IsZeroVector(const float* vector, int v_size) { - return PortableIsZeroVector(vector, v_size); -} - -// Check if all entries of a vector are zero for int8_t. -bool IsZeroVector(const int8_t* vector, int v_size) { - return PortableIsZeroVector(vector, v_size); -} - -void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, float* max, - float* scaling_factor) { - PortableSymmetricQuantizeFloats(values, size, quantized_values, min, max, - scaling_factor); -} - -void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor) { - PortableSymmetricQuantizeFloats(values, size, quantized_values, min_value, - max_value, scaling_factor); -} - -void AsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* scaling_factor, - int32_t* offset) { - PortableAsymmetricQuantizeFloats(values, size, quantized_values, - scaling_factor, offset); -} - -void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, - int m_cols, const float* vector, - int n_batch, float* result) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - n_batch, result); -} - -void MatrixBatchVectorMultiplyAccumulate(const int8_t* __restrict__ matrix, - const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, - const float* scaling_factors, - int n_batch, - float* __restrict__ result) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - scaling_factors, n_batch, result); -} - -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result, - per_channel_scale, input_offset, scratch, row_sums, compute_row_sums, - context); -} - -void MatrixBatchVectorMultiplyAccumulate(const int8_t* __restrict__ matrix, - const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, - const float* scaling_factors, - int n_batch, int32_t* scratch, - float* __restrict__ result, - CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - scaling_factors, n_batch, result); -} - -void SparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( - matrix, segments, indices, m_rows, m_cols, vector, n_batch, result); -} - -void SparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate( - matrix, ledger, m_rows, m_cols, vector, n_batch, result); -} - -void SparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - - int8_t* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( - matrix, segments, indices, m_rows, m_cols, vector, bias_vector, n_batch, - input_offset, output_multiplier, output_shift, output_offset, - output_activation_min, output_activation_max, result); -} - -void SparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, - const int m_cols, const int8_t* __restrict__ vectors, - const float* scaling_factors, int n_batch, float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate( - matrix, ledger, m_rows, m_cols, vectors, scaling_factors, n_batch, - result); -} - -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, scratch, output, context); -} - -void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, scratch, output, context); -} - -void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, - int32_t n_row, int32_t n_col, - int32_t* output) { - PortableMatrixScalarMultiplyAccumulate(matrix, scalar, n_row, n_col, output); -} - -void MatrixBatchVectorMultiply(const int8_t* input, int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, int32_t n_cell, - int8_t* gate_output, int8_t gate_output_zp) { - PortableMatrixBatchVectorMultiply( - input, input_zeropoint, input_to_gate_weights, - input_to_gate_effective_scale_a, input_to_gate_effective_scale_b, n_batch, - n_input, n_cell, gate_output, gate_output_zp); -} - -void MatrixBatchVectorMultiply(const int16_t* hidden, - const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, - int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, - int32_t n_hidden, int32_t n_output, - int32_t output_zp, int8_t* proj_output) { - PortableMatrixBatchVectorMultiply(hidden, hidden_to_output_weights, - proj_effective_scale_a, - proj_effective_scale_b, gate_bias, n_batch, - n_hidden, n_output, output_zp, proj_output); -} - -void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output) { - PortableApplyLayerNorm(input, layer_norm_weights, bias, layer_norm_scale_a, - layer_norm_scale_b, variance_limit, n_batch, n_input, - output); -} - -void ApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, int32_t layer_norm_scale_b, - const int32_t* bias, int n_batch, int n_input, - int16_t* output) { - PortableApplyLayerNormFloat(input, layer_norm_weights, layer_norm_scale_a, - layer_norm_scale_b, bias, n_batch, n_input, - output); -} - -void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, - int16_t* output) { - PortableApplySigmoid(input, n_batch, n_input, output); -} - -void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, int32_t n_input, - int16_t* output) { - PortableApplySigmoidFloat(input, n_batch, n_input, output); -} - -void ApplyTanh(int32_t integer_bits, const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output) { - PortableApplyTanh(integer_bits, input, n_batch, n_input, output); -} - -void ApplyTanhFloat(const int16_t* input, int32_t n_batch, int32_t n_input, - int32_t integer_bits, int16_t* output) { - PortableApplyTanhFloat(input, n_batch, n_input, integer_bits, output); -} - -void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int shift, int16_t* output) { - PortableCwiseMul(input_1, input_2, n_batch, n_input, shift, output); -} - -void CwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output) { - PortableCwiseMul(input_1, input_2, multiplier, shift, n_batch, n_input, - output_zp, output); -} - -void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int16_t* output) { - PortableCwiseAdd(input_1, input_2, n_batch, n_input, output); -} - -void CwiseClipping(float* vector, const int v_size, - const float clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -void CwiseClipping(int16_t* vector, const int v_size, - const int16_t clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -void CwiseClipping(int8_t* vector, const int v_size, - const int8_t clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size, - const int16_t* batch_vector, - int n_batch, int32_t multiplier, - int shift, int16_t* result) { - PortableVectorBatchVectorCwiseProductAccumulate( - vector, v_size, batch_vector, n_batch, multiplier, shift, result); -} - -float VectorVectorDotProduct(const float* vector1, const float* vector2, - int v_size) { - return PortableVectorVectorDotProduct(vector1, vector2, v_size); -} - -void BatchVectorBatchVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, int v_size, - int n_batch, int32_t* result) { - PortableBatchVectorBatchVectorDotProduct(vector1, vector2, v_size, n_batch, - result); -} - -void Sub1Vector(const float* vector, int v_size, float* result) { - PortableSub1Vector(vector, v_size, result); -} - -void Sub1Vector(const int16_t* vector, int v_size, int16_t* result) { - PortableSub1Vector(vector, v_size, result); -} - -// Multiply all elements of vector with a scalar. -void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, - float* result) { - PortableVectorScalarMultiply(vector, v_size, scale, result); -} - -void ReductionSumVector(const float* input_vector, float* output_vector, - int output_size, int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -void ReductionSumVector(const int32_t* input_vector, int32_t* output_vector, - int output_size, int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -void ReductionSumVector(const int8_t* input_vector, int32_t* output_vector, - int output_size, int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -void MeanStddevNormalization(const float* input_vector, float* output_vector, - int v_size, int n_batch) { - PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch); -} - -void TwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, int32_t n_batch, - int32_t n_cell, int16_t* output) { - PortableTwoGateSaturatingAdd( - input, input_zp, recurrent, recurrent_zp, input_effective_scale_a, - input_effective_scale_b, recurrent_effective_scale_a, - recurrent_effective_scale_b, n_batch, n_cell, output); -} - -} // namespace tensor_utils -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h deleted file mode 100644 index 88d9c640..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h +++ /dev/null @@ -1,244 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ - -#include -#include - -#if defined(_MSC_VER) -#define __restrict__ __restrict -#endif - -namespace tflite_micro { - -// Not all backends support CpuBackendContext usage, so forward declare to avoid -// pulling in its implementation. -class CpuBackendContext; - -namespace tensor_utils { - -template -bool PortableIsZeroVector(const T* vector, int v_size) { - for (int i = 0; i < v_size; ++i) { - if (vector[i] != 0) { - return false; - } - } - return true; -} - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min_value, - float* max_value, float* scaling_factor); - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor); - -void PortableAsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, - float* scaling_factor, int32_t* offset); - -// Multiply a matrix by a batch vector, and store results in a batch-size -// vector. -void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, - int m_rows, int m_cols, - const float* vector, - int n_batch, float* result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, const float* scaling_factors, - int n_batch, int32_t* scratch, float* __restrict__ result, - CpuBackendContext* context); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - int8_t* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, - const int m_cols, const int8_t* __restrict__ vectors, - const float* scaling_factors, int n_batch, float* __restrict__ result); - -// Dot product of two vectors. -float PortableVectorVectorDotProduct(const float* vector1, const float* vector2, - int v_size); - -void PortableBatchVectorBatchVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, - int v_size, int n_batch, - int32_t* result); - -void PortableVectorBatchVectorCwiseProductAccumulate( - const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch, - int32_t multiplier, int shift, int16_t* result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiply(const int8_t* input, - int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, - int32_t n_cell, int8_t* gate_output, - int8_t gate_output_zp); - -void PortableMatrixBatchVectorMultiply( - const int16_t* hidden, const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, int32_t n_hidden, - int32_t n_output, int32_t output_zp, int8_t* proj_output); - -void PortableMatrixScalarMultiplyAccumulate(const int8_t* matrix, - int32_t scalar, int32_t n_row, - int32_t n_col, int32_t* output); - -void PortableApplyLayerNorm(const int16_t* input, - const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output); - -void PortableApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, - const int32_t* bias, int n_batch, int n_input, - int16_t* output); - -void PortableApplySigmoid(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output); - -void PortableApplySigmoidFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output); - -void PortableApplyTanh(int32_t integer_bits, const int16_t* input, - int32_t n_batch, int32_t n_input, int16_t* output); - -void PortableApplyTanhFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int32_t integer_bits, - int16_t* output); - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int shift, int16_t* output); - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output); - -void PortableCwiseAdd(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int16_t* output); - -template -void PortableCwiseClipping(T* vector, const int v_size, - const T& clipping_value) { - for (int i = 0; i < v_size; i++) { - vector[i] = std::max(std::min(clipping_value, vector[i]), - static_cast(-clipping_value)); - } -} - -// Batch vector initialization with another vector. -void PortableVectorBatchVectorAssign(const float* vector, int v_size, - int n_batch, float* batch_vector); - -// Compute "1.0f - elements of vector" (used in CIFG). -void PortableSub1Vector(const float* vector, int v_size, float* result); - -void PortableSub1Vector(const int16_t* vector, int v_size, int16_t* result); - -// Multiply all elements of vector with a scalar. -void PortableVectorScalarMultiply(const int8_t* vector, int v_size, float scale, - float* result); - -// Reduce-sum on a vector: -// input_vector: pointer to input vector. -// output_vector: pointer to vector. -// output_size: output vector size. -// reduction_size: number of consecutive elements from input vector which are -// added to get one element of output. -template -void PortableReductionSumVector(const INPUT* input_vector, - OUTPUT* output_vector, int output_size, - int reduction_size) { - for (int o = 0; o < output_size; o++) { - OUTPUT result = 0; - for (int r = 0; r < reduction_size; r++) { - result += input_vector[r]; - } - output_vector[o] = result; - input_vector += reduction_size; - } -} - -// Layer norm for each batch. -void PortableMeanStddevNormalization(const float* __restrict__ input_vector, - float* __restrict__ output_vector, - int v_size, int n_batch); - -// Saturate Add. -void PortableTwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, - int32_t n_batch, int32_t n_cell, - int16_t* output); - -} // namespace tensor_utils -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_IMPL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h deleted file mode 100644 index 89d4be10..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/prelu.h +++ /dev/null @@ -1,111 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -// Broadcast prelu to output_shape for quantized uint8_t/int8_t data. -template -inline void BroadcastPrelu4DSlow( - const PreluParams& params, const RuntimeShape& input_shape, - const T* input_data, const RuntimeShape& alpha_shape, const T* alpha_data, - const RuntimeShape& output_shape, T* output_data) { - TFLITE_DCHECK_LE(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(alpha_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 4); - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(4, output_shape); - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input_shape, alpha_shape, &desc1, &desc2); - - for (int b = 0; b < extended_output_shape.Dims(0); ++b) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) { - int output_index = Offset(extended_output_shape, b, y, x, c); - int input_index = SubscriptToIndex(desc1, b, y, x, c); - const int32_t input_value = - params.input_offset + input_data[input_index]; - int32_t output_value; - if (input_value >= 0) { - output_value = MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_1, params.output_shift_1); - } else { - auto alpha_index = SubscriptToIndex(desc2, b, y, x, c); - const int32_t alpha_value = - params.alpha_offset + alpha_data[alpha_index]; - - output_value = MultiplyByQuantizedMultiplier( - input_value * alpha_value, params.output_multiplier_2, - params.output_shift_2); - } - output_value += params.output_offset; - - const int32_t quantized_min = std::numeric_limits::min(); - const int32_t quantized_max = std::numeric_limits::max(); - const int32_t clamped_output = - std::min(quantized_max, std::max(quantized_min, output_value)); - output_data[output_index] = static_cast(clamped_output); - } - } - } - } -} - -template -inline void Prelu(const PreluParams& params, const RuntimeShape& input_shape, - const T* input_data, const RuntimeShape& alpha_shape, - const T* alpha_data, const RuntimeShape& output_shape, - T* output_data) { - const int32_t quantized_min = std::numeric_limits::min(); - const int32_t quantized_max = std::numeric_limits::max(); - - const int flat_size = - MatchingElementsSize(input_shape, alpha_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - const int32_t input_value = params.input_offset + input_data[i]; - int32_t output_value; - if (input_value >= 0) { - output_value = MultiplyByQuantizedMultiplier( - input_value, params.output_multiplier_1, params.output_shift_1); - } else { - const int32_t alpha_value = params.alpha_offset + alpha_data[i]; - - output_value = MultiplyByQuantizedMultiplier(input_value * alpha_value, - params.output_multiplier_2, - params.output_shift_2); - } - output_value += params.output_offset; - - const int32_t clamped_output = - std::min(quantized_max, std::max(quantized_min, output_value)); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PRELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h deleted file mode 100644 index 0cae0eb3..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h +++ /dev/null @@ -1,140 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -// Consolidates dimensions in broadcast inputs, checks for five-fold pattern. -// -// For example, if sequence of dimensions of one input is -// ..., 1, 3, 1, 7, 9, 5,... and the other is ..., 2, 3, 1, 7, 1, 1, ... -// we can consolidate these as -// ..., 1, 3*7, 9*5, ... and 2, 3*7, 1. -// -// The category is updated in the less-frequent case of shapes that are -// not suited to a fivefold-loop broadcast. -// -// Falls back to generic pattern when it does not know how to process properly. -// -// Returns true iff there is some sort of broadcast, which includes five-fold -// patterns and falling back to generic broadcast. -inline bool ProcessBroadcastShapes(const RuntimeShape& shape0, - const RuntimeShape& shape1, - tflite_micro::ArithmeticParams* params) { - const int dims_count = - std::max(shape0.DimensionsCount(), shape1.DimensionsCount()); - - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - RuntimeShape scalar_shape(dims_count, 1); - - auto extended_shape0 = RuntimeShape::ExtendedShape(dims_count, shape0); - auto extended_shape1 = RuntimeShape::ExtendedShape(dims_count, shape1); - - // Check for "exact" match, implicitly accepting any scalar shapes. - if (extended_shape0 == extended_shape1) { - params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; - return false; - } - - for (int i = dims_count - 1; i >= 0; --i) { - if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) { - continue; - } else if (extended_shape0.Dims(i) == 1) { - params->broadcast_category = - BroadcastableOpCategory::kFirstInputBroadcastsFast; - break; - } else if (extended_shape1.Dims(i) == 1) { - params->broadcast_category = - BroadcastableOpCategory::kSecondInputBroadcastsFast; - break; - } else { - // This case is erroneous: there is a dimension that does not match and - // is not a broadcast from one shape to the other. - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - return true; - } - } - - if (params->broadcast_category != - BroadcastableOpCategory::kFirstInputBroadcastsFast && - params->broadcast_category != - BroadcastableOpCategory::kSecondInputBroadcastsFast) { - // This is unreachable because at least one else clause in the above loop - // must be reached. - TFLITE_DCHECK(false); - params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; - return false; - } - - // From this point it is assumed contractually that corresponding dimensions - // in shape0 and shape1 are either (a) equal or (b) one or other equals 1. - const bool swap_inputs = params->broadcast_category == - BroadcastableOpCategory::kSecondInputBroadcastsFast; - const RuntimeShape* shape_a = - swap_inputs ? &extended_shape1 : &extended_shape0; - const RuntimeShape* shape_b = - swap_inputs ? &extended_shape0 : &extended_shape1; - - int i = dims_count - 1; - params->broadcast_shape[0] = 1; - params->broadcast_shape[1] = 1; - params->broadcast_shape[2] = 1; - params->broadcast_shape[3] = 1; - params->broadcast_shape[4] = 1; - // y_0 is greedy: include dims if both or neither equal 1: in other words, - // test for equality rather than (shape_a->Dims(i) != 1). - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[4] *= shape_b->Dims(i); - --i; - } - // Here either input_a or input_b has dim of 1 (if i >= 0). If it is input_b - // that has the unit dimension, the next two loops are not entered. - while (i >= 0 && shape_a->Dims(i) == 1) { - params->broadcast_shape[3] *= shape_b->Dims(i); - --i; - } - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[2] *= shape_a->Dims(i); - --i; - } - // Here either input_a or input_b has dim of 1 (if i >= 0). - while (i >= 0 && shape_b->Dims(i) == 1) { - params->broadcast_shape[1] *= shape_a->Dims(i); - --i; - } - while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) { - params->broadcast_shape[0] *= shape_b->Dims(i); - --i; - } - - // Rarer case is when the broadcast dimensions cannot be handled by a fivefold - // loop. - if (i >= 0) { - params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; - } - return true; -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_PROCESS_BROADCAST_SHAPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h deleted file mode 100644 index 05af1959..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/quantize.h +++ /dev/null @@ -1,89 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void AffineQuantize(const tflite_micro::QuantizationParams& op_params, - const RuntimeShape& input_shape, - const InputT* input_data, - const RuntimeShape& output_shape, - OutputT* output_data) { - const int32_t zero_point = op_params.zero_point; - const double scale = op_params.scale; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - static constexpr int32_t min_val = std::numeric_limits::min(); - static constexpr int32_t max_val = std::numeric_limits::max(); - - for (int i = 0; i < flat_size; i++) { - const InputT val = input_data[i]; - int32_t unclamped = - static_cast(TfLiteRound(val / static_cast(scale))) + - zero_point; - int32_t clamped = std::min(std::max(unclamped, min_val), max_val); - output_data[i] = clamped; - } -} - -// Quantizes per-channel. -template -inline void PerChannelQuantize( - const tflite_micro::PerChannelQuantizationParams& op_params, - const RuntimeShape& input_shape, const InputT* input_data, - const RuntimeShape& output_shape, OutputT* output_data) { - // Ensure flat size is same. - MatchingFlatSize(input_shape, output_shape); - - const int32_t* zero_point = op_params.zero_point; - const float* scale = op_params.scale; - const int32_t quantized_dimension = op_params.quantized_dimension; - const int32_t num_dims = input_shape.DimensionsCount(); - const int32_t* dims_data = input_shape.DimsData(); - std::vector current_dim(num_dims, 0); - static constexpr int32_t min_val = std::numeric_limits::min(); - static constexpr int32_t max_val = std::numeric_limits::max(); - - do { - size_t offset = - ReducedOutputOffset(num_dims, reinterpret_cast(dims_data), - current_dim.data(), 0, nullptr); - const InputT val = input_data[offset]; - const int channel = current_dim[quantized_dimension]; - int32_t unclamped = static_cast(TfLiteRound( - val / static_cast(scale[channel]))) + - zero_point[channel]; - int32_t clamped = std::min(std::max(unclamped, min_val), max_val); - output_data[offset] = static_cast(clamped); - } while (NextIndex(num_dims, reinterpret_cast(dims_data), - current_dim.data())); -} - -} // namespace reference_ops - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_QUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h deleted file mode 100644 index 6016a8df..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/reduce.h +++ /dev/null @@ -1,491 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/max.h" -#include "tensorflow/lite/kernels/internal/min.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" - -// Check if the reduction at index is the first one along the dimensions given -// in axis. -inline bool IsFirstReduction(const int* index, const int num_axis, - const int* axis) { - if (num_axis == 0) { - return true; - } - - TFLITE_DCHECK(index != nullptr); - TFLITE_DCHECK(axis != nullptr); - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { - if (index[axis[axis_idx]] != 0) { - return false; - } - } - - return true; -} - -namespace tflite_micro { - -namespace reference_ops { - -// A generic reduce method that can be used for reduce_sum, reduce_mean, etc. -// This method iterates through input data and reduce elements along the -// dimensions given in axis. -template -inline bool Reduce(const In* input_data, const int* input_dims, - const int* output_dims, const int input_num_dims, - const int output_num_dims, const int* axis, - const int num_axis, int* input_iter, - Out reducer(Out current, const In in), Out* output_data) { - // Reset input iterator. - for (int idx = 0; idx < input_num_dims; ++idx) { - input_iter[idx] = 0; - } - // Iterate through input_data. - do { - size_t input_offset = - ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, - input_iter, num_axis, axis); - output_data[output_offset] = - reducer(output_data[output_offset], input_data[input_offset]); - } while (NextIndex(input_num_dims, input_dims, input_iter)); - return true; -} - -// Similar to above Reduce function but takes two reducer functions. -// The 'reducer_first' is called with the first value of the reduction, -// 'reducer_next' is then called for all the others. -template -inline bool Reduce(const In* input_data, const int* input_dims, - const int* output_dims, const int input_num_dims, - const int output_num_dims, const int* axis, - const int num_axis, int* input_iter, - const std::function& reducer_first, - const std::function& reducer_next, - Out* output_data) { - // Reset input iterator. - for (int idx = 0; idx < input_num_dims; ++idx) { - input_iter[idx] = 0; - } - // Iterate through input_data. - do { - size_t input_offset = - ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, - input_iter, num_axis, axis); - if (IsFirstReduction(input_iter, num_axis, axis)) { - output_data[output_offset] = reducer_first(input_data[input_offset]); - } else { - output_data[output_offset] = - reducer_next(output_data[output_offset], input_data[input_offset]); - } - } while (NextIndex(input_num_dims, input_dims, input_iter)); - return true; -} - -// This method parses the input 'axis' to remove duplicates and handle negative -// values, and returns a valid 'out_axis' -inline bool ResolveAxis(const int num_dims, const int* axis, - const int64_t num_axis, int* out_axis, - int* out_num_axis) { - *out_num_axis = 0; // Just in case. - // Short-circuit axis resolution for scalars; the axis will go unused. - if (num_dims == 0) { - return true; - } - // o(n^2) is fine since out_num_axis should be really small, mostly <= 4 - for (int64_t idx = 0; idx < num_axis; ++idx) { - // Handle negative index. A positive index 'p_idx' can be represented as a - // negative index 'n_idx' as: n_idx = p_idx-num_dims - // eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1] */ - int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx]; - TFLITE_DCHECK(current >= 0 && current < num_dims); - if (current < 0 || current >= num_dims) { - return false; - } - bool is_dup = false; - for (int j = 0; j < *out_num_axis; ++j) { - if (out_axis[j] == current) { - is_dup = true; - break; - } - } - if (!is_dup) { - out_axis[*out_num_axis] = current; - *out_num_axis += 1; - } - } - return true; -} - -// This method expects that output_data has been initialized. -template -inline bool ReduceSumImpl(const In* input_data, const int* input_dims, - const int* output_dims, const int input_num_dims, - const int output_num_dims, const int* axis, - const int num_axis, int* input_iter, - Out* output_data) { - auto reducer = [](const Out current, const In in) -> Out { - const Out actual_in = static_cast(in); - return current + actual_in; - }; - return Reduce(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, axis, num_axis, input_iter, reducer, - output_data); -} - -template -inline bool InitTensorDataForReduce(const int* dims, const int num_dims, - const T init_value, T* data) { - size_t num_elements = 1; - for (int idx = 0; idx < num_dims; ++idx) { - size_t current = static_cast(dims[idx]); - // Overflow prevention. - if (current > 0 && - num_elements > std::numeric_limits::max() / current) { - return false; - } - num_elements *= current; - } - for (size_t idx = 0; idx < num_elements; ++idx) { - data[idx] = init_value; - } - return true; -} - -// Computes the generic value (i.e., sum/max/min/prod) of elements across -// dimensions given in axis. It needs to pass in init_value and reducer. -template -inline bool ReduceGeneric(const T* input_data, const int* input_dims, - const int input_num_dims, T* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int64_t num_axis_dimensions, - bool keep_dims, int* temp_index, int* resolved_axis, - T init_value, - T reducer(const T current, const T in)) { - // Reset output data. - if (!InitTensorDataForReduce(output_dims, output_num_dims, init_value, - output_data)) { - return false; - } - - // Return early when input shape has zero dim. This is done after initializing - // data for output tensor because there are cases that the input tensor is - // empty but output tensor is not. In that case, output tensor should be - // filled with init_value. - for (int i = 0; i < input_num_dims; ++i) { - if (input_dims[i] == 0) return true; - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - return Reduce(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, reducer, output_data); -} - -// Computes the mean of elements across dimensions given in axis. -// It does so in two stages, first calculates the sum of elements along the axis -// then divides it by the number of element in axis. -template -inline bool Mean(const T* input_data, const int* input_dims, - const int input_num_dims, T* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int num_axis_dimensions, bool keep_dims, - int* temp_index, int* resolved_axis, U* temp_sum) { - ruy::profiler::ScopeLabel label("Mean"); - // Reset output data. - size_t num_outputs = 1; - for (int idx = 0; idx < output_num_dims; ++idx) { - size_t current = static_cast(output_dims[idx]); - // Overflow prevention. - if (num_outputs > std::numeric_limits::max() / current) { - return false; - } - num_outputs *= current; - } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = T(); - temp_sum[idx] = U(); - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, temp_sum)) { - return false; - } - - // Calculate mean by dividing output_data by num of aggregated element. - size_t num_elements_in_axis = 1; - for (int idx = 0; idx < num_resolved_axis; ++idx) { - size_t current = static_cast(input_dims[resolved_axis[idx]]); - // Overflow prevention. - if (current > (std::numeric_limits::max() / num_elements_in_axis)) { - return false; - } - num_elements_in_axis *= current; - } - - if (num_elements_in_axis > 0) { - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = - static_cast(temp_sum[idx] / static_cast(num_elements_in_axis)); - } - } - return true; -} - -inline void Mean(const tflite_micro::MeanParams& op_params, - const RuntimeShape& unextended_input_shape, - const float* input_data, - const RuntimeShape& unextended_output_shape, - float* output_data) { - ruy::profiler::ScopeLabel label("Mean4D"); - - // Current implementation only supports dimension equals 4 and simultaneous - // reduction over width and height. - TFLITE_CHECK_EQ(unextended_input_shape.DimensionsCount(), 4); - TFLITE_CHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - const int output_batch = output_shape.Dims(0); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int output_depth = output_shape.Dims(3); - - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - - TFLITE_CHECK_EQ(op_params.axis_count, 2); - TFLITE_CHECK((op_params.axis[0] == 1 && op_params.axis[1] == 2) || - (op_params.axis[0] == 2 && op_params.axis[1] == 1)); - TFLITE_CHECK_EQ(output_height, 1); - TFLITE_CHECK_EQ(output_width, 1); - - for (int out_b = 0; out_b < output_batch; ++out_b) { - for (int out_d = 0; out_d < output_depth; ++out_d) { - float value = 0; - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - value += input_data[Offset(input_shape, out_b, in_h, in_w, out_d)]; - } - } - output_data[Offset(output_shape, out_b, 0, 0, out_d)] = - value / (input_width * input_height); - } - } -} - -// Computes the mean of elements across dimensions given in axis. -// It does so in two stages, first calculates the sum of elements along the axis -// then divides it by the number of element in axis for quantized values. -template -inline bool QuantizedMeanOrSum(const T* input_data, int32_t input_zero_point, - const int* input_dims, const int input_num_dims, - T* output_data, int32_t output_multiplier, - int output_shift, int32_t output_zero_point, - const int* output_dims, - const int output_num_dims, const int* axis, - const int num_axis_dimensions, bool keep_dims, - int* temp_index, int* resolved_axis, U* temp_sum, - bool compute_sum) { - const int32_t kMinValue = std::numeric_limits::min(); - const int32_t kMaxValue = std::numeric_limits::max(); - const bool uint8_case = std::is_same::value; - const bool int16_case = std::is_same::value; - if (uint8_case) { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Uint8" : "Mean/Uint8"); - } else if (int16_case) { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int16" : "Mean/Int16"); - } else { - ruy::profiler::ScopeLabel label(compute_sum ? "Sum/Int8" : "Mean/Int8"); - } - // Reset output data. - size_t num_outputs = 1; - for (int idx = 0; idx < output_num_dims; ++idx) { - size_t current = static_cast(output_dims[idx]); - // Overflow prevention. - if (num_outputs > std::numeric_limits::max() / current) { - return false; - } - num_outputs *= current; - } - for (size_t idx = 0; idx < num_outputs; ++idx) { - output_data[idx] = T(); - temp_sum[idx] = U(); - } - - // Return early when input shape has zero dim. This is done after initializing - // data for output tensor because there are cases that the input tensor is - // empty but output tensor is not. In that case, output tensor should be - // filled with init_value. - for (int i = 0; i < input_num_dims; ++i) { - if (input_dims[i] == 0) return true; - } - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, - &num_resolved_axis)) { - return false; - } - - if (!ReduceSumImpl(input_data, input_dims, output_dims, input_num_dims, - output_num_dims, resolved_axis, num_resolved_axis, - temp_index, temp_sum)) { - return false; - } - - // Calculate mean by dividing output_data by num of aggregated element. - int64_t num_elements_in_axis = 1; - for (int idx = 0; idx < num_resolved_axis; ++idx) { - size_t current = static_cast(input_dims[resolved_axis[idx]]); - // Overflow prevention. - if (current > static_cast(std::numeric_limits::max() / - num_elements_in_axis)) { - return false; - } - num_elements_in_axis *= current; - } - - if (num_elements_in_axis == 0) { - return true; - } - - // Readapt output rescaling when calculating the mean to integrate a - // 1/num_elements_in_axis multiplier. - if (!compute_sum) { - TFLITE_DCHECK_GE(num_elements_in_axis, 0); - int shift = - 63 - CountLeadingZeros(static_cast(num_elements_in_axis)); - // To avoid any overflow risk 'shift' should be <= 32 and to satisfy - // 'MultiplyByQuantizedMultiplier' pre-conditions 'output_shift - shift' - // should be >= -31. Clamp the value at the price of some precision loss. - shift = std::min(shift, 32); - shift = std::min(shift, 31 + output_shift); - output_multiplier = static_cast( - (static_cast(output_multiplier) << shift) / - num_elements_in_axis); - output_shift = output_shift - shift; - } - - for (size_t idx = 0; idx < num_outputs; ++idx) { - const U shifted_sum = - static_cast(temp_sum[idx] - input_zero_point * num_elements_in_axis); - int32_t output = MultiplyByQuantizedMultiplier( - shifted_sum, output_multiplier, output_shift) + - output_zero_point; - output = std::min(std::max(output, kMinValue), kMaxValue); - output_data[idx] = static_cast(output); - } - return true; -} - -template -inline bool QuantizedMeanOrSumExtraArgs( - const T* input_data, int32_t input_zero_point, float input_scale, - const int* input_dims, const int input_num_dims, T* output_data, - float output_scale, int32_t output_multiplier, int output_shift, - int32_t output_zero_point, const int* output_dims, - const int output_num_dims, const int* axis, const int num_axis_dimensions, - bool keep_dims, int* temp_index, int* resolved_axis, U* temp_sum, - bool compute_sum) { - return QuantizedMeanOrSum( - input_data, input_zero_point, input_dims, input_num_dims, output_data, - output_multiplier, output_shift, output_zero_point, output_dims, - output_num_dims, axis, num_axis_dimensions, keep_dims, temp_index, - resolved_axis, temp_sum, compute_sum); -} - -template -inline bool QuantizedReduceProd(const T* input_data, int32_t input_zero_point, - const RuntimeShape& input_shape, T* output_data, - int32_t output_zero_point, - const RuntimeShape& output_shape, - const int* axis, - const int64_t num_axis_dimensions, - bool keep_dims, int* temp_index, - int* resolved_axis, int32_t* temp_prod, - int32_t scaling_multiplier, int scaling_shift) { - const int32_t kMinValue = std::numeric_limits::min(); - const int32_t kMaxValue = std::numeric_limits::max(); - - // Resolve axis. - int num_resolved_axis = 0; - if (!ResolveAxis(input_shape.DimensionsCount(), axis, num_axis_dimensions, - resolved_axis, &num_resolved_axis)) { - return false; - } - - // Calculate the reduced product by rescaling each multiplication step to - // avoid an overflow. - auto reducer_first = [&](T in) -> int32_t { return in - input_zero_point; }; - - auto reducer_next = [&](int32_t current, T in) -> int32_t { - const int64_t result = - static_cast(current) * (in - input_zero_point); - return MultiplyByQuantizedMultiplier(result, scaling_multiplier, - scaling_shift); - }; - - if (!Reduce( - input_data, input_shape.DimsData(), output_shape.DimsData(), - input_shape.DimensionsCount(), output_shape.DimensionsCount(), - resolved_axis, num_resolved_axis, temp_index, reducer_first, - reducer_next, temp_prod)) { - return false; - } - - for (int i = 0; i < output_shape.FlatSize(); i++) { - int32_t result = - MultiplyByQuantizedMultiplier(static_cast(temp_prod[i]), - scaling_multiplier, scaling_shift) + - output_zero_point; - result = std::min(std::max(result, kMinValue), kMaxValue); - output_data[i] = static_cast(result); - } - - return true; -} - -} // namespace reference_ops - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h deleted file mode 100644 index db89dc14..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/requantize.h +++ /dev/null @@ -1,70 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void Requantize(const input_type* input_data, int32_t size, - int32_t effective_scale_multiplier, - int32_t effective_scale_shift, int32_t input_zeropoint, - int32_t output_zeropoint, output_type* output_data) { - ruy::profiler::ScopeLabel label("Requantize"); - const bool same_scale = - (effective_scale_multiplier == 1 << 30 && effective_scale_shift == 1); - if (same_scale) { - const bool mixed_type_int8_uint8 = - std::is_same::value && - std::is_same::value; - const bool mixed_type_uint8_int8 = - std::is_same::value && - std::is_same::value; - const int32_t zero_point_diff = input_zeropoint - output_zeropoint; - // Fast path to do requantization for the case when just a shift of 128 is - // needed. - if ((mixed_type_int8_uint8 && zero_point_diff == -128) || - (mixed_type_uint8_int8 && zero_point_diff == 128)) { - for (int i = 0; i < size; ++i) { - output_data[i] = input_data[i] ^ 0x80; - } - return; - } - } - static constexpr int32_t kMinOutput = std::numeric_limits::min(); - static constexpr int32_t kMaxOutput = std::numeric_limits::max(); - for (int i = 0; i < size; ++i) { - const int32_t input = input_data[i] - input_zeropoint; - const int32_t output = - MultiplyByQuantizedMultiplier(input, effective_scale_multiplier, - effective_scale_shift) + - output_zeropoint; - const int32_t clamped_output = - std::max(std::min(output, kMaxOutput), kMinOutput); - output_data[i] = static_cast(clamped_output); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_REQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h deleted file mode 100644 index 73108fe7..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_bilinear.h +++ /dev/null @@ -1,233 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ - -#include -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void ComputeInterpolationValues(const float value, const float scale, - const bool half_pixel_centers, - int32_t input_size, float* scaled_value, - int32_t* lower_bound, - int32_t* upper_bound) { - if (half_pixel_centers) { - *scaled_value = (value + 0.5f) * scale - 0.5f; - } else { - *scaled_value = value * scale; - } - float scaled_value_floor = std::floor(*scaled_value); - *lower_bound = std::max(static_cast(scaled_value_floor), - static_cast(0)); - *upper_bound = - std::min(static_cast(std::ceil(*scaled_value)), input_size - 1); -} - -template -inline void ResizeBilinear(const tflite_micro::ResizeBilinearParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_size_shape, - const int32_t* output_size_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - // If half_pixel_centers is True, align_corners must be False. - TFLITE_DCHECK(!op_params.half_pixel_centers || !op_params.align_corners); - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_size_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_size_shape = - RuntimeShape::ExtendedShape(4, unextended_output_size_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); - int32_t input_height = input_shape.Dims(1); - int32_t input_width = input_shape.Dims(2); - int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); - - TFLITE_DCHECK_EQ(output_size_shape.Dims(0), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(1), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(2), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(3), 2); - int32_t output_height = - output_size_data[Offset(output_size_shape, 0, 0, 0, 0)]; - int32_t output_width = - output_size_data[Offset(output_size_shape, 0, 0, 0, 1)]; - - float height_scale = static_cast(input_height) / output_height; - float width_scale = static_cast(input_width) / output_width; - if (op_params.align_corners && output_height > 1) { - height_scale = static_cast(input_height - 1) / (output_height - 1); - } - if (op_params.align_corners && output_width > 1) { - width_scale = static_cast(input_width - 1) / (output_width - 1); - } - const float rounding_offset = std::numeric_limits::is_integer ? .5f : .0f; - - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - float input_y; - int32_t y0, y1; - ComputeInterpolationValues(y, height_scale, op_params.half_pixel_centers, - input_height, &input_y, &y0, &y1); - for (int x = 0; x < output_width; ++x) { - float input_x; - int32_t x0, x1; - ComputeInterpolationValues(x, width_scale, op_params.half_pixel_centers, - input_width, &input_x, &x0, &x1); - for (int c = 0; c < depth; ++c) { - T interpolation = - static_cast(input_data[Offset(input_shape, b, y0, x0, c)] * - (1 - (input_y - y0)) * (1 - (input_x - x0)) + - input_data[Offset(input_shape, b, y1, x0, c)] * - (input_y - y0) * (1 - (input_x - x0)) + - input_data[Offset(input_shape, b, y0, x1, c)] * - (1 - (input_y - y0)) * (input_x - x0) + - input_data[Offset(input_shape, b, y1, x1, c)] * - (input_y - y0) * (input_x - x0) + - rounding_offset); - output_data[Offset(output_shape, b, y, x, c)] = interpolation; - } - } - } - } -} - -inline void ComputeInterpolationValuesInteger( - const int32_t value, const int32_t scale_10, const bool half_pixel_centers, - int32_t input_size, int32_t* scaled_value, int32_t* lower_bound, - int32_t* upper_bound) { - if (half_pixel_centers) { - *scaled_value = value * scale_10 + scale_10 / 2 - (1 << 9); - } else { - *scaled_value = value * scale_10; - } - constexpr int32_t zero = 0; - *lower_bound = std::max(*scaled_value / (1 << 10), zero); - *upper_bound = - std::min((*scaled_value + (1 << 10) - 1) / (1 << 10), input_size - 1); -} - -// Same as above but doesn't use any floating-point for the resize -template -inline void ResizeBilinearInteger( - const tflite_micro::ResizeBilinearParams& op_params, - const RuntimeShape& unextended_input_shape, const T* input_data, - const RuntimeShape& unextended_output_size_shape, - const int32_t* output_size_data, - const RuntimeShape& unextended_output_shape, T* output_data) { - // If half_pixel_centers is True, align_corners must be False. - TFLITE_DCHECK(!op_params.half_pixel_centers || !op_params.align_corners); - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_size_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_size_shape = - RuntimeShape::ExtendedShape(4, unextended_output_size_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); - const int32_t input_height = input_shape.Dims(1); - const int32_t input_width = input_shape.Dims(2); - const int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); - - TFLITE_DCHECK_EQ(output_size_shape.Dims(0), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(1), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(2), 1); - TFLITE_DCHECK_EQ(output_size_shape.Dims(3), 2); - const int32_t output_height = - output_size_data[Offset(output_size_shape, 0, 0, 0, 0)]; - const int32_t output_width = - output_size_data[Offset(output_size_shape, 0, 0, 0, 1)]; - - int32_t height_scale_10 = - ((1 << 10) * input_height + output_height / 2) / output_height; - int32_t width_scale_10 = - ((1 << 10) * input_width + output_width / 2) / output_width; - if (op_params.align_corners && output_height > 1) { - height_scale_10 = - ((1 << 10) * (input_height - 1) + (output_height - 1) / 2) / - (output_height - 1); - } - if (op_params.align_corners && output_width > 1) { - width_scale_10 = ((1 << 10) * (input_width - 1) + (output_width - 1) / 2) / - (output_width - 1); - } - - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - int32_t input_y, y0, y1; - ComputeInterpolationValuesInteger(y, height_scale_10, - op_params.half_pixel_centers, - input_height, &input_y, &y0, &y1); - for (int x = 0; x < output_width; ++x) { - int32_t input_x, x0, x1; - ComputeInterpolationValuesInteger(x, width_scale_10, - op_params.half_pixel_centers, - input_width, &input_x, &x0, &x1); - for (int c = 0; c < depth; ++c) { - const int64_t output_20_ll = - static_cast( - input_data[Offset(input_shape, b, y0, x0, c)]) * - ((1 << 10) - (input_y - (1 << 10) * y0)) * - ((1 << 10) - (input_x - (1 << 10) * x0)); - const int64_t output_20_lu = - static_cast( - input_data[Offset(input_shape, b, y1, x0, c)]) * - (input_y - (1 << 10) * y0) * - ((1 << 10) - (input_x - (1 << 10) * x0)); - const int64_t output_20_rl = - static_cast( - input_data[Offset(input_shape, b, y0, x1, c)]) * - ((1 << 10) - (input_y - (1 << 10) * y0)) * - (input_x - (1 << 10) * x0); - const int64_t output_20_ru = - static_cast( - input_data[Offset(input_shape, b, y1, x1, c)]) * - (input_y - (1 << 10) * y0) * (input_x - (1 << 10) * x0); - const int64_t output_20 = - output_20_ll + output_20_lu + output_20_rl + output_20_ru; -#if TFLITE_SINGLE_ROUNDING - const int64_t round = 1 << 19; - const T interpolation = static_cast((output_20 + round) >> 20); -#else - const int64_t round = (output_20 > 0) ? (1 << 19) : -(1 << 19); - const T interpolation = - static_cast((output_20 + round) / (1 << 20)); -#endif // TFLITE_SINGLE_ROUNDING - output_data[Offset(output_shape, b, y, x, c)] = interpolation; - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_BILINEAR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h deleted file mode 100644 index 2860df3e..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h +++ /dev/null @@ -1,102 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline int32_t GetNearestNeighbor(const int input_value, - const int32_t input_size, - const int32_t output_size, - const bool align_corners, - const bool half_pixel_centers) { - const float scale = - (align_corners && output_size > 1) - ? (input_size - 1) / static_cast(output_size - 1) - : input_size / static_cast(output_size); - const float offset = half_pixel_centers ? 0.5f : 0.0f; - int32_t output_value = std::min( - align_corners - ? static_cast(TfLiteRound((input_value + offset) * scale)) - : static_cast(std::floor((input_value + offset) * scale)), - input_size - 1); - if (half_pixel_centers) { - output_value = std::max(static_cast(0), output_value); - } - return output_value; -} - -template -inline void ResizeNearestNeighbor( - const tflite_micro::ResizeNearestNeighborParams& op_params, - const RuntimeShape& unextended_input_shape, const T* input_data, - const RuntimeShape& output_size_shape, const int32_t* output_size_data, - const RuntimeShape& unextended_output_shape, T* output_data) { - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - int32_t batches = MatchingDim(input_shape, 0, output_shape, 0); - int32_t input_height = input_shape.Dims(1); - int32_t input_width = input_shape.Dims(2); - int32_t depth = MatchingDim(input_shape, 3, output_shape, 3); - - // The Tensorflow version of this op allows resize on the width and height - // axis only. - TFLITE_DCHECK_EQ(output_size_shape.FlatSize(), 2); - int32_t output_height = output_size_data[0]; - int32_t output_width = output_size_data[1]; - - const int col_offset = input_shape.Dims(3); - const int row_offset = input_shape.Dims(2) * col_offset; - const int batch_offset = input_shape.Dims(1) * row_offset; - - const T* input_ptr = input_data; - T* output_ptr = output_data; - for (int b = 0; b < batches; ++b) { - for (int y = 0; y < output_height; ++y) { - int32_t in_y = GetNearestNeighbor(y, input_height, output_height, - op_params.align_corners, - op_params.half_pixel_centers); - const T* y_input_ptr = input_ptr + in_y * row_offset; - for (int x = 0; x < output_width; ++x) { - int32_t in_x = GetNearestNeighbor(x, input_width, output_width, - op_params.align_corners, - op_params.half_pixel_centers); - const T* x_input_ptr = y_input_ptr + in_x * col_offset; - memcpy(output_ptr, x_input_ptr, depth * sizeof(T)); - output_ptr += depth; - } - } - input_ptr += batch_offset; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_RESIZE_NEAREST_NEIGHBOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h deleted file mode 100644 index c8b4552c..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/round.h +++ /dev/null @@ -1,51 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline float RoundToNearest(float value) { - auto floor_val = std::floor(value); - auto diff = value - floor_val; - if ((diff < 0.5f) || - ((diff == 0.5f) && (static_cast(floor_val) % 2 == 0))) { - return floor_val; - } else { - return floor_val = floor_val + 1.0f; - } -} - -inline void Round(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - for (int i = 0; i < flat_size; ++i) { - // Note that this implementation matches that of tensorFlow tf.round - // and corresponds to the bankers rounding method. - // cfenv (for fesetround) is not yet supported universally on Android, so - // using a work around. - output_data[i] = RoundToNearest(input_data[i]); - } -} - -} // namespace reference_ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_ROUND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h deleted file mode 100644 index e4347246..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/select.h +++ /dev/null @@ -1,151 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -void Select(const RuntimeShape& input_condition_shape, - const D* input_condition_data, const RuntimeShape& input_x_shape, - const T* input_x_data, const RuntimeShape& input_y_shape, - const T* input_y_data, const RuntimeShape& output_shape, - T* output_data) { - ruy::profiler::ScopeLabel label("Select"); - int64_t flatsize; - // Allow select operator executions on mixed scalar tensors and one element - // tensors. - if (input_condition_shape.FlatSize() == 1 && input_x_shape.FlatSize() == 1 && - input_y_shape.FlatSize() == 1 && output_shape.FlatSize() == 1) { - flatsize = 1; - } else { - flatsize = MatchingFlatSize(input_condition_shape, input_x_shape, - input_y_shape, output_shape); - } - for (int64_t i = 0; i < flatsize; ++i) { - output_data[i] = - input_condition_data[i] ? input_x_data[i] : input_y_data[i]; - } -} - -template -void RankOneSelect(const RuntimeShape& input_condition_shape, - const D* input_condition_data, - const RuntimeShape& input_x_shape, const T* input_x_data, - const RuntimeShape& input_y_shape, const T* input_y_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("Select/RankOneSelect"); - const int64_t outer_size = input_condition_shape.FlatSize(); - int64_t inner_size; - if (input_condition_shape.DimensionsCount() == 0) { - inner_size = MatchingFlatSize(input_x_shape, input_y_shape, output_shape); - } else { - TFLITE_DCHECK_EQ( - MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0), - outer_size); - inner_size = - MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape); - } - - int64_t offset = 0; - for (int64_t i = 0; i < outer_size; i++) { - const T* input_data = input_condition_data[i] ? input_x_data : input_y_data; - memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T)); - offset += inner_size; - } -} - -template -void BroadcastSelect5DSlow(const RuntimeShape& input_condition_shape, - const D* input_condition_data, - const RuntimeShape& input_x_shape, - const T* input_x_data, - const RuntimeShape& input_y_shape, - const T* input_y_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("Select/BroadcastSelectSlow"); - TFLITE_DCHECK_LE(input_condition_shape.DimensionsCount(), 5); - TFLITE_DCHECK_LE(input_x_shape.DimensionsCount(), 5); - TFLITE_DCHECK_LE(input_y_shape.DimensionsCount(), 5); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 5); - - NdArrayDesc<5> desc_condition; - NdArrayDesc<5> desc_x; - NdArrayDesc<5> desc_y; - NdArrayDesc<5> desc_output; - const RuntimeShape extended_output_shape = - RuntimeShape::ExtendedShape(5, output_shape); - CopyDimsToDesc(extended_output_shape, &desc_output); - NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape, - input_y_shape, &desc_condition, &desc_x, - &desc_y); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest - // stride, typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for - // the best cache behavior. - for (int n = 0; n < desc_output.extents[0]; ++n) { - int out_idx_n = desc_output.extents[1] * n; - int cond_idx_n = desc_condition.strides[0] * n; - int in_idx1_n = desc_x.strides[0] * n; - int in_idx2_n = desc_y.strides[0] * n; - for (int b = 0; b < desc_output.extents[1]; ++b) { - int out_idx_b = (out_idx_n + b) * desc_output.extents[2]; - int cond_idx_b = cond_idx_n + desc_condition.strides[1] * b; - int in_idx1_b = in_idx1_n + desc_x.strides[1] * b; - int in_idx2_b = in_idx2_n + desc_y.strides[1] * b; - for (int y = 0; y < desc_output.extents[2]; ++y) { - int out_idx_y = (out_idx_b + y) * desc_output.extents[3]; - int cond_idx_y = cond_idx_b + desc_condition.strides[2] * y; - int in_idx1_y = in_idx1_b + desc_x.strides[2] * y; - int in_idx2_y = in_idx2_b + desc_y.strides[2] * y; - for (int x = 0; x < desc_output.extents[3]; ++x) { - int out_idx = (out_idx_y + x) * desc_output.extents[4]; - int cond_idx = cond_idx_y + desc_condition.strides[3] * x; - int in_idx1 = in_idx1_y + desc_x.strides[3] * x; - int in_idx2 = in_idx2_y + desc_y.strides[3] * x; - for (int c = 0; c < desc_output.extents[4]; ++c) { - output_data[out_idx] = input_condition_data[cond_idx] - ? input_x_data[in_idx1] - : input_y_data[in_idx2]; - out_idx++; - cond_idx += desc_condition.strides[4]; - in_idx1 += desc_x.strides[4]; - in_idx2 += desc_y.strides[4]; - } - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h deleted file mode 100644 index 277d25ff..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/slice.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ - -#include "tensorflow/lite/kernels/internal/portable_tensor.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void Slice(const tflite_micro::SliceParams& op_params, - const RuntimeShape& input_shape, - const RuntimeShape& output_shape, - SequentialTensorWriter* writer) { - const RuntimeShape ext_shape = RuntimeShape::ExtendedShape(5, input_shape); - TFLITE_DCHECK_LE(op_params.begin_count, 5); - TFLITE_DCHECK_LE(op_params.size_count, 5); - const int begin_count = op_params.begin_count; - const int size_count = op_params.size_count; - // We front-pad the begin and size vectors. - int start[5]; - int stop[5]; - for (int i = 0; i < 5; ++i) { - int padded_i = 5 - i; - start[i] = - begin_count < padded_i ? 0 : op_params.begin[begin_count - padded_i]; - stop[i] = - (size_count < padded_i || op_params.size[size_count - padded_i] == -1) - ? ext_shape.Dims(i) - : start[i] + op_params.size[size_count - padded_i]; - } - - for (int i0 = start[0]; i0 < stop[0]; ++i0) { - for (int i1 = start[1]; i1 < stop[1]; ++i1) { - for (int i2 = start[2]; i2 < stop[2]; ++i2) { - for (int i3 = start[3]; i3 < stop[3]; ++i3) { - for (int i4 = start[4]; i4 < stop[4]; ++i4) { - writer->Write(Offset(ext_shape, i0, i1, i2, i3, i4)); - } - } - } - } - } -} - -template -inline void Slice(const tflite_micro::SliceParams& op_params, - const RuntimeShape& input_shape, const T* input_data, - const RuntimeShape& output_shape, T* output_data) { - SequentialTensorWriter writer(input_data, output_data); - return Slice(op_params, input_shape, output_shape, &writer); -} - -template -inline void Slice(const tflite_micro::SliceParams& op_params, - const RuntimeShape& input_shape, const TfLiteTensor* input, - const RuntimeShape& output_shape, TfLiteTensor* output) { - SequentialTensorWriter writer(input, output); - return Slice(op_params, input_shape, output_shape, &writer); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h deleted file mode 100644 index 81274043..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/softmax.h +++ /dev/null @@ -1,233 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ - -#include -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void Softmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - // Find max element value which we'll use to ensure numerical stability - // taking advantage of the following equality: - // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C)) - float max = std::numeric_limits::lowest(); - for (int c = 0; c < depth; ++c) { - max = std::max(max, input_data[i * depth + c]); - } - - // Compute sum. - float sum = 0.f; - for (int c = 0; c < depth; ++c) { - const float exp_c = std::exp((input_data[i * depth + c] - max) * - static_cast(params.beta)); - output_data[i * depth + c] = exp_c; - sum += exp_c; - } - - // Compute result. - for (int c = 0; c < depth; ++c) { - output_data[i * depth + c] = output_data[i * depth + c] / sum; - } - } -} - -// Quantized softmax with int8_t/uint8_t input and int8_t/uint8_t/int16_t -// output. -template -inline void Softmax(const SoftmaxParams& params, - const RuntimeShape& input_shape, const InputT* input_data, - const RuntimeShape& output_shape, OutputT* output_data) { - const int32_t input_beta_multiplier = params.input_multiplier; - const int32_t input_beta_left_shift = params.input_left_shift; - const int diff_min = params.diff_min; - // The representation chosen for the input to the exp() function is Q5.26. - // We need to leave extra space since values that we skip might be as large as - // -32 before multiplying by input_beta_multiplier, and therefore as large as - // -16 afterwards. Note that exp(-8) is definitely not insignificant to - // accumulation, but exp(-16) definitely is. - static const int kScaledDiffIntegerBits = 5; - static const int kAccumulationIntegerBits = 12; - using FixedPointScaledDiff = - gemmlowp::FixedPoint; - using FixedPointAccum = - gemmlowp::FixedPoint; - using FixedPoint0 = gemmlowp::FixedPoint; - - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - InputT max_in_row = std::numeric_limits::min(); - for (int c = 0; c < depth; ++c) { - max_in_row = std::max(max_in_row, input_data[i * depth + c]); - } - - FixedPointAccum sum_of_exps = FixedPointAccum::Zero(); - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - sum_of_exps = sum_of_exps + gemmlowp::Rescale( - exp_on_negative_values(scaled_diff_f8)); - } - } - - int num_bits_over_unit; - FixedPoint0 shifted_scale = FixedPoint0::FromRaw(GetReciprocal( - sum_of_exps.raw(), kAccumulationIntegerBits, &num_bits_over_unit)); - - for (int c = 0; c < depth; ++c) { - int32_t input_diff = - static_cast(input_data[i * depth + c]) - max_in_row; - if (input_diff >= diff_min) { - const int32_t input_diff_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_diff, input_beta_multiplier, input_beta_left_shift); - const FixedPointScaledDiff scaled_diff_f8 = - FixedPointScaledDiff::FromRaw(input_diff_rescaled); - - FixedPoint0 exp_in_0 = exp_on_negative_values(scaled_diff_f8); - int32_t unsat_output = gemmlowp::RoundingDivideByPOT( - (shifted_scale * exp_in_0).raw(), - num_bits_over_unit + 31 - (sizeof(OutputT) * 8)); - - const int32_t shifted_output = - unsat_output + - static_cast(std::numeric_limits::min()); - - output_data[i * depth + c] = static_cast(std::max( - std::min(shifted_output, - static_cast(std::numeric_limits::max())), - static_cast(std::numeric_limits::min()))); - } else { - output_data[i * depth + c] = std::numeric_limits::min(); - } - } - } -} - -// Computes exp(input - max_input) -inline int16_t SoftMaxCalculateExp(const SoftmaxParams& params, - const int16_t* input_data, const int depth, - int16_t max_in_row, int i, int c) { - int32_t input_diff = input_data[i * depth + c] - max_in_row; - // scale the input_diff such that [-65535, 0] correspond to [-10.0, 0.0] - // exp lut generated with range [-10, 0], as exp(-10) is negligible. - int32_t scaled_diff = MultiplyByQuantizedMultiplier( - input_diff, params.input_multiplier, params.input_left_shift); - // recenter to [-32768, 32767] - int32_t sym_scaled_diff = scaled_diff + 32767; - int16_t sat_sym_scaled_diff = - std::min(std::max(sym_scaled_diff, static_cast(-32768)), - static_cast(32767)); - // apply the exp() LUT activation function - return LUTLookup(sat_sym_scaled_diff, params.exp_lut); -} -// Quantized softmax with int16_t input and int16_t output. -inline void SoftmaxInt16(const SoftmaxParams& params, - const RuntimeShape& input_shape, - const int16_t* input_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - - for (int i = 0; i < outer_size; ++i) { - // Find the largest element - int16_t max_in_row = std::numeric_limits::min(); - for (int c = 0; c < depth; ++c) { - max_in_row = std::max(max_in_row, input_data[i * depth + c]); - } - - // This loops computes the exp values and their sum. We will need the exp - // values later on in the function so we cache them in the output_data - // buffer. This is an optimization done to avoid calculating the exp values - // twice making use of the output_data buffer as scratch memory. - int32_t sum_of_exps = 0; // Q16.15 fixed point format. - int16_t* exp_results_Q015 = output_data + i * depth; - for (int c = 0; c < depth; ++c) { - exp_results_Q015[c] = - SoftMaxCalculateExp(params, input_data, depth, max_in_row, i, c); - sum_of_exps += exp_results_Q015[c]; - } - - // Compute the reciprocal 1/sum_of_exps - uint8_t headroom_plus_one = - CountLeadingZeros(static_cast(sum_of_exps)); - int32_t shifted_sum = - ((static_cast(sum_of_exps) << (headroom_plus_one - 1)) + - (1 << 13)) >> - 14; - // since the LUT computes 1/(1 + x) we need to first compute x = (sum - 1). - // also, the LUT expects a symmetrical input, so we must also recenter x - // from [0, 65535] to [-32768, 32767]. - int32_t sym_shifted_sum = shifted_sum + (-((1 << 15) + (1 << 16))); - int16_t sat_sym_shifted_sum = static_cast( - std::min(std::max(sym_shifted_sum, static_cast(-32768)), - static_cast(32767))); - // apply 1/(1 + x) LUT activation function - int16_t reciprocal_scale_Q015 = - LUTLookup(sat_sym_shifted_sum, params.one_over_one_plus_x_lut); - - // Rescale the exp_result with reciprocal - // range of output is [0, 32767] correspond to [0.0, 1.0] - for (int c = 0; c < depth; ++c) { - uint8_t right_shift = 31 - headroom_plus_one; - int64_t round = 1 << (right_shift - 1); - int32_t result = (static_cast(exp_results_Q015[c]) * - static_cast(reciprocal_scale_Q015) + - round) >> - right_shift; - output_data[i * depth + c] = static_cast( - std::min(std::max(result, static_cast(0)), - static_cast(32767))); - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h deleted file mode 100644 index 77d5bd5b..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h +++ /dev/null @@ -1,109 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ - -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -// TODO(b/135760455): Move this method anonymous namespace in a cc file. -inline RuntimeShape ExtendShapeSpaceToBatch(const RuntimeShape& shape) { - if (shape.DimensionsCount() == 4) { - return shape; - } - RuntimeShape new_shape(4, 1); - new_shape.SetDim(0, shape.Dims(0)); - new_shape.SetDim(1, shape.Dims(1)); - new_shape.SetDim(3, shape.Dims(2)); - return new_shape; -} - -template -inline void SpaceToBatchND(const SpaceToBatchParams& params, - const RuntimeShape& unextended_input1_shape, - const T* input1_data, - const RuntimeShape& unextended_input2_shape, - const int32_t* block_shape_data, - const RuntimeShape& unextended_input3_shape, - const int32_t* paddings_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - ruy::profiler::ScopeLabel label("SpaceToBatchND"); - TFLITE_DCHECK_GE(unextended_input1_shape.DimensionsCount(), 3); - TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(unextended_input1_shape.DimensionsCount(), - unextended_output_shape.DimensionsCount()); - - // Extends the input/output shape from 3D to 4D if needed, NHC -> NH1C. - const RuntimeShape input1_shape = - ExtendShapeSpaceToBatch(unextended_input1_shape); - const RuntimeShape output_shape = - ExtendShapeSpaceToBatch(unextended_output_shape); - - const int depth = input1_shape.Dims(3); - const int input_width = input1_shape.Dims(2); - const int input_height = input1_shape.Dims(1); - const int input_batch_size = input1_shape.Dims(0); - - const int output_width = output_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_batch_size = output_shape.Dims(0); - - const int block_shape_height = block_shape_data[0]; - const int block_shape_width = - unextended_input1_shape.DimensionsCount() == 4 ? block_shape_data[1] : 1; - const int padding_top = paddings_data[0]; - const int padding_left = - unextended_input1_shape.DimensionsCount() == 4 ? paddings_data[2] : 0; - - // For uint8 quantized, the correct padding "zero value" is the output offset. - const int32_t pad_value = params.output_offset; - for (int out_b = 0; out_b < output_batch_size; ++out_b) { - int input_batch = out_b % input_batch_size; - int shift_w = (out_b / input_batch_size) % block_shape_width; - int shift_h = (out_b / input_batch_size) / block_shape_width; - for (int out_h = 0; out_h < output_height; ++out_h) { - for (int out_w = 0; out_w < output_width; ++out_w) { - T* out = output_data + Offset(output_shape, out_b, out_h, out_w, 0); - if (out_h * block_shape_height + shift_h < padding_top || - out_h * block_shape_height + shift_h >= - padding_top + input_height || - out_w * block_shape_width + shift_w < padding_left || - out_w * block_shape_width + shift_w >= padding_left + input_width) { - // This may not execute correctly when pad_value != 0 and T != uint8. - memset(out, pad_value, depth * sizeof(T)); - } else { - const T* in = - input1_data + - Offset(input1_shape, input_batch, - (out_h * block_shape_height + shift_h) - padding_top, - (out_w * block_shape_width + shift_w) - padding_left, 0); - memcpy(out, in, depth * sizeof(T)); - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_BATCH_ND_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h deleted file mode 100644 index c576e3db..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/space_to_depth.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace reference_ops { - -template -inline void SpaceToDepth(const tflite_micro::SpaceToDepthParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(4, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(4, unextended_output_shape); - - const int input_depth = input_shape.Dims(3); - const int input_width = input_shape.Dims(2); - const int input_height = input_shape.Dims(1); - const int input_batch = input_shape.Dims(0); - - const int output_depth = output_shape.Dims(3); - const int output_width = output_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_batch = output_shape.Dims(0); - - const int32_t block_size = op_params.block_size; - - TFLITE_DCHECK_EQ(input_width, output_width * block_size); - TFLITE_DCHECK_EQ(input_height, output_height * block_size); - TFLITE_DCHECK_EQ(input_depth * block_size * block_size, output_depth); - TFLITE_DCHECK_EQ(input_batch, output_batch); - - for (int in_b = 0; in_b < input_batch; ++in_b) { - for (int in_h = 0; in_h < input_height; ++in_h) { - for (int in_w = 0; in_w < input_width; ++in_w) { - for (int in_d = 0; in_d < input_depth; ++in_d) { - const int out_d = - in_d + ((in_h % block_size) * block_size + in_w % block_size) * - input_depth; - const int out_w = in_w / block_size; - const int out_h = in_h / block_size; - const int out_b = in_b; - - const int input_index = Offset(input_shape, in_b, in_h, in_w, in_d); - const int output_index = - Offset(output_shape, out_b, out_h, out_w, out_d); - - output_data[output_index] = input_data[input_index]; - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SPACE_TO_DEPTH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h deleted file mode 100644 index 4eefdd19..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/strided_slice.h +++ /dev/null @@ -1,147 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/portable_tensor.h" -#include "tensorflow/lite/kernels/internal/strided_slice_logic.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, - const RuntimeShape& unextended_input_shape, - const RuntimeShape& unextended_output_shape, - SequentialTensorWriter* writer) { - ruy::profiler::ScopeLabel label("StridedSlice"); - - // Note that the output_shape is not used herein. - tflite_micro::StridedSliceParams params_copy = op_params; - - TFLITE_DCHECK_LE(unextended_input_shape.DimensionsCount(), 5); - TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 5); - const RuntimeShape input_shape = - RuntimeShape::ExtendedShape(5, unextended_input_shape); - const RuntimeShape output_shape = - RuntimeShape::ExtendedShape(5, unextended_output_shape); - - // Reverse and pad to 5 dimensions because that is what the runtime code - // requires (ie. all shapes must be 5D and are given backwards). - strided_slice::StridedSlicePadIndices(¶ms_copy, 5); - - const int start_0 = - strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 0); - const int stop_0 = strided_slice::StridedSliceEndForAxis( - params_copy, input_shape, 0, start_0); - const int start_1 = - strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 1); - const int stop_1 = strided_slice::StridedSliceEndForAxis( - params_copy, input_shape, 1, start_1); - const int start_2 = - strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 2); - const int stop_2 = strided_slice::StridedSliceEndForAxis( - params_copy, input_shape, 2, start_2); - const int start_3 = - strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 3); - const int stop_3 = strided_slice::StridedSliceEndForAxis( - params_copy, input_shape, 3, start_3); - const int start_4 = - strided_slice::StridedSliceStartForAxis(params_copy, input_shape, 4); - const int stop_4 = strided_slice::StridedSliceEndForAxis( - params_copy, input_shape, 4, start_4); - - auto lc = [&](int end, int stride, int index) { - if (stride < 0) { - return index > end; - } else { - return index < end; - } - }; - // With a static_cast it is not possible to initialize - // a variable of type 'const int *' - // with an rvalue of type 'const int32_t *' (aka 'const long *'). - // reinterpret_cast is required to handle this casting. - const int* shape = reinterpret_cast(input_shape.DimsData()); - const int* stride = reinterpret_cast(params_copy.strides); - const bool inner_stride_is_1 = params_copy.strides[4] == 1; - - for (int offset_0 = start_0; lc(stop_0, stride[0], offset_0); - offset_0 += stride[0]) { - for (int offset_1 = start_1; lc(stop_1, stride[1], offset_1); - offset_1 += stride[1]) { - for (int offset_2 = start_2; lc(stop_2, stride[2], offset_2); - offset_2 += stride[2]) { - for (int offset_3 = start_3; lc(stop_3, stride[3], offset_3); - offset_3 += stride[3]) { - // When the stride is 1, the inner loop is equivalent to the - // optimized slice inner loop. Otherwise, it is identical to the - // strided_slice reference implementation inner loop. - if (inner_stride_is_1) { - const int len = stop_4 - start_4; - int index = start_4 + offset_3 * shape[4] + - offset_2 * shape[3] * shape[4] + - offset_1 * shape[2] * shape[3] * shape[4] + - offset_0 * shape[1] * shape[2] * shape[3] * shape[4]; - if (len > 0) { - writer->WriteN(index, len); - } - } else { - for (int offset_4 = start_4; lc(stop_4, stride[4], offset_4); - offset_4 += stride[4]) { - int index = offset_4 + offset_3 * shape[4] + - offset_2 * shape[3] * shape[4] + - offset_1 * shape[2] * shape[3] * shape[4] + - offset_0 * shape[1] * shape[2] * shape[3] * shape[4]; - writer->Write(index); - } - } - } - } - } - } -} - -template -inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, - const RuntimeShape& unextended_input_shape, - const T* input_data, - const RuntimeShape& unextended_output_shape, - T* output_data) { - SequentialTensorWriter writer(input_data, output_data); - StridedSlice(op_params, unextended_input_shape, unextended_output_shape, - &writer); -} - -template -inline void StridedSlice(const tflite_micro::StridedSliceParams& op_params, - const RuntimeShape& unextended_input_shape, - const TfLiteTensor* input, - const RuntimeShape& unextended_output_shape, - TfLiteTensor* output) { - SequentialTensorWriter writer(input, output); - StridedSlice(op_params, unextended_input_shape, unextended_output_shape, - &writer); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_STRIDED_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h deleted file mode 100644 index e80b3c27..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/sub.h +++ /dev/null @@ -1,465 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ - -#include - -#include -#include -#include - -#include "ruy/profiler/instrumentation.h" // from @ruy -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -template -struct SubImpl { - template - static void BroadcastInput1(const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data, size_t size, F binary_func) { - for (size_t c = 0; c < size; ++c) { - output_data[c] = binary_func(input1_data[0], input2_data[c], params); - } - } - - template - static void BroadcastInput2(const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data, size_t size, F binary_func) { - for (size_t c = 0; c < size; ++c) { - output_data[c] = binary_func(input1_data[c], input2_data[0], params); - } - } - - template - static void ElementWise(const ArithmeticParams& params, const T* input1_data, - const T* input2_data, T* output_data, size_t size, - F binary_func) { - for (size_t c = 0; c < size; ++c) { - output_data[c] = binary_func(input1_data[c], input2_data[c], params); - } - } -}; - -template <> -struct SubImpl { - template - static void BroadcastInput1(const ArithmeticParams& params, - const int32_t* input1_data, - const int32_t* input2_data, int32_t* output_data, - size_t size, F binary_func) { - size_t c = 0; - int32_t activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); -#ifdef USE_NEON - const int32x4_t vmax = vdupq_n_s32(activation_max); - const int32x4_t vmin = vdupq_n_s32(activation_min); - const int32x4_t va = vdupq_n_s32(input1_data[0]); - for (; c + 4 <= size; c += 4) { - const int32x4_t vb = vld1q_s32(&input2_data[c]); - int32x4_t vres = vsubq_s32(va, vb); - vres = vmaxq_s32(vmin, vres); - vres = vminq_s32(vmax, vres); - vst1q_s32(&output_data[c], vres); - } -#endif - for (; c < size; ++c) { - output_data[c] = binary_func(input1_data[0], input2_data[c], params); - } - } - - template - static void BroadcastInput2(const ArithmeticParams& params, - const int32_t* input1_data, - const int32_t* input2_data, int32_t* output_data, - size_t size, F binary_func) { - size_t c = 0; - int32_t activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); -#ifdef USE_NEON - const int32x4_t vmax = vdupq_n_s32(activation_max); - const int32x4_t vmin = vdupq_n_s32(activation_min); - const int32x4_t vb = vdupq_n_s32(input2_data[0]); - for (; c + 4 <= size; c += 4) { - const int32x4_t va = vld1q_s32(&input1_data[c]); - int32x4_t vres = vsubq_s32(va, vb); - vres = vmaxq_s32(vmin, vres); - vres = vminq_s32(vmax, vres); - vst1q_s32(&output_data[c], vres); - } -#endif - for (; c < size; ++c) { - output_data[c] = binary_func(input1_data[c], input2_data[0], params); - } - } - - template - static void ElementWise(const ArithmeticParams& params, - const int32_t* input1_data, - const int32_t* input2_data, int32_t* output_data, - size_t size, F binary_func) { - size_t c = 0; - int32_t activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); -#ifdef USE_NEON - int32x4_t vmax = vdupq_n_s32(activation_max); - int32x4_t vmin = vdupq_n_s32(activation_min); - for (; c + 4 <= size; c += 4) { - const int32x4_t va = vld1q_s32(&input1_data[c]); - const int32x4_t vb = vld1q_s32(&input2_data[c]); - int32x4_t vres = vsubq_s32(va, vb); - vres = vmaxq_s32(vmin, vres); - vres = vminq_s32(vmax, vres); - vst1q_s32(&output_data[c], vres); - } -#endif - for (; c < size; ++c) { - output_data[c] = binary_func(input1_data[c], input2_data[c], params); - } - } -}; - -template -inline void BroadcastSubRecursiveDimensions( - int dimension, const ArithmeticParams& params, const T* input1_data, - const T* input2_data, T* output_data, size_t* input1_offset_p, - size_t* input2_offset_p, size_t* output_offset, - size_t* compressed_input1_stride, size_t* compressed_input2_stride, - size_t* compressed_output_shape, F binary_func) { - if (dimension > 0) { - for (size_t c = 0; c < compressed_output_shape[dimension]; ++c) { - size_t input1_offset_c = *input1_offset_p; - size_t input2_offset_c = *input2_offset_p; - BroadcastSubRecursiveDimensions( - dimension - 1, params, input1_data, input2_data, output_data, - &input1_offset_c, &input2_offset_c, output_offset, - compressed_input1_stride, compressed_input2_stride, - compressed_output_shape, binary_func); - *input1_offset_p += compressed_input1_stride[dimension]; - *input2_offset_p += compressed_input2_stride[dimension]; - } - } else { - TFLITE_DCHECK(dimension == 0); - bool input1_is_broadcast = compressed_input1_stride[dimension] == 0; - bool input2_is_broadcast = compressed_input2_stride[dimension] == 0; - TFLITE_DCHECK(!(input1_is_broadcast && input2_is_broadcast)); - const T* input1_data_ptr = input1_data + *input1_offset_p; - const T* input2_data_ptr = input2_data + *input2_offset_p; - T* output_data_ptr = output_data + *output_offset; - if (input1_is_broadcast) { - // input1 is broadcast. - SubImpl::BroadcastInput1( - params, input1_data_ptr, input2_data_ptr, output_data_ptr, - compressed_output_shape[dimension], binary_func); - *input2_offset_p += compressed_output_shape[dimension]; - } else if (input2_is_broadcast) { - // input2 is broadcast. - SubImpl::BroadcastInput2( - params, input1_data_ptr, input2_data_ptr, output_data_ptr, - compressed_output_shape[dimension], binary_func); - *input1_offset_p += compressed_output_shape[dimension]; - } else { - // Add element-wise. - SubImpl::ElementWise(params, input1_data_ptr, input2_data_ptr, - output_data_ptr, - compressed_output_shape[dimension], binary_func); - *input1_offset_p += compressed_output_shape[dimension]; - *input2_offset_p += compressed_output_shape[dimension]; - } - *output_offset += compressed_output_shape[dimension]; - } -} - -// TODO: b/296510380 - we may be able to factor out this to common.h for all -// binary arithmetic ops (add, sub, mul). -template -inline void BroadcastSubCommon(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const T* input1_data, - const RuntimeShape& input2_shape, - const T* input2_data, - const RuntimeShape& output_shape, T* output_data, - F binary_func) { - constexpr int kMaxBroadcastDim = 6; - TFLITE_DCHECK_LE(input1_shape.DimensionsCount(), kMaxBroadcastDim); - TFLITE_DCHECK_LE(input2_shape.DimensionsCount(), kMaxBroadcastDim); - TFLITE_DCHECK_LE(output_shape.DimensionsCount(), kMaxBroadcastDim); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - - size_t compressed_input1_stride[kMaxBroadcastDim]; - size_t compressed_input2_stride[kMaxBroadcastDim]; - size_t compressed_output_shape[kMaxBroadcastDim]; - bool broadcastable_shape = ReduceDimensionsForBroadcast( - input1_shape, input2_shape, compressed_input1_stride, - compressed_input2_stride, compressed_output_shape); - // Skip broadcasting for degenerate shapes. - if (!broadcastable_shape) { - return; - } - - size_t input1_offset = 0; - size_t input2_offset = 0; - size_t output_offset = 0; - BroadcastSubRecursiveDimensions( - kMaxBroadcastDim - 1, params, input1_data, input2_data, output_data, - &input1_offset, &input2_offset, &output_offset, compressed_input1_stride, - compressed_input2_stride, compressed_output_shape, binary_func); -} - -// TODO(b/151345304): We can implement BroadcastSub on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -template -void BroadcastSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const T* input1_data, - const RuntimeShape& input2_shape, const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSubSlow/T"); - BroadcastSubCommon( - params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, - [](T input1_val, T input2_val, const ArithmeticParams& params) { - T activation_min, activation_max; - GetActivationParams(params, &activation_min, &activation_max); - return ActivationFunctionWithMinMax(input1_val - input2_val, - activation_min, activation_max); - }); -} - -inline void BroadcastSub16POTSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const int16_t* input1_data, - const RuntimeShape& input2_shape, - const int16_t* input2_data, - const RuntimeShape& output_shape, - int16_t* output_data) { - ruy::profiler::ScopeLabel label("BroadcastSub16POTSlow/int16_t"); - BroadcastSubCommon( - params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, - [](int16_t input1_val, int16_t input2_val, - const ArithmeticParams& params) { - const int32_t scaled_input1_val = - gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift); - const int32_t scaled_input2_val = - gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift); - const int32_t raw_output = scaled_input1_val - scaled_input2_val; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - return static_cast(clamped_output); - }); -} - -template -void BroadcastQuantSubSlow(const ArithmeticParams& params, - const RuntimeShape& input1_shape, - const T* input1_data, - const RuntimeShape& input2_shape, - const T* input2_data, - const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("BroadcastQuantSubSlow/T"); - BroadcastSubCommon( - params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, - [](T input1_val, T input2_val, const ArithmeticParams& params) { - const int32_t shifted_input1_val = - (params.input1_offset + input1_val) * (1 << params.left_shift); - const int32_t shifted_input2_val = - (params.input2_offset + input2_val) * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, - params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, - params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - return static_cast(clamped_output); - }); -} - -// Element-wise add that can often be used for inner loop of broadcast add as -// well as the non-broadcast add. -template -inline void SubElementwise(int size, const ArithmeticParams& params, - const T* input1_data, const T* input2_data, - T* output_data) { - for (int i = 0; i < size; ++i) { - const int32_t input1_val = params.input1_offset + input1_data[i]; - const int32_t input2_val = params.input2_offset + input2_data[i]; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_sub = scaled_input1_val - scaled_input2_val; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - output_data[i] = static_cast(clamped_output); - } -} - -inline void Sub(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const uint8_t* input1_data, - const RuntimeShape& input2_shape, const uint8_t* input2_data, - const RuntimeShape& output_shape, uint8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_GT(params.input1_offset, -256); - TFLITE_DCHECK_GT(params.input2_offset, -256); - TFLITE_DCHECK_LT(params.input1_offset, 256); - TFLITE_DCHECK_LT(params.input2_offset, 256); - SubElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Sub(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int8_t* input1_data, - const RuntimeShape& input2_shape, const int8_t* input2_data, - const RuntimeShape& output_shape, int8_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_GE(params.input1_offset, -128); - TFLITE_DCHECK_GE(params.input2_offset, -128); - // offset = -quantization_params.zero_point in PrepareGeneralSubOp(). - // So it's maximum can be 128 not 127. - TFLITE_DCHECK_LE(params.input1_offset, 128); - TFLITE_DCHECK_LE(params.input2_offset, 128); - SubElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -inline void Sub(const ArithmeticParams& params, - const RuntimeShape& input1_shape, const int16_t* input1_data, - const RuntimeShape& input2_shape, const int16_t* input2_data, - const RuntimeShape& output_shape, int16_t* output_data) { - TFLITE_DCHECK_LE(params.quantized_activation_min, - params.quantized_activation_max); - - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - - TFLITE_DCHECK_EQ(params.input1_offset, 0); - TFLITE_DCHECK_EQ(params.input2_offset, 0); - SubElementwise(flat_size, params, input1_data, input2_data, output_data); -} - -template -void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, - T* output_data) { - BroadcastSubCommon( - params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, - [](T input1_val, T input2_val, const ArithmeticParams& params) { - return input1_val - input2_val; - }); -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - int32_t* activation_min, - int32_t* activation_max) { - *activation_min = params.quantized_activation_min; - *activation_max = params.quantized_activation_max; -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - float* activation_min, float* activation_max) { - *activation_min = params.float_activation_min; - *activation_max = params.float_activation_max; -} - -inline void SetActivationMinMax(const ArithmeticParams& params, - int64_t* activation_min, - int64_t* activation_max) { - *activation_min = params.int64_activation_min; - *activation_max = params.int64_activation_max; -} - -template -inline void SubWithActivation( - const ArithmeticParams& params, const RuntimeShape& input1_shape, - const T* input1_data, const RuntimeShape& input2_shape, - const T* input2_data, const RuntimeShape& output_shape, T* output_data) { - ruy::profiler::ScopeLabel label("SubWithActivation"); - const int flat_size = - MatchingElementsSize(input1_shape, input2_shape, output_shape); - T activation_min, activation_max; - SetActivationMinMax(params, &activation_min, &activation_max); - - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], activation_min, activation_max); - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h deleted file mode 100644 index 578a1a90..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/tanh.h +++ /dev/null @@ -1,129 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ - -#include - -#include "fixedpoint/fixedpoint.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite_micro { -namespace reference_ops { - -inline void Tanh(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data) { - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - float val = input_data[i]; - float result = std::tanh(val); - output_data[i] = result; - } -} - -// Convenience version that allows, for example, generated-code calls to be -// uniform between data types. -inline void Tanh(const TanhParams&, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& output_shape, - float* output_data) { - // Drop params: not needed. - Tanh(input_shape, input_data, output_shape, output_data); -} - -inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& output_shape, - int16_t* output_data) { - const int input_left_shift = params.input_left_shift; - // Support for shifts is limited until we have a parameterized version of - // SaturatingRoundingMultiplyByPOT(). - TFLITE_DCHECK_GE(input_left_shift, 0); - TFLITE_DCHECK_LE(input_left_shift, 1); - - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - // F0 uses 0 integer bits, range [-1, 1]. - // This is the return type of math functions such as tanh, logistic, - // whose range is in [-1, 1]. - using F0 = gemmlowp::FixedPoint; - // F3 uses 3 integer bits, range [-8, 8], the input range expected here. - using F3 = gemmlowp::FixedPoint; - - if (input_left_shift == 0) { - for (int i = 0; i < flat_size; i++) { - F3 input = F3::FromRaw(input_data[i]); - F0 output = gemmlowp::tanh(input); - output_data[i] = output.raw(); - } - } else { - for (int i = 0; i < flat_size; i++) { - F3 input = F3::FromRaw( - gemmlowp::SaturatingRoundingMultiplyByPOT<1>(input_data[i])); - F0 output = gemmlowp::tanh(input); - output_data[i] = output.raw(); - } - } -} - -inline void Tanh(const TanhParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& output_shape, - uint8_t* output_data) { - const int32_t input_zero_point = params.input_zero_point; - const int32_t input_range_radius = params.input_range_radius; - const int32_t input_multiplier = params.input_multiplier; - const int input_left_shift = params.input_left_shift; - const int32_t output_zero_point = 128; - const int flat_size = MatchingFlatSize(input_shape, output_shape); - - for (int i = 0; i < flat_size; i++) { - const uint8_t input_val_u8 = input_data[i]; - const int32_t input_val_centered = - static_cast(input_val_u8) - input_zero_point; - uint8_t output_val; - if (input_val_centered <= -input_range_radius) { - output_val = 0; - } else if (input_val_centered >= input_range_radius) { - output_val = 255; - } else { - const int32_t input_val_rescaled = - MultiplyByQuantizedMultiplierGreaterThanOne( - input_val_centered, input_multiplier, input_left_shift); - using FixedPoint4 = gemmlowp::FixedPoint; - using FixedPoint0 = gemmlowp::FixedPoint; - const FixedPoint4 input_val_f4 = FixedPoint4::FromRaw(input_val_rescaled); - const FixedPoint0 output_val_f0 = gemmlowp::tanh(input_val_f4); - // Convert from Q0.31 to Q24.7. - using gemmlowp::RoundingDivideByPOT; - int32_t output_val_s32 = RoundingDivideByPOT(output_val_f0.raw(), 24); - output_val_s32 += output_zero_point; - if (output_val_s32 == 256) { - output_val_s32 = 255; - } - // Reinterpret as Q0.7, encoded in uint8_t. - TFLITE_DCHECK_GE(output_val_s32, 0); - TFLITE_DCHECK_LE(output_val_s32, 255); - output_val = static_cast(output_val_s32); - } - output_data[i] = output_val; - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TANH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h deleted file mode 100644 index 195202e6..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose.h +++ /dev/null @@ -1,203 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ - -#include - -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -namespace transpose_internal { - -// Recursively explores all the dimensions of the output tensor and writes the -// corresponding input tensor data. -// -// - depth: the current depth of the recursion. -// - dims: tensor dimension count, also `perm` size. -// - perm: permutation array. -// - input_data: Running input data pointer. If depth == num_dims-1, this points -// to the first element of the last dimension to traverse. -// - input_stride: Reverse partial product of input shapes. -// - output_data: Running output data pointer. If depth == num_dims-1, this -// points to the first element of the last dimension to traverse. -// - output_stride: Reverse partial product of output shapes. -// - output_shape: Shape of the output tensor. -// -// ## Algorithm explanation -// -// Assume a 3D tensor T with a shape of [I, J, K] stored in row major order. -// T[i, j, k] is at position `i*J*K + j*K + k` in the tensor buffer. -// -// If we want to go through the whole tensor iteratively, we can use loops. -// -// ``` -// for(i = 0; i < I; ++i) { -// for(j = 0; j < J; ++j) { -// for(k = 0; k < K; ++k) { -// T.data[i*J*K + j*K + k] = ... -// } -// } -// } -// ``` -// -// We can also compute the offset as we go through the loops. -// -// ``` -// stride_i = K * J; -// stride_j = K; -// stride_k = 1; -// for(i = 0; i < I; ++i) { -// offset_i = i * stride_i; -// offset_j = 0; -// for(j = 0; j < J; ++j) { -// offset_j += stride_j; -// offset_k = 0; -// for(k = 0; k < K; ++k) { -// offset_k += stride_k; -// T.data[offset_i + offset_j + offset_k] = ... -// } -// } -// } -// ``` -// -// This nicely extends to a recursive version which is the base of this -// algorithm and supports any number of dimensions. -// -// ``` -// shape = [I, J, K] -// strides = [K*J, K, 1] -// void recurse(T* data, shape, strides, depth = 0) { -// if(depth == shape.size) { -// *data = ... -// } else { -// for(a = 0; a < shape[depth]; ++a) { -// recurse(data, shape, strides, depth+1); -// data += strides[depth]; -// } -// } -// } -// ``` -template -void TransposeImpl(const int depth, const int dims, const int32_t* perm, - const T* input_data, const int* input_stride, T* output_data, - const int* output_stride, const int32_t* output_shape) { - const int dimension_size = output_shape[depth]; - if (depth == dims - 1) { - const int loop_stride = input_stride[perm[depth]]; - for (int i = 0; i < dimension_size; ++i) { - output_data[i] = *input_data; - input_data += loop_stride; - } - } else { - for (int i = 0; i < dimension_size; ++i) { - TransposeImpl(depth + 1, dims, perm, input_data, input_stride, - output_data, output_stride, output_shape); - - input_data += input_stride[perm[depth]]; - output_data += output_stride[depth]; - } - } -} - -// Compile-time switch to get the storage type of the transposition. -template -struct TransposeStorageType; - -template <> -struct TransposeStorageType<1> { - using type = int8_t; -}; - -template <> -struct TransposeStorageType<2> { - using type = int16_t; -}; - -template <> -struct TransposeStorageType<4> { - using type = int32_t; -}; - -template <> -struct TransposeStorageType<8> { - using type = int64_t; -}; - -// Sets up the stride arrays for the recursive transpose algorithm. -// -// Implementation notes: -// -// This is a reverse partial product. We could use standard algorithms to -// implement this but the result is not a readable and is tricky to get right -// because the first element must be set to 1, which leads to offset -// shenanigans: -// -// ``` -// stride[dims - 1] = 1; -// std::partial_sum(std::make_reverse_iterator(shape + dims), -// std::make_reverse_iterator(shape + 1), -// stride.rend() - input_rank + 1, std::multiplies()); -// ``` -// -// Note that Abseil isn't used in kernels implementation. That would make the -// above solution more readable. -inline void SetupTransposeStrides( - std::array& stride, const int32_t* shape, - const int dims) { - stride[dims - 1] = 1; - for (int i = dims - 2; i >= 0; --i) { - stride[i] = stride[i + 1] * shape[i + 1]; - } -} - -} // namespace transpose_internal - -// Copies a tensor to an other buffer and permutes its dimensions. -// -// Note: template parameter N is not used anymore. It is kept for API -// compatibility with TFLite micro. -template -void Transpose(const TransposeParams& params, const RuntimeShape& input_shape, - const T* input_data, const RuntimeShape& output_shape, - T* output_data) { - using transpose_internal::SetupTransposeStrides; - using transpose_internal::TransposeImpl; - using transpose_internal::TransposeStorageType; - // Transpose kernel only does rearranging values not numeric evaluations on - // each cell. It's safe to implement per size of scalar type and this trick - // keeps the total code size in a reasonable range. - using StorageType = typename TransposeStorageType::type; - const StorageType* const input_data_storage = - reinterpret_cast(input_data); - StorageType* const output_data_storage = - reinterpret_cast(output_data); - - const int dims = input_shape.DimensionsCount(); - std::array input_stride, output_stride; - SetupTransposeStrides(input_stride, input_shape.DimsData(), dims); - SetupTransposeStrides(output_stride, output_shape.DimsData(), dims); - TransposeImpl(0, dims, ¶ms.perm[0], input_data_storage, - input_stride.data(), output_data_storage, output_stride.data(), - output_shape.DimsData()); -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h deleted file mode 100644 index 4a588a34..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/reference/transpose_conv.h +++ /dev/null @@ -1,225 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ - -#include - -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -namespace reference_ops { - -inline void TransposeConv( - const ConvParams& params, const RuntimeShape& input_shape, - const float* input_data, const RuntimeShape& filter_shape, - const float* filter_data, const RuntimeShape& bias_shape, - const float* bias_data, const RuntimeShape& output_shape, - float* output_data, const RuntimeShape& im2col_shape, float* im2col_data) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const float output_activation_min = params.float_activation_min; - const float output_activation_max = params.float_activation_max; - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - // Although transpose convolution simplifies to convolution with transposed - // weights for strides of 1, non-unitary striding complicates matters. To - // keep this reference implementation as clear as possible, we use a - // "scatter" access pattern, where we loop through all the input elements, - // computing their influence on the output, rather than looping through the - // output elements in the typical "gather" access pattern of a conv. We - // therefore must initialize the output array to zero. - const int num_elements = output_shape.FlatSize(); - for (int i = 0; i < num_elements; i++) { - output_data[i] = 0.0f; - } - - // Loop through input elements one at a time. - for (int batch = 0; batch < batches; ++batch) { - for (int in_y = 0; in_y < input_height; ++in_y) { - for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - float input_value = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - float filter_value = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - output_data[Offset(output_shape, batch, out_y, out_x, - out_channel)] += - input_value * filter_value; - } - } - } - } - } - } - } - } - - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - float acc = output_data[Offset(output_shape, batch, out_y, out_x, - out_channel)]; - if (bias_data) acc += bias_data[out_channel]; - - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - ActivationFunctionWithMinMax(acc, output_activation_min, - output_activation_max); - } - } - } - } -} - -inline void TransposeConv( - const ConvParams& params, const RuntimeShape& input_shape, - const uint8_t* input_data, const RuntimeShape& filter_shape, - const uint8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - uint8_t* output_data, const RuntimeShape& im2col_shape, - uint8_t* im2col_data, int32_t* scratch_buffer) { - const int stride_width = params.stride_width; - const int stride_height = params.stride_height; - const int pad_width = params.padding_values.width; - const int pad_height = params.padding_values.height; - TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4); - TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4); - (void)im2col_data; // only used in optimized code. - (void)im2col_shape; // only used in optimized code. - - const int batches = MatchingDim(input_shape, 0, output_shape, 0); - const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3); - const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3); - const int input_height = input_shape.Dims(1); - const int input_width = input_shape.Dims(2); - const int filter_height = filter_shape.Dims(1); - const int filter_width = filter_shape.Dims(2); - const int output_height = output_shape.Dims(1); - const int output_width = output_shape.Dims(2); - const int32_t input_offset = params.input_offset; - const int32_t filter_offset = params.weights_offset; - const int32_t output_offset = params.output_offset; - const int32_t output_multiplier = params.output_multiplier; - const int output_shift = params.output_shift; - const int32_t output_activation_min = params.quantized_activation_min; - const int32_t output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (bias_data) { - TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth); - } - - const int num_elements = output_shape.FlatSize(); - // We need to initialize scratch_buffer to all 0s, as we apply the same - // 'scatter' based trick as in float version. - memset(scratch_buffer, 0, num_elements * sizeof(int32_t)); - - // Loop through input elements one at a time. - for (int batch = 0; batch < batches; ++batch) { - for (int in_y = 0; in_y < input_height; ++in_y) { - for (int in_x = 0; in_x < input_width; ++in_x) { - for (int in_channel = 0; in_channel < input_depth; ++in_channel) { - // Loop through the output elements it will influence. - const int out_x_origin = (in_x * stride_width) - pad_width; - const int out_y_origin = (in_y * stride_height) - pad_height; - for (int filter_y = 0; filter_y < filter_height; ++filter_y) { - for (int filter_x = 0; filter_x < filter_width; ++filter_x) { - for (int out_channel = 0; out_channel < output_depth; - ++out_channel) { - // Compute output element location. - const int out_x = out_x_origin + filter_x; - const int out_y = out_y_origin + filter_y; - // We cannot accumulate out of bounds. - if ((out_x >= 0) && (out_x < output_width) && (out_y >= 0) && - (out_y < output_height)) { - uint8_t input_value = input_data[Offset( - input_shape, batch, in_y, in_x, in_channel)]; - uint8_t filter_value = - filter_data[Offset(filter_shape, out_channel, filter_y, - filter_x, in_channel)]; - scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)] += - (input_value + input_offset) * - (filter_value + filter_offset); - } - } - } - } - } - } - } - } - for (int batch = 0; batch < batches; ++batch) { - for (int out_y = 0; out_y < output_height; ++out_y) { - for (int out_x = 0; out_x < output_width; ++out_x) { - for (int out_channel = 0; out_channel < output_depth; ++out_channel) { - int32_t acc = scratch_buffer[Offset(output_shape, batch, out_y, out_x, - out_channel)]; - if (bias_data) { - acc += bias_data[out_channel]; - } - int32_t scaled_acc = MultiplyByQuantizedMultiplier( - acc, output_multiplier, output_shift); - scaled_acc += output_offset; - scaled_acc = std::max(scaled_acc, output_activation_min); - scaled_acc = std::min(scaled_acc, output_activation_max); - output_data[Offset(output_shape, batch, out_y, out_x, out_channel)] = - static_cast(scaled_acc); - } - } - } - } -} - -} // namespace reference_ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_TRANSPOSE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h deleted file mode 100644 index 28fbfe27..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/runtime_shape.h +++ /dev/null @@ -1,168 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ - -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite_micro { - -template -struct Dims { - int sizes[N]; - int strides[N]; -}; - -class RuntimeShape { - public: - RuntimeShape& operator=(RuntimeShape const&) = delete; - - // RuntimeShape in TFLM supports up to 6 dimensions. - // The name kMaxSmallSize comes from the same file of the upstream - // tensorflow lite repo and need to be kept the same for max reuse. - static constexpr int kMaxSmallSize = 6; - - RuntimeShape() : size_(0) {} - - explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { - TFLITE_DCHECK_LE(dimensions_count, kMaxSmallSize); - } - - RuntimeShape(int shape_size, int32_t value) : size_(shape_size) { - TFLITE_DCHECK_LE(shape_size, kMaxSmallSize); - for (int i = 0; i < shape_size; ++i) { - SetDim(i, value); - } - } - - RuntimeShape(int dimensions_count, const int32_t* dims_data) - : size_(dimensions_count) { - // check of dimensions_count handled by ReplaceWith() - ReplaceWith(dimensions_count, dims_data); - } - - bool operator==(const RuntimeShape& comp) const { - return this->size_ == comp.size_ && - std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32_t)) == - 0; - } - - ~RuntimeShape() {} - - int32_t DimensionsCount() const { return size_; } - int32_t Dims(int i) const { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - return dims_[i]; - } - void SetDim(int i, int32_t val) { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - dims_[i] = val; - } - - static RuntimeShape ExtendedShape(int new_shape_size, - const RuntimeShape& shape) { - TFLITE_DCHECK_LE(new_shape_size, kMaxSmallSize); - return RuntimeShape(new_shape_size, shape, 1); - } - int32_t* DimsData() { return dims_; } - const int32_t* DimsData() const { return dims_; } - const int32_t* DimsDataUpTo5D() const { return dims_; } - - void ReplaceWith(int dimensions_count, const int32_t* dims_data) { - TFLITE_DCHECK_LE(dimensions_count, kMaxSmallSize); - size_ = dimensions_count; - int32_t* dst_dims = DimsData(); - std::memcpy(dst_dims, dims_data, dimensions_count * sizeof(int32_t)); - } - - // Returns the total count of elements, that is the size when flattened into a - // vector. - int FlatSize() const { - int buffer_size = 1; - const int* dims_data = reinterpret_cast(DimsData()); - for (int i = 0; i < size_; i++) { - buffer_size *= dims_data[i]; - } - return buffer_size; - } - - private: - // For use only by ExtendedShape(), written to guarantee (return-value) copy - // elision in C++17. - // This creates a shape padded to the desired size with the specified value. - RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) - : size_(new_shape_size) { - // If the following check fails, it is likely because a 4D-only kernel is - // being used with an array of larger dimension count. - TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); - const int size_increase = new_shape_size - shape.DimensionsCount(); - for (int i = 0; i < size_increase; ++i) { - SetDim(i, pad_value); - } - std::memcpy(DimsData() + size_increase, shape.DimsData(), - sizeof(int32_t) * shape.DimensionsCount()); - } - - int32_t size_; - union { - int32_t dims_[kMaxSmallSize]; - }; -}; - -// Since tensors with '0' in their shape are valid in TF, these offset functions -// allow that as long as the corresponding index is also 0. It is upto the -// calling ops to ensure that they perform verification checks on tensor shapes -// if they don't support a particular behavior. - -inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), 4); - const int* dims_data = reinterpret_cast(shape.DimsData()); - TFLITE_DCHECK((dims_data[0] == 0 && i0 == 0) || - (i0 >= 0 && i0 < dims_data[0])); - TFLITE_DCHECK((dims_data[1] == 0 && i1 == 0) || - (i1 >= 0 && i1 < dims_data[1])); - TFLITE_DCHECK((dims_data[2] == 0 && i2 == 0) || - (i2 >= 0 && i2 < dims_data[2])); - TFLITE_DCHECK((dims_data[3] == 0 && i3 == 0) || - (i3 >= 0 && i3 < dims_data[3])); - return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; -} - -inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3, - int i4) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), 5); - const int* dims_data = reinterpret_cast(shape.DimsData()); - TFLITE_DCHECK((dims_data[0] == 0 && i0 == 0) || - (i0 >= 0 && i0 < dims_data[0])); - TFLITE_DCHECK((dims_data[1] == 0 && i1 == 0) || - (i1 >= 0 && i1 < dims_data[1])); - TFLITE_DCHECK((dims_data[2] == 0 && i2 == 0) || - (i2 >= 0 && i2 < dims_data[2])); - TFLITE_DCHECK((dims_data[3] == 0 && i3 == 0) || - (i3 >= 0 && i3 < dims_data[3])); - TFLITE_DCHECK((dims_data[4] == 0 && i4 == 0) || - (i4 >= 0 && i4 < dims_data[4])); - return (((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3) * - dims_data[4] + - i4; -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_RUNTIME_SHAPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h deleted file mode 100644 index 85849365..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/strided_slice_logic.h +++ /dev/null @@ -1,278 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ - -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { -namespace strided_slice { - -// Use until std::clamp() is available from C++17. -inline int Clamp(const int v, const int lo, const int hi) { - TFLITE_DCHECK(!(hi < lo)); - if (hi < v) return hi; - if (v < lo) return lo; - return v; -} - -inline void StridedSlicePadIndices(tflite_micro::StridedSliceParams* p, - int dim_count) { - // Add indices and mask bits to fully include extra dimensions - TFLITE_CHECK_LE(dim_count, 5); - TFLITE_CHECK_GE(dim_count, p->start_indices_count); - TFLITE_CHECK_EQ(p->start_indices_count, p->stop_indices_count); - TFLITE_CHECK_EQ(p->stop_indices_count, p->strides_count); - - const int pad_count = dim_count - p->start_indices_count; - - // Pad indices at start, so move arrays by pad_count. - for (int i = p->start_indices_count - 1; i >= 0; --i) { - p->strides[i + pad_count] = p->strides[i]; - p->start_indices[i + pad_count] = p->start_indices[i]; - p->stop_indices[i + pad_count] = p->stop_indices[i]; - } - for (int i = 0; i < pad_count; ++i) { - p->start_indices[i] = 0; - p->stop_indices[i] = 1; - p->strides[i] = 1; - } - - // Pad masks with 0s or 1s as required. - p->shrink_axis_mask <<= pad_count; - p->ellipsis_mask <<= pad_count; - p->new_axis_mask <<= pad_count; - p->begin_mask <<= pad_count; - p->end_mask <<= pad_count; - p->begin_mask |= (1 << pad_count) - 1; - p->end_mask |= (1 << pad_count) - 1; - - p->start_indices_count = dim_count; - p->stop_indices_count = dim_count; - p->strides_count = dim_count; -} - -// Return the index for the first element along that axis. This index will be a -// positive integer between [0, axis_size] (or [-1, axis_size -1] if stride < 0) -// that can be used to index directly into the data. -inline int StridedSliceStartForAxis(const tflite_micro::StridedSliceParams& params, - const RuntimeShape& input_shape, - int32_t axis) { - const int32_t axis_size = input_shape.Dims(axis); - int32_t start = params.start_indices[axis]; - const int32_t stride = params.strides[axis]; - const int32_t begin_mask = (params.begin_mask & 1 << axis); - if (start < 0) { - start += axis_size; - } - if (stride > 0) { - start = Clamp(start, 0, axis_size); - } else { - start = Clamp(start, -1, axis_size - 1); - } - if (begin_mask) { - if (stride > 0) { - start = 0; - } else { - start = axis_size - 1; - } - } - return start; -} - -inline int StridedSliceEndForAxis(const tflite_micro::StridedSliceParams& params, - const RuntimeShape& input_shape, int axis, - int start) { - const auto shrink_axis_mask = params.shrink_axis_mask; - const bool shrink_axis = shrink_axis_mask & (1 << axis); - const int axis_size = input_shape.Dims(axis); - const bool offset = params.offset; - if (shrink_axis) { - if (start >= axis_size) { - return start; - } else { - return start + 1; - } - } - const auto* indices = params.stop_indices; - int end = indices[axis]; - if (offset) { - end += start; - } - const int32_t stride = params.strides[axis]; - const int32_t end_mask = (params.end_mask & 1 << axis); - if (end < 0) { - end += axis_size; - } - if (stride > 0) { - end = Clamp(end, 0, axis_size); - } else { - end = Clamp(end, -1, axis_size - 1); - } - if (end_mask) { - if (stride > 0) { - end = axis_size; - } else { - end = -1; - } - } - return end; -} - -// Return the index for the first element along that axis. This index will be a -// positive integer between [0, axis_size] (or [-1, axis_size -1] if stride < 0) -// that can be used to index directly into the data. -inline int StartForAxis(const tflite_micro::StridedSliceParams& params, - const RuntimeShape& input_shape, int axis) { - const auto begin_mask = params.begin_mask; - const auto* start_indices = params.start_indices; - const auto* strides = params.strides; - const int axis_size = input_shape.Dims(axis); - if (axis_size == 0) { - return 0; - } - // Begin with the specified index. - int start = start_indices[axis]; - - // begin_mask override - if (begin_mask & 1 << axis) { - if (strides[axis] > 0) { - // Forward iteration - use the first element. These values will get - // clamped below (Note: We could have set them to 0 and axis_size-1, but - // use lowest() and max() to maintain symmetry with StopForAxis()) - start = std::numeric_limits::lowest(); - } else { - // Backward iteration - use the last element. - start = std::numeric_limits::max(); - } - } - - // Handle negative indices - if (start < 0) { - start += axis_size; - } - - // Clamping - if (strides[axis] > 0) { - // Forward iteration - start = Clamp(start, 0, axis_size); - } else { - // Backward iteration - start = Clamp(start, -1, axis_size - 1); - } - - return start; -} - -// Return the "real" index for the end of iteration along that axis. This is an -// "end" in the traditional C sense, in that it points to one past the last -// element. ie. So if you were iterating through all elements of a 1D array of -// size 4, this function would return 4 as the stop, because it is one past the -// "real" indices of 0, 1, 2 & 3. -inline int StopForAxis(const tflite_micro::StridedSliceParams& params, - const RuntimeShape& input_shape, int axis, - int start_for_axis) { - const auto end_mask = params.end_mask; - const auto shrink_axis_mask = params.shrink_axis_mask; - const auto* stop_indices = params.stop_indices; - const auto* strides = params.strides; - const int axis_size = input_shape.Dims(axis); - if (axis_size == 0) { - return 0; - } - - // Begin with the specified index - const bool shrink_axis = shrink_axis_mask & (1 << axis); - int stop = stop_indices[axis]; - - // When shrinking an axis, the end position does not matter (and can be - // incorrect when negative indexing is used, see Issue #19260). Always use - // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has - // already been adjusted for negative indices. - if (shrink_axis) { - return start_for_axis + 1; - } - - // end_mask override - if (end_mask & (1 << axis)) { - if (strides[axis] > 0) { - // Forward iteration - use the last element. These values will get - // clamped below - stop = std::numeric_limits::max(); - } else { - // Backward iteration - use the first element. - stop = std::numeric_limits::lowest(); - } - } - - // Handle negative indices - if (stop < 0) { - stop += axis_size; - } - - // Clamping - // Because the end index points one past the last element, we need slightly - // different clamping ranges depending on the direction. - if (strides[axis] > 0) { - // Forward iteration - stop = Clamp(stop, 0, axis_size); - } else { - // Backward iteration - stop = Clamp(stop, -1, axis_size - 1); - } - - return stop; -} - -inline bool LoopCondition(int index, int stop, int stride) { - // True when we have reached the end of an axis and should loop. - return stride > 0 ? index >= stop : index <= stop; -} - -inline tflite_micro::StridedSliceParams BuildStridedSliceParams( - int begin_mask, int end_mask, int shrink_axis_mask, - const std::vector& start_indices, const std::vector& stop_indices, - const std::vector& strides) { - tflite_micro::StridedSliceParams op_params{}; - const int dims_count = start_indices.size(); - - op_params.start_indices_count = dims_count; - op_params.stop_indices_count = dims_count; - op_params.strides_count = dims_count; - for (int i = 0; i < dims_count; ++i) { - op_params.start_indices[i] = start_indices[i]; - op_params.stop_indices[i] = stop_indices[i]; - op_params.strides[i] = strides[i]; - } - - op_params.begin_mask = begin_mask; - op_params.ellipsis_mask = 0; - op_params.end_mask = end_mask; - op_params.new_axis_mask = 0; - op_params.shrink_axis_mask = shrink_axis_mask; - - return op_params; -} - -} // namespace strided_slice - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_STRIDED_SLICE_LOGIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h deleted file mode 100644 index 7cc05ff4..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/tensor_ctypes.h +++ /dev/null @@ -1,42 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ - -#include - -#include "tensorflow/lite/core/c/common.h" -#include "tensorflow/lite/core/macros.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -template -inline T* GetTensorData(TfLiteTensor* tensor) { - return tensor != nullptr ? reinterpret_cast(tensor->data.raw) : nullptr; -} - -template -inline const T* GetTensorData(const TfLiteTensor* tensor) { - return tensor != nullptr ? reinterpret_cast(tensor->data.raw) - : nullptr; -} - -TFLITE_NOINLINE RuntimeShape GetTensorShape(const TfLiteTensor* tensor); -RuntimeShape GetTensorShape(std::vector data); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TENSOR_CTYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h b/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h deleted file mode 100644 index bddd87b5..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/internal/types.h +++ /dev/null @@ -1,1096 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ -#define TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ - -#include -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/runtime_shape.h" - -namespace tflite_micro { - -enum class FusedActivationFunctionType : uint8_t { - kNone, - kRelu6, - kRelu1, - kRelu -}; -enum class PaddingType : uint8_t { kNone, kSame, kValid }; - -struct PaddingValues { - int16_t width; - int16_t height; - // offset is used for calculating "remaining" padding, for example, `width` - // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is - // 1 + 1 = 2. - int16_t width_offset; - // Same as width_offset except it's over the height dimension. - int16_t height_offset; -}; - -struct Padding3DValues { - int16_t width; - int16_t height; - int16_t depth; - // offset is used for calculating "remaining" padding, for example, `width` - // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is - // 1 + 1 = 2. - int16_t width_offset; - // Same as width_offset except it's over the height dimension. - int16_t height_offset; - // Same as width_offset except it's over the depth dimension. - int16_t depth_offset; -}; - -// This enumeration allows for non-default formats for the weights array -// of a fully-connected operator, allowing the use of special optimized -// runtime paths. -enum class FullyConnectedWeightsFormat : uint8_t { - // Default format (flat 2D layout, the inner contiguous dimension - // is input_depth, the outer non-contiguous dimension is output_depth) - kDefault, - // Summary: optimized layout for fast CPU runtime implementation, - // aimed specifically at ARM CPUs at the moment, and specialized for - // 8-bit quantized layers. - // - // The use case we're concerned with here is: 8-bit quantization, - // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in - // a key application that drove this), very small batch size (e.g. 1 -- 4). - // - // Even with 8-bit quantization of weights, the performance of memory - // accesses to the weights can become the dominant issue when - // the batch size is small, so each weight value is used in only a few - // arithmetic ops, i.e. the fully-connected node has a low arithmetic - // intensity. The specific issues that arise are of three kinds: - // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory - // bound. That's the "good" issue to run into. - // (2) One may run into sub-optimal pre-fetching: the data hasn't been - // prefetched into the cache by the time we need it. - // (3) One may run into cache aliasing: multiple values that are - // pre-fetched, alias each other in the L1 cache (which typically - // has only 4-way set associativity in ARM CPUs) and thus evict - // each other before we get to using them. - // - // The point of this shuffling is to avoid issues (2) and (3) so that - // we get as fast as possible given only the hard constraint (1). - // This is achieved by turning the difficulty into a solution: the - // difficulty, that each value loaded from memory is used only in - // one kernel iteration, making this operation memory-intensive, hints at - // the solution, of shuffling the weights so that they are stored in the - // exact order as the kernel needs to load them, so that the memory - // accesses made by the kernel are trivial. This solves (2) because the - // trivial memory access pattern allows the CPU's automatic prefetching - // to perform very well (no need even for preload instructions), and this - // solves (3) because the values being loaded concurrently are now - // contiguous in the address space, thus don't alias each other in the cache. - // - // On ARM, we typically want our kernel to process a 4x16 block of weights - // at a time, because: - // - 16 is the number of bytes in a NEON register. - // - 4 is how many rows we need to handle concurrently in the kernel in - // order to have sufficient mutual independence of instructions to - // maximize arithmetic throughput. - // - // Finally, the 'Int8' part in the name refers to the fact that this - // weights format has each weights value encoded as a signed int8_t value, - // even if the data type of the weights buffer is uint8_t. This is intended - // to save runtime kernels the effort to have to XOR the top bit of these - // bytes before using them in signed arithmetic, see this file for more - // explanations on the 'signed int8_t trick' in matrix multiplication kernels: - // - // tensorflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc - // - kShuffled4x16Int8, -}; - -// Quantization parameters, determining the mapping of quantized values -// to real values (i.e. determining how quantized values are mathematically -// interpreted). -// -// The correspondence is as follows: -// -// real_value = scale * (quantized_value - zero_point); -// -// In other words, zero_point designates which quantized value corresponds to -// the real 0 value, and scale designates the difference between the real values -// corresponding to consecutive quantized values differing by 1. -struct QuantizationParams { - int32_t zero_point = 0; - double scale = 0.0; -}; - -inline bool operator==(const QuantizationParams& qp1, - const QuantizationParams& qp2) { - return qp1.zero_point == qp2.zero_point && qp1.scale == qp2.scale; -} - -// Quantization parameters for each channel, determining the mapping of -// quantized values to real values. See QuantizationParams for a single set of -// parameters per tensor. This has one parameters set per each channel. -// -// The correspondence is as follows: -// -// real_value = scale[channel] * (quantized_value - zero_point[channel]); -// -struct PerChannelQuantizationParams { - // The following members typically point to the corresponding members of a - // TfLiteAffineQuantization struct. - const float* scale; - const int32_t* zero_point; - int32_t quantized_dimension; -}; - -// Gets next index to iterate through a multidimensional array. -template -inline bool NextIndex(const int num_dims, const int* dims, IndexType* current) { - if (num_dims == 0) { - return false; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(current != nullptr); - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) { - IndexType current_val = current[idx] + carry; - TFLITE_DCHECK_GE(dims[idx], current_val); - if (dims[idx] == current_val) { - current[idx] = 0; - } else { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int* dims, - const int* index, const int num_axis, - const int* axis) { - if (num_dims == 0) { - return 0; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(index != nullptr); - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { - if (idx == axis[axis_idx]) { - is_axis = true; - break; - } - } - } - if (!is_axis) { - offset = offset * static_cast(dims[idx]) + - static_cast(index[idx]); - } - } - return offset; -} - -// Since tensors with '0' in their shape are valid in TF, these offset functions -// allow that as long as the corresponding index is also 0. It is upto the -// calling ops to ensure that they perform verification checks on tensor shapes -// if they don't support a particular behavior. - -inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK((i0 == 0 && dims.sizes[0] == 0) || - (i0 >= 0 && i0 < dims.sizes[0])); - TFLITE_DCHECK((i1 == 0 && dims.sizes[1] == 0) || - (i1 >= 0 && i1 < dims.sizes[1])); - TFLITE_DCHECK((i2 == 0 && dims.sizes[2] == 0) || - (i2 >= 0 && i2 < dims.sizes[2])); - TFLITE_DCHECK((i3 == 0 && dims.sizes[3] == 0) || - (i3 >= 0 && i3 < dims.sizes[3])); - return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + - i3 * dims.strides[3]; -} - -inline int Offset(const Dims<4>& dims, int* index) { - return Offset(dims, index[0], index[1], index[2], index[3]); -} - -// Get array size, DCHECKing that the dim index is in range. -// -// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() -// already performs this check. -template -int ArraySize(const Dims& array, int index) { - TFLITE_DCHECK(index >= 0 && index < N); - return array.sizes[index]; -} - -// Get common array size, DCHECKing that they all agree. -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return ArraySize(array1, index1); -} - -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2, Args... args) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return MatchingArraySize(array1, index1, args...); -} - -// Get common shape dim, DCHECKing that they all agree. -inline int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return std::min(shape1.Dims(index1), shape2.Dims(index2)); -} - -template -int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2, Args... args) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return MatchingDim(shape1, index1, args...); -} - -// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). -template -inline int FlatSize(const Dims& dims) { - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= dims.sizes[i]; - } - return flat_size; -} - -TFLITE_DEPRECATED("Prefer FlatSize.") -inline int RequiredBufferSizeForDims(const Dims<4>& dims) { - return FlatSize(dims); -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - return size_1; -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - const int size_3 = check_shape_1.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - TFLITE_CHECK_EQ(size_2, size_3); - return size_1; -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return shape.FlatSize(); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return FlatSize(dims); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); -} - -// Flat size calculation, checking if their extended shapes match. -inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - const int shape_dims = shape.DimensionsCount(); - const int check_shape_0_dims = check_shape_0.DimensionsCount(); - const int min_dims = std::min(shape_dims, check_shape_0_dims); - - for (int i = 0; i < min_dims; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(shape_dims - 1 - i), - check_shape_0.Dims(check_shape_0_dims - 1 - i)); - } - for (int i = min_dims; i < shape_dims; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(shape_dims - 1 - i), 1); - } - for (int i = min_dims; i < check_shape_0_dims; ++i) { - TFLITE_DCHECK_EQ(check_shape_0.Dims(check_shape_0_dims - 1 - i), 1); - } - return shape.FlatSize(); -} - -inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); - TFLITE_DCHECK_EQ(MatchingExtendedShapeFlatSize(shape, check_shape_1), - flat_size); - return flat_size; -} - -inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); - TFLITE_DCHECK_EQ( - MatchingExtendedShapeFlatSize(shape, check_shape_1, check_shape_2), - flat_size); - return flat_size; -} - -inline int MatchingExtendedShapeFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - const int flat_size = MatchingExtendedShapeFlatSize(shape, check_shape_0); - TFLITE_DCHECK_EQ(MatchingExtendedShapeFlatSize(shape, check_shape_1, - check_shape_2, check_shape_3), - flat_size); - return flat_size; -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -template -inline int FlatSizeSkipDim(const Dims& dims, int skip_dim) { - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < N); - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims.sizes[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return FlatSizeSkipDim(dims, skip_dim); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2, - check_dims_3); -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { - const int dims_count = shape.DimensionsCount(); - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); - const auto* dims_data = shape.DimsData(); - int flat_size = 1; - for (int i = 0; i < dims_count; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims_data[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return FlatSizeSkipDim(shape, skip_dim); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, - check_shape_3); -} - -template -bool IsPackedWithoutStrides(const Dims& dims) { - int expected_stride = 1; - for (int d = 0; d < N; d++) { - if (dims.strides[d] != expected_stride) return false; - expected_stride *= dims.sizes[d]; - } - return true; -} - -template -void ComputeStrides(Dims* dims) { - dims->strides[0] = 1; - for (int d = 1; d < N; d++) { - dims->strides[d] = dims->strides[d - 1] * dims->sizes[d - 1]; - } -} - -enum class BroadcastableOpCategory : uint8_t { - kNone, - kNonBroadcast, // Matching input shapes. - kFirstInputBroadcastsFast, // Fivefold nested loops. - kSecondInputBroadcastsFast, // Fivefold nested loops. - kGenericBroadcast, // Fall-back. -}; - -struct MinMax { - float min; - float max; -}; -static_assert(sizeof(MinMax) == 8, ""); - -struct ActivationParams { - FusedActivationFunctionType activation_type; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; -}; - -struct ReluParams : public ActivationParams { - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; -}; - -// Styles of resizing op usages. For example, kImageStyle can be used with a Pad -// op for pattern-specific optimization. -enum class ResizingCategory : uint8_t { - kNone, - kImageStyle, // 4D, operating on inner dimensions, say {0, a, b, 0}. - kGenericResize, -}; - -// For Add, Sub, Mul ops. -struct ArithmeticParams { - // Shape dependent / common to data / op types. - BroadcastableOpCategory broadcast_category; - // uint8_t inference params. - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // Add / Sub, not Mul, uint8_t inference params. - int left_shift; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_multiplier; - int input2_shift; - - // TODO(b/158622529): Union the following activation params. - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - // int64_t activation params. - int64_t int64_activation_min; - int64_t int64_activation_max; - // int16_t activation params. - int16_t int16_activation_min; - int16_t int16_activation_max; - - // Processed output dimensions. - // Let input "a" be the one that broadcasts in the faster-changing dimension. - // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and - // {b0, b1, b2, b3, b4}, - // broadcast_shape[4] = b0 = a0. - // broadcast_shape[3] = b1; a1 = 1. - // broadcast_shape[2] = b2 = a2. - // broadcast_shape[1] = a3; b3 = 1. - // broadcast_shape[0] = b4 = a4. - int broadcast_shape[5]; -}; - -struct ConcatenationParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16_t inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct ComparisonParams { - // uint8_t inference params. - int left_shift; - int32_t input1_offset; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_offset; - int32_t input2_multiplier; - int input2_shift; - // Shape dependent / common to inference types. - bool is_broadcast; -}; - -struct ConvParams { - PaddingType padding_type; - PaddingValues padding_values; - // TODO(starka): This was just "stride", so check that width+height is OK. - int16_t stride_width; - int16_t stride_height; - int16_t dilation_width_factor; - int16_t dilation_height_factor; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct Conv3DParams { - Padding3DValues padding_values; - int stride_width; - int stride_height; - int stride_depth; - int dilation_width; - int dilation_height; - int dilation_depth; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -typedef Conv3DParams Conv3DTransposeParams; - -struct DepthToSpaceParams { - int32_t block_size; -}; - -struct DepthwiseParams { - PaddingType padding_type; - PaddingValues padding_values; - int16_t stride_width; - int16_t stride_height; - int16_t dilation_width_factor; - int16_t dilation_height_factor; - int16_t depth_multiplier; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - const int32_t* output_multiplier_per_channel; - const int32_t* output_shift_per_channel; -}; - -struct DequantizationParams { - double scale; - int32_t zero_point; -}; - -struct PerChannelDequantizationParams { - const float* scale; - const int32_t* zero_point; - int32_t quantized_dimension; -}; - -struct FakeQuantParams { - MinMax minmax; - int32_t num_bits; -}; - -struct FullyConnectedParams { - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - // Mark the operands as cacheable if they are unchanging, e.g. weights. - bool lhs_cacheable; - bool rhs_cacheable; - FullyConnectedWeightsFormat weights_format; -}; - -struct GatherParams { - int16_t axis; - int16_t batch_dims; -}; - -struct L2NormalizationParams { - // uint8_t inference params. - int32_t input_zero_point; -}; - -struct LocalResponseNormalizationParams { - int32_t range; - double bias; - double alpha; - double beta; -}; - -struct HardSwishParams { - // zero_point of the input activations. - int16_t input_zero_point; - // zero_point of the output activations. - int16_t output_zero_point; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // "relu-ish scale", which 3.0/32768. - // See the implementation of HardSwishPrepare. - int16_t reluish_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int reluish_multiplier_exponent; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // output scale. - // See the implementation of HardSwishPrepare. - int16_t output_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int output_multiplier_exponent; -}; - -struct LogisticParams { - // uint8_t inference params. - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -struct LstmCellParams { - int32_t weights_zero_point; - int32_t accum_multiplier; - int accum_shift; - int state_integer_bits; -}; - -struct MeanParams { - int8_t axis_count; - int16_t axis[4]; -}; - -struct PackParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16_t inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct PadParams { - int8_t left_padding_count; - int32_t left_padding[5]; - int8_t right_padding_count; - int32_t right_padding[5]; - ResizingCategory resizing_category; -}; - -struct PreluParams { - int32_t input_offset; - int32_t alpha_offset; - int32_t output_offset; - int32_t output_multiplier_1; - int output_shift_1; - int32_t output_multiplier_2; - int output_shift_2; -}; - -struct PoolParams { - FusedActivationFunctionType activation; - PaddingType padding_type; - PaddingValues padding_values; - int stride_height; - int stride_width; - int filter_height; - int filter_width; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct ReshapeParams { - int8_t shape_count; - int32_t shape[4]; -}; - -struct ResizeBilinearParams { - bool align_corners; - // half_pixel_centers assumes pixels are of half the actual dimensions, and - // yields more accurate resizes. Corresponds to the same argument for the - // original TensorFlow op in TF2.0. - bool half_pixel_centers; -}; - -struct ResizeNearestNeighborParams { - bool align_corners; - bool half_pixel_centers; -}; - -struct SliceParams { - int8_t begin_count; - int32_t begin[5]; - int8_t size_count; - int32_t size[5]; -}; - -struct SoftmaxParams { - // beta is not really used (not a Tensorflow parameter) and not implemented - // for LogSoftmax. - double beta; - // uint8_t inference params. Used even when beta defaults to 1.0. - int32_t input_multiplier; - int32_t input_left_shift; - // Reverse scaling is only used by LogSoftmax. - int32_t reverse_scaling_divisor; - int32_t reverse_scaling_right_shift; - int diff_min; - int32_t zero_point; - float scale; - float* table; - // int16 LUT for exp(x), where x uniform distributed between [-10.0 , 0.0] - int16_t* exp_lut; - // int16 LUT for 1 / (1 + x), where x uniform distributed between [0.0 , 1.0] - int16_t* one_over_one_plus_x_lut; - uint8_t* uint8_table1; - uint8_t* uint8_table2; -}; - -struct SpaceToBatchParams { - // "Zero" padding for uint8_t means padding with the output offset. - int32_t output_offset; -}; - -struct SpaceToDepthParams { - int32_t block_size; -}; - -struct SplitParams { - // Graphs that split into, say, 2000 nodes are encountered. The indices in - // OperatorEdges are of type uint16_t. - uint16_t num_split; - int16_t axis; -}; - -struct SqueezeParams { - int8_t squeeze_dims_count; - int32_t squeeze_dims[4]; -}; - -struct StridedSliceParams { - int8_t start_indices_count; - int32_t start_indices[5]; - int8_t stop_indices_count; - int32_t stop_indices[5]; - int8_t strides_count; - int32_t strides[5]; - - uint16_t begin_mask; - uint16_t ellipsis_mask; - uint16_t end_mask; - uint16_t new_axis_mask; - uint16_t shrink_axis_mask; - bool offset; -}; - -struct TanhParams { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -constexpr int kTransposeMaxDimensions = 6; - -struct TransposeParams { - int8_t perm_count; - int32_t perm[kTransposeMaxDimensions]; -}; - -struct UnpackParams { - uint16_t num_split; - int16_t axis; -}; - -struct LeakyReluParams { - float alpha; - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier_alpha; - int32_t output_shift_alpha; - int32_t output_multiplier_identity; - int32_t output_shift_identity; -}; - -template -inline void SetActivationParams(float min, float max, P* params) { - params->float_activation_min = min; - params->float_activation_max = max; -} - -template -inline void SetActivationParams(int32_t min, int32_t max, P* params) { - params->quantized_activation_min = min; - params->quantized_activation_max = max; -} - -template -inline void SetActivationParams(uint32_t min, uint32_t max, P* params) { - params->quantized_activation_min = min; - params->quantized_activation_max = max; -} - -template -inline void SetActivationParams(int16_t min, int16_t max, P* params) { - params->int16_activation_min = min; - params->int16_activation_max = max; -} - -template -inline void SetActivationParams(int64_t min, int64_t max, P* params) { - params->int64_activation_min = min; - params->int64_activation_max = max; -} - -template -inline void GetActivationParams(const P& params, int32_t* min, int32_t* max) { - *min = params.quantized_activation_min; - *max = params.quantized_activation_max; -} - -template -inline void GetActivationParams(const P& params, uint32_t* min, uint32_t* max) { - *min = params.quantized_activation_min; - *max = params.quantized_activation_max; -} - -template -inline void GetActivationParams(const P& params, int16_t* min, int16_t* max) { - *min = params.int16_activation_min; - *max = params.int16_activation_max; -} - -template -inline void GetActivationParams(const P& params, float* min, float* max) { - *min = params.float_activation_min; - *max = params.float_activation_max; -} - -template -inline void GetActivationParams(const P& params, int64_t* min, int64_t* max) { - *min = params.int64_activation_min; - *max = params.int64_activation_max; -} - -// Type trait to check of given type has size smaller than 4 bytes. -template -struct is_small_integer - : public std::integral_constant::value || - std::is_same::value || - std::is_same::value || - std::is_same::value> {}; - -// Type trait to check of given type is int32 or int64. -template -struct is_int32_or_int64 - : public std::integral_constant::value || - std::is_same::value> { -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h b/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h deleted file mode 100644 index 4ca74708..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/kernel_util.h +++ /dev/null @@ -1,350 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ -#define TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ - -#include -#include - -#include -#ifndef TF_LITE_STATIC_MEMORY -#include -#endif // TF_LITE_STATIC_MEMORY - -#include "tensorflow/lite/core/c/builtin_op_data.h" -#include "tensorflow/lite/core/c/common.h" -#ifndef NDEBUG -#include "tensorflow/lite/kernels/op_macros.h" -#endif - -namespace tflite_micro { - -// A fair number of functions in this header have historically been inline. -// It is ok to change functions to not be inline if the latency with -// benchmark_model for MobileNet + MobileBERT is unaffected. If such a change is -// made, move the newly non-inlined function declarations to the top of this -// header file. - -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetInput(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -const TfLiteTensor* GetInput(const TfLiteContext* context, - const TfLiteNode* node, int index); - -// Same as `GetInput` but returns boolean and uses output argument for tensor. -// -// TfLiteTensor* my_tensor; -// TF_LITE_ENSURE_OK(context, -// GetInputSafe(context, node, kMyTensorIdx, &my_tensor)); -// // can use my_tensor directly from here onwards, it is not nullptr -// -// Should be used in cases where the binary size is too large. -TfLiteStatus GetInputSafe(const TfLiteContext* context, const TfLiteNode* node, - int index, const TfLiteTensor** tensor); - -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetVariableInput(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -TfLiteTensor* GetVariableInput(TfLiteContext* context, const TfLiteNode* node, - int index); - -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetOutput(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node, - int index); - -// Same as `GetOutput` but returns boolean and uses output argument for tensor. -// -// TfLiteTensor* my_tensor; -// TF_LITE_ENSURE_OK(context, -// GetOutputSafe(context, node, kMyTensorIdx, &my_tensor)); -// // can use my_tensor directly from here onwards, it is not nullptr -// -// Should be used in cases where the binary size is too large. -TfLiteStatus GetOutputSafe(const TfLiteContext* context, const TfLiteNode* node, - int index, TfLiteTensor** tensor); - -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetOptionalInputTensor(context, node, kIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -// -// Deprecated. GetInput has the same functionality. -const TfLiteTensor* GetOptionalInputTensor(const TfLiteContext* context, - const TfLiteNode* node, int index); - -#ifndef TF_LITE_STATIC_MEMORY -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetTemporary(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -TfLiteTensor* GetTemporary(TfLiteContext* context, const TfLiteNode* node, - int index); - -// Same as `GetTemporary` but returns boolean and uses output argument for -// tensor. -// -// TfLiteTensor* my_tensor; -// TF_LITE_ENSURE_OK(context, -// GetTemporarySafe(context, node, kMyTensorIdx, -// &my_tensor)); -// // can use my_tensor directly from here onwards, it is not nullptr -// -// Should be used in cases where the binary size is too large. -TfLiteStatus GetTemporarySafe(const TfLiteContext* context, - const TfLiteNode* node, int index, - TfLiteTensor** tensor); - -// Note: You must check if result is not null: -// -// TfLiteTensor* my_tensor = GetIntermediates(context, node, kMyTensorIdx); -// TF_LITE_ENSURE(context, my_tensor != nullptr); -// -// This is because the index might point to the optional tensor constant -// (kTfLiteOptionalTensor) in which case there is no tensor to return. -const TfLiteTensor* GetIntermediates(TfLiteContext* context, - const TfLiteNode* node, int index); - -// Same as `GetIntermediates` but returns boolean and uses output argument for -// tensor. -// -// TfLiteTensor* my_tensor; -// TF_LITE_ENSURE_OK(context, -// GetIntermediatesSafe(context, node, kMyTensorIdx, -// &my_tensor)); -// // can use my_tensor directly from here onwards, it is not nullptr -// -// Should be used in cases where the binary size is too large. -TfLiteStatus GetIntermediatesSafe(const TfLiteContext* context, - const TfLiteNode* node, int index, - TfLiteTensor** tensor); -#endif // TF_LITE_STATIC_MEMORY - -inline int NumDimensions(const TfLiteTensor* t) { return t->dims->size; } -inline int SizeOfDimension(const TfLiteTensor* t, int dim) { - return t->dims->data[dim]; -} - -inline int NumInputs(const TfLiteNode* node) { - return node->inputs == nullptr ? 0 : node->inputs->size; -} -inline int NumOutputs(const TfLiteNode* node) { - return node->outputs == nullptr ? 0 : node->outputs->size; -} - -#ifndef TF_LITE_STATIC_MEMORY -inline int NumIntermediates(const TfLiteNode* node) { - return node->intermediates->size; -} -#endif // TF_LITE_STATIC_MEMORY - -inline int64_t NumElements(const int* dims, int num_dims) { - int64_t count = 1; - for (int i = 0; i < num_dims; ++i) { -#ifndef NDEBUG - if (count <= 0) { - break; - } - // Check that number of elements can fit in 32 bit int. Most of tflite - // assumes the result of `NumElements` is < MAX_INT and static or implicit - // casts to `int32_t` without any checks. It is more meaningful to check - // that the result fits into 32 bits than for standard overflow on 64 bit - // type. - TF_LITE_ASSERT(dims[i] < std::numeric_limits::max() / count); -#endif - count *= dims[i]; - } - return count; -} - -inline int64_t NumElements(const TfLiteIntArray* dims) { - return NumElements(dims->data, dims->size); -} - -inline int64_t NumElements(const TfLiteTensor* t) { - return NumElements(t->dims); -} - -// Determines whether tensor is constant. -// TODO(b/138199592): Introduce new query which checks for constant OR -// persistent-read-only, which would be useful for most tensor kernels that -// are potentially dynamic based on the input tensor value availability at the -// time of prepare. -inline bool IsConstantTensor(TfLiteContext *context, TfLiteTensor* tensor) { - #ifndef NO_INTERPRETER - return tensor->allocation_type == kTfLiteMmapRo; - #else - return context->IsConstantTensor(context, tensor); - #endif -} - -inline bool IsConstantOrPersistentTensor(const TfLiteTensor* tensor) { - assert(false && "Disabled function!"); - return false; - // return IsConstantTensor(tensor) || - // (tensor->allocation_type == kTfLitePersistentRo); -} - -// Determines whether tensor is dynamic. Note that a tensor can be non-const and -// not dynamic. This function specifically checks for a dynamic tensor. -inline bool IsDynamicTensor(const TfLiteTensor* tensor) { - assert(false && "Disabled function!"); - return false; - // return tensor->allocation_type == kTfLiteDynamic; -} -#ifndef TF_LITE_STATIC_MEMORY -// Sets tensor to dynamic. -inline void SetTensorToDynamic(TfLiteTensor* tensor) { - if (tensor->allocation_type != kTfLiteDynamic) { - TfLiteTensorDataFree(tensor); - tensor->allocation_type = kTfLiteDynamic; - } -} - -// Sets tensor to persistent and read-only. -inline void SetTensorToPersistentRo(TfLiteTensor* tensor) { - if (tensor->allocation_type != kTfLitePersistentRo) { - TfLiteTensorDataFree(tensor); - tensor->allocation_type = kTfLitePersistentRo; - } -} -#endif // TF_LITE_STATIC_MEMORY - -// Determines whether it is a hybrid op - one that has float inputs and -// quantized weights. -inline bool IsHybridOp(const TfLiteTensor* input, const TfLiteTensor* weight) { - return ((weight->type == kTfLiteUInt8 || weight->type == kTfLiteInt8) && - input->type == kTfLiteFloat32); -} - -// Check dimensionality match and populate OpData for Conv and DepthwiseConv. -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int32_t* per_channel_shift); - -TfLiteStatus PopulateConvolutionQuantizationParams( - TfLiteContext* context, const TfLiteTensor* input, - const TfLiteTensor* filter, const TfLiteTensor* bias, TfLiteTensor* output, - const TfLiteFusedActivation& activation, int32_t* multiplier, int* shift, - int32_t* output_activation_min, int32_t* output_activation_max, - int32_t* per_channel_multiplier, int32_t* per_channel_shift, - int num_channels); - -// Calculates the multiplication factor for a quantized convolution (or -// quantized depthwise convolution) involving the given tensors. Returns an -// error if the scales of the tensors are not compatible. -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - const TfLiteTensor* bias, - TfLiteTensor* output, - double* multiplier); - -TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context, - const TfLiteTensor* input, - const TfLiteTensor* filter, - TfLiteTensor* output, - double* multiplier); - -// Calculates the useful quantized range of an activation layer given its -// activation tensor. -TfLiteStatus CalculateActivationRangeQuantized(TfLiteContext* context, - TfLiteFusedActivation activation, - TfLiteTensor* output, - int32_t* act_min, - int32_t* act_max); - -// Calculates the useful range of an activation layer given its activation -// tensor.a -template -void CalculateActivationRange(TfLiteFusedActivation activation, - T* activation_min, T* activation_max) { - if (activation == kTfLiteActRelu) { - *activation_min = 0; - *activation_max = std::numeric_limits::max(); - } else if (activation == kTfLiteActRelu6) { - *activation_min = 0; - *activation_max = 6; - } else if (activation == kTfLiteActReluN1To1) { - *activation_min = -1; - *activation_max = 1; - } else { - *activation_min = std::numeric_limits::lowest(); - *activation_max = std::numeric_limits::max(); - } -} - -// Return true if the given tensors have the same shape. -bool HaveSameShapes(const TfLiteTensor* input1, const TfLiteTensor* input2); - -#if !defined(TF_LITE_STATIC_MEMORY) -// Gets the output shape from the input tensor. -TfLiteStatus GetOutputShapeFromInput(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteIntArray** output_shape); - -std::string GetShapeDebugString(const TfLiteIntArray* shape); - -#endif // !defined(TF_LITE_STATIC_MEMORY) - -// Calculates the output_shape that is necessary for element-wise operations -// with broadcasting involving the two input tensors. -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteIntArray** output_shape); - -// Calculates the output_shape that is necessary for element-wise operations -// with broadcasting involving the three input tensors. -TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - const TfLiteTensor* input3, - TfLiteIntArray** output_shape); - -// Return the size of given type in bytes. Return 0 in case of string. -int TfLiteTypeGetSize(TfLiteType type); - -// Whether the current platform is mobile (Android or iOS). -bool IsMobilePlatform(); - -// Returns whether there is unspecified dimension in the tensor's dim signature. -bool HasUnspecifiedDimension(const TfLiteTensor* tensor); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_KERNEL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h b/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h deleted file mode 100644 index 9c4e2fd0..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/op_macros.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ -#define TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ - -#include "tensorflow/lite/micro/micro_log.h" - -#if !defined(TF_LITE_MCU_DEBUG_LOG) -#include -#define TFLITE_ABORT abort() -#else -inline void AbortImpl() { - MicroPrintf("HALTED"); - while (1) { - } -} -#define TFLITE_ABORT AbortImpl(); -#endif - -#if defined(NDEBUG) -#define TFLITE_ASSERT_FALSE (static_cast(0)) -#else -#define TFLITE_ASSERT_FALSE TFLITE_ABORT -#endif - -#define TF_LITE_FATAL(msg) \ - do { \ - MicroPrintf("%s", (msg)); \ - TFLITE_ABORT; \ - } while (0) - -#define TF_LITE_ASSERT(x) \ - do { \ - if (!(x)) TF_LITE_FATAL(#x); \ - } while (0) - -#endif // TENSORFLOW_LITE_KERNELS_OP_MACROS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/kernels/padding.h b/new_ai_tools/include/tensorflow/lite/kernels/padding.h deleted file mode 100644 index 02437cb4..00000000 --- a/new_ai_tools/include/tensorflow/lite/kernels/padding.h +++ /dev/null @@ -1,115 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_PADDING_H_ -#define TENSORFLOW_LITE_KERNELS_PADDING_H_ - -#include "tensorflow/lite/core/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -inline int ComputePadding(int stride, int dilation_rate, int in_size, - int filter_size, int out_size) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - int padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2; - return padding > 0 ? padding : 0; -} - -// It's not guaranteed that padding is symmetric. It's important to keep -// offset for algorithms need all paddings. -inline int ComputePaddingWithOffset(int stride, int dilation_rate, int in_size, - int filter_size, int out_size, - int* offset) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - int total_padding = - ((out_size - 1) * stride + effective_filter_size - in_size); - total_padding = total_padding > 0 ? total_padding : 0; - *offset = total_padding % 2; - return total_padding / 2; -} - -// Matching GetWindowedOutputSize in TensorFlow. -inline int ComputeOutSize(TfLitePadding padding, int image_size, - int filter_size, int stride, int dilation_rate = 1) { - int effective_filter_size = (filter_size - 1) * dilation_rate + 1; - - // TODO(b/186448822): This uses 0 since the function has no other way to - // report error case - if (stride == 0) return 0; - - switch (padding) { - case kTfLitePaddingSame: - return (image_size + stride - 1) / stride; - case kTfLitePaddingValid: - return (image_size + stride - effective_filter_size) / stride; - default: - return 0; - } -} - -inline TfLitePaddingValues ComputePaddingHeightWidth( - int stride_height, int stride_width, int dilation_rate_height, - int dilation_rate_width, int in_height, int in_width, int filter_height, - int filter_width, TfLitePadding padding, int* out_height, int* out_width) { - *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width, - dilation_rate_width); - *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height, - dilation_rate_height); - - TfLitePaddingValues padding_values; - int offset = 0; - padding_values.height = - ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height, - filter_height, *out_height, &offset); - padding_values.height_offset = offset; - padding_values.width = - ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width, - filter_width, *out_width, &offset); - padding_values.width_offset = offset; - return padding_values; -} - -inline Padding3DValues ComputePadding3DValues( - int stride_height, int stride_width, int stride_depth, - int dilation_rate_height, int dilation_rate_width, int dilation_rate_depth, - int in_height, int in_width, int in_depth, int filter_height, - int filter_width, int filter_depth, TfLitePadding padding, int* out_height, - int* out_width, int* out_depth) { - *out_width = ComputeOutSize(padding, in_width, filter_width, stride_width, - dilation_rate_width); - *out_height = ComputeOutSize(padding, in_height, filter_height, stride_height, - dilation_rate_height); - *out_depth = ComputeOutSize(padding, in_depth, filter_depth, stride_depth, - dilation_rate_depth); - - Padding3DValues padding_values; - int offset = 0; - padding_values.depth = - ComputePaddingWithOffset(stride_depth, dilation_rate_depth, in_depth, - filter_depth, *out_depth, &offset); - padding_values.depth_offset = offset; - padding_values.height = - ComputePaddingWithOffset(stride_height, dilation_rate_height, in_height, - filter_height, *out_height, &offset); - padding_values.height_offset = offset; - padding_values.width = - ComputePaddingWithOffset(stride_width, dilation_rate_width, in_width, - filter_width, *out_width, &offset); - padding_values.width_offset = offset; - return padding_values; -} -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_KERNELS_PADDING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h deleted file mode 100644 index 41e32c9a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h +++ /dev/null @@ -1,100 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/c_api_types.h" - -namespace tflite_micro { -// Interface classes that the TFLM framework relies on to get buffers it needs. -// There are two types of buffers that the TFLM framework requires: persistent -// and non-persistent. Persistent buffers, once allocated, are never freed by -// the TFLM framework. Non-persist buffers can be allocated and deallocated by -// the TFLM framework. This file defines two interfaces classes that TFLM -// framework will rely on to manage these buffers. - -// Interface class for managing persistent buffers. -class IPersistentBufferAllocator { - public: - IPersistentBufferAllocator() {} - virtual ~IPersistentBufferAllocator() {} - - // Allocates persistent memory. The persistent buffer is never freed. - virtual uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) = 0; - - // Returns the size of all persistent allocations in bytes. - virtual size_t GetPersistentUsedBytes() const = 0; -}; - -// Interface class for managing non-persistent buffers. -// The default non-persistent buffers are temp buffers that are not resizable. -// Support of at least one resizable buffer is required. -class INonPersistentBufferAllocator { - public: - INonPersistentBufferAllocator() {} - virtual ~INonPersistentBufferAllocator() {} - - // Allocates a temporary buffer. This buffer is not resizable. - virtual uint8_t* AllocateTemp(size_t size, size_t alignment) = 0; - - // Signals that a temporary buffer is no longer needed. - virtual void DeallocateTemp(uint8_t* buf) = 0; - - // Returns true if all temporary buffers are already deallocated. - virtual bool IsAllTempDeallocated() = 0; - - // Signals that all temporary allocations can be reclaimed. TFLM calls this - // API when it knows that all temporary buffers that it requested has been - // deallocated. The goal of API is to facilitate implementations of - // INonPersistentBufferAllocator can reuse buffer with some reasonable - // complexity. - virtual TfLiteStatus ResetTempAllocations() = 0; - - // Returns a buffer that is resizable viable ResizeBuffer(). - virtual uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) = 0; - - // Resizes a buffer that is previously returned by the - // AllocateResizableBuffer. - virtual TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, - size_t alignment) = 0; - - // Frees up the memory occupied by the resizable buffer. - virtual TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) = 0; - - // Returns a pointer pointing to the start of the overlay memory, which is - // used for activation tensors and scratch buffers by kernels at Invoke stage. - virtual uint8_t* GetOverlayMemoryAddress() const = 0; - - // Reserves the size of the overlay memory. This overlay is reserved for the - // kernels at Invoke stage. This is referred to as the overlay because before - // Invoket state, the same memory can be used for temp buffers. The layout of - // the memory is planned by the memory planner separately at Invoke stage. - virtual TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size, - size_t alignment) = 0; - - // Returns the size of non-persistent buffer in use. - virtual size_t GetNonPersistentUsedBytes() const = 0; - - // Returns the number of bytes available with a given alignment. This number - // takes in account any temporary allocations. - virtual size_t GetAvailableMemory(size_t alignment) const = 0; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h deleted file mode 100644 index 8707e5a4..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h +++ /dev/null @@ -1,104 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite_micro { - -// Implement INonPersistentBufferAllocator on an arena that is dedicated for -// non-persistent buffers. -class NonPersistentArenaBufferAllocator : public INonPersistentBufferAllocator { - public: - NonPersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); - virtual ~NonPersistentArenaBufferAllocator(); - - // Allocates a temporary buffer. This buffer is not resizable. - uint8_t* AllocateTemp(size_t size, size_t alignment) override; - - // Signals that a temporary buffer is no longer needed. - void DeallocateTemp(uint8_t* buf) override; - - // Returns true if all temporary buffers are already deallocated. - bool IsAllTempDeallocated() override; - - // Signals that all temporary allocations can be reclaimed. TFLM calls this - // API when it knows that all temporary buffers that it requested has been - // deallocated. - TfLiteStatus ResetTempAllocations() override; - - // Returns a buffer that is resizable viable ResizeBuffer(). - uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) override; - - // Resizes a buffer that is previously returned by the - // AllocateResizableBuffer. - TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, - size_t alignment) override; - - // Frees up the memory occupied by the resizable buffer. - TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) override; - - // Returns a pointer pointing to the start of the overlay memory, which is - // used for activation tensors and scratch buffers by kernels at Invoke stage. - uint8_t* GetOverlayMemoryAddress() const override; - - // Reserves the size of the overlay memory. This overlay is reserved for the - // kernels at Invoke stage. This is referred to as the overlay because before - // Invoket state, the same memory can be used for temp buffers. The layout of - // the memory is planned by the memory planner separately at Invoke stage. - TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size, - size_t alignment) override; - - // Returns the size of non-persistent buffer in use. - size_t GetNonPersistentUsedBytes() const override; - - // Returns the number of bytes available with a given alignment. This number - // takes in account any temporary allocations. - size_t GetAvailableMemory(size_t alignment) const override; - - TF_LITE_REMOVE_VIRTUAL_DELETE - - private: - // The memory arena that this allocator manages. - uint8_t* const buffer_head_; - uint8_t* const buffer_tail_; - - // The whole region is split into two parts: - // buffer_head_ to head_temp_ - 1 belongs to the only resizable buffer. - // head_temp_ to buffer_tail_ can be used for (non-resizable) temp buffers. - uint8_t* head_temp_; - - // next_temp_ points to the next available temp buffer allocation address and - // its range is between head_temp_ and buffer_tail_ - uint8_t* next_temp_; - - // XOR Check sum for outstanding temp buffers. - // If all temp buffers are deallocated OR no temp buffers are allocated, - // temp_buffer_ptr_check_sum_ == nullptr. - intptr_t temp_buffer_ptr_check_sum_ = 0; - // Count of outstanding temp buffers. - int temp_buffer_count_ = 0; - bool resizable_buffer_allocated_ = false; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h deleted file mode 100644 index a4ca701c..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h +++ /dev/null @@ -1,58 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite_micro { - -// PersistentArenaBufferAllocator is an implementatation of -// IPersistentBufferAllocator interface on an arena that is dedicated for -// persistent buffers. -class PersistentArenaBufferAllocator : public IPersistentBufferAllocator { - public: - PersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); - virtual ~PersistentArenaBufferAllocator(); - - // Allocates persistent memory. The persistent buffer is never freed. - // Returns nullptr if errors occured. - uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override; - - // Returns the size of all persistent allocations in bytes. - size_t GetPersistentUsedBytes() const override; - - TF_LITE_REMOVE_VIRTUAL_DELETE - private: - // The memory arena that this allocator manages. - uint8_t* const buffer_head_; - uint8_t* const buffer_tail_; - - // The whole region is split into two parts: - // tail_temp_ to buffer_tail_ contains allocated buffers; - // buffer_head_ to tail_temp_ - 1 belongs to still available spaces. - // So in essence, the allocated region grows from the bottom and emulates - // SingleArenaBufferAllocator's persistent part. - uint8_t* tail_temp_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h deleted file mode 100644 index e24530e6..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h +++ /dev/null @@ -1,63 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ - -#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite_micro { - -// Utility class used to log allocations of a SingleArenaBufferAllocator. Should -// only be used in debug/evaluation settings or unit tests to evaluate -// allocation usage. -class RecordingSingleArenaBufferAllocator : public SingleArenaBufferAllocator { - public: - RecordingSingleArenaBufferAllocator(uint8_t* buffer_head, size_t buffer_size); - // TODO(b/157615197): Cleanup constructors/destructor and use factory - // functions. - ~RecordingSingleArenaBufferAllocator() override; - - static RecordingSingleArenaBufferAllocator* Create(uint8_t* buffer_head, - size_t buffer_size); - - // Returns the number of bytes requested from the head or tail. - size_t GetRequestedBytes() const; - - // Returns the number of bytes actually allocated from the head or tail. This - // value will be >= to the number of requested bytes due to padding and - // alignment. - size_t GetUsedBytes() const; - - // Returns the number of alloc calls from the head or tail. - size_t GetAllocatedCount() const; - - TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, - size_t alignment) override; - uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override; - - private: - size_t requested_head_bytes_; - size_t requested_tail_bytes_; - size_t used_bytes_; - size_t alloc_count_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h deleted file mode 100644 index 91464c89..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h +++ /dev/null @@ -1,144 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite_micro { - -// TODO(petewarden): This allocator never frees up or reuses any memory, even -// though we have enough information about lifetimes of the tensors to do so. -// This makes it pretty wasteful, so we should use a more intelligent method. -class SingleArenaBufferAllocator : public INonPersistentBufferAllocator, - public IPersistentBufferAllocator { - public: - // TODO(b/157615197): Cleanup constructors/destructor and use factory - // functions. - SingleArenaBufferAllocator(uint8_t* buffer_head, uint8_t* buffer_tail); - SingleArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); - virtual ~SingleArenaBufferAllocator(); - - // Creates a new SingleArenaBufferAllocator from a given buffer head and size. - static SingleArenaBufferAllocator* Create(uint8_t* buffer_head, - size_t buffer_size); - - // Resizes a buffer that is previously returned by the - // AllocateResizableBuffer. In current implementation, it Adjusts the head - // (lowest address and moving upwards) memory allocation to a given size. - // Calls to this method will also invalidate all temporary allocation values - // (it sets the location of temp space at the end of the head section). This - // call will fail if a chain of allocations through AllocateTemp() have not - // been cleaned up with a call to ResetTempAllocations(). - virtual TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, - size_t alignment) override; - - // Returns a buffer that is resizable viable ResizeBuffer(). Only one - // resizable buffer is currently supported. - virtual uint8_t* AllocateResizableBuffer(size_t size, - size_t alignment) override; - - // Frees up the memory occupied by the resizable buffer - virtual TfLiteStatus DeallocateResizableBuffer( - uint8_t* resizable_buf) override; - - // Reserves the non-persistent memory that is planned by the memory planner. - virtual TfLiteStatus ReserveNonPersistentOverlayMemory( - size_t size, size_t alignment) override; - - // Allocates persistent memory starting at the tail of the arena (highest - // address and moving downwards). - virtual uint8_t* AllocatePersistentBuffer(size_t size, - size_t alignment) override; - - // Allocates a temporary buffer from the head of the arena (lowest address and - // moving upwards) but does not update the actual head allocation size or - // position. The returned buffer is guaranteed until either - // ResetTempAllocations() is called or another call to AllocateFromHead(). - // Repeat calls to this function will create a chain of temp allocations. All - // calls to AllocateTemp() must end with a call to ResetTempAllocations(). If - // AllocateFromHead() is called before a call to ResetTempAllocations(), it - // will fail with an error message. - virtual uint8_t* AllocateTemp(size_t size, size_t alignment) override; - - // Signals that a temporary buffer is no longer needed. This is currently for - // book-keeping purpose and the memory region are not immediately available - // for re-use. The deallocated memory region are only reclaimed after - // ResetTempAllocations is called as it is right now. - virtual void DeallocateTemp(uint8_t* buf) override; - - // Returns true if all temporary buffers are already deallocated. - virtual bool IsAllTempDeallocated() override; - - // Resets a chain of temporary allocations back to the current head of the - // arena (lowest address). - virtual TfLiteStatus ResetTempAllocations() override; - - // Returns a pointer to the buffer currently assigned to the head section. - // This buffer is set by calling SetHeadSize(). - uint8_t* GetOverlayMemoryAddress() const override; - - // Returns the size of the head section in bytes. - size_t GetNonPersistentUsedBytes() const override; - - // Returns the size of all allocations in the tail section in bytes. - size_t GetPersistentUsedBytes() const override; - - // Returns the number of bytes available with a given alignment. This number - // takes in account any temporary allocations. - size_t GetAvailableMemory(size_t alignment) const override; - - // Returns the number of used bytes in the allocator. This number takes in - // account any temporary allocations. - size_t GetUsedBytes() const; - - TF_LITE_REMOVE_VIRTUAL_DELETE - - protected: - // Returns a pointer to the current end of the head buffer. - uint8_t* head() const; - - // Returns a pointer to the current end of the tail buffer. - uint8_t* tail() const; - - private: - size_t GetBufferSize() const; - uint8_t* buffer_head_; - uint8_t* buffer_tail_; - uint8_t* head_; - uint8_t* tail_; - uint8_t* temp_; - - // The combination of the checksum of outstanding temporary buffer pointers - // AND the count of outstanding temporary buffer provide a low cost mechanism - // to audit temporary buffers' allocation and deallocation. - // - // XOR Check sum for outstanding temp buffers. - // If all temp buffers are deallocated OR no temp buffers are allocated, - // temp_buffer_ptr_check_sum_ == nullptr. - intptr_t temp_buffer_ptr_check_sum_ = 0; - // Count of outstanding temp buffers. - int temp_buffer_count_ = 0; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SINGLE_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h b/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h deleted file mode 100644 index 01c06a4a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/benchmarks/micro_benchmark.h +++ /dev/null @@ -1,95 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ -#define TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ - -#include - -#include "tensorflow/lite/micro/micro_log.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/micro/micro_profiler_interface.h" -#include "tensorflow/lite/micro/micro_resource_variable.h" -#include "tensorflow/lite/micro/micro_time.h" -#include "tensorflow/lite/micro/recording_micro_interpreter.h" - -namespace tflite_micro { - -template -class MicroBenchmarkRunner { - public: - // The lifetimes of model, op_resolver, tensor_arena, profiler must exceed - // that of the created MicroBenchmarkRunner object. - MicroBenchmarkRunner(const uint8_t* model, - const tflite_micro::MicroOpResolver* op_resolver, - uint8_t* tensor_arena, int tensor_arena_size, - MicroProfilerInterface* profiler, - int num_resource_variables = 0) - : allocator_( - RecordingMicroAllocator::Create(tensor_arena, tensor_arena_size)), - interpreter_( - GetModel(model), *op_resolver, allocator_, - MicroResourceVariables::Create(allocator_, num_resource_variables), - profiler) { - interpreter_.AllocateTensors(); - } - - void RunSingleIteration() { - // Run the model on this input and make sure it succeeds. - TfLiteStatus invoke_status = interpreter_.Invoke(); - if (invoke_status == kTfLiteError) { - MicroPrintf("Invoke failed."); - } - } - - int NumInputs() { return interpreter_.inputs().size(); } - - void SetRandomInput(const int random_seed, int input_index = 0) { - // The pseudo-random number generator is initialized to a constant seed - std::srand(random_seed); - TfLiteTensor* input = interpreter_.input(input_index); - - // Pre-populate input tensor with random values. - int input_length = input->bytes / sizeof(inputT); - inputT* input_values = tflite_micro::GetTensorData(input); - for (int i = 0; i < input_length; i++) { - // Pre-populate input tensor with a random value based on a constant seed. - input_values[i] = static_cast( - std::rand() % (std::numeric_limits::max() - - std::numeric_limits::min() + 1)); - } - } - - void SetInput(const inputT* custom_input, int input_index = 0) { - TfLiteTensor* input = interpreter_.input(input_index); - inputT* input_buffer = tflite_micro::GetTensorData(input); - int input_length = input->bytes / sizeof(inputT); - for (int i = 0; i < input_length; i++) { - input_buffer[i] = custom_input[i]; - } - } - - void PrintAllocations() const { - interpreter_.GetMicroAllocator().PrintAllocations(); - } - - private: - tflite_micro::RecordingMicroAllocator* allocator_; - tflite_micro::RecordingMicroInterpreter interpreter_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_BENCHMARKS_MICRO_BENCHMARK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/compatibility.h b/new_ai_tools/include/tensorflow/lite/micro/compatibility.h deleted file mode 100644 index 49acb28f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/compatibility.h +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ -#define TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ - -// C++ will automatically create class-specific delete operators for virtual -// objects, which by default call the global delete function. For embedded -// applications we want to avoid this, and won't be calling new/delete on these -// objects, so we need to override the default implementation with one that does -// nothing to avoid linking in ::delete(). -// This macro needs to be included in all subclasses of a virtual base class in -// the private section. -#ifdef TF_LITE_STATIC_MEMORY -#define TF_LITE_REMOVE_VIRTUAL_DELETE \ - void operator delete(void* p) {} -#else -#define TF_LITE_REMOVE_VIRTUAL_DELETE -#endif - -#endif // TENSORFLOW_LITE_MICRO_COMPATIBILITY_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h b/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h deleted file mode 100644 index c1afd19a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ -#define TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ - -// The application layer must implement and register a callback before calling -// the network in a way similar to -// -// void debug_log_printf(const char* s) -// { -// printf(s); -// } -// -// int main(void) -// { -// // Register callback for printing debug log -// RegisterDebugLogCallback(debug_log_printf); -// -// // now call the network -// TfLiteStatus invoke_status = interpreter->Invoke(); -// } - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -typedef void (*DebugLogCallback)(const char* s); - -// Registers and application-specific callback for debug logging. It must be -// called before the first call to DebugLog(). -void RegisterDebugLogCallback(DebugLogCallback callback); - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus - -#endif // TENSORFLOW_LITE_MICRO_CORTEX_M_GENERIC_DEBUG_LOG_CALLBACK_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/debug_log.h b/new_ai_tools/include/tensorflow/lite/micro/debug_log.h deleted file mode 100644 index c0cf6995..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/debug_log.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ -#define TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ - -#ifdef __cplusplus -#include -#else -#include -#endif // __cplusplus - -#ifdef __cplusplus -extern "C" { -#endif // __cplusplus - -// This function should be implemented by each target platform, and provide a -// way for strings to be output to some text stream. For more information, see -// the tensorflow/lite/micro/debug_log.cc file. This function should support -// standard C/C++ stdio style formatting operations. -void DebugLog(const char* format, va_list args); - -#ifdef __cplusplus -} // extern "C" -#endif // __cplusplus - -#endif // TENSORFLOW_LITE_MICRO_DEBUG_LOG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h b/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h deleted file mode 100644 index 9d5b0462..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/micro_speech/micro_model_settings.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ - -// The following values are derived from values used during model training. -// If you change the way you preprocess the input, update all these constants. -constexpr int kAudioSampleFrequency = 16000; -constexpr int kFeatureSize = 40; -constexpr int kFeatureCount = 49; -constexpr int kFeatureElementCount = (kFeatureSize * kFeatureCount); -constexpr int kFeatureStrideMs = 20; -constexpr int kFeatureDurationMs = 30; - -// Variables for the model's output categories. -constexpr int kCategoryCount = 4; -constexpr const char* kCategoryLabels[kCategoryCount] = { - "silence", - "unknown", - "yes", - "no", -}; - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_MODEL_SETTINGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h deleted file mode 100644 index 3eb62e69..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/expected_output_data.h +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ - -#ifdef ETHOS_U -static unsigned char expected_output_data[] = {143, 113}; -#else -static unsigned char expected_output_data[] = { - 0x07, 0xf3, 0x16, 0xf3, 0x3a, 0x1f, 0xb1, 0x45, 0xce, 0x12, 0xc2, 0xbb, - 0xf5, 0x47, 0x45, 0x91, 0x13, 0x21, 0xc7, 0x07, 0xa2, 0x2c, 0xf2, 0xf7, - 0xd1, 0xe1, 0x08, 0xf5, 0x0a, 0x58, 0x40, 0xb0, 0xd9, 0xf1, 0x37, 0x1e, - 0xf1, 0x0d, 0xea, 0x05, 0xf9, 0xee, 0xfa, 0xca, 0x37, 0xb9, 0xc4, 0xd8, - 0x15, 0xe3, 0xd7, 0xd3, 0x36, 0x14, 0xb7, 0xed, 0x43, 0xfa, 0x29, 0xbe, - 0xff, 0xdb, 0x38, 0xc7, 0x00, 0xf1, 0x06, 0xbe, 0x31, 0x5b, 0xe9, 0x02, - 0xf0, 0x07, 0x13, 0x25, 0xe1, 0xf0, 0x0e, 0x12, 0x17, 0xd2, 0x51, 0x48, - 0x2a, 0x24, 0xf2, 0x0a, 0x3d, 0x05, 0x0b, 0x67, 0xc3, 0xea, 0x0a, 0xbf, - 0x22, 0x40, 0x20, 0x18, 0x54, 0xd3, 0x31, 0xef, 0xe7, 0x04, 0x3c, 0xf6, - 0xcf, 0xe2, 0x79, 0xed, 0xec, 0xff, 0xfa, 0xcf, 0xc2, 0x42, 0x1f, 0x0d, - 0xf8, 0xc2, 0x36, 0x1d, 0x09, 0x01, 0xd2, 0x09, 0xda, 0xdf, 0xe8, 0x1f, - 0x06, 0xdf, 0xf7, 0x3c, 0x4d, 0xe5, 0xb4, 0xc1, 0xfa, 0x03, 0x22, 0xbc, - 0x08, 0xea, 0x72, 0x3a, 0xc8, 0x0b, 0xe6, 0x1c, 0x00, 0xef, 0xf1, 0x34, - 0x01, 0xbe, 0xf7, 0x92, 0xe6, 0xc1, 0xe3, 0x25, 0xe5, 0x4c, 0xf1, 0xbb, - 0x6f, 0x07, 0x63, 0x80, 0x2a, 0xae, 0x46, 0xf1, 0x3c, 0x01, 0xe8, 0xda, - 0xed, 0x15, 0xfc, 0x56, 0x18, 0x16, 0x16, 0xc1, 0xf5, 0xef, 0x64, 0x03, - 0xa5, 0x0c, 0x15, 0x25, 0xe0, 0x51, 0x23, 0xec, 0x31, 0xfe, 0xe9, 0xd9, - 0x0c, 0x09, 0x42, 0xd8, 0xbc, 0x15, 0xe1, 0x21, 0xe4, 0x09, 0x4f, 0xb5, - 0xd6, 0x0c, 0x04, 0x20, 0xdf, 0x99, 0x2e, 0xe8, 0x06, 0xad, 0xf5, 0x04, - 0xf7, 0x1b, 0x5b, 0x63, 0xaa, 0xf3, 0xf0, 0xfc, 0x05, 0x35, 0xf1, 0x1d, - 0x40, 0x2d, 0xf6, 0xfa, 0xdd, 0x07, 0xfe, 0x32, 0x6d, 0x1b, 0x01, 0x31, - 0x39, 0xcb, 0x22, 0xf8}; -#endif - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_EXPECTED_OUTPUT_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h b/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h deleted file mode 100644 index 460d0ca5..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/network_tester/input_data.h +++ /dev/null @@ -1,108 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_INPUT_DATA_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_INPUT_DATA_H_ - -static const int input_data_len = 1024; -static const unsigned char input_data[] = { - 0x2c, 0x8a, 0xff, 0x0c, 0xaf, 0x2a, 0x44, 0x17, 0xf5, 0x26, 0x96, 0x37, - 0x40, 0x4c, 0xa1, 0x58, 0xc3, 0x33, 0xce, 0x1a, 0x7b, 0xd2, 0x22, 0x5b, - 0x43, 0xf6, 0xfd, 0x0b, 0xe7, 0xfd, 0x65, 0x58, 0x89, 0x24, 0xf4, 0xec, - 0x53, 0x5e, 0x21, 0x1f, 0x95, 0xd1, 0xd9, 0x25, 0x72, 0x56, 0xe6, 0xe2, - 0xa4, 0x37, 0x85, 0xf0, 0xd7, 0xba, 0xab, 0xcc, 0xc6, 0xbc, 0xcb, 0x64, - 0x58, 0x3d, 0x04, 0x8e, 0xd8, 0x1a, 0x32, 0x76, 0x0c, 0x4d, 0x4c, 0xc5, - 0xba, 0xb9, 0xa9, 0xe2, 0x41, 0xc2, 0xc8, 0xfa, 0x66, 0xfd, 0x2e, 0x4a, - 0xa7, 0xca, 0x6a, 0x4f, 0xd7, 0x28, 0xe5, 0x07, 0x2e, 0x48, 0x5f, 0xfa, - 0xd8, 0xde, 0xeb, 0x11, 0xd1, 0x0b, 0x0d, 0xe4, 0x25, 0x66, 0x73, 0x6c, - 0x99, 0xc2, 0x89, 0x56, 0xcd, 0xeb, 0xaf, 0x92, 0xc8, 0x18, 0xdf, 0xd6, - 0x89, 0x9b, 0xce, 0x96, 0x14, 0x17, 0x6e, 0x25, 0xf3, 0x88, 0xad, 0x85, - 0x50, 0x93, 0xc5, 0xde, 0x73, 0x12, 0xa6, 0x55, 0x45, 0x9e, 0x88, 0x75, - 0x7e, 0xc7, 0xb5, 0x47, 0xcf, 0x87, 0x6e, 0xa3, 0x2f, 0x9d, 0x39, 0x5e, - 0x40, 0x89, 0x3e, 0x5e, 0xd2, 0x3b, 0x06, 0x7a, 0xe3, 0xe0, 0xbe, 0xf9, - 0x58, 0x27, 0x15, 0x4c, 0x31, 0xfc, 0x88, 0x4d, 0x73, 0x02, 0xa3, 0xf6, - 0x9d, 0x83, 0xbe, 0x05, 0x13, 0x42, 0x50, 0x47, 0x13, 0x8c, 0x4b, 0x2d, - 0x0e, 0xbb, 0xc7, 0x9e, 0x27, 0xe1, 0xfb, 0x38, 0xa0, 0x5d, 0x51, 0x23, - 0x41, 0xbf, 0x7f, 0x14, 0x89, 0x4d, 0x32, 0xa4, 0x39, 0xca, 0x11, 0x09, - 0xff, 0x52, 0x55, 0x71, 0xa0, 0x22, 0xb4, 0x42, 0x9f, 0x5a, 0x17, 0x05, - 0x4a, 0x42, 0x9c, 0x9b, 0x74, 0xc8, 0xa7, 0xea, 0x17, 0x60, 0xbd, 0xf9, - 0x23, 0x6a, 0x39, 0xc3, 0x7e, 0xf9, 0x33, 0xaf, 0x4b, 0xad, 0xb4, 0x46, - 0xf2, 0x2b, 0x26, 0x3c, 0x37, 0xc4, 0x46, 0xf4, 0x9c, 0x58, 0xef, 0xb7, - 0xa2, 0x63, 0x04, 0x11, 0x00, 0x28, 0x1d, 0xa2, 0x00, 0xe8, 0x4f, 0x84, - 0x24, 0x67, 0x7a, 0xd2, 0xb5, 0xdc, 0x9d, 0x1e, 0x05, 0x97, 0xd1, 0xa6, - 0xa6, 0xce, 0x32, 0x1c, 0x68, 0x8d, 0x6a, 0xb8, 0x74, 0xe2, 0x6f, 0x1d, - 0x91, 0x71, 0x70, 0xe2, 0xcf, 0xf9, 0x19, 0x71, 0x04, 0x31, 0x90, 0x67, - 0xe9, 0x46, 0x0b, 0x2e, 0xaa, 0x6c, 0xaf, 0xf3, 0x3a, 0x35, 0x88, 0xdc, - 0x9f, 0x3a, 0x71, 0x1c, 0xf8, 0xd3, 0x61, 0xef, 0x81, 0xd1, 0x80, 0x67, - 0xc1, 0x75, 0x17, 0x32, 0x67, 0xef, 0xea, 0x99, 0x29, 0x6f, 0xea, 0x8f, - 0xb9, 0xe8, 0xc5, 0x78, 0xa3, 0xb9, 0x31, 0x2a, 0xe6, 0xaf, 0xb4, 0x27, - 0xf7, 0x6c, 0x2e, 0x2b, 0x8b, 0x3b, 0x40, 0xab, 0x2e, 0x21, 0x5b, 0xb5, - 0xd2, 0x5c, 0x2f, 0xdb, 0xdb, 0xd0, 0x81, 0xa1, 0x00, 0x77, 0xac, 0x40, - 0x0e, 0x69, 0x3f, 0xd0, 0xe3, 0x4a, 0x1a, 0x1b, 0xb5, 0xa6, 0x9c, 0xdd, - 0x0c, 0xcb, 0xb6, 0xd3, 0xf9, 0xea, 0x78, 0x11, 0x2a, 0xbf, 0x56, 0x89, - 0xd4, 0xb0, 0xc8, 0xf0, 0x4b, 0x48, 0xdf, 0x22, 0xc4, 0x91, 0x86, 0x7d, - 0x86, 0x33, 0xfd, 0x75, 0x44, 0x1b, 0x9f, 0x9a, 0xaf, 0xc4, 0x9d, 0x27, - 0xff, 0x72, 0xf8, 0xb5, 0x74, 0xb7, 0x13, 0x22, 0x03, 0x14, 0xcb, 0xa9, - 0x4c, 0x73, 0x4b, 0x7e, 0xe4, 0x9b, 0xb2, 0x27, 0x34, 0xa4, 0x74, 0x14, - 0x68, 0x37, 0x57, 0xc0, 0xce, 0x8f, 0x94, 0xea, 0x0f, 0xa4, 0xe0, 0xf7, - 0x14, 0x46, 0x05, 0xf9, 0x63, 0x2c, 0x12, 0xbe, 0x3a, 0xdb, 0x1e, 0x1f, - 0x97, 0xbb, 0x32, 0xa8, 0x4f, 0x2a, 0x07, 0xd6, 0x0d, 0x0b, 0xc1, 0x9c, - 0xf5, 0x11, 0x3c, 0x2c, 0xd5, 0xb7, 0x85, 0x28, 0xb0, 0xa2, 0xb4, 0xd1, - 0xb1, 0x75, 0xdf, 0xa3, 0xc5, 0x26, 0xc1, 0x47, 0x29, 0xb8, 0xd3, 0xb2, - 0x23, 0x96, 0x97, 0x73, 0x40, 0x45, 0xe4, 0xde, 0xdf, 0xdc, 0x1b, 0x29, - 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All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ - -alignas(4) const unsigned char network_model[] = { - 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00, - 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, - 0x00, 0x00, 0x04, 0x00, 0x12, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, - 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0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, - 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x3e, 0x68, 0x00, 0x3c, - 0x01, 0x00, 0x00, 0x00, 0xe9, 0xe6, 0x7f, 0x3f, 0x01, 0x00, 0x00, 0x00, - 0x70, 0xb8, 0x7f, 0xbf, 0x16, 0x00, 0x00, 0x00, 0x64, 0x61, 0x74, 0x61, - 0x30, 0x2f, 0x50, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, 0x64, 0x65, - 0x72, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, - 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, - 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, - 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xd8, 0xff, 0xff, 0xff, - 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, - 0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, - 0x0c, 0x00, 0x00, 0x00, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, - 0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, - 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x04}; -const unsigned int network_model_len = 1720; - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_NETWORK_TESTER_NETWORK_MODEL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h deleted file mode 100644 index d2d945f4..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/detection_responder.h +++ /dev/null @@ -1,32 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Provides an interface to take an action based on the output from the person -// detection model. - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ - -#include "tensorflow/lite/c/common.h" - -// Called every time the results of a person detection run are available. The -// `person_score` has the numerical confidence that the captured image contains -// a person, and `no_person_score` has the numerical confidence that the image -// does not contain a person. Typically if person_score > no person score, the -// image is considered to contain a person. This threshold may be adjusted for -// particular applications. -void RespondToDetection(int8_t person_score, int8_t no_person_score); - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_DETECTION_RESPONDER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h deleted file mode 100644 index f3799922..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/image_provider.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ - -#include "tensorflow/lite/c/common.h" - -// This is an abstraction around an image source like a camera, and is -// expected to return 8-bit sample data. The assumption is that this will be -// called in a low duty-cycle fashion in a low-power application. In these -// cases, the imaging sensor need not be run in a streaming mode, but rather can -// be idled in a relatively low-power mode between calls to GetImage(). The -// assumption is that the overhead and time of bringing the low-power sensor out -// of this standby mode is commensurate with the expected duty cycle of the -// application. The underlying sensor may actually be put into a streaming -// configuration, but the image buffer provided to GetImage should not be -// overwritten by the driver code until the next call to GetImage(); -// -// The reference implementation can have no platform-specific dependencies, so -// it just returns a static image. For real applications, you should -// ensure there's a specialized implementation that accesses hardware APIs. -TfLiteStatus GetImage(int image_width, int image_height, int channels, - int8_t* image_data); - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_IMAGE_PROVIDER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h deleted file mode 100644 index 2620097a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/main_functions.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ - -// Expose a C friendly interface for main functions. -#ifdef __cplusplus -extern "C" { -#endif - -// Initializes all data needed for the example. The name is important, and needs -// to be setup() for Arduino compatibility. -void setup(); - -// Runs one iteration of data gathering and inference. This should be called -// repeatedly from the application code. The name needs to be loop() for Arduino -// compatibility. -void loop(); - -#ifdef __cplusplus -} -#endif - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MAIN_FUNCTIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h b/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h deleted file mode 100644 index f94d58ed..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/examples/person_detection/model_settings.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ -#define TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ - -// Keeping these as constant expressions allow us to allocate fixed-sized arrays -// on the stack for our working memory. - -// All of these values are derived from the values used during model training, -// if you change your model you'll need to update these constants. -constexpr int kNumCols = 96; -constexpr int kNumRows = 96; -constexpr int kNumChannels = 1; - -constexpr int kMaxImageSize = kNumCols * kNumRows * kNumChannels; - -constexpr int kCategoryCount = 2; -constexpr int kPersonIndex = 1; -constexpr int kNotAPersonIndex = 0; -extern const char* kCategoryLabels[kCategoryCount]; - -#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_PERSON_DETECTION_MODEL_SETTINGS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h b/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h deleted file mode 100644 index cd2f9e6c..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/fake_micro_context.h +++ /dev/null @@ -1,70 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ -#define TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ - -#include "tensorflow/lite/micro/micro_context.h" -#include "tensorflow/lite/micro/micro_graph.h" - -namespace tflite_micro { -// A fake of MicroContext for kernel util tests. -// TODO(b/272759060): FakeMicroContext currently inherits from MicroContext. -// Which allow tests to use functions from MicroContext that weren't added to -// FakeMicroContext in tests. This should be looked into further. - -class FakeMicroContext : public MicroContext { - public: - ~FakeMicroContext() = default; - - FakeMicroContext(TfLiteTensor* tensors, SingleArenaBufferAllocator* allocator, - MicroGraph* micro_graph); - - void* AllocatePersistentBuffer(size_t bytes) override; - TfLiteStatus RequestScratchBufferInArena(size_t bytes, - int* buffer_index) override; - void* GetScratchBuffer(int buffer_index) override; - - TfLiteTensor* AllocateTempTfLiteTensor(int tensor_index) override; - void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override; - bool IsAllTempTfLiteTensorDeallocated(); - - uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override; - void DeallocateTempBuffer(uint8_t* buffer) override; - - TfLiteEvalTensor* GetEvalTensor(int tensor_index) override; - - TfLiteStatus set_external_context(void* external_context_payload) override; - void* external_context() override; - MicroGraph& graph() override; - - private: - static constexpr int kNumScratchBuffers_ = 12; - - MicroGraph& graph_; - int scratch_buffer_count_ = 0; - uint8_t* scratch_buffers_[kNumScratchBuffers_]; - - TfLiteTensor* tensors_; - int allocated_temp_count_ = 0; - - SingleArenaBufferAllocator* allocator_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_FAKE_MICRO_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h b/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h deleted file mode 100644 index 374be3fa..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/flatbuffer_utils.h +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ -#define THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ - -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/flexbuffers.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { -// Kernels use flexbuffers::Map to pack their init parameters in a tflite file, -// with the parameter names as map keys and the parameter values as the -// corresponding map values. -// Accessing the map values using the flexbuffers:Map class is inline heavy, -// which can cause the code size to bloat beyond what's reasonable for a micro -// application. Use this class instead, when possible. -// FlexbufferWrapper takes advantage of the following properties of -// flexbuffers::Map: -// 1. It can be viewed as a flexbuffers::Vector of the values. -// 2. The values in the vector are ordered alphabetically by their keys. -// 3. All integer and Boolean values are stored as 64-bit numbers. -// 4. All floating point values are stored as double precision numbers. -// The properties are mentioned in the flexbuffers docs, but we rely on -// a unit test to catch design changes. -class FlexbufferWrapper : public flexbuffers::Vector { - public: - // Construct with a serialized flexbuffer 'buffer' of 'size' bytes - explicit FlexbufferWrapper(const uint8_t* buffer, size_t size); - int64_t ElementAsInt64(size_t i) const; - uint64_t ElementAsUInt64(size_t i) const; - int32_t ElementAsInt32(size_t i) const; - bool ElementAsBool(size_t i) const; - double ElementAsDouble(size_t i) const; - float ElementAsFloat(size_t i) const; -}; - -// Return the number of operators in a subgraph tflite -uint32_t NumSubgraphOperators(const SubGraph* subgraph); -uint32_t NumSubgraphOperators(const Model* model, int subgraph_idx); - -// Converts a flatbuffer array to a TfLiteArray. -// TODO(b/188459715): These function convert a const input to a non-const via a -// const_cast. It is unclear exactly why this is required. -TfLiteIntArray* FlatBufferVectorToTfLiteTypeArray( - const flatbuffers::Vector* flatbuffer_array); -TfLiteFloatArray* FlatBufferVectorToTfLiteTypeArray( - const flatbuffers::Vector* flatbuffer_array); - -} // namespace tflite_micro - -#endif // THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_FLATBUFFER_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h deleted file mode 100644 index 5b8c016d..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/activation_utils.h +++ /dev/null @@ -1,57 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/internal/max.h" -#include "tensorflow/lite/kernels/internal/min.h" - -namespace tflite_micro { -namespace ops { -namespace micro { - -// Returns the floating point value for a fused activation: -inline float ActivationValFloat(TfLiteFusedActivation act, float a) { - switch (act) { - case kTfLiteActNone: - return a; - case kTfLiteActRelu: - return TfLiteMax(0.0f, a); - case kTfLiteActReluN1To1: - return TfLiteMax(-1.0f, TfLiteMin(a, 1.0f)); - case kTfLiteActRelu6: - return TfLiteMax(0.0f, TfLiteMin(a, 6.0f)); - case kTfLiteActTanh: - return std::tanh(a); - case kTfLiteActSignBit: - return std::signbit(a); - case kTfLiteActSigmoid: - return 1.0f / (1.0f + std::exp(-a)); - } - return 0.0f; // To indicate an unsupported activation (i.e. when a new fused - // activation is added to the enum and not handled here). -} - -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATION_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h deleted file mode 100644 index 0f83c033..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/activations.h +++ /dev/null @@ -1,68 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -extern const int kActivationsInputTensor; -extern const int kActivationsOutputTensor; - -struct ReluOpData { - ReluParams params; -}; - -struct Relu6OpData { - int8_t six_int8; - int8_t zero_int8; -}; - -template -void ReluQuantized(const ReluOpData& data, const RuntimeShape& input_shape, - const RuntimeShape& output_shape, const T* input_data, - T* output_data); - -template -void CalculateReluOpData(const TfLiteTensor* input, TfLiteTensor* output, - ReluOpData* data); - -void ReluFloat(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data); - -void Relu6Float(const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& output_shape, float* output_data); - -void Relu6Quantized(int8_t lower, int8_t upper, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& output_shape, - int8_t* output_data); - -void Relu6Quantized(int16_t lower, int16_t upper, const RuntimeShape& input_shape, - const int16_t* input_data, const RuntimeShape& output_shape, - int16_t* output_data); - -TfLiteStatus ReluPrepare(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus Relu6Prepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ACTIVATIONS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h deleted file mode 100644 index 201ec9f8..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/add.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -extern const int kAddInputTensor1; -extern const int kAddInputTensor2; -extern const int kAddOutputTensor; - -struct OpDataAdd { - bool requires_broadcast; - - // These fields are used in both the general 8-bit -> 8bit quantized path, - // and the special 16-bit -> 16bit quantized path - int input1_shift; - int input2_shift; - int32_t output_activation_min; - int32_t output_activation_max; - - // These fields are used only in the general 8-bit -> 8bit quantized path - int32_t input1_multiplier; - int32_t input2_multiplier; - int32_t output_multiplier; - int output_shift; - int left_shift; - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; - - // Used only for float evals: - float output_activation_min_f32; - float output_activation_max_f32; -}; - -TfLiteStatus CalculateOpDataAdd(TfLiteContext* context, TfLiteAddParams* params, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteTensor* output, OpDataAdd* data); - -TfLiteStatus AddPrepare(TfLiteContext* context, TfLiteNode* node); - -// Generic must define registration function. -TFLMRegistration Register_ADD(); - -#if defined(CMSIS_NN) -TFLMRegistration Register_ADD_INT8(); - -TFLMRegistration Register_ADD_INT16(); -#else -// Fallback registration -inline TFLMRegistration Register_ADD_INT8() { return Register_ADD(); } - -inline TFLMRegistration Register_ADD_INT16() { return Register_ADD(); } -#endif -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h deleted file mode 100644 index 95fb2b2d..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h +++ /dev/null @@ -1,141 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "mli_api.h" // NOLINT - -namespace tflite_micro { - -// Convolution specialized function. -typedef mli_status (*conv_func_ptr)(const mli_tensor* /*in*/, - const mli_tensor* /*weights*/, - const mli_tensor* /*bias*/, - const mli_conv2d_cfg* /*cfg*/, - mli_tensor* /*out*/); - -#ifdef MLI_2_0 -conv_func_ptr __attribute__((weak)) -mli_krn_conv2d_hwcn(const mli_tensor* weights) { - int filter_w = weights->shape[KRNL_W_DIM_HWCN]; - int filter_h = weights->shape[KRNL_H_DIM_HWCN]; - - if (filter_w == 1 && filter_h == 1) { - return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k1x1; - } else if (filter_w == 3 && filter_h == 3) { - return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k3x3; - } else if (filter_w == 5 && filter_h == 5) { - return mli_krn_conv2d_hwcn_sa8_sa8_sa32_k5x5; - } else { - return mli_krn_conv2d_hwcn_sa8_sa8_sa32; - } -} -#else -conv_func_ptr __attribute__((weak)) -mli_krn_conv2d_hwcn(const mli_tensor* weights, const mli_conv2d_cfg* cfg) { - return mli_krn_conv2d_nhwc_sa8_sa8_sa32; -} -#endif - -// Depthwise convolution specialized function. -typedef mli_status (*depthwise_func_ptr)(const mli_tensor* /*in*/, - const mli_tensor* /*weights*/, - const mli_tensor* /*bias*/, - const mli_conv2d_cfg* /*cfg*/, - mli_tensor* /*out*/); - -#ifdef MLI_2_0 -depthwise_func_ptr __attribute__((weak)) -mli_krn_depthwise_conv2d(const mli_tensor* weights) { - int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N]; - int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N]; - - if (filter_w == 3 && filter_h == 3) { - return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k3x3; - } else if (filter_w == 5 && filter_h == 5) { - return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32_k5x5; - } else { - return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32; - } -} -#else -depthwise_func_ptr __attribute__((weak)) -mli_krn_depthwise_conv2d(const mli_tensor* weights, const mli_conv2d_cfg* cfg) { - return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32; -} -#endif - -#ifdef MLI_2_0 -depthwise_func_ptr __attribute__((weak)) -mli_krn_group_conv2d(const mli_tensor* weights) { - int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N]; - int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N]; - - if (filter_w == 3 && filter_h == 3) { - return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32_k3x3; - } else if (filter_w == 5 && filter_h == 5) { - return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32_k5x5; - } else { - return mli_krn_group_conv2d_hwcn_sa8_sa8_sa32; - } -} -#endif - -// Pooling specialized functions. -typedef mli_status (*pooling_func_ptr)(const mli_tensor* /*in*/, - const mli_pool_cfg* /*cfg*/, - mli_tensor* /*out*/); - -#ifdef MLI_2_0 -pooling_func_ptr __attribute__((weak)) -mli_krn_avepool(const mli_pool_cfg* cfg) { - int filter_w = cfg->kernel_width; - int filter_h = cfg->kernel_height; - - if (filter_w == 2 && filter_h == 2) { - return mli_krn_avepool_hwc_sa8_k2x2; - } else if (filter_w == 3 && filter_h == 3) { - return mli_krn_avepool_hwc_sa8_k3x3; - } else { - return mli_krn_avepool_hwc_sa8; - } -} -#else -pooling_func_ptr __attribute__((weak)) -mli_krn_avepool(const mli_pool_cfg* cfg) { - return mli_krn_avepool_hwc_sa8; -} -#endif - -#ifdef MLI_2_0 -pooling_func_ptr __attribute__((weak)) -mli_krn_maxpool(const mli_pool_cfg* cfg) { - int filter_w = cfg->kernel_width; - int filter_h = cfg->kernel_height; - - if (filter_w == 2 && filter_h == 2) { - return mli_krn_maxpool_hwc_sa8_k2x2; - } else if (filter_w == 3 && filter_h == 3) { - return mli_krn_maxpool_hwc_sa8_k3x3; - } else { - return mli_krn_maxpool_hwc_sa8; - } -} -#else -pooling_func_ptr __attribute__((weak)) -mli_krn_maxpool(const mli_pool_cfg* cfg) { - return mli_krn_maxpool_hwc_sa8; -} -#endif - -} // namespace tflite_micro \ No newline at end of file diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h deleted file mode 100644 index 1977b2e2..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_interface.h +++ /dev/null @@ -1,75 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_INTERFACE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_INTERFACE_H_ - -#include "mli_api.h" // NOLINT -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -namespace tflite_micro { -namespace ops { -namespace micro { - -// Abstracts access to mli_tensor fields to use different versions of MLI -// Library (1.x and 2.x) -// Example: -// ops::micro::MliTensorInterface mli_in = -// ops::micro::MliTensorInterface(static_cast( -// context->AllocatePersistentBuffer(context, sizeof(mli_tensor)))); - -class MliTensorInterface { - public: - // Make sure that lifetime of MliTensorInterface instance isn't bigger than - // related mli_tensor. - MliTensorInterface(mli_tensor* tensor) : tensor_(tensor){}; - MliTensorInterface() = default; - ~MliTensorInterface() = default; - - template - T* Data(); - template - T Scale(); - template - T ZeroPoint(); - template - T ScaleFracBits(); - mli_tensor* MliTensor(); - const mli_tensor* MliTensor() const; - int32_t* Dim(); - uint32_t* Rank(); - uint32_t* Shape(); - const uint32_t* Shape() const; - const uint32_t* DataCapacity() const; - uint32_t* ScaleCapacity(); - mli_element_type* ElType(); - uint32_t* ScaleFracBitsCapacity(); - int32_t* MemStride(); - uint32_t* ZeroPointCapacity(); - - template - void SetData(T* data, uint32_t capacity) const; - void SetScale(float fscale); - void SetScalePerChannel(float* fscale, const int num_channels); - void SetElType(TfLiteType type); - - private: - mli_tensor* tensor_; -}; - -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h deleted file mode 100644 index f92eb98a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_slicers.h +++ /dev/null @@ -1,56 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ - -#include "mli_api.h" // NOLINT -namespace tflite_micro { -namespace ops { -namespace micro { - -class TensorSlicer { - public: - TensorSlicer(const mli_tensor* full_tensor, int slice_dim, int slice_size, - int padding_pre = 0, int padding_post = 0, int overlap = 0, - bool interleave_mode = false); - ~TensorSlicer() = default; - - void Next(); - bool Done(); - int GetPaddingPre(); - int GetPaddingPost(); - - mli_tensor* Sub(); - - // Default constructor is deleted - TensorSlicer() = delete; - - private: - const mli_tensor* full_tensor_; - mli_tensor sub_tensor_; - mli_sub_tensor_cfg sub_cfg_; - bool done_; - int sliceDim_; - int pad_pre_, pad_post_, overlap_; - int actual_padding_pre, actual_padding_post; - - void ComputeSubTensor(); -}; - -} // namespace micro -} // namespace ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_SLICERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h deleted file mode 100644 index 67c84e93..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/mli_tf_utils.h +++ /dev/null @@ -1,310 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ - -#include "mli_api.h" // NOLINT -#include "mli_interface.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_log.h" - -#define KRNL_C_DIM_NHWC 0 // output channels - -namespace tflite_micro { -namespace ops { -namespace micro { - -inline void ConvertToMliTensorData(const TfLiteTensor* tfT, - MliTensorInterface* mliT, - bool is_bias_tensor) { - // Data is NULL until MliTensorAttachBuffer is called. - mliT->SetElType(tfT->type); - if (tfT->type == kTfLiteInt8) { - mliT->SetData(nullptr, tfT->bytes); - } else if (tfT->type == kTfLiteInt32) { - mliT->SetData(nullptr, tfT->bytes); - } else { - MicroPrintf("Wrong data type. Expected int8_t or int32_t."); - TFLITE_ABORT; - } - const int32_t dims_count = GetTensorShape(tfT).DimensionsCount(); - *mliT->Rank() = is_bias_tensor ? 1 : dims_count; - - int mli_tensor_memstride = 1; - if (is_bias_tensor) { - mliT->Shape()[0] = GetTensorShape(tfT).Dims(dims_count - 1); - mliT->MemStride()[0] = mli_tensor_memstride; - } else { - for (int i = dims_count - 1; i >= 0; --i) { - mliT->Shape()[i] = GetTensorShape(tfT).Dims(i); - mliT->MemStride()[i] = mli_tensor_memstride; - mli_tensor_memstride *= GetTensorShape(tfT).Dims(i); - } - } -} - -inline void ConvertToMliQuantParams(const TfLiteTensor* tfT, - MliTensorInterface* mliT) { - *mliT->Dim() = -1; -#ifdef MLI_2_0 - *mliT->ZeroPointCapacity() = 0; -#endif - *mliT->ZeroPoint() = tfT->params.zero_point; - float fscale = tfT->params.scale; - mliT->SetScale(fscale); -} - -inline void ConvertToMliQuantParamsPerChannel(const TfLiteTensor* tfT, - MliTensorInterface* mliT, - bool is_bias_tensor) { - // mli tensor scale and zero_point arrays should be allocated at this point -#ifdef MLI_2_0 - TFLITE_DCHECK_NE(*mliT->Scale(), 0); - TFLITE_DCHECK_NE(*mliT->ZeroPoint(), 0); -#else - TFLITE_DCHECK_NE(*mliT->Scale(), 0); - TFLITE_DCHECK_NE(*mliT->ZeroPoint(), 0); -#endif - - // get per channel quantization parameters - const auto* affine_quantization = - reinterpret_cast(tfT->quantization.params); - int32_t quantized_dimension = - is_bias_tensor ? 0 : affine_quantization->quantized_dimension; - const int num_channels = mliT->Shape()[quantized_dimension]; - - *mliT->Dim() = quantized_dimension; - - // set capacities -#ifdef MLI_2_0 - *mliT->ScaleFracBitsCapacity() = num_channels * sizeof(int8_t); - *mliT->ScaleCapacity() = num_channels * sizeof(int16_t); - *mliT->ZeroPointCapacity() = num_channels * sizeof(int16_t); -#endif - float* fscale = affine_quantization->scale->data; - mliT->SetScalePerChannel(fscale, num_channels); - -#ifdef MLI_2_0 - int16_t* zero_point = *mliT->ZeroPoint(); - for (int i = 0; i < num_channels; i++) { - zero_point[i] = tfT->params.zero_point; - } -#endif -} - -template -inline void MliTensorAttachBuffer(const TfLiteEvalTensor*, - const MliTensorInterface*); - -template <> -inline void MliTensorAttachBuffer(const TfLiteEvalTensor* tfT, - const MliTensorInterface* mliT) { - // "const_cast" here used to attach const data buffer to the initially - // non-const mli_tensor. This is required by current implementation of MLI - // backend and planned for redesign due to this and some other aspects. - mliT->SetData( - const_cast(tflite_micro::micro::GetTensorData(tfT)), - *mliT->DataCapacity()); -} - -template <> -inline void MliTensorAttachBuffer(const TfLiteEvalTensor* tfT, - const MliTensorInterface* mliT) { - // "const_cast" here used to attach const data buffer to the initially - // non-const mli_tensor. This is required by current implementation of MLI - // backend and planned for redesign due to this and some other aspects. - mliT->SetData( - const_cast(tflite_micro::micro::GetTensorData(tfT)), - *mliT->DataCapacity()); -} - -inline void ConvertToMliTensor(const TfLiteTensor* tfT, - MliTensorInterface* mliT) { - ConvertToMliTensorData(tfT, mliT, false); - ConvertToMliQuantParams(tfT, mliT); -} - -inline void ConvertToMliTensorPerChannel(const TfLiteTensor* tfT, - MliTensorInterface* mliT, - bool is_bias_tensor) { - ConvertToMliTensorData(tfT, mliT, is_bias_tensor); - ConvertToMliQuantParamsPerChannel(tfT, mliT, is_bias_tensor); -} - -inline void PrepareLocalTensor(mli_tensor* tensor, mli_tensor* tensor_local) { -#ifdef MLI_2_0 - int8_t* local_data = tensor_local->data.mem.pi8; - *tensor_local = *tensor; - tensor_local->data.mem.pi8 = local_data; -#else - int8_t* local_data = static_cast(tensor_local->data); - *tensor_local = *tensor; - tensor_local->data = local_data; -#endif -} - -inline void AdjustBiasTensor(MliTensorInterface* bias, MliTensorInterface* in, - MliTensorInterface* weights) { - int32_t quantized_dimension = *bias->Dim(); - const int num_channels = - quantized_dimension < 0 ? 1 : bias->Shape()[quantized_dimension]; - for (int i = 0; i < num_channels; i++) { - int32_t adjusted_bias_scale = - (*in->Scale()) * (*weights->Scale())[i]; - int in_shift = *in->ScaleFracBits(); - int w_shift = (*weights->ScaleFracBits())[i]; - int b_shift = (*bias->ScaleFracBits())[i]; - int bias_shift = in_shift + w_shift - b_shift; - (*bias->Scale())[i] = - (int16_t)(adjusted_bias_scale >> bias_shift); - } -} - -#ifdef MLI_2_0_KRNL_TEST -// Reorder an array according to given indexes. If backward is true, order of -// index array must be reversed. -inline static void reorder(uint32_t* arr, const uint8_t index[], - bool backward) { - uint32_t temp[MLI_MAX_RANK]; - for (int8_t i = 0; i < MLI_MAX_RANK; i++) { - if (backward) - temp[index[i]] = arr[i]; - else - temp[i] = arr[index[i]]; - } - for (int8_t i = 0; i < MLI_MAX_RANK; i++) { - arr[i] = temp[i]; - } -} - -// Change shape of mli tensor and recalculate mem strides. -inline void change_shape(mli_tensor* mliT, const uint8_t dim_order[]) { - reorder(mliT->shape, dim_order, false); - - // Calculate strides for new layout - int mli_tensor_memstride = 1; - for (int shape_idx = mliT->rank - 1; shape_idx >= 0; --shape_idx) { - mliT->mem_stride[shape_idx] = mli_tensor_memstride; - mli_tensor_memstride *= mliT->shape[shape_idx]; - } -} - -inline void permute_weights(const mli_tensor* weights_src, - const mli_permute_cfg* permute_cfg, - mli_tensor* weights_dst, - mli_data_container* buffer_data) { - mli_tensor buffer = {}; - buffer.el_params = weights_dst->el_params; - buffer.data = *buffer_data; - // Compare weights tensor size and avaliable buffer capacity. - int buffer_size = buffer_data->capacity; - int weights_size = mli_hlp_count_elem_num(weights_src, 0) * - mli_hlp_tensor_element_size(weights_src); - - // Need to change shape of distanation weights buffer according to permute - // dimensions order to calculate slice sizes - change_shape(weights_dst, permute_cfg->perm_dim); - - if (buffer_size >= weights_size) { - mli_mov_cfg_t copy_config; - mli_mov_cfg_for_copy(©_config); - mli_mov_tensor_sync(weights_src, ©_config, &buffer); - mli_krn_permute_sa8(&buffer, permute_cfg, weights_dst); - } else { - // Weights shape is NHWC and output (buffer) shape is HWC where N_w = C_o. - // Buffer size (H_o * W_o) must be more or equal then the weights size (H_w - // * W_w * C_w). So, this is the reason, why buffer size (output tensor) is - // divided by channel shape. - uint32_t slice_size = buffer_size / weights_src->shape[KRNL_C_DIM_NHWC]; - - mli_mov_cfg_t copy_config = {}; - uint32_t src_offsets[] = {0, 0, 0, 0}; - uint32_t src_sizes[] = {0, 0, 0, 0}; - int dst_mem_stride[] = {0, 0, 0, 0}; - - mli_tensor weights_dst_sub_tensor; - mli_sub_tensor_cfg sub_tensor_cfg = {}; - sub_tensor_cfg.sub_tensor_rank = weights_src->rank; - - // Calculate dimensions for slice accroding to buffer capacity. - // Now, after calling change_shape() function, dst weights buffer has the - // MLI layout (HWCN). This means, the innermost dimension (N) of dst weights - // tensor is equal to the innermost dimension of output tensor (N). - sub_tensor_cfg.size[weights_dst->rank - 1] = - src_sizes[weights_dst->rank - 1] = weights_src->shape[KRNL_C_DIM_NHWC]; - // Now need to calculate other shapes for weights slice. Total slice size is - // H*W*C*N, so to calculate sizes for each axis, avaliable slice size is - // divided by shape for each axis. - uint32_t slice_size_left = slice_size; - for (uint32_t i = 0; i < weights_dst->rank - 1; i++) { - sub_tensor_cfg.size[i] = src_sizes[i] = - slice_size_left / weights_dst->shape[i] > 0 ? weights_dst->shape[i] - : slice_size_left; - slice_size_left /= weights_dst->shape[i]; - slice_size_left = slice_size_left > 0 ? slice_size_left : 1; - } - // Need to reorder src tensor sizes because it is still in TFLM format - // (NHWC) and src_sizes array calculated as (HWCN). - reorder(src_sizes, permute_cfg->perm_dim, true); - - sub_tensor_cfg.offset[KRNL_C_DIM_HWCN] = src_offsets[KRNL_H_DIM_HWCN] = 0; - sub_tensor_cfg.offset[KRNL_H_DIM_HWCN] = src_offsets[KRNL_W_DIM_HWCN] = 0; - sub_tensor_cfg.offset[KRNL_W_DIM_HWCN] = src_offsets[KRNL_D_DIM_HWCN] = 0; - sub_tensor_cfg.offset[KRNL_D_DIM_HWCN] = src_offsets[KRNL_C_DIM_HWCN] = 0; - do { - do { - do { - do { - mli_mov_cfg_for_slice(©_config, (int*)src_offsets, - (int*)src_sizes, dst_mem_stride); - mli_mov_tensor_sync(weights_src, ©_config, &buffer); - - mli_hlp_create_subtensor(weights_dst, &sub_tensor_cfg, - &weights_dst_sub_tensor); - mli_krn_permute_sa8(&buffer, permute_cfg, &weights_dst_sub_tensor); - - // For each axis, it is necessary to recalculate the offsets and - // slice sizes. - sub_tensor_cfg.offset[2] = src_offsets[3] += src_sizes[3]; - src_sizes[3] = - std::min(src_sizes[3], weights_src->shape[3] - src_offsets[3]); - } while (src_offsets[3] < weights_src->shape[3]); - - sub_tensor_cfg.offset[1] = src_offsets[2] += src_sizes[2]; - src_sizes[2] = - std::min(src_sizes[2], weights_src->shape[2] - src_offsets[2]); - } while (src_offsets[2] < weights_src->shape[2]); - - sub_tensor_cfg.offset[0] = src_offsets[1] += src_sizes[1]; - src_sizes[1] = - std::min(src_sizes[1], weights_src->shape[1] - src_offsets[1]); - } while (src_offsets[1] < weights_src->shape[1]); - - sub_tensor_cfg.offset[3] = src_offsets[0] += src_sizes[0]; - src_sizes[0] = - std::min(src_sizes[0], weights_src->shape[0] - src_offsets[0]); - } while (src_offsets[0] < weights_src->shape[0]); - } -} -#endif - -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ARC_MLI_TF_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h deleted file mode 100644 index fd14cf39..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buf_mgr.h +++ /dev/null @@ -1,145 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ -#define TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ - -#include "mli_api.h" // NOLINT -#include "mli_interface.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { -namespace ops { -namespace micro { - -/** - * @brief Function to allocate scratch buffers for the convolution tensors - * - * @detail This function will update the data pointers in the 4 tensors with - * pointers to scratch buffers in fast local memory. - * - * @param context [I] pointer to TfLite context (needed for error handling) - * @param in [IO] pointer to the input tensor - * @param weights [IO] pointer to the weights tensor - * @param bias [IO] pointer to the bias tensor - * @param output [IO] pointer to the output tensor - * - * @return Tf Lite status code - */ -TfLiteStatus get_arc_scratch_buffer_for_conv_tensors( - TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* weights, - MliTensorInterface* bias, MliTensorInterface* out); - -/** - * @brief Function to allocate scratch buffers for pooling kernels with only - * input and output buffers - * - * @detail This function will update the data pointers in the 2 tensors with - * pointers to scratch buffers in fast local memory. - * - * @param context [I] pointer to TfLite context (needed for error handling) - * @param in [IO] pointer to the input tensor - * @param output [IO] pointer to the output tensor - * - * @return Tf Lite status code - */ -TfLiteStatus get_arc_scratch_buffer_for_pooling_tensors( - TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* out); - -/** - * @brief Function to allocate scratch buffers for the fully connect tensors - * - * @detail This function will update the data pointers in the 4 tensors with - * pointers to scratch buffers in fast local memory. - * - * @param context [I] pointer to TfLite context (needed for error handling) - * @param in [IO] pointer to the input tensor - * @param weights [IO] pointer to the weights tensor - * @param bias [IO] pointer to the bias tensor - * @param output [IO] pointer to the output tensor - * - * @return Tf Lite status code - */ -TfLiteStatus get_arc_scratch_buffer_for_fully_connect_tensors( - TfLiteContext* context, MliTensorInterface* in, MliTensorInterface* weights, - MliTensorInterface* bias, MliTensorInterface* out); - -/** - * @brief Function to allocate scratch buffers for the eltwise function tensors - * - * @detail This function will update the data pointers in the 3 tensors with - * pointers to scratch buffers in fast local memory. - * - * @param context [I] pointer to TfLite context (needed for error handling) - * @param in1 [IO] pointer to the first input tensor - * @param in2 [IO] pointer to the second input tensor - * @param output [IO] pointer to the output tensor - * - * @return Tf Lite status code - */ -TfLiteStatus get_arc_scratch_buffer_for_eltwise_tensors( - TfLiteContext* context, MliTensorInterface* in1, MliTensorInterface* in2, - MliTensorInterface* out); - -/** - * @brief Function to calculate slice size for io tensors - * - * @detail This function will calculate the slice size in the height dimension - * for input and output tensors. it takes into account the kernel size and the - * padding. the function will look at the capacity filed in the in and out - * tensor to determine the available buffersize. - * - * @param in [I] pointer to the input tensor - * @param out [I] pointer to the output tensor - * @param kernelHeight [I] size of the kernel in height dimension - * @param strideHeight [I] input stride in height dimension - * @param padding_top [I] number of lines with zeros at the top - * @param padding_bot [I] number of lines with zeros at the bottom - * @param inSliceHeight [O] slice size in height dimension for the input - * tensor - * @param outSliceHeight [O] slice size in height dimension for the output - * tensor - * - * @return Tf Lite status code - */ -TfLiteStatus arc_scratch_buffer_calc_slice_size_io( - const MliTensorInterface* in, const MliTensorInterface* out, - const int kernelHeight, const int strideHeight, const int padding_top, - const int padding_bot, int* in_slice_height, int* out_slice_height); - -/** - * @brief Function to calculate slice size for weight slicing - * - * @detail This function will calculate the slice size in the output channel - * dimension for weight and bias tensors. the function will look at the capacity - * filed in the weights and bias tensor to determine the available buffersize. - * - * @param weights [I] pointer to the input tensor - * @param bias [I] pointer to the output tensor - * @param weightOutChDimension [I] dimension of the output channels in the - * weights tensor - * @param sliceChannels [O] slice size in output channel dimension - * - * @return Tf Lite status code - */ -TfLiteStatus arc_scratch_buffer_calc_slice_size_weights( - const MliTensorInterface* weights, const MliTensorInterface* bias, - const int weight_out_ch_dimension, int* slice_channels); - -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUF_MGR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h deleted file mode 100644 index 0e5dd02b..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/arc_mli/scratch_buffers.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ -#define TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ - -#include "mli_api.h" // NOLINT -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { -namespace ops { -namespace micro { - -void init_arc_scratch_buffers(void); -void* get_arc_scratch_buffer(int size); // Function to assign fast memory - // from one of 3 scratch buffers. - -void get_arc_scratch_buffer_max_size(int* size); -void get_arc_scratch_buffer_two_max_sizes(int* size1, int* size2); - -static inline bool inside_arc_dccm(void* p) { -#if core_config_dccm_present - return ((unsigned)p >= core_config_dccm_base) && - ((unsigned)p < core_config_dccm_base + core_config_dccm_size); -#else - return false; -#endif -} - -static inline bool inside_arc_xccm(void* p) { -#if core_config_xy - return ((unsigned)p >= core_config_xy_x_base) && - ((unsigned)p < core_config_xy_x_base + core_config_xy_size); -#else - return false; -#endif -} - -static inline bool inside_arc_yccm(void* p) { -#if core_config_xy_size - return ((unsigned)p >= core_config_xy_y_base) && - ((unsigned)p < core_config_xy_y_base + core_config_xy_size); -#else - return false; -#endif -} - -static inline bool inside_arc_vccm(void* p) { -#if core_config_vec_mem_size - return ((unsigned)p >= core_config_vec_mem_base) && - ((unsigned)p < core_config_vec_mem_base + core_config_vec_mem_size); -#else - return false; -#endif -} - -static inline bool inside_arc_ccm(void* p) { - return inside_arc_dccm(p) || inside_arc_xccm(p) || inside_arc_yccm(p) || - inside_arc_vccm(p); -} - -} // namespace micro -} // namespace ops -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_ARC_SCRATCH_BUFFERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h deleted file mode 100644 index dd2dcadd..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_common.h +++ /dev/null @@ -1,24 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_COMMON_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_COMMON_H_ - -#if defined(CEVA_BX1) || defined(CEVA_SP500) -extern int32_t CEVA_TFLM_KERNELS_SCRATCH[]; -extern int32_t CEVA_TFLM_KERNELS_SCRATCH_SIZE_VAL; -#endif - -#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h deleted file mode 100644 index 2b404278..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/ceva_tflm_lib.h +++ /dev/null @@ -1,613 +0,0 @@ - -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// API header for CEVA TFLM optimized kernel library - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ - -#include "tensorflow/lite/micro/kernels/ceva/types.h" - -#if defined(__cplusplus) -extern "C" { -#endif /* __cplusplus */ - -void CEVA_TFLM_ResizeNearestNeighbor_float32( - const bool align_corners, int32_t output_height, int32_t output_width, - int32_t row_offset, int32_t input_height, int32_t input_width, - int32_t col_offset, int32_t depth, const int32_t* input_ptr, - int32_t* output_ptr, const bool half_pixel_centers, int32_t* scratch); -void CEVA_TFLM_ResizeNearestNeighbor_int8( - const bool align_corners, int32_t output_height, int32_t output_width, - int32_t row_offset, int32_t input_height, int32_t input_width, - int32_t col_offset, int32_t depth, const int8_t* input_ptr, - int8_t* output_ptr, const bool half_pixel_centers, int32_t* scratch); - -void CEVA_TFLM_Abs_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Sqrt_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Rsqrt_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Square_Float32(const float* input_data, float* output_data, - int flat_size); - -void CEVA_TFLM_Cos_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Sin_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Tanh_Float32(const float* input_data, float* output_data, - int flat_size); - -void CEVA_TFLM_Sigmoid_Float32(const float* input_data, float* output_data, - int flat_size); -void CEVA_TFLM_Log_Float32(const float* input_data, float* output_data, - int flat_size); - -void CEVA_TFLM_LogicalNot(const bool* input_data, bool* output_data, - int flat_size); - -void CEVA_TFLM_AffineQuantize_Int8(const float_32* input_data, - int8_t* output_data, int flat_size, - float_32 scale, int zero_point); - -void CEVA_TFLM_Softmax_Float32(const float* input_data, float* output_data, - const float beta, const int depth); - -void CEVA_TFLM_Neg_Float32(const float_32* input_data, float_32* output_data, - const int flat_size); - -void CEVA_TFLM_RoundToNearest_asm(const float* input_arr, float* output_arr, - const int size); -float RoundToNearest(float value); - -void CEVA_TFLM_Round_float32(const float* input_data, float* output_data, - const int flat_size); - -void CEVA_TFLM_Softmax_Int8(const int8_t* input_data, int8_t* output_data, - const int32_t input_beta_multiplier, - const int32_t input_beta_left_shift, - const int32_t depth, void* scratch); - -void CEVA_TFLM_Min_Max_Float32(const float* input_data, - const float float_activation_min, - const float float_activation_max, - const int flat_size, float* output_data); - -void CEVA_TFLM_Add_Float32(const void* params_inp, const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastAdd4DSlow_Float32(const void* params_inp, - const float* input1_data, - const float* input2_data, - float* output_data, const int* Dims, - const int* desc1, const int* desc2); - -void CEVA_TFLM_BroadcastSubSlow_Float32( - const void* params_inp, const float* input1_data, const float* input2_data, - float* output_data, const int* strides1, const int* strides2, - const int* output_strides, const int* output_extents); - -void CEVA_TFLM_BroadcastSubSlow_Float32_loop( - const void* params_inp, const float* input1_data, const float* input2_data, - float* output_data, const int* output_extents, const int* strides1, - const int* strides2, const int* output_strides); - -void CEVA_TFLM_SubWithActivation_Float32(const void* params_inp, - const float* input1_data, - const float* input2_data, - float* output_data, - const int flat_size); - -void CEVA_TFLM_MaximumBroadcastSlow_Float32( - const float* input1_data, const float* input2_data, float* output_data, - const int* strides1, const int* strides2, const int* output_strides, - const int* output_extents); -void CEVA_TFLM_MinimumBroadcastSlow_Float32( - const float* input1_data, const float* input2_data, float* output_data, - const int* strides1, const int* strides2, const int* output_strides, - const int* output_extents); - -void CEVA_TFLM_Maximum_Float32(const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); -void CEVA_TFLM_Minimum_Float32(const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); -void CEVA_TFLM_Maximum_Float32_asm(const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); -void CEVA_TFLM_Minimum_Float32_asm(const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); -void CEVA_TFLM_DepthwiseConv_Float32( - // const DepthwiseParams& params, - // const int batches, // always 1 - const int stride_width, const int stride_height, const int pad_width, - const int pad_height, const int depth_multiplier, const int input_height, - const int input_width, const int input_depth, const float* input_data, - const int filter_height, const int filter_width, const int filter_depth, - const float* filter_data, const float* bias_data, const int output_height, - const int output_width, const int output_depth, float* output_data, - const int dilation_width_factor, const int dilation_height_factor, - const float output_activation_min, const float output_activation_max - -); -void CEVA_TFLM_DepthwiseConvPerChannel_int8( - const int stride_width, const int stride_height, const int pad_width, - const int pad_height, const int depth_multiplier_, - const int32_t input_offset_, const int32_t output_offset, - const int32_t* output_multiplier, const int32_t* output_shift, - const int input_height, const int input_width_, const int input_depth_, - const int8_t* input_data, const int filter_height, const int filter_width, - const int filter_depth_, const int8_t* filter_data, - const int32_t* bias_data, const int output_height, const int output_width, - const int output_depth, - - int8_t* output_data, int32_t* scratch_ - - , - const int dilation_width_factor_, const int dilation_height_factor, - const int32_t output_activation_min, const int32_t output_activation_max); - -void CEVA_TFLM_ConvPerChannel_Int8( - const int stride_width, const int stride_height, const int pad_width, - const int pad_height, // const int depth_multiplier, - const int32_t input_offset, const int32_t output_offset, - const int32_t* output_multiplier, const int32_t* output_shift, - const int input_height, const int input_width, const int input_depth_Dims3, - const int input_depth, const int8_t* input_data, const int filter_height, - const int filter_width, const int filter_depth, const int8_t* filter_data, - const int32_t* bias_data, const int output_height, const int output_width, - const int output_depth_Dims3, const int output_depth, int8_t* output_data, - int32_t* scratch, const int dilation_width_factor, - const int dilation_height_factor, const int32_t output_activation_min, - const int32_t output_activation_max); - -void CEVA_TFLM_Conv_Float32( - // const int batches, - const int stride_width, const int stride_height, const int pad_width, - const int pad_height, // const int depth_multiplier, - const int input_height, const int input_width, const int input_depth_Dims3, - const int input_depth, const float* input_data, const int filter_height, - const int filter_width, const int filter_depth, const float* filter_data, - const float* bias_data, const int output_height, const int output_width, - const int output_depth_Dims3, const int output_depth, float* output_data, - const int dilation_width_factor, const int dilation_height_factor, - const float output_activation_min, const float output_activation_max - -); - -/////////////////// -void CEVA_TFLM_MaximumBroadcastSlow_Int8( - const int8_t* input1_data, const int8_t* input2_data, int8_t* output_data, - const int* strides1, const int* strides2, const int* output_strides, - const int* output_extents); -void CEVA_TFLM_MinimumBroadcastSlow_Int8( - const int8_t* input1_data, const int8_t* input2_data, int8_t* output_data, - const int* strides1, const int* strides2, const int* output_strides, - const int* output_extents); - -void CEVA_TFLM_Maximum_Int8(const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); -void CEVA_TFLM_Minimum_Int8(const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastSubSlow_Int8( - const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, const int* strides1, - const int* strides2, const int* output_strides, const int* output_extents); - -void CEVA_TFLM_BroadcastSubSlow_Int8_loop( - const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, const int* output_extents, - const int* strides1, const int* strides2, const int* output_strides); - -void CEVA_TFLM_BroadcastAddSlow_Int8(const void* params_inp, - const int8_t* input1_data, - const int8_t* input2_data, - int8_t* output_data, const int* strides1, - const int* strides2, - const int* output_extents); - -void CEVA_TFLM_BroadcastAddSlow_Int8_loop( - const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, const int* output_extents, - const int* strides1, const int* strides2); - -void CEVA_TFLM_Sub_Int8(const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_Sub_Uint8(const void* params_inp, const uint8_t* input1_data, - const uint8_t* input2_data, uint8_t* output_data, - const int flat_size); - -void CEVA_TFLM_Add_Uint8(const void* params, const uint8_t* input1_data, - const uint8_t* input2_data, uint8_t* output_data, - const int flat_size); - -void CEVA_TFLM_Add_Int8(const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastAdd4DSlow_Uint8(const void* params, - const uint8_t* input1_data, - const uint8_t* input2_data, - uint8_t* output_data, const int* Dims, - const int* desc1, const int* desc2, - const int* dims_data); -void CEVA_TFLM_svdf_Float32(float_32* vector1_ptr, float_32* vector2_ptr, - int32_t num_units, int32_t memory_size_rank, - float_32* output_ptr_batch); -void CEVA_TFLM_svdf_Int8(int n_memory, const int8_t* matrix_ptr, - const int8_t* vector_in_batch_t, - int16_t* result_in_batch, int input_zp, int n_input, - int effective_scale_1_a, int effective_scale_1_b, - int n_filter, int* scratch); -void CEVA_TFLM_AffineQuantize_Int8(const float_32* input_data, - int8_t* output_data, int flat_size, - float_32 scale, int zero_point); - -// int32_t MultiplyByQuantizedMultiplier_t(int32_t x, int32_t -// quantized_multiplier, int shift); int32_t -// MultiplyByQuantizedMultiplier_t1(int32_t x, int32_t quantized_multiplier, int -// shift); - -void CEVA_TFLM_L2Normalization_Float32(const float* input_data, - float* output_data, float epsilon, - const int outer_size, const int depth); -void CEVA_TFLM_L2Normalization_Int8(int32_t input_zero_point, - int32_t outer_size, int32_t depth, - const int8_t* input_data, - int8_t* output_data); - -void CEVA_TFLM_prelu_Float32(const float* in1_data, const int32_t* in1_strides, - const float* in2_data, const int32_t* in2_strides, - float* out_data, const int32_t* out_strides, - const int32_t* dims); - -void CEVA_TFLM_prelu_Int8(const int8_t* in1_data, const int32_t* in1_strides, - const int8_t* alpha_data, - const int32_t* alpha_strides, int8_t* out_data, - const int32_t* out_strides, const int32_t* dims, - const int32_t* params); -void CEVA_TFLM_FullyConnected_Float32( - const void* params_inp, const int input_shape, const float* input_data, - const int weights_shape_DimensionsCount, const int* weights_shape_DimsData, - const float* weights_data, const int bias_shape, const float* bias_data, - const int output_shape_DimensionsCount, const int* output_shape_DimsData, - float* output_data); -void CEVA_TFLM_FullyConnected_int8( - const void* params_inp, const int input_shape, const int8_t* input_data, - const int filter_shape_DimensionsCount, const int* filter_shape_DimsData, - const int8_t* filter_data, const int bias_shape, const int32_t* bias_data, - const int output_shape_DimensionsCount, const int* output_shape_DimsData, - int8_t* output_data, int* scratch); - -void CEVA_TFLM_tanh_Int8(int32_t input_zero_point, int32_t input_range_radius, - int32_t input_multiplier, int32_t input_shift, - int32_t input_size, const int8_t* input_data, - int8_t* output_data); - -void CEVA_TFLM_Logistic_Int8(int32_t input_zero_point, - int32_t input_range_radius, - int32_t input_multiplier, int32_t input_left_shift, - int32_t input_size, const int8_t* input_data, - int8_t* output_data); - -void CEVA_TFLM_Tanh_float32(const float_32* input_data, float_32* output_data, - const int flat_size); -void CEVA_TFLM_Logistic_float32(const float_32* input_data, - float_32* output_data, const int flat_size); - -void CEVA_TFLM_PackImplLoop_float(const float* input_ptr, float* output_ptr, - int outer_size, int copy_size, - int step_vcount_copy_size); -void CEVA_TFLM_PackUnpackImplLoopInitSizes(int* const copy_size, - int* const outer_size, - const int* const outputDimsData, - const int dimensions, int axis); -void CEVA_TFLM_PackImplLoop_Int8(const int8_t* input_ptr, int8_t* output_ptr, - int outer_size, int copy_size, - int step_vcount_copy_size); -void CEVA_TFLM_UnpackImplLoop_float(const float* input_ptr, float* output_ptr, - int outer_size, int copy_size, - int step_vcount_copy_size); -void CEVA_TFLM_UnpackImplLoop_Int8(const int8_t* input_ptr, int8_t* output_ptr, - int outer_size, int copy_size, - int step_vcount_copy_size); - -void CEVA_TFLM_ComparisonEqual_Float32(const float* input1, const float* input2, - bool* output, const int32_t size); -void CEVA_TFLM_ComparisonNotEqual_Float32(const float* input1, - const float* input2, bool* output, - const int32_t size); -void CEVA_TFLM_ComparisonGreater_Float32(const float* input1, - const float* input2, bool* output, - const int32_t size); -void CEVA_TFLM_ComparisonGreaterEqual_Float32(const float* input1, - const float* input2, bool* output, - const int32_t size); -void CEVA_TFLM_ComparisonLess_Float32(const float* input1, const float* input2, - bool* output, const int32_t size); -void CEVA_TFLM_ComparisonLessEqual_Float32(const float* input1, - const float* input2, bool* output, - const int32_t size); - -void CEVA_TFLM_ComparisonEqual_Float32_Broadcast(const float* input1, - const float* input2, - bool* output, - const int32_t* dims, - const int32_t** op_param); - -void CEVA_TFLM_ComparisonNotEqual_Float32_Broadcast(const float* input1, - const float* input2, - bool* output, - const int32_t* dims, - const int32_t** op_param); - -void CEVA_TFLM_ComparisonGreater_Float32_Broadcast(const float* input1, - const float* input2, - bool* output, - const int32_t* dims, - const int32_t** op_param); -void CEVA_TFLM_ComparisonGreaterEqual_Float32_Broadcast( - const float* input1, const float* input2, bool* output, const int32_t* dims, - const int32_t** op_param); - -void CEVA_TFLM_ComparisonLess_Float32_Broadcast(const float* input1, - const float* input2, - bool* output, - const int32_t* dims, - const int32_t** op_param); - -void CEVA_TFLM_ComparisonLessEqual_Float32_Broadcast(const float* input1, - const float* input2, - bool* output, - const int32_t* dims, - const int32_t** op_param); - -void CEVA_TFLM_ComparisonEqual_Int8(const int8_t* input1, const int8_t* input2, - bool* output, const int32_t flatsize, - void* op_params); -void CEVA_TFLM_ComparisonNotEqual_Int8(const int8_t* input1, - const int8_t* input2, bool* output, - const int32_t flatsize, void* op_params); -void CEVA_TFLM_ComparisonGreater_Int8(const int8_t* input1, - const int8_t* input2, bool* output, - const int32_t flatsize, void* op_params); -void CEVA_TFLM_ComparisonGreaterEqual_Int8(const int8_t* input1, - const int8_t* input2, bool* output, - const int32_t flatsize, - void* op_params); -void CEVA_TFLM_ComparisonLess_Int8(const int8_t* input1, const int8_t* input2, - bool* output, const int32_t flatsize, - void* op_params); -void CEVA_TFLM_ComparisonLessEqual_Int8(const int8_t* input1, - const int8_t* input2, bool* output, - const int32_t flatsize, - void* op_params); - -void CEVA_TFLM_ComparisonEqual_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, - bool* output, const int32_t* dims, - void* op_params); -void CEVA_TFLM_ComparisonNotEqual_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, - bool* output, - const int32_t* dims, - void* op_params); -void CEVA_TFLM_ComparisonGreater_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, - bool* output, - const int32_t* dims, - void* op_params); -void CEVA_TFLM_ComparisonGreaterEqual_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, - bool* output, - const int32_t* dims, - void* op_params); -void CEVA_TFLM_ComparisonLess_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, bool* output, - const int32_t* dims, - void* op_params); -void CEVA_TFLM_ComparisonLessEqual_Int8_Broadcast(const int8_t* input1, - const int8_t* input2, - bool* output, - const int32_t* dims, - void* op_params); - -void CEVA_TFLM_Mul_Float32(const void* params_inp, const float* input1_data, - const float* input2_data, float* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastMul4DSlow_Float32(const void* params_inp, - const float* input1_data, - const float* input2_data, - float* output_data, const int* Dims, - const int* desc1, const int* desc2); - -void CEVA_TFLM_AveragePool_Float32(const void* params, const int* input_shape, - const float* input_data, - const int* output_shape, float* output_data); - -void CEVA_TFLM_AveragePool_Int8(const void* params_inp, const int* input_shape, - const int8_t* input_data, - const int* output_shape, int8_t* output_data); - -void CEVA_TFLM_AveragePool_Int8_Loop( - const int* input_shape, const int8_t* input_data, int8_t* output_data, - const int depth, int batch, int in_y, const int filter_y_start, - const int filter_y_end, const int in_x_origin, const int filter_x_start, - const int filter_x_end, int filter_count, int32_t quantized_activation_min, - int32_t quantized_activation_max, int indx_out); - -void CEVA_TFLM_MaxPool_Float32(const void* params_inp, const int* input_shape, - const float* input_data, const int* output_shape, - float* output_data); - -void CEVA_TFLM_MaxPool_Int8(const void* params_inp, const int* input_shape, - const int8_t* input_data, const int* output_shape, - int8_t* output_data); - -void CEVA_TFLM_MaxPool_Int8_Loop( - const int* input_shape, const int8_t* input_data, int8_t* output_data, - const int depth, int batch, int in_y, const int filter_y_start, - const int filter_y_end, const int in_x_origin, const int filter_x_start, - const int filter_x_end, int32_t quantized_activation_min, - int32_t quantized_activation_max, int indx_out); - -void CEVA_TFLM_Mul_Int8(const void* params_inp, const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastMul4DSlow_Int8(const void* params_inp, - const int8_t* input1_data, - const int8_t* input2_data, - int8_t* output_data, const int* Dims, - const int* desc1, const int* desc2); - -void CEVA_TFLM_Dequantize_Float32(const int8_t* input_data, - float_32* output_data, int flat_size, - float_32 scale, int zero_point); - -void CEVA_TFLM_Ceil_Float32(const float* input_data, float* output_data, - const int flat_size); - -void CEVA_TFLM_Logical_And_Int8(const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastLogicalAnd4DSlow_Int8(const int8_t* input1_data, - const int8_t* input2_data, - int8_t* output_data, - const int* Dims, const int* desc1, - const int* desc2); - -void CEVA_TFLM_Logical_Or_Int8(const int8_t* input1_data, - const int8_t* input2_data, int8_t* output_data, - const int flat_size); - -void CEVA_TFLM_BroadcastLogicalOr4DSlow_Int8(const int8_t* input1_data, - const int8_t* input2_data, - int8_t* output_data, - const int* Dims, const int* desc1, - const int* desc2); - -void CEVA_TFLM_SplitLoops_Float32(float** out_ptrs, const int* dataIndex, - const float* input_ptr, int outer_size, - int output_count, int copy_size); -void CEVA_TFLM_SplitLoops_int8(int8_t** out_ptrs, const int* dataIndex, - const int8_t* input_ptr, int outer_size, - int output_count, int copy_size); - -void CEVA_TFLM_Relu_Float32(const float* input_data, float* output_data, - const int flat_size); -void CEVA_TFLM_Relu6_Float32(const float* input_data, float* output_data, - const int flat_size); -void CEVA_TFLM_Relu_int8(const void* params, const int8_t* input_data, - int8_t* output_data, const int flat_size); -void CEVA_TFLM_Relu6_int8(const int8_t lower, const int8_t upper, - const int8_t* input_data, int8_t* output_data, - const int flat_size); -void CEVA_TFLM_Floor_float32(const float* input_data, float* output_data, - const int flat_size); - -void CEVA_TFLM_Concatenation_Float32(const void* params_inp, - const int** input_shape, - const float** input_data, - const int output_shape_DimensionsCount, - const int* output_shape_DimsData, - float* output_data); - -void CEVA_TFLM_Concatenation_int8(const void* params_inp, - const int** input_shape, - const int8_t** input_data, - const int output_shape_DimensionsCount, - const int* output_shape_DimsData, - int8_t* output_data); - -void CEVA_TFLM_Mean4D_Float32(const float* input_data, float* output_data, - const int* Dims, const int* Dims_inp, - const int* dims_data, const int* dims_data_inp); -bool CEVA_TFLM_Mean_Float32(const float* input_data, const int* input_dims, - const int input_num_dims, float* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int num_axis_dimensions, - bool keep_dims, int* temp_index, int* resolved_axis, - float* temp_sum); -void CEVA_TFLM_Mean_Float32_loop(float* temp_sum, float* output_data, - int num_elements_in_axis, size_t num_outputs); -void CEVA_TFLM_Mean4D_Int8(int32_t multiplier, int32_t shift, - const int8_t* input_data, int32_t input_zero_point, - int8_t* output_data, int32_t output_zero_point, - int* input_shape, int* output_shape); -bool CEVA_TFLM_Mean_Int8(const int8_t* input_data, const int* input_dims, - const int input_num_dims, int8_t* output_data, - const int* output_dims, const int output_num_dims, - const int* axis, const int num_axis_dimensions, - bool keep_dims, int* temp_index, int* resolved_axis, - int32_t* temp_sum); -void CEVA_TFLM_Mean_Int8_loop(int32_t* temp_sum, int8_t* output_data, - int num_elements_in_axis, size_t num_outputs); -void CEVA_TFLM_StridedSlice_Float32(void* op_params, - int unextended_input_shape_DimensionsCount, - int* unextended_input_shape_DimsData, - float* input_data, - - float* output_data); - -void CEVA_TFLM_StridedSlice_Float32(void* op_params, - int unextended_input_shape_DimensionsCount, - int* unextended_input_shape_DimsData, - float* input_data, float* output_data); - -void CEVA_TFLM_StridedSlice_loop_Float32(float* input_data, float* output_data, - void* params); - -void CEVA_TFLM_StridedSlice_int8(void* op_params, - int unextended_input_shape_DimensionsCount, - int* unextended_input_shape_DimsData, - int8_t* input_data, int8_t* output_data); - -void CEVA_TFLM_StridedSlice_loop_int8(int8_t* input_data, int8_t* output_data, - void* params); - -void CEVA_TFLM_Pad_Float32(void* op_params, int input_shape, int* output_shape, - const float* input_data, const float* pad_value_ptr, - float* output_data); - -void CEVA_TFLM_Pad_Int8(void* op_params, int input_shape, int* output_shape, - const int8_t* input_data, const int8_t* pad_value_ptr, - int8_t* output_data); - -int CEVA_TFLM_ReshapeOutput(int input_type, const int input_size, - const int* input_data, int output_type, - int* output_size, int* output_data, - int node_in_size); - -int CEVA_TFLM_EvalRashape(const int8_t* input, int8_t* output, - unsigned int N_cnt); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_CEVA_CEVA_TFLM_LIB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h deleted file mode 100644 index 0d51e5ac..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/mcps_macros.h +++ /dev/null @@ -1,115 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// MCPS measurement macros for CEVA optimized kernels - -#ifndef MCPS_MACROS_ -#define MCPS_MACROS_ - -#ifndef WIN32 -#include -#endif - -#ifdef MCPS_MEASUREMENT - -#ifdef STACK_MEASUREMENT -#if defined(__cplusplus) -extern "C" { -#endif /* __cplusplus */ -void CEVA_BX_Stack_Marking(const int32_t _count); -int32_t CEVA_BX_Stack_Measurement(const int32_t count); -#if defined(__cplusplus) -} -#endif /* __cplusplus */ -#endif - -#define MCPS_CALL_RET_VALUE 4 - -#ifdef STACK_MEASUREMENT -#define MCPS_VARIBLES \ - clock_t c1, c2; \ - int ClockCEVA, Constant_cycles; \ - int StackSize; \ - FILE* f_mcps_report; -#else -#define MCPS_VARIBLES \ - clock_t c1, c2; \ - int ClockCEVA, Constant_cycles; \ - FILE* f_mcps_report; -#endif -#define MCPS_OPEN_FILE f_mcps_report = fopen("mcps_report.txt", "at"); - -#define MCPS_CLOSE_FILE fclose(f_mcps_report); - -#ifdef STACK_MEASUREMENT -#define MCPS_START_CLOCK \ - CEVA_BX_Stack_Marking(0x800); \ - reset_clock(); \ - start_clock(); \ - c1 = clock(); \ - c2 = clock(); \ - Constant_cycles = c2 - c1; \ - c1 = clock(); - -#define MCPS_STOP_AND_LOG(...) \ - c2 = clock(); \ - ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ - StackSize = CEVA_BX_Stack_Measurement(0x800) * 4; \ - fprintf(f_mcps_report, __VA_ARGS__); \ - fprintf(f_mcps_report, ":cycles:%d:Stack:%d\r\n", ClockCEVA, StackSize); - -#else // STACK_MEASUREMENT -#define MCPS_START_CLOCK \ - reset_clock(); \ - start_clock(); \ - c1 = clock(); \ - c2 = clock(); \ - Constant_cycles = c2 - c1; \ - c1 = clock(); - -#define MCPS_STOP_AND_LOG(...) \ - c2 = clock(); \ - ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ - fprintf(f_mcps_report, __VA_ARGS__); \ - fprintf(f_mcps_report, ":cycles:%d\r\n", ClockCEVA); -#endif // STACK_MEASUREMENT - -#define MCPS_STOP_AND_PRINT(...) \ - c2 = clock(); \ - ClockCEVA = c2 - c1 - Constant_cycles - MCPS_CALL_RET_VALUE; \ - fprintf(stdout, __VA_ARGS__); \ - fprintf(stdout, ":cycles=%d\n", ClockCEVA); - -#define MCPS_START_ONE \ - MCPS_VARIBLES; \ - MCPS_OPEN_FILE; \ - MCPS_START_CLOCK; -#define MCPS_STOP_ONE(...) \ - MCPS_STOP_AND_LOG(__VA_ARGS__); \ - MCPS_CLOSE_FILE; - -#else -#define MCPS_VARIBLES -#define MCPS_OPEN_FILE -#define MCPS_START_CLOCK -#define MCPS_STOP_AND_LOG(...) -#define MCPS_STOP_AND_PRINT(...) -#define MCPS_CLOSE_FILE - -#define MCPS_START_ONE -#define MCPS_STOP_ONE(...) -#endif - -#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h deleted file mode 100644 index 9b766c0d..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/ceva/types.h +++ /dev/null @@ -1,1286 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef CEVA_TYPES_H_ -#define CEVA_TYPES_H_ - -#include -#include -#include - -// typedef int8_t int8_t; -// typedef int16_t int16; -// typedef int32_t int32_t; -// typedef uint8_t uint8; -// typedef uint16_t uint16; -// typedef uint32_t uint32; - -typedef float float_32; -typedef unsigned long long uint64; -typedef long long int64; - -#if 1 -enum BroadcastableOpCategory { - kNone, - kNonBroadcast, // Matching input shapes. - kFirstInputBroadcastsFast, // Fivefold nested loops. - kSecondInputBroadcastsFast, // Fivefold nested loops. - kGenericBroadcast, // Fall-back. -}; - -#else -enum class BroadcastableOpCategory : uint8_t { - kNone, - kNonBroadcast, // Matching input shapes. - kFirstInputBroadcastsFast, // Fivefold nested loops. - kSecondInputBroadcastsFast, // Fivefold nested loops. - kGenericBroadcast, // Fall-back. -}; -#endif - -struct ArithmeticParams_ceva { - // Shape dependent / common to data / op types. - uint8_t broadcast_category; // BroadcastableOpCategory broadcast_category; - // uint8_t inference params. - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // Add / Sub, not Mul, uint8_t inference params. - int left_shift; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_multiplier; - int input2_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - - // Processed output dimensions. - // Let input "a" be the one that broadcasts in the faster-changing dimension. - // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and - // {b0, b1, b2, b3, b4}, - // broadcast_shape[4] = b0 = a0. - // broadcast_shape[3] = b1; a1 = 1. - // broadcast_shape[2] = b2 = a2. - // broadcast_shape[1] = a3; b3 = 1. - // broadcast_shape[0] = b4 = a4. - int broadcast_shape[5]; -}; - -struct SoftmaxParams_ceva { - // beta is not really used (not a Tensorflow parameter) and not implemented - // for LogSoftmax. - double beta; - // uint8_t inference params. Used even when beta defaults to 1.0. - int32_t input_multiplier; - int32_t input_left_shift; - // Reverse scaling is only used by LogSoftmax. - int32_t reverse_scaling_divisor; - int32_t reverse_scaling_right_shift; - int diff_min; - int32_t zero_point; - float scale; - float* table; - int16_t* exp_lut; - int16_t* one_over_one_plus_x_lut; - uint8_t* uint8_table1; - uint8_t* uint8_table2; -}; - -enum class FusedActivationFunctionType_ceva : uint8_t { - kNone, - kRelu6, - kRelu1, - kRelu -}; -enum class PaddingType_ceva : uint8_t { kNone, kSame, kValid }; - -struct PaddingValues_ceva { - int16_t width; - int16_t height; - // offset is used for calculating "remaining" padding, for example, `width` - // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is - // 1 + 1 = 2. - int16_t width_offset; - // Same as width_offset except it's over the height dimension. - int16_t height_offset; -}; - -struct StridedSliceParams_ceva { - int8_t start_indices_count; - int32_t start_indices[5]; - int8_t stop_indices_count; - int32_t stop_indices[5]; - int8_t strides_count; - int32_t strides[5]; - - int16_t begin_mask; - int16_t ellipsis_mask; - int16_t end_mask; - int16_t new_axis_mask; - int16_t shrink_axis_mask; -}; - -struct PoolParams_ceva { - FusedActivationFunctionType_ceva activation; - PaddingType_ceva padding_type; - PaddingValues_ceva padding_values; - int stride_height; - int stride_width; - int filter_height; - int filter_width; - // uint8, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -inline size_t ReducedOutputOffset(const int num_dims, const int* dims, - const int* index, const int num_axis, - const int* axis) { - if (num_dims == 0) { - return 0; - } - // TFLITE_DCHECK(dims != nullptr); - // TFLITE_DCHECK(index != nullptr); - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { - if (idx == axis[axis_idx]) { - is_axis = true; - break; - } - } - } - if (!is_axis) { - offset = offset * static_cast(dims[idx]) + - static_cast(index[idx]); - } - } - return offset; -} -inline bool NextIndex(const int num_dims, const int* dims, int* current) { - if (num_dims == 0) { - return false; - } - // TFLITE_DCHECK(dims != nullptr); - // TFLITE_DCHECK(current != nullptr); - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) { - int current_val = current[idx] + carry; - // TFLITE_DCHECK_GE(dims[idx], current_val); - if (dims[idx] == current_val) { - current[idx] = 0; - } else { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -#if 0 -#include -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" - -namespace tflite_micro { - -enum class FusedActivationFunctionType : uint8_t { kNone, kRelu6, kRelu1, kRelu }; -enum class PaddingType : uint8_t { kNone, kSame, kValid }; - -struct PaddingValues { - int16 width; - int16 height; - // offset is used for calculating "remaining" padding, for example, `width` - // is 1 and `width_offset` is 1, so padding_left is 1 while padding_right is - // 1 + 1 = 2. - int16 width_offset; - // Same as width_offset except it's over the height dimension. - int16 height_offset; -}; - -// This enumeration allows for non-default formats for the weights array -// of a fully-connected operator, allowing the use of special optimized -// runtime paths. -enum class FullyConnectedWeightsFormat : uint8_t { - // Default format (flat 2D layout, the inner contiguous dimension - // is input_depth, the outer non-contiguous dimension is output_depth) - kDefault, - // Summary: optimized layout for fast CPU runtime implementation, - // aimed specifically at ARM CPUs at the moment, and specialized for - // 8-bit quantized layers. - // - // The use case we're concerned with here is: 8-bit quantization, - // large weights matrix that doesn't fit in cache (e.g. 4096x2048 in - // a key application that drove this), very small batch size (e.g. 1 -- 4). - // - // Even with 8-bit quantization of weights, the performance of memory - // accesses to the weights can become the dominant issue when - // the batch size is small, so each weight value is used in only a few - // arithmetic ops, i.e. the fully-connected node has a low arithmetic - // intensity. The specific issues that arise are of three kinds: - // (1) One may, ideally, max out DRAM bandwidth, i.e. be truly memory - // bound. That's the "good" issue to run into. - // (2) One may run into sub-optimal pre-fetching: the data hasn't been - // prefetched into the cache by the time we need it. - // (3) One may run into cache aliasing: multiple values that are - // pre-fetched, alias each other in the L1 cache (which typically - // has only 4-way set associativity in ARM CPUs) and thus evict - // each other before we get to using them. - // - // The point of this shuffling is to avoid issues (2) and (3) so that - // we get as fast as possible given only the hard constraint (1). - // This is achieved by turning the difficulty into a solution: the - // difficulty, that each value loaded from memory is used only in - // one kernel iteration, making this operation memory-intensive, hints at - // the solution, of shuffling the weights so that they are stored in the - // exact order as the kernel needs to load them, so that the memory - // accesses made by the kernel are trivial. This solves (2) because the - // trivial memory access pattern allows the CPU's automatic prefetching - // to perform very well (no need even for preload instructions), and this - // solves (3) because the values being loaded concurrently are now - // contiguous in the address space, thus don't alias each other in the cache. - // - // On ARM, we typically want our kernel to process a 4x16 block of weights - // at a time, because: - // - 16 is the number of bytes in a NEON register. - // - 4 is how many rows we need to handle concurrently in the kernel in - // order to have sufficient mutual independence of instructions to - // maximize arithmetic throughput. - // - // Finally, the 'int8_t' part in the name refers to the fact that this - // weights format has each weights value encoded as a signed int8_t value, - // even if the data type of the weights buffer is uint8_t. This is intended - // to save runtime kernels the effort to have to XOR the top bit of these - // bytes before using them in signed arithmetic, see this file for more - // explanations on the 'signed int8_t trick' in matrix multiplication kernels: - // - // tensorflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc - // - kShuffled4x16Int8, -}; - -// Quantization parameters, determining the mapping of quantized values -// to real values (i.e. determining how quantized values are mathematically -// interpreted). -// -// The correspondence is as follows: -// -// real_value = scale * (quantized_value - zero_point); -// -// In other words, zero_point designates which quantized value corresponds to -// the real 0 value, and scale designates the difference between the real values -// corresponding to consecutive quantized values differing by 1. -struct QuantizationParams { - int32_t zero_point = 0; - double scale = 0.0; -}; - -inline bool operator==(const QuantizationParams& qp1, - const QuantizationParams& qp2) { - return qp1.zero_point == qp2.zero_point && qp1.scale == qp2.scale; -} - -template -struct Dims { - int sizes[N]; - int strides[N]; -}; - -class RuntimeShape { - public: - // Shapes with dimensions up to 5 are stored directly in the structure, while - // larger shapes are separately allocated. - static constexpr int kMaxSmallSize = 5; - - RuntimeShape& operator=(RuntimeShape const&) = delete; - - RuntimeShape() : size_(0) {} - - explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { - if (dimensions_count > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - dims_pointer_ = new int32_t[dimensions_count]; -#endif // TF_LITE_STATIC_MEMORY - } - } - - RuntimeShape(int shape_size, int32_t value) : size_(0) { - Resize(shape_size); - for (int i = 0; i < shape_size; ++i) { - SetDim(i, value); - } - } - - RuntimeShape(int dimensions_count, const int32_t* dims_data) : size_(0) { - ReplaceWith(dimensions_count, dims_data); - } - - RuntimeShape(const std::initializer_list init_list) : size_(0) { - BuildFrom(init_list); - } - - // Avoid using this constructor. We should be able to delete it when C++17 - // rolls out. - RuntimeShape(RuntimeShape const& other) : size_(other.DimensionsCount()) { - if (size_ > kMaxSmallSize) { - dims_pointer_ = new int32_t[size_]; - } - std::memcpy(DimsData(), other.DimsData(), sizeof(int32_t) * size_); - } - - bool operator==(const RuntimeShape& comp) const { - return this->size_ == comp.size_ && - std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32_t)) == 0; - } - - ~RuntimeShape() { - if (size_ > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - delete[] dims_pointer_; -#endif // TF_LITE_STATIC_MEMORY - } - } - - inline int32_t DimensionsCount() const { return size_; } - inline int32_t Dims(int i) const { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - return size_ > kMaxSmallSize ? dims_pointer_[i] : dims_[i]; - } - inline void SetDim(int i, int32_t val) { - TFLITE_DCHECK_GE(i, 0); - TFLITE_DCHECK_LT(i, size_); - if (size_ > kMaxSmallSize) { - dims_pointer_[i] = val; - } else { - dims_[i] = val; - } - } - - inline int32_t* DimsData() { - return size_ > kMaxSmallSize ? dims_pointer_ : dims_; - } - inline const int32_t* DimsData() const { - return size_ > kMaxSmallSize ? dims_pointer_ : dims_; - } - // The caller must ensure that the shape is no bigger than 5-D. - inline const int32_t* DimsDataUpTo5D() const { return dims_; } - - inline void Resize(int dimensions_count) { - if (size_ > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - delete[] dims_pointer_; -#endif // TF_LITE_STATIC_MEMORY - } - size_ = dimensions_count; - if (dimensions_count > kMaxSmallSize) { -#ifdef TF_LITE_STATIC_MEMORY - TFLITE_CHECK(false && "No shape resizing supported on this platform"); -#else // TF_LITE_STATIC_MEMORY - dims_pointer_ = new int32_t[dimensions_count]; -#endif // TF_LITE_STATIC_MEMORY - } - } - - inline void ReplaceWith(int dimensions_count, const int32_t* dims_data) { - Resize(dimensions_count); - int32_t* dst_dims = DimsData(); - std::memcpy(dst_dims, dims_data, dimensions_count * sizeof(int32_t)); - } - - template - inline void BuildFrom(const T& src_iterable) { - const int dimensions_count = - std::distance(src_iterable.begin(), src_iterable.end()); - Resize(dimensions_count); - int32_t* data = DimsData(); - for (auto it : src_iterable) { - *data = it; - ++data; - } - } - - // This will probably be factored out. Old code made substantial use of 4-D - // shapes, and so this function is used to extend smaller shapes. Note that - // (a) as Dims<4>-dependent code is eliminated, the reliance on this should be - // reduced, and (b) some kernels are stricly 4-D, but then the shapes of their - // inputs should already be 4-D, so this function should not be needed. - inline static RuntimeShape ExtendedShape(int new_shape_size, - const RuntimeShape& shape) { - return RuntimeShape(new_shape_size, shape, 1); - } - - inline void BuildFrom(const std::initializer_list init_list) { - BuildFrom>(init_list); - } - - // Returns the total count of elements, that is the size when flattened into a - // vector. - inline int FlatSize() const { - int buffer_size = 1; - const int* dims_data = reinterpret_cast(DimsData()); - for (int i = 0; i < size_; i++) { - buffer_size *= dims_data[i]; - } - return buffer_size; - } - - bool operator!=(const RuntimeShape& comp) const { return !((*this) == comp); } - - private: - // For use only by ExtendedShape(), written to guarantee (return-value) copy - // elision in C++17. - // This creates a shape padded to the desired size with the specified value. - RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) - : size_(0) { - // If the following check fails, it is likely because a 4D-only kernel is - // being used with an array of larger dimension count. - TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); - Resize(new_shape_size); - const int size_increase = new_shape_size - shape.DimensionsCount(); - for (int i = 0; i < size_increase; ++i) { - SetDim(i, pad_value); - } - std::memcpy(DimsData() + size_increase, shape.DimsData(), - sizeof(int32_t) * shape.DimensionsCount()); - } - - int32_t size_; - union { - int32_t dims_[kMaxSmallSize]; - int32_t* dims_pointer_; - }; -}; - -// Converts inference-style shape to legacy tflite_micro::Dims<4>. -inline tflite_micro::Dims<4> ToRuntimeDims(const tflite_micro::RuntimeShape& array_shape) { - tflite_micro::Dims<4> result; - const int dimensions_count = array_shape.DimensionsCount(); - TFLITE_CHECK_LE(dimensions_count, 4); - int cum_prod = 1; - for (int i = 0; i < 4; i++) { - const int new_dim = - (i < dimensions_count) ? array_shape.Dims(dimensions_count - 1 - i) : 1; - result.sizes[i] = new_dim; - result.strides[i] = cum_prod; - cum_prod *= new_dim; - } - return result; -} - -// TODO(b/80418076): Move to legacy ops file, update invocations. -inline RuntimeShape DimsToShape(const tflite_micro::Dims<4>& dims) { - return RuntimeShape( - {dims.sizes[3], dims.sizes[2], dims.sizes[1], dims.sizes[0]}); -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(const int num_dims, const int* dims, int* current) { - if (num_dims == 0) { - return false; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(current != nullptr); - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) { - int current_val = current[idx] + carry; - TFLITE_DCHECK_GE(dims[idx], current_val); - if (dims[idx] == current_val) { - current[idx] = 0; - } else { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int* dims, - const int* index, const int num_axis, - const int* axis) { - if (num_dims == 0) { - return 0; - } - TFLITE_DCHECK(dims != nullptr); - TFLITE_DCHECK(index != nullptr); - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) { - if (idx == axis[axis_idx]) { - is_axis = true; - break; - } - } - } - if (!is_axis) { - offset = offset * static_cast(dims[idx]) + - static_cast(index[idx]); - } - } - return offset; -} - -inline int Offset(const RuntimeShape& shape, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), 4); - const int* dims_data = reinterpret_cast(shape.DimsDataUpTo5D()); - TFLITE_DCHECK(i0 >= 0 && i0 < dims_data[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < dims_data[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < dims_data[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < dims_data[3]); - return ((i0 * dims_data[1] + i1) * dims_data[2] + i2) * dims_data[3] + i3; -} - -inline int Offset(const Dims<4>& dims, int i0, int i1, int i2, int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < dims.sizes[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < dims.sizes[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < dims.sizes[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < dims.sizes[3]); - return i0 * dims.strides[0] + i1 * dims.strides[1] + i2 * dims.strides[2] + - i3 * dims.strides[3]; -} - -inline int Offset(const Dims<4>& dims, int* index) { - return Offset(dims, index[0], index[1], index[2], index[3]); -} - -inline int Offset(const RuntimeShape& shape, int* index) { - return Offset(shape, index[0], index[1], index[2], index[3]); -} - -// Get array size, DCHECKing that the dim index is in range. -// -// Note that this will be phased out with Dims<4>, since RuntimeShape::Dims() -// already performs this check. -template -int ArraySize(const Dims& array, int index) { - TFLITE_DCHECK(index >= 0 && index < N); - return array.sizes[index]; -} - -// Get common array size, DCHECKing that they all agree. -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return ArraySize(array1, index1); -} - -template -int MatchingArraySize(const ArrayType1& array1, int index1, - const ArrayType2& array2, int index2, Args... args) { - TFLITE_DCHECK_EQ(ArraySize(array1, index1), ArraySize(array2, index2)); - return MatchingArraySize(array1, index1, args...); -} - -// Get common shape dim, DCHECKing that they all agree. -inline int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return shape1.Dims(index1); -} - -template -int MatchingDim(const RuntimeShape& shape1, int index1, - const RuntimeShape& shape2, int index2, Args... args) { - TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2)); - return MatchingDim(shape1, index1, args...); -} - -// Will be phased out with Dims<4>, replaced by RuntimeShape::FlatSize(). -template -inline int FlatSize(const Dims& dims) { - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= dims.sizes[i]; - } - return flat_size; -} - -TFLITE_DEPRECATED("Prefer FlatSize.") -inline int RequiredBufferSizeForDims(const Dims<4>& dims) { - return FlatSize(dims); -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - return size_1; -} - -inline int MatchingElementsSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int size_1 = shape.FlatSize(); - const int size_2 = check_shape_0.FlatSize(); - const int size_3 = check_shape_1.FlatSize(); - TFLITE_CHECK_EQ(size_1, size_2); - TFLITE_CHECK_EQ(size_2, size_3); - return size_1; -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return shape.FlatSize(); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSize(const RuntimeShape& shape, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - return MatchingFlatSize(shape, check_shape_1, check_shape_2, check_shape_3); -} - -// Flat size calculation, checking that dimensions match with one or more other -// arrays. -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return FlatSize(dims); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSize(const Dims& dims, const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - return MatchingFlatSize(dims, check_dims_1, check_dims_2, check_dims_3); -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -template -inline int FlatSizeSkipDim(const Dims& dims, int skip_dim) { - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < N); - int flat_size = 1; - for (int i = 0; i < N; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims.sizes[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return FlatSizeSkipDim(dims, skip_dim); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2); -} - -template -inline int MatchingFlatSizeSkipDim(const Dims& dims, int skip_dim, - const Dims& check_dims_0, - const Dims& check_dims_1, - const Dims& check_dims_2, - const Dims& check_dims_3) { - for (int i = 0; i < N; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(ArraySize(dims, i), ArraySize(check_dims_0, i)); - } - } - return MatchingFlatSizeSkipDim(dims, skip_dim, check_dims_1, check_dims_2, - check_dims_3); -} - -// Data is required to be contiguous, and so many operators can use either the -// full array flat size or the flat size with one dimension skipped (commonly -// the depth). -inline int FlatSizeSkipDim(const RuntimeShape& shape, int skip_dim) { - const int dims_count = shape.DimensionsCount(); - TFLITE_DCHECK(skip_dim >= 0 && skip_dim < dims_count); - const auto* dims_data = shape.DimsData(); - int flat_size = 1; - for (int i = 0; i < dims_count; ++i) { - flat_size *= (i == skip_dim) ? 1 : dims_data[i]; - } - return flat_size; -} - -// A combination of MatchingFlatSize() and FlatSizeSkipDim(). -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return FlatSizeSkipDim(shape, skip_dim); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2); -} - -inline int MatchingFlatSizeSkipDim(const RuntimeShape& shape, int skip_dim, - const RuntimeShape& check_shape_0, - const RuntimeShape& check_shape_1, - const RuntimeShape& check_shape_2, - const RuntimeShape& check_shape_3) { - const int dims_count = shape.DimensionsCount(); - for (int i = 0; i < dims_count; ++i) { - if (i != skip_dim) { - TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); - } - } - return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1, check_shape_2, - check_shape_3); -} - -template -bool IsPackedWithoutStrides(const Dims& dims) { - int expected_stride = 1; - for (int d = 0; d < N; d++) { - if (dims.strides[d] != expected_stride) return false; - expected_stride *= dims.sizes[d]; - } - return true; -} - -template -void ComputeStrides(Dims* dims) { - dims->strides[0] = 1; - for (int d = 1; d < N; d++) { - dims->strides[d] = dims->strides[d - 1] * dims->sizes[d - 1]; - } -} - -enum class BroadcastableOpCategory : uint8_t { - kNone, - kNonBroadcast, // Matching input shapes. - kFirstInputBroadcastsFast, // Fivefold nested loops. - kSecondInputBroadcastsFast, // Fivefold nested loops. - kGenericBroadcast, // Fall-back. -}; - -struct MinMax { - float min; - float max; -}; -static_assert(sizeof(MinMax) == 8, ""); - -struct ActivationParams { - FusedActivationFunctionType activation_type; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; -}; - -struct ReluParams : public ActivationParams { - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier; - int32_t output_shift; -}; - -// Styles of resizing op usages. For example, kImageStyle can be used with a Pad -// op for pattern-specific optimization. -enum class ResizingCategory : uint8_t { - kNone, - kImageStyle, // 4D, operating on inner dimensions, say {0, a, b, 0}. - kGenericResize, -}; - -// For Add, Sub, Mul ops. - - -struct ConcatenationParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16 inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct ComparisonParams { - // uint8_t inference params. - int left_shift; - int32_t input1_offset; - int32_t input1_multiplier; - int input1_shift; - int32_t input2_offset; - int32_t input2_multiplier; - int input2_shift; - // Shape dependent / common to inference types. - bool is_broadcast; -}; - -struct ConvParams { - PaddingType padding_type; - PaddingValues padding_values; - // TODO(starka): This was just "stride", so check that width+height is OK. - int16 stride_width; - int16 stride_height; - int16 dilation_width_factor; - int16 dilation_height_factor; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct DepthToSpaceParams { - int32_t block_size; -}; - -struct DepthwiseParams { - PaddingType padding_type; - PaddingValues padding_values; - int16 stride_width; - int16 stride_height; - int16 dilation_width_factor; - int16 dilation_height_factor; - int16 depth_multiplier; - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - const int32_t* output_multiplier_per_channel; - const int32_t* output_shift_per_channel; -}; - -struct DequantizationParams { - double scale; - int32_t zero_point; -}; - -struct PerChannelDequantizationParams { - const float* scale; - const int32_t* zero_point; - int32_t quantized_dimension; -}; - -struct FakeQuantParams { - MinMax minmax; - int32_t num_bits; -}; - -struct FullyConnectedParams { - // uint8_t inference params. - // TODO(b/65838351): Use smaller types if appropriate. - int32_t input_offset; - int32_t weights_offset; - int32_t output_offset; - int32_t output_multiplier; - int output_shift; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; - // Mark the operands as cacheable if they are unchanging, e.g. weights. - bool lhs_cacheable; - bool rhs_cacheable; - FullyConnectedWeightsFormat weights_format; -}; - -struct GatherParams { - int16 axis; -}; - -struct L2NormalizationParams { - // uint8_t inference params. - int32_t input_zero_point; -}; - -struct LocalResponseNormalizationParams { - int32_t range; - double bias; - double alpha; - double beta; -}; - -struct HardSwishParams { - // zero_point of the input activations. - int16_t input_zero_point; - // zero_point of the output activations. - int16_t output_zero_point; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // "relu-ish scale", which 3.0/32768. - // See the implementation of HardSwishPrepare. - int16_t reluish_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int reluish_multiplier_exponent; - // 16bit fixed-point component of the multiplier to apply to go from the - // "high-res input scale", which is the input scale multiplied by 2^7, to the - // output scale. - // See the implementation of HardSwishPrepare. - int16_t output_multiplier_fixedpoint_int16; - // exponent/bit-shift component of the aforementioned multiplier. - int output_multiplier_exponent; -}; - -struct LogisticParams { - // uint8_t inference params. - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -struct LstmCellParams { - int32_t weights_zero_point; - int32_t accum_multiplier; - int accum_shift; - int state_integer_bits; -}; - -struct MeanParams { - int8_t axis_count; - int16 axis[4]; -}; - -struct PackParams { - int8_t axis; - const int32_t* input_zeropoint; - const float* input_scale; - uint16 inputs_count; - int32_t output_zeropoint; - float output_scale; -}; - -struct PadParams { - int8_t left_padding_count; - int32_t left_padding[4]; - int8_t right_padding_count; - int32_t right_padding[4]; - ResizingCategory resizing_category; -}; - -struct PreluParams { - int32_t input_offset; - int32_t alpha_offset; - int32_t output_offset; - int32_t output_multiplier_1; - int32_t output_shift_1; - int32_t output_multiplier_2; - int32_t output_shift_2; -}; - -struct PoolParams { - FusedActivationFunctionType activation; - PaddingType padding_type; - PaddingValues padding_values; - int stride_height; - int stride_width; - int filter_height; - int filter_width; - // uint8_t, etc, activation params. - int32_t quantized_activation_min; - int32_t quantized_activation_max; - // float activation params. - float float_activation_min; - float float_activation_max; -}; - -struct ReshapeParams { - int8_t shape_count; - int32_t shape[4]; -}; - -struct ResizeBilinearParams { - bool align_corners; - // half_pixel_centers assumes pixels are of half the actual dimensions, and - // yields more accurate resizes. Corresponds to the same argument for the - // original TensorFlow op in TF2.0. - bool half_pixel_centers; -}; - -struct ResizeNearestNeighborParams { - bool align_corners; - bool half_pixel_centers; -}; - -struct SliceParams { - int8_t begin_count; - int32_t begin[4]; - int8_t size_count; - int32_t size[4]; -}; - -struct SoftmaxParams { - // beta is not really used (not a Tensorflow parameter) and not implemented - // for LogSoftmax. - double beta; - // uint8_t inference params. Used even when beta defaults to 1.0. - int32_t input_multiplier; - int32_t input_left_shift; - // Reverse scaling is only used by LogSoftmax. - int32_t reverse_scaling_divisor; - int32_t reverse_scaling_right_shift; - int diff_min; - int32_t zero_point; - float scale; - float* table; - int16_t* exp_lut; - int16_t* one_over_one_plus_x_lut; - uint8_t* uint8_table1; - uint8_t* uint8_table2; -}; - -struct SpaceToBatchParams { - // "Zero" padding for uint8_t means padding with the output offset. - int32_t output_offset; -}; - -struct SpaceToDepthParams { - int32_t block_size; -}; - -struct SplitParams { - // Graphs that split into, say, 2000 nodes are encountered. The indices in - // OperatorEdges are of type uint16. - uint16 num_split; - int16 axis; -}; - -struct SqueezeParams { - int8_t squeeze_dims_count; - int32_t squeeze_dims[4]; -}; - -struct StridedSliceParams { - int8_t start_indices_count; - int32_t start_indices[5]; - int8_t stop_indices_count; - int32_t stop_indices[5]; - int8_t strides_count; - int32_t strides[5]; - - int16 begin_mask; - int16 ellipsis_mask; - int16 end_mask; - int16 new_axis_mask; - int16 shrink_axis_mask; -}; - -struct TanhParams { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -struct TransposeParams { - int8_t perm_count; - int32_t perm[5]; -}; - -struct UnpackParams { - uint16 num_split; - int16 axis; -}; - -struct LeakyReluParams { - float alpha; - int32_t input_offset; - int32_t output_offset; - int32_t output_multiplier_alpha; - int32_t output_shift_alpha; - int32_t output_multiplier_identity; - int32_t output_shift_identity; -}; - -template -inline void SetActivationParams(float min, float max, P* params) { - params->float_activation_min = min; - params->float_activation_max = max; -} - -template -inline void SetActivationParams(int32_t min, int32_t max, P* params) { - params->quantized_activation_min = min; - params->quantized_activation_max = max; -} - -template -inline void GetActivationParams(const P& params, int32_t* min, int32_t* max) { - *min = params.quantized_activation_min; - *max = params.quantized_activation_max; -} - -template -inline void GetActivationParams(const P& params, float* min, float* max) { - *min = params.float_activation_min; - *max = params.float_activation_max; -} - -} // namespace tflite_micro -#endif -#endif // CEVA_TYPES_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h deleted file mode 100644 index dd3ffcb2..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer.h +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -// The CircularBuffer op has one input and one output tensor. -extern const int kCircularBufferInputTensor; -extern const int kCircularBufferOutputTensor; - -// Indices into the init flexbuffer's vector. -// The parameter's name is in the comment that follows. -// Elements in the vectors are ordered alphabetically by parameter name. -extern const int kCircularBufferCyclesMaxIndex; // 'cycles_max' - -// These fields control the stride period of a strided streaming model. This op -// returns kTfLiteAbort until cycles_until_run-- is zero. At this time, -// cycles_until_run is reset to cycles_max. -struct OpDataCircularBuffer { - int cycles_until_run; - int cycles_max; -}; - -TfLiteStatus CircularBufferPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_CIRCULAR_BUFFER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h deleted file mode 100644 index 2fbf4fe9..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/circular_buffer_flexbuffers_generated_data.h +++ /dev/null @@ -1,22 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H -#define TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H - -extern const int g_gen_data_size_circular_buffer_config; -extern const unsigned char g_gen_data_circular_buffer_config[]; - -#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h deleted file mode 100644 index 45f3b406..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv.h +++ /dev/null @@ -1,117 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -struct OpDataConv { - TfLitePaddingValues padding; - - // Cached tensor zero point values for quantized operations. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - - // Per channel output multiplier and shift. - int32_t* per_channel_output_multiplier; - int32_t* per_channel_output_shift; - - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - - // A buffer used to store unpacked filter values. This is used if the source - // tensor is of n-bit precision that cannot be easily processed by kernels. - int filter_buffer_index; -}; - -extern const int kConvInputTensor; -extern const int kConvWeightsTensor; -extern const int kConvBiasTensor; -extern const int kConvOutputTensor; -extern const int kConvQuantizedDimension; - -// Returns a ConvParams struct with all the parameters needed for a -// float computation. -ConvParams ConvParamsFloat(const TfLiteConvParams& params, - const OpDataConv& data); - -// Returns a ConvParams struct with all the parameters needed for a -// quantized computation. -ConvParams ConvParamsQuantized(const TfLiteConvParams& params, - const OpDataConv& data); - -TfLiteStatus CalculateOpDataConv(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams& params, int width, - int height, int filter_width, - int filter_height, int out_width, - int out_height, const TfLiteType data_type, - OpDataConv* data); - -void* ConvInit(TfLiteContext* context, const char* buffer, size_t length); - -TfLiteStatus ConvPrepare(TfLiteContext* context, TfLiteNode* node); - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -TFLMRegistration Register_CONV_2D(); - -#if defined(XTENSA) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 activations and int8 weights and always calls the reference -// implementation. -TFLMRegistration Register_CONV_2D_INT8REF(); - -#else -inline TFLMRegistration Register_CONV_2D_INT8REF() { - return Register_CONV_2D(); -} -#endif // defined(XTENSA) - -#if defined(CMSIS_NN) || defined(XTENSA) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 activations and int8 weights and uses the latency optimized -// implementations. -TFLMRegistration Register_CONV_2D_INT8(); - -// Returns a TFLMRegistration struct for kernel variant that only supports -// int16 activations and int8 weights and uses the latency optimized -// implementations. -TFLMRegistration Register_CONV_2D_INT16(); - -#else -inline TFLMRegistration Register_CONV_2D_INT8() { return Register_CONV_2D(); } - -inline TFLMRegistration Register_CONV_2D_INT16() { return Register_CONV_2D(); } -#endif // defined(CMSIS_NN) || defined(XTENSA) - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h deleted file mode 100644 index d23f18c9..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/conv_test.h +++ /dev/null @@ -1,94 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/kernels/kernel_runner.h" -#include "tensorflow/lite/micro/kernels/micro_ops.h" -#include "tensorflow/lite/micro/test_helpers.h" -#include "tensorflow/lite/micro/testing/micro_test.h" - -namespace tflite_micro { -namespace testing { - -TfLiteStatus InvokeConv(TfLiteTensor* tensors, int tensors_size, - int output_length, TfLiteConvParams* conv_params, - TFLMRegistration registration, float* output_data); - -TfLiteStatus InvokeConv(TfLiteTensor* tensors, int tensors_size, - int output_length, TfLiteConvParams* conv_params, - TFLMRegistration registration, int8_t* output_data); - -TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size, - const float* expected_output_data, - int output_length, - TfLiteConvParams* conv_params, - TFLMRegistration registration, - float* output_data, float tolerance = 1e-5); - -TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size, - const int8_t* expected_output_data, - int output_length, - TfLiteConvParams* conv_params, - TFLMRegistration registration, - int8_t* output_data, float tolerance = 1e-5); - -TfLiteStatus TestConvFloat(int* input_dims_data, const float* input_data, - int* filter_dims_data, const float* filter_data, - int* bias_dims_data, const float* bias_data, - int* output_dims_data, - const float* expected_output_data, - TfLiteConvParams* conv_params, - TFLMRegistration registration, float* output_data); - -TfLiteStatus TestConvQuantizedPerChannel( - int* input_dims_data, const float* input_data, int8_t* input_quantized, - float input_scale, int input_zero_point, int* filter_dims_data, - const float* filter_data, int8_t* filter_data_quantized, - int* bias_dims_data, const float* bias_data, int32_t* bias_data_quantized, - float* bias_scales, int* bias_zero_points, int* output_dims_data, - const float* expected_output_data, int8_t* expected_output_data_quantized, - float output_scale, int output_zero_point, TfLiteConvParams* conv_params, - TFLMRegistration registration, int8_t* output_data, - TfLiteType tensor_weight_type = kTfLiteNoType); - -TfLiteStatus TestConvQuantizedPerChannel( - int* input_dims_data, const float* input_data, int16_t* input_quantized, - float input_scale, int input_zero_point, int* filter_dims_data, - const float* filter_data, int8_t* filter_data_quantized, - int* bias_dims_data, const float* bias_data, - std::int64_t* bias_data_quantized, float* bias_scales, - int* bias_zero_points, int* output_dims_data, - const float* expected_output_data, int16_t* expected_output_data_quantized, - float output_scale, int output_zero_point, TfLiteConvParams* conv_params, - TFLMRegistration registration, int16_t* output_data); - -TfLiteStatus TestConvQuantizedPerChannel( - int* input_dims_data, const float* input_data, int16_t* input_quantized, - float input_scale, int input_zero_point, int* filter_dims_data, - const float* filter_data, int8_t* filter_data_quantized, - int* bias_dims_data, const float* bias_data, int32_t* bias_data_quantized, - float* bias_scales, int* bias_zero_points, int* output_dims_data, - const float* expected_output_data, int16_t* expected_output_data_quantized, - float output_scale, int output_zero_point, TfLiteConvParams* conv_params, - TFLMRegistration registration, int16_t* output_data); - -} // namespace testing -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h deleted file mode 100644 index a035d580..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/depthwise_conv.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/conv.h" - -namespace tflite_micro { - -extern const int kDepthwiseConvInputTensor; -extern const int kDepthwiseConvWeightsTensor; -extern const int kDepthwiseConvBiasTensor; -extern const int kDepthwiseConvOutputTensor; -extern const int kDepthwiseConvQuantizedDimension; - -// Returns a DepthwiseParams struct with all the parameters needed for a -// float computation. -DepthwiseParams DepthwiseConvParamsFloat( - const TfLiteDepthwiseConvParams& params, const OpDataConv& data); - -// Returns a DepthwiseParams struct with all the parameters needed for a -// quantized computation. -DepthwiseParams DepthwiseConvParamsQuantized( - const TfLiteDepthwiseConvParams& params, const OpDataConv& data); - -TfLiteStatus CalculateOpDataDepthwiseConv( - TfLiteContext* context, TfLiteNode* node, - const TfLiteDepthwiseConvParams& params, int width, int height, - int filter_width, int filter_height, int out_width, int out_height, - const TfLiteType data_type, OpDataConv* data); - -TfLiteStatus DepthwiseConvPrepare(TfLiteContext* context, TfLiteNode* node); - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -TFLMRegistration Register_DEPTHWISE_CONV_2D(); - -#if defined(CMSIS_NN) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 activations and int8 weights and uses the latency optimized -// implementations. -TFLMRegistration Register_DEPTHWISE_CONV_2D_INT8(); - -// Returns a TFLMRegistration struct for kernel variant that only supports -// int16 activations and int8 weights and uses the latency optimized -// implementations. -TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16(); - -#else -inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT8() { - return Register_DEPTHWISE_CONV_2D(); -} - -inline TFLMRegistration Register_DEPTHWISE_CONV_2D_INT16() { - return Register_DEPTHWISE_CONV_2D(); -} -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h deleted file mode 100644 index 5915cea1..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/dequantize.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -struct DequantizeOpData { - tflite_micro::DequantizationParams quantization_params; - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - int32_t output_zero_point; -}; - -TfLiteStatus DequantizePrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_DEQUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h deleted file mode 100644 index f5b9eae0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/detection_postprocess_flexbuffers_generated_data.h +++ /dev/null @@ -1,25 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H -#define TENSORFLOW_LITE_MICRO_KERNELS_FLEXBUFFERS_GENERATED_DATA_H - -extern const int g_gen_data_size_none_regular_nms; -extern const unsigned char g_gen_data_none_regular_nms[]; - -extern const int g_gen_data_size_regular_nms; -extern const unsigned char g_gen_data_regular_nms[]; - -#endif diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h deleted file mode 100644 index da91066b..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/ethosu.h +++ /dev/null @@ -1,28 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -TFLMRegistration* Register_ETHOSU(); - -const char* GetString_ETHOSU(); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_ETHOSU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h deleted file mode 100644 index 9a7adb8a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/fully_connected.h +++ /dev/null @@ -1,112 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -struct OpDataFullyConnected { - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t output_multiplier; - int output_shift; - // The range of the fused activation layer. For example for kNone and - // uint8_t these would be 0 and 255. - int32_t output_activation_min; - int32_t output_activation_max; - // The index of the temporary tensor where the quantized inputs are cached. - int input_quantized_index; - // Cached zero point values of tensors. - int32_t input_zero_point; - int32_t filter_zero_point; - int32_t output_zero_point; - -// TODO(b/258710417): enable by default once optimized fully-connected works for -// all targets. -#if !defined(HEXAGON) - // A buffer used to store unpacked filter values. This is used if the source - // tensor is of n-bit precision that cannot be easily processed by kernels. - int filter_buffer_index; -#endif -}; - -extern const int kFullyConnectedInputTensor; -extern const int kFullyConnectedWeightsTensor; -extern const int kFullyConnectedBiasTensor; -extern const int kFullyConnectedOutputTensor; - -// Returns a FullyConnectedParams struct with all the parameters needed for a -// float computation. -FullyConnectedParams FullyConnectedParamsFloat( - TfLiteFusedActivation activation); - -// Returns a FullyConnectedParams struct with all the parameters needed for a -// quantized computation. -FullyConnectedParams FullyConnectedParamsQuantized( - const OpDataFullyConnected& op_data); - -TfLiteStatus CalculateOpDataFullyConnected( - TfLiteContext* context, TfLiteFusedActivation activation, - TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter, - const TfLiteTensor* bias, TfLiteTensor* output, OpDataFullyConnected* data); - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -TFLMRegistration Register_FULLY_CONNECTED(); - -#if defined(CMSIS_NN) || defined(HEXAGON) || defined(XTENSA) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8. -TFLMRegistration Register_FULLY_CONNECTED_INT8(); - -#else -// Note that while this block gets used for both reference and optimized kernels -// that do not have any specialized implementations, the only goal here is to -// define fallback implementation that allow reference kernels to still be used -// from applications that call a more specific kernel variant. - -inline TFLMRegistration Register_FULLY_CONNECTED_INT8() { - return Register_FULLY_CONNECTED(); -} - -#endif - -#if defined(CMSIS_NN) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int16. -TFLMRegistration Register_FULLY_CONNECTED_INT16(); - -#else -// Note that while this block gets used for both reference and optimized kernels -// that do not have any specialized implementations, the only goal here is to -// define fallback implementation that allow reference kernels to still be used -// from applications that call a more specific kernel variant. - -inline TFLMRegistration Register_FULLY_CONNECTED_INT16() { - return Register_FULLY_CONNECTED(); -} - -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h deleted file mode 100644 index 18bbf3bb..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/hard_swish.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -extern const int kHardSwishInputTensor; -extern const int kHardSwishOutputTensor; - -TfLiteStatus HardSwishPrepare(TfLiteContext* context, TfLiteNode* node); -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_HARD_SWISH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h deleted file mode 100644 index a0be324f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_runner.h +++ /dev/null @@ -1,86 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" -#include "tensorflow/lite/micro/fake_micro_context.h" -#include "tensorflow/lite/micro/mock_micro_graph.h" - -namespace tflite_micro { -namespace micro { - -// Helper class to perform a simulated kernel (i.e. TFLMRegistration) -// lifecycle (init, prepare, invoke). All internal allocations are handled by -// this class. Simply pass in the registration, list of required tensors, inputs -// array, outputs array, and any pre-builtin data. Calling Invoke() will -// automatically walk the kernel and outputs will be ready on the TfLiteTensor -// output provided during construction. -class KernelRunner { - public: - KernelRunner(const TFLMRegistration& registration, TfLiteTensor* tensors, - int tensors_size, TfLiteIntArray* inputs, - TfLiteIntArray* outputs, const void* builtin_data, - TfLiteIntArray* intermediates = nullptr); - - // Calls init and prepare on the kernel (i.e. TFLMRegistration) struct. - // Any exceptions will be DebugLog'd and returned as a status code. - TfLiteStatus InitAndPrepare(const char* init_data = nullptr, - size_t length = 0); - - // Calls invoke on a given TFLMRegistration pointer. After successful - // invoke, results will be available in the output tensor as passed into the - // constructor of this class. - TfLiteStatus Invoke(); - - // Calls Free on a given TFLMRegistration pointer(if it's implemented). - // After successful Free, kTfLiteOk status will be returned. If Free is not - // implemented for a given kernel kTfLiteError will be returned. - TfLiteStatus Free(); - - // Calls Reset on a given TFLMRegistration pointer(if it's implemented). - // After successful Reset, kTfLiteOk status will be returned. If Free is not - // implemented for a given kernel kTfLiteError will be returned. - TfLiteStatus Reset(); - - // Returns a pointer to the internal MockMicroGraph which KernelRunner uses - // to stub out MicroGraph methods and track invocations on each subgraph. - MockMicroGraph* GetMockGraph() { return &mock_micro_graph_; } - - // Returns true if all temp buffer in tests are deallocated. - // TODO(b/209453859): move this function to private after deallocation checks - // are enabled for all kernel tests. - bool ValidateTempBufferDeallocated(); - - private: - static constexpr int kKernelRunnerBufferSize_ = 10000; - static uint8_t kKernelRunnerBuffer_[kKernelRunnerBufferSize_]; - - TfLiteContext context_ = {}; - TfLiteNode node_ = {}; - const TFLMRegistration& registration_; - - SingleArenaBufferAllocator* allocator_; - MockMicroGraph mock_micro_graph_; - FakeMicroContext fake_micro_context_; -}; - -} // namespace micro -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h deleted file mode 100644 index f8a534fa..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/kernel_util.h +++ /dev/null @@ -1,150 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/micro_context.h" - -namespace tflite_micro { -namespace micro { - -TFLMRegistration RegisterOp( - void* (*init)(TfLiteContext* context, const char* buffer, size_t length), - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node), - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node), - void (*free)(TfLiteContext* context, void* buffer) = nullptr, - void (*reset)(TfLiteContext* context, void* buffer) = nullptr); - -TFLMInferenceRegistration RegisterOp( - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node), - void (*reset)(TfLiteContext* context, void* buffer) = nullptr); - -// Prints out n bytes in a int8_t buffer as hex -void PrintNBytes(const int8_t* tensor_data, int n_bytes, - const char* prefix = nullptr); - -// Prints out the n bytes in a TfLiteEvalTensor as hex -void PrintNBytes(const TfLiteEvalTensor* tensor, int n_bytes, - const char* prefix = nullptr); - -// Prints out n bytes in a TfLiteTensor as hex -void PrintNBytes(const TfLiteTensor* tensor, int n_bytes, - const char* prefix = nullptr); - -// Returns a mutable tensor for a given input index. is_variable must be checked -// during prepare when the full TfLiteTensor is available. -TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context, - const TfLiteNode* node, int index); - -// Returns the TfLiteEvalTensor struct for a given input index in a node. -const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context, - const TfLiteNode* node, int index); - -// Returns the TfLiteEvalTensor struct for a given output index in a node. -TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context, - const TfLiteNode* node, int index); - -// Returns data for a TfLiteEvalTensor struct that are expected to exist. -template -T* GetTensorData(TfLiteEvalTensor* tensor) { - TFLITE_DCHECK(tensor != nullptr); - return reinterpret_cast(tensor->data.raw); -} - -// Returns const data for a TfLiteEvalTensor struct that are expected to exist. -template -const T* GetTensorData(const TfLiteEvalTensor* tensor) { - TFLITE_DCHECK(tensor != nullptr); - return reinterpret_cast(tensor->data.raw); -} - -// Returns data for a TfLiteEvalTensor struct that could be null. -template -T* GetOptionalTensorData(TfLiteEvalTensor* tensor) { - return tensor == nullptr ? nullptr : reinterpret_cast(tensor->data.raw); -} - -// Returns const data for a TfLiteEvalTensor struct that could be null. -template -const T* GetOptionalTensorData(const TfLiteEvalTensor* tensor) { - return tensor == nullptr ? nullptr - : reinterpret_cast(tensor->data.raw); -} - -// Returns the shape of a TfLiteEvalTensor struct. -const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor); - -// Return true if the given tensors have the same shape. -bool HaveSameShapes(const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2); - -PaddingType RuntimePaddingType(TfLitePadding padding); - -// Relocate tensor dims from FlatBuffer to the persistent storage arena. -// The old dims data is copied to the new storage area. -// The tensor and eval_tensor must be the same tensor. -// Only use during Prepare phase. -TfLiteStatus CreateWritableTensorDimsWithCopy(TfLiteContext* context, - TfLiteTensor* tensor, - TfLiteEvalTensor* eval_tensor); - -// Copy all op input tensors to op output tensors. Requires all op input tensor -// shapes and types to be identical to op output tensor shapes and types. -TfLiteStatus CopyOpInputsToOpOutputs(TfLiteContext* context, TfLiteNode* node); - -// Copy all op input tensors to subgraph input tensors. Requires all op input -// tensor shapes and types to be identical to subgraph input tensor shapes and -// types. -TfLiteStatus CopyOpInputsToSubgraphInputs(TfLiteContext* context, - TfLiteNode* node, - MicroGraph* graph_info, - int subgraph_idx, - int first_tensor_idx); - -// Copy all op output tensors to subgraph input tensors. Requires all op output -// tensor shapes and types to be identical to subgraph input tensor shapes and -// types. -TfLiteStatus CopyOpOutputsToSubgraphInputs(TfLiteContext* context, - TfLiteNode* node, - MicroGraph* graph_info, - int subgraph_idx); - -// Copy all subgraph output tensors to op outputs. Requires all subgraph output -// tensor shapes and types to be identical to op output tensor shapes and types. -TfLiteStatus CopySubgraphOutputsToOpOutputs(TfLiteContext* context, - TfLiteNode* node, - MicroGraph* graph_info, - int subgraph_idx); - -// If tensor is INT4, make a new TfLiteEvalTensor with data unpacked into -// a scratch buffer. The returned tensor will have the kTfLiteInt8 type. -// Assume scratch buffer is previously requested in Prepare, and -// scratch_buffer_index can be used to retrieve that buffer. -// If the tensor is not INT4, a shallow copy is returned. -TfLiteEvalTensor MakeUnpackedInt4Tensor(TfLiteContext* context, - int scratch_buffer_index, - const TfLiteEvalTensor* tensor); -} // namespace micro -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h deleted file mode 100644 index 5a05869a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/leaky_relu.h +++ /dev/null @@ -1,43 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -// Input/output tensor index. -extern const int kInputTensor; -extern const int kOutputTensor; - -struct LeakyReluOpData { - // quantization parameters - int32_t output_multiplier_alpha; - int32_t output_shift_alpha; - int32_t output_multiplier_identity; - int32_t output_shift_identity; - int32_t input_zero_point; - int32_t output_zero_point; -}; - -TfLiteStatus CalculateOpDataLeakyRelu(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus LeakyReluPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LEAKY_RELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h deleted file mode 100644 index a2b59ae3..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/logical.h +++ /dev/null @@ -1,35 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { -// Input/output tensor index. -extern const int kLogicalInputTensor1; -extern const int kLogicalInputTensor2; -extern const int kLogicalOutputTensor; - -TfLiteStatus LogicalImpl(TfLiteContext* context, TfLiteNode* node, - bool (*func)(bool, bool)); - -bool LogicalOr(bool x, bool y); -bool LogicalAnd(bool x, bool y); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LOGICAL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h deleted file mode 100644 index 8c7a8ce8..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/logistic.h +++ /dev/null @@ -1,42 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { -extern const int kLogisticInputTensor; -extern const int kLogisticOutputTensor; - -struct OpDataLogistic { - int32_t input_zero_point; - int32_t input_range_radius; - int32_t input_multiplier; - int input_left_shift; -}; - -TfLiteStatus CalculateArithmeticOpDataLogistic(TfLiteContext* context, - TfLiteNode* node, - OpDataLogistic* data); - -TfLiteStatus LogisticPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LOGISTIC_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h deleted file mode 100644 index 8db0971f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval.h +++ /dev/null @@ -1,541 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// Functions to perform integer evaulation for standard LSTM (e.g., defined in -// the keras lstm layer, no peephole etc.). Currently used by the 16 bits -// activation case only - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_GENERAL_H_ -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/lstm_shared.h" -#include "tensorflow/lite/micro/micro_log.h" - -namespace tflite_micro { - -// Interface to access all the TempTfLiteTensors of the LSTM kernel during the -// preparation phase. Can only be constructed through the constructor to avoid -// memory leakage. All TempTfLiteTensors will be deallocated through the -// destructor. -class LstmTensors { - public: - LstmTensors(const LstmTensors& other) = delete; - LstmTensors& operator=(const LstmTensors& other) = delete; - - LstmTensors(TfLiteContext* context, TfLiteNode* node); - ~LstmTensors(); - - // Verify the LSTM internal tensor properties (e.g., type checks) - // Input/output/states/fc weights tensors are required for kernel evaulation. - // The state tensors should be variables. Variants of the standard LSTM - // are not supported here, therefore their corresponding tensors should be - // invalid - TfLiteStatus ValidateTensorStatus(TfLiteContext* context) const; - - // Internal tensors. see lstm_shared.h for tensor names - const TfLiteTensor* GetInternalTensor(const int tensor_index) const { - return internal_tensors_[tensor_index]; - } - - const TfLiteTensor* HiddenStateTensor() const { - return internal_tensors_[kLstmOutputStateTensor]; - } - const TfLiteTensor* CellStateTensor() const { - return internal_tensors_[kLstmCellStateTensor]; - } - const TfLiteTensor* OutputTensor() const { return output_tensor_; } - - private: - // see lstm_shared.h for tensor names - MicroContext* micro_context_; - TfLiteTensor* internal_tensors_[24]; - TfLiteTensor* output_tensor_; -}; - -// Deduce the size information (Batch (B), Time Steps (T), Input dimension (I), -// State dimension (S)) that defines the LSTM using the input and hidden state -// tensor -LstmSizeInfo CreateLstmSizeInfo( - const bool time_major, const TfLiteIntArray* input_tensor_shape, - const TfLiteIntArray* hidden_state_tensor_shape); - -TfLiteStatus ValidateWeightTensorSize(TfLiteContext* context, - const TfLiteTensor* tensor, int dim1_size, - int dim2_size); - -TfLiteStatus ValidateBiasTensorSize(TfLiteContext* context, - const TfLiteTensor* tensor, int size); - -// Go through every tensors and make sure their shape match the kernel -// configuration -TfLiteStatus ValidateTensorSize(TfLiteContext* context, - const LstmTensors& tensors, - const LstmSizeInfo& size_info); - -// Wrapper function to create gate parameters for the four internal LSTM gates -TfLiteStatus CreateGateParams( - TfLiteContext* context, - /*Input tensors*/ - const TfLiteTensor* input, const TfLiteTensor* input_weight, - const TfLiteTensor* input_bias, - /*Hidden state tensors*/ - const TfLiteTensor* hidden_state, const TfLiteTensor* hidden_state_weight, - const TfLiteTensor* hidden_state_bias, - /*Scale of the fc output (input to non-linear activation)*/ - const float nonlinear_activation_input_scale, const TfLiteType cell_type, - const tflite_micro::GateParameters& gate_params); - -// Create parameters for element wise multiplication that happens in a) cell -// state update ; b) hidden state update -// Note that all the output of gates are symmetrically quantized so only scales -// are required for input. However, during the hidden state update phase, the -// output is the updated hidden state, which is asymmetrically quantized. Thus -// output may require zero point -tflite_micro::ArithmeticParams CreateInterGateMulParams(const float input1_scale, - const float input2_scale, - const float output_scale, - const TfLiteType output_type, - const int output_zp = 0); - -// Create the additional information about the cell state, which include: -// cell_state_scale_power: used in integer nonlinear function (e.g., tanh) -// quantized_cell_clip: quantized cell clip range -CellStateInfo CreateLstmCellStateInfo(const float cell_state_scale, - const float cell_clip); - -CellStateInfo CreateLstmCellStateInfoFloat(const float cell_clip); -tflite_micro::FullyConnectedParams CreateFCParamsFloat(); - -tflite_micro::GateParameters CreateGateParamsFloat(); - -tflite_micro::ArithmeticParams CreateInterGateMulParamsFloat(); - -TfLiteStatus PrepareGateParametersFloat(TfLiteContext* context, - const LstmTensors& lstm_tensors, - OpDataLSTM* op_data_lstm); - -TfLiteStatus PrepareGateParametersInteger(TfLiteContext* context, - const LstmTensors& lstm_tensors, - OpDataLSTM* op_data_lstm); - -LSTMKernelContents CreateLSTMKernelContent(TfLiteContext* context, - TfLiteNode* node); - -template -LSTMBuffers CreateLSTMBuffers(TfLiteContext* context, - const int* buffer_indices) { - LSTMBuffers buffers; - buffers.buffer0 = reinterpret_cast( - context->GetScratchBuffer(context, buffer_indices[0])); - buffers.buffer1 = reinterpret_cast( - context->GetScratchBuffer(context, buffer_indices[1])); - buffers.buffer2 = reinterpret_cast( - context->GetScratchBuffer(context, buffer_indices[2])); - buffers.buffer3 = reinterpret_cast( - context->GetScratchBuffer(context, buffer_indices[3])); - return buffers; -} - -// Since LSTM includes multiple intermediate stages, introducing the internal -// namespace to expose them for testing -namespace lstm_internal { - -void Sigmoid(const RuntimeShape& data_shape, int16_t* data); - -void Sigmoid(const RuntimeShape& data_shape, float* data); - -void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape, - int16_t* input_data, const RuntimeShape& output_data_shape, - int16_t* output_data); - -void Tanh(int32_t cell_state_scale_power, const RuntimeShape& input_data_shape, - float* input_data, const RuntimeShape& output_data_shape, - float* output_data); - -void Mul(const RuntimeShape& shape, const ArithmeticParams& params, - const int16_t* input1_data, const int16_t* input2_data, - int8_t* output_data); - -void Mul(const RuntimeShape& shape, const ArithmeticParams& params, - const int16_t* input1_data, const int16_t* input2_data, - int16_t* output_data); - -void Mul(const RuntimeShape& shape, const ArithmeticParams& params, - const float* input1_data, const float* input2_data, - float* output_data); - -void FullyConnected(const FullyConnectedParams& params, - const RuntimeShape& input_shape, const int8_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const int32_t* bias_data, - const RuntimeShape& output_shape, int16_t* output_data); - -void FullyConnected(const FullyConnectedParams& params, - const RuntimeShape& input_shape, const int16_t* input_data, - const RuntimeShape& filter_shape, const int8_t* filter_data, - const RuntimeShape& bias_shape, const int64_t* bias_data, - const RuntimeShape& output_shape, int16_t* output_data); - -void FullyConnected(const FullyConnectedParams& params, - const RuntimeShape& input_shape, const float* input_data, - const RuntimeShape& filter_shape, const float* filter_data, - const RuntimeShape& bias_shape, const float* bias_data, - const RuntimeShape& output_shape, float* output_data); - -void AddElementWise(const int16_t* input_1, const int16_t* input_2, int n_batch, - int n_input, int16_t* output); - -void AddElementWise(const float* input_1, const float* input_2, int n_batch, - int n_input, float* output); - -void Clipping(const int v_size, const CellStateInfo& cell_state_info, - int16_t* vector); - -void Clipping(const int v_size, const CellStateInfo& cell_state_info, - float* vector); - -// Manages the slice position (offset), slice length (sliced tensor shape), -// and update rules for input/output/hidden state/cell state tensors at each -// time step. -class LstmStepManager { - public: - LstmStepManager() = delete; - // Does not take any ownership, and all pointers must refer to valid objects - // that outlive the one constructed. - explicit LstmStepManager(const LstmSizeInfo* size_info) - : size_info_(*size_info) {} - - void UpdateTime(); - void UpdateBatch(); - - void ResetTime() { current_time_ = 0; } - RuntimeShape InputShape() const; - RuntimeShape StateShape() const; - - int InputOffset() const { return input_offset_; } - int OutputOffset() const { return output_offset_; } - int HiddenStateOffset() const { return hidden_state_offset_; } - int CellStateOffset() const { return cell_state_offset_; } - - private: - int current_time_ = 0; - int current_batch_ = 0; - int input_offset_ = 0; - int output_offset_ = 0; - int hidden_state_offset_ = 0; - int cell_state_offset_ = 0; - // Sizeinfo is from LstmOpData, which reside in the memory arena - // (guarante to outlast LSTMStepManager, which reside in stack) - const LstmSizeInfo& size_info_; -}; - -// Calculates a single LSTM gate. -// Implements the following formula: -// gate = activate(FC(input) + FC(recurrent)) -// Activation is sigmoid except for the "cell" gate (configurable, usually tanh) -template -void CalculateLstmGate( - const LstmStepManager& step_info, const GateParameters& gate_params, - // Input FC - const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight, - const TfLiteEvalTensor* input_bias, - // Recurrent FC - const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight, - const TfLiteEvalTensor* recurrent_bias, - // Output - CellType* gate_output, - // Scratch arrays - CellType* fc_output_buffer, const TfLiteFusedActivation activation) { - const auto gate_output_shape = step_info.StateShape(); - // Check offset validity to avoid memory overflow - TFLITE_DCHECK_LE(step_info.InputOffset() + step_info.InputShape().FlatSize(), - tflite_micro::micro::GetTensorShape(input).FlatSize()); - TFLITE_DCHECK_LE( - step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(), - tflite_micro::micro::GetTensorShape(recurrent).FlatSize()); - - // Input FC - FullyConnected(gate_params.input_fc_params, step_info.InputShape(), - tflite_micro::micro::GetTensorData(input) + - step_info.InputOffset(), - micro::GetTensorShape(input_weight), - tflite_micro::micro::GetTensorData(input_weight), - tflite_micro::micro::GetTensorShape(input_bias), - tflite_micro::micro::GetOptionalTensorData(input_bias), - gate_output_shape, gate_output); - - // Recurrent FC - FullyConnected(gate_params.recurrent_fc_params, step_info.StateShape(), - tflite_micro::micro::GetTensorData(recurrent) + - step_info.HiddenStateOffset(), - tflite_micro::micro::GetTensorShape(recurrent_weight), - tflite_micro::micro::GetTensorData(recurrent_weight), - tflite_micro::micro::GetTensorShape(recurrent_bias), - tflite_micro::micro::GetOptionalTensorData(recurrent_bias), - gate_output_shape, fc_output_buffer); - - AddElementWise(gate_output, fc_output_buffer, - /*n_batch=*/gate_output_shape.DimsData()[0], - /*n_state=*/gate_output_shape.DimsData()[1], gate_output); - // Apply activation - switch (activation) { - case kTfLiteActSigmoid: - Sigmoid(gate_output_shape, gate_output); - break; - case kTfLiteActTanh: { - // Set the scale power to -12 to avoid shift - Tanh(/*cell_state_scale_power=*/-12, gate_output_shape, gate_output, - gate_output_shape, gate_output); - } break; - default: - // Only Sigmoid or Tanh is used. - TFLITE_ASSERT_FALSE; - } -} - -// Update the cell state using the output from the forget gate, input gate, and -// cell gate Formula: updated_cell_state = forget_gate_output*cell_state + -// input_gate_output * cell_gate_output, where * denotes element wise -// multiplication -template -void UpdateLstmCell(const LstmStepManager& step_info, - TfLiteEvalTensor* cell_state, - // Gate outputs - CellType* forget_gate_output, - const CellType* input_gate_output, - const CellType* cell_gate_output, - // Mul parameters - const ArithmeticParams& forget_cell_mul_params, - const ArithmeticParams& input_mul_params, - const CellStateInfo& cell_state_info, CellType* buffer) { - // Check offset validity to avoid memory overflow - TFLITE_DCHECK_LE( - step_info.CellStateOffset() + step_info.StateShape().FlatSize(), - tflite_micro::micro::GetTensorShape(cell_state).FlatSize()); - - auto cell_state_shape = step_info.StateShape(); - // Forget Gate x Cell State - Mul(cell_state_shape, forget_cell_mul_params, forget_gate_output, - tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset(), - tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset()); - // Input Gate x Cell Gate - Mul(cell_state_shape, input_mul_params, input_gate_output, cell_gate_output, - buffer); - - // Update the cell state - AddElementWise(tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset(), - buffer, - /*n_batch=*/cell_state_shape.DimsData()[0], - /*n_state=*/cell_state_shape.DimsData()[1], - tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset()); - - if (cell_state_info.cell_clip > 0) { - Clipping(cell_state_shape.FlatSize(), cell_state_info, - tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset()); - } -} - -// Update the hidden state of the LSTM kernel using the following formula: -// updated_hidden_state = Tanh(updated_cell_state) * output_gate_output, * means -// element wise multiplication -template -void UpdateLstmHidden(const LstmStepManager& step_info, - TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* hidden_state, - const CellType* output_gate_output, - const ArithmeticParams& mul_params, - int32_t cell_state_scale_power, CellType* buffer) { - // Check offset validity to avoid memory overflow - TFLITE_DCHECK_LE( - step_info.CellStateOffset() + step_info.StateShape().FlatSize(), - tflite_micro::micro::GetTensorShape(cell_state).FlatSize()); - TFLITE_DCHECK_LE( - step_info.HiddenStateOffset() + step_info.StateShape().FlatSize(), - tflite_micro::micro::GetTensorShape(hidden_state).FlatSize()); - - auto cell_state_shape = step_info.StateShape(); - CellType* cell_state_data = - tflite_micro::micro::GetTensorData(cell_state) + - step_info.CellStateOffset(); - // Tanh(cell_state) - Tanh(cell_state_scale_power, cell_state_shape, cell_state_data, - cell_state_shape, buffer); - // Update the hidden state - Mul(cell_state_shape, mul_params, buffer, output_gate_output, - tflite_micro::micro::GetTensorData(hidden_state) + - step_info.HiddenStateOffset()); -} - -template -void LstmStep(const LstmStepManager& step_info, const OpDataLSTM& op_data, - LSTMKernelContents& kernel_content, - const LSTMBuffers& buffers) { - /*Step1: Calculate gate outputs to prepare cell state update*/ - CellType* gate_internal_buffer = buffers.buffer3; - CellType* forget_gate_output = buffers.buffer0; - CalculateLstmGate( - step_info, op_data.forget_gate_parameters, - // Input FC - kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmInputToForgetWeightsTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmForgetGateBiasTensor), - // Recurrent FC - kernel_content.HiddenStateTensor(), - kernel_content.GetInternalTensor( - tflite_micro::kLstmRecurrentToForgetWeightsTensor), - /*recurrent_bias*/ nullptr, - // Output - forget_gate_output, - // Scratch arrays - gate_internal_buffer, kTfLiteActSigmoid); - - // Input Gate calculation; - CellType* input_gate_output = buffers.buffer1; - CalculateLstmGate( - step_info, op_data.input_gate_parameters, - // Input FC - kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmInputToInputWeightsTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmInputGateBiasTensor), - // Recurrent FC - kernel_content.HiddenStateTensor(), - kernel_content.GetInternalTensor( - tflite_micro::kLstmRecurrentToInputWeightsTensor), - /*recurrent_bias*/ nullptr, - // Output - input_gate_output, - // Scratch arrays - gate_internal_buffer, kTfLiteActSigmoid); - - // Cell Gate calculation - CellType* cell_gate_output = buffers.buffer2; - CalculateLstmGate( - step_info, op_data.cell_gate_parameters, - // Input FC - kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmInputToCellWeightsTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmCellGateBiasTensor), - // Recurrent FC - kernel_content.HiddenStateTensor(), - kernel_content.GetInternalTensor( - tflite_micro::kLstmRecurrentToCellWeightsTensor), - /*recurrent_bias*/ nullptr, - // Output - cell_gate_output, - // Scratch arrays - gate_internal_buffer, op_data.cell_gate_nonlinear_type); - - /*Step2: update the cell state */ - const InterGateParameters& inter_gate_params = op_data.inter_gate_parameters; - CellType* updated_input_buffer = buffers.buffer1; // reuse buffer - - UpdateLstmCell(step_info, kernel_content.CellStateTensor(), - forget_gate_output, input_gate_output, - cell_gate_output, - inter_gate_params.forget_cell_mul_params, - inter_gate_params.input_mul_params, - op_data.cell_state_info, updated_input_buffer); - - /*Step3: update the hidden state */ - CellType* output_gate_output = buffers.buffer1; // reuse buffer - CalculateLstmGate( - step_info, op_data.output_gate_parameters, - // Input FC - kernel_content.GetInternalTensor(tflite_micro::kLstmInputTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmInputToOutputWeightsTensor), - kernel_content.GetInternalTensor(tflite_micro::kLstmOutputGateBiasTensor), - // Recurrent FC - kernel_content.HiddenStateTensor(), - kernel_content.GetInternalTensor( - tflite_micro::kLstmRecurrentToOutputWeightsTensor), - /*recurrent_bias*/ nullptr, - // Output - output_gate_output, - // Scratch arrays - gate_internal_buffer, kTfLiteActSigmoid); - - CellType* tanh_activated_cell_buffer = buffers.buffer0; // reuse buffer - tflite_micro::lstm_internal::UpdateLstmHidden( - step_info, kernel_content.CellStateTensor(), - kernel_content.HiddenStateTensor(), output_gate_output, - inter_gate_params.output_mul_params, - op_data.cell_state_info.cell_state_scale_power, - tanh_activated_cell_buffer); - - /*Step4: copy the update the hidden state to output*/ - // Check offset validity to avoid memory overflow - TFLITE_DCHECK_LE( - step_info.OutputOffset() + step_info.StateShape().FlatSize(), - tflite_micro::micro::GetTensorShape(kernel_content.output_tensor).FlatSize()); - // record the output (from the updated hidden state) - ActivationType* output_ptr = tflite_micro::micro::GetTensorData( - kernel_content.output_tensor); - const auto* hidden_state = kernel_content.HiddenStateTensor(); - std::memcpy(output_ptr + step_info.OutputOffset(), - tflite_micro::micro::GetTensorData(hidden_state) + - step_info.HiddenStateOffset(), - step_info.StateShape().FlatSize() * sizeof(ActivationType)); -} - -} // namespace lstm_internal - -// Evaulate the LSTM kernel with (potential) multi-steps and multi-batch input -// Since -template -TfLiteStatus EvalLstm(const OpDataLSTM& op_data, - LSTMKernelContents& kernel_content, - const LSTMBuffers& buffers) { - lstm_internal::LstmStepManager step_info(&op_data.size_info); - const auto& size_info = op_data.size_info; - // time is the first dimention, enable batch computation - if (size_info.time_major) { - for (int t = 0; t < size_info.time_steps; t++) { - lstm_internal::LstmStep( - step_info, op_data, kernel_content, buffers); - // prepare for the next time step - step_info.UpdateTime(); - } - } else { - // batch first, unable to size the input data. single batch inference - for (int b = 0; b < size_info.batch_size; b++) { - for (int t = 0; t < size_info.time_steps; t++) { - lstm_internal::LstmStep( - step_info, op_data, kernel_content, buffers); - // prepare for the next time step - step_info.UpdateTime(); - } - // prepare for the next batch - step_info.UpdateBatch(); - step_info.ResetTime(); - } - } - return kTfLiteOk; -} -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_16ACT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h deleted file mode 100644 index 10e443de..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_eval_test.h +++ /dev/null @@ -1,817 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ - -#include -#include - -#include "tensorflow/lite/micro/kernels/lstm_eval.h" -#include "tensorflow/lite/micro/kernels/testdata/lstm_test_data.h" -#include "tensorflow/lite/micro/test_helpers.h" -#include "tensorflow/lite/micro/testing/micro_test.h" - -namespace tflite_micro { -namespace testing { - -/*Helper Functions (mainly about mimicking the kernel preparation)*/ - -// Create fully connected parameters using quantization settings of input and -// weight tensors. -// Since TfLiteContext is not available during the kernel test, here we mimic -// (put into stack memory) CalculateOpDataFullyConnected in -// tensorflow/lite/micro/kernels/fully_connected_common.cc -template -tflite_micro::FullyConnectedParams CreateFCParams( - const TensorQuantizationParameters& input_quant_params, - const TensorQuantizationParameters& weight_quant_params, - const float nonlinear_activation_input_scale) { - OpDataFullyConnected data; - const double input_product_scale = - input_quant_params.scale * weight_quant_params.scale; - double effective_scale = - input_product_scale / - static_cast(nonlinear_activation_input_scale); - - QuantizeMultiplier(effective_scale, &data.output_multiplier, - &data.output_shift); - - data.input_zero_point = input_quant_params.zero_point; - - data.filter_zero_point = 0; // symmetrically quantized - data.output_zero_point = 0; // symmetrically quantized - - data.output_activation_min = std::numeric_limits::min(); - data.output_activation_max = std::numeric_limits::max(); - - return tflite_micro::FullyConnectedParamsQuantized(data); -} - -inline tflite_micro::FullyConnectedParams CreateFCParamsFloat() { - FullyConnectedParams op_params; - CalculateActivationRange(kTfLiteActNone, &op_params.float_activation_min, - &op_params.float_activation_max); - return op_params; -} - -// Wrapper function to create gate parameters for the four internal LSTM gates -template -tflite_micro::GateParameters CreateGateParams( - const TensorQuantizationParameters& input_quant_params, - const TensorQuantizationParameters& hidden_state_quant_params, - const GateQuantizationParameters& gate_quantization_settings, - const float nonlinear_activation_input_scale) { - tflite_micro::GateParameters gate_params = {}; - gate_params.input_fc_params = CreateFCParams( - input_quant_params, gate_quantization_settings.activation_weight, - nonlinear_activation_input_scale); - gate_params.recurrent_fc_params = CreateFCParams( - hidden_state_quant_params, gate_quantization_settings.recurrent_weight, - nonlinear_activation_input_scale); - return gate_params; -} - -inline tflite_micro::GateParameters CreateGateParamsFloat() { - tflite_micro::GateParameters gate_params = {}; - gate_params.input_fc_params = CreateFCParamsFloat(); - gate_params.recurrent_fc_params = CreateFCParamsFloat(); - return gate_params; -} -// Create parameters for element wise multiplication that happens in a) cell -// state update ; b) hidden state update -// Note that all the output of gates are symmetrically quantized so only scales -// are required for input. However, during the hidden state update phase, the -// output is the updated hidden state, which is asymmetrically quantized. Thus -// output may require zero point -template -tflite_micro::ArithmeticParams CreateInterGateMulParams(const float input1_scale, - const float input2_scale, - const float output_scale, - const int output_zp = 0) { - tflite_micro::ArithmeticParams op_params = {}; - op_params.quantized_activation_min = std::numeric_limits::min(); - op_params.quantized_activation_max = std::numeric_limits::max(); - op_params.input1_offset = 0; - op_params.input2_offset = 0; - op_params.output_offset = output_zp; - - const double input_product_scale = - static_cast(input1_scale) * static_cast(input2_scale); - double effective_scale = - input_product_scale / static_cast(output_scale); - - QuantizeMultiplier(effective_scale, &op_params.output_multiplier, - &op_params.output_shift); - return op_params; -} - -inline tflite_micro::ArithmeticParams CreateInterGateMulParamsFloat() { - tflite_micro::ArithmeticParams op_params = {}; - CalculateActivationRange(kTfLiteActNone, &op_params.float_activation_min, - &op_params.float_activation_max); - return op_params; -} - -// Create the additional information about the cell state, which include: -// cell_state_scale_power: used in integer nonlinear function (e.g., tanh) -// quantized_cell_clip: quantized cell clip range -CellStateInfo CreateLstmCellStateInfo(const float cell_state_scale, - const float cell_clip) { - CellStateInfo cell_state_info; - // cell_state_scale_power: 2^-cell_state_scale_power = cell state scale - int buffer; - tflite_micro::CheckedLog2(cell_state_scale, &buffer); - cell_state_info.cell_state_scale_power = buffer; - // Cell state specifics - cell_state_info.cell_clip = cell_clip; - cell_state_info.quantized_cell_clip = static_cast( - std::min(std::max(static_cast(cell_clip) / - static_cast(cell_state_scale), - -32768.0), - 32767.0)); - return cell_state_info; -} - -// Create LSTMKernelContents from LstmNodeContent by copying TfLiteEvalTensor -// pointers -template -LSTMKernelContents CreateLSTMKernelContent( - LstmNodeContent& - node_contents) { - LSTMKernelContents kernel_content; - // Point to correct tensors - kernel_content.internal_tensors[kLstmInputTensor] = - node_contents.GetEvalTensor(kLstmInputTensor); - kernel_content.internal_tensors[kLstmInputToInputWeightsTensor] = - node_contents.GetEvalTensor(kLstmInputToInputWeightsTensor); - kernel_content.internal_tensors[kLstmInputToForgetWeightsTensor] = - node_contents.GetEvalTensor(kLstmInputToForgetWeightsTensor); - kernel_content.internal_tensors[kLstmInputToCellWeightsTensor] = - node_contents.GetEvalTensor(kLstmInputToCellWeightsTensor); - kernel_content.internal_tensors[kLstmInputToOutputWeightsTensor] = - node_contents.GetEvalTensor(kLstmInputToOutputWeightsTensor); - kernel_content.internal_tensors[kLstmRecurrentToInputWeightsTensor] = - node_contents.GetEvalTensor(kLstmRecurrentToInputWeightsTensor); - kernel_content.internal_tensors[kLstmRecurrentToForgetWeightsTensor] = - node_contents.GetEvalTensor(kLstmRecurrentToForgetWeightsTensor); - kernel_content.internal_tensors[kLstmRecurrentToCellWeightsTensor] = - node_contents.GetEvalTensor(kLstmRecurrentToCellWeightsTensor); - kernel_content.internal_tensors[kLstmRecurrentToOutputWeightsTensor] = - node_contents.GetEvalTensor(kLstmRecurrentToOutputWeightsTensor); - kernel_content.internal_tensors[kLstmInputGateBiasTensor] = - node_contents.GetEvalTensor(kLstmInputGateBiasTensor); - kernel_content.internal_tensors[kLstmForgetGateBiasTensor] = - node_contents.GetEvalTensor(kLstmForgetGateBiasTensor); - kernel_content.internal_tensors[kLstmCellGateBiasTensor] = - node_contents.GetEvalTensor(kLstmCellGateBiasTensor); - kernel_content.internal_tensors[kLstmOutputGateBiasTensor] = - node_contents.GetEvalTensor(kLstmOutputGateBiasTensor); - kernel_content.internal_tensors[kLstmOutputStateTensor] = - node_contents.GetEvalTensor(kLstmOutputStateTensor); - kernel_content.internal_tensors[kLstmOutputGateBiasTensor] = - node_contents.GetEvalTensor(kLstmOutputGateBiasTensor); - kernel_content.internal_tensors[kLstmCellStateTensor] = - node_contents.GetEvalTensor(kLstmCellStateTensor); - // Not used internal tensors - kernel_content.internal_tensors[kLstmCellToInputWeightsTensor] = nullptr; - kernel_content.internal_tensors[kLstmCellToForgetWeightsTensor] = nullptr; - kernel_content.internal_tensors[kLstmCellToOutputWeightsTensor] = nullptr; - kernel_content.internal_tensors[kLstmProjectionWeightsTensor] = nullptr; - kernel_content.internal_tensors[kLstmProjectionBiasTensor] = nullptr; - kernel_content.internal_tensors[kLstmInputLayerNormCoefficientsTensor] = - nullptr; - kernel_content.internal_tensors[kLstmForgetLayerNormCoefficientsTensor] = - nullptr; - kernel_content.internal_tensors[kLstmInputLayerNormCoefficientsTensor] = - nullptr; - kernel_content.internal_tensors[kLstmCellLayerNormCoefficientsTensor] = - nullptr; - kernel_content.internal_tensors[kLstmOutputLayerNormCoefficientsTensor] = - nullptr; - // Output tensor - kernel_content.output_tensor = node_contents.OutputEvalTensor(); - return kernel_content; -} - -// Deduce the size information (Batch (B), Time Steps (T), Input dimension (I), -// State dimension (S)) that defines the LSTM using the input and hidden state -// tensor -LstmSizeInfo CreateLstmSizeInfo( - const bool time_major, const TfLiteIntArray* input_tensor_shape, - const TfLiteIntArray* hidden_state_tensor_shape) { - LstmSizeInfo size_info; - size_info.time_major = time_major; - size_info.batch_size = - time_major ? input_tensor_shape->data[1] : input_tensor_shape->data[0]; - size_info.time_steps = - time_major ? input_tensor_shape->data[0] : input_tensor_shape->data[1]; - size_info.input_dimension = input_tensor_shape->data[2]; - size_info.state_dimension = hidden_state_tensor_shape->data[1]; - return size_info; -} - -// Create the LstmOpData using the LstmNodeContent and -// NodeQuantizationParameters (defined in test_data/lstm_test_data) During the -// actual inference phase, OpDataLSTM is created using information from the -// flatbuffer file. The test divide the complete LSTM node information into -// LstmNodeContent and NodeQuantizationParameters for easy construction -// purposes -template -OpDataLSTM CreateLstmOpData( - LstmNodeContent& - node_contents) { - const auto& builtin_data = node_contents.BuiltinData(); - const auto& quantization_settings = node_contents.QuantizationSettings(); - OpDataLSTM op_data; - - op_data.cell_gate_nonlinear_type = builtin_data.activation; - op_data.size_info = - CreateLstmSizeInfo(builtin_data.time_major, - node_contents.GetEvalTensor(kLstmInputTensor)->dims, - node_contents.HiddenStateEvalTensor()->dims); - - op_data.cell_state_info = CreateLstmCellStateInfo( - quantization_settings.cell_state.scale, builtin_data.cell_clip); - - // Gate Parameters - op_data.forget_gate_parameters = CreateGateParams( - quantization_settings.input, quantization_settings.hidden_state, - quantization_settings.forget_gate, - quantization_settings.nonlinear_activation_input_scale); - op_data.input_gate_parameters = CreateGateParams( - quantization_settings.input, quantization_settings.hidden_state, - quantization_settings.input_gate, - quantization_settings.nonlinear_activation_input_scale); - op_data.cell_gate_parameters = CreateGateParams( - quantization_settings.input, quantization_settings.hidden_state, - quantization_settings.cell_gate, - quantization_settings.nonlinear_activation_input_scale); - op_data.output_gate_parameters = CreateGateParams( - quantization_settings.input, quantization_settings.hidden_state, - quantization_settings.output_gate, - quantization_settings.nonlinear_activation_input_scale); - // Inter gate multiplication parameters - op_data.inter_gate_parameters.forget_cell_mul_params = - CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.cell_state.scale, - quantization_settings.cell_state.scale); - op_data.inter_gate_parameters.input_mul_params = - CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.cell_state.scale); - op_data.inter_gate_parameters.output_mul_params = - CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point); - return op_data; -} - -template -OpDataLSTM CreateLstmOpDataFloat( - LstmNodeContent& node_contents) { - const auto& builtin_data = node_contents.BuiltinData(); - OpDataLSTM op_data; - - op_data.cell_gate_nonlinear_type = builtin_data.activation; - op_data.size_info = - CreateLstmSizeInfo(builtin_data.time_major, - node_contents.GetEvalTensor(kLstmInputTensor)->dims, - node_contents.HiddenStateEvalTensor()->dims); - op_data.cell_state_info.cell_clip = builtin_data.cell_clip; - op_data.cell_state_info.quantized_cell_clip = 0; // No quantization - op_data.cell_state_info.cell_state_scale_power = 0; // No quantization - - // Gate Parameters - op_data.forget_gate_parameters = CreateGateParamsFloat(); - op_data.input_gate_parameters = CreateGateParamsFloat(); - op_data.cell_gate_parameters = CreateGateParamsFloat(); - op_data.output_gate_parameters = CreateGateParamsFloat(); - // Inter gate multiplication parameters - op_data.inter_gate_parameters.forget_cell_mul_params = - CreateInterGateMulParamsFloat(); - op_data.inter_gate_parameters.input_mul_params = - CreateInterGateMulParamsFloat(); - op_data.inter_gate_parameters.output_mul_params = - CreateInterGateMulParamsFloat(); - return op_data; -} - -/*Test Functions Below Here*/ -template -void ValidateResultGoldens(const T* golden, const T* output_data, - const int output_len, const float tolerance) { - for (int i = 0; i < output_len; ++i) { - TF_LITE_MICRO_EXPECT_NEAR(golden[i], output_data[i], tolerance); - } -} - -template -void TestCalculateLstmGateFloat(const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_weight, - const TfLiteEvalTensor* input_bias, - // Recurrent FC - const TfLiteEvalTensor* recurrent, - const TfLiteEvalTensor* recurrent_weight, - const TfLiteEvalTensor* recurrent_bias, - // Result comparison - TfLiteFusedActivation nonlinear_type, - const float* expected_vals, float tolerance) { - float gate_output[batch_size * state_dimension] = {}; - float fc_output_buffer[batch_size * state_dimension] = {}; - - tflite_micro::GateParameters gate_params = CreateGateParamsFloat(); - - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, input->dims, recurrent->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - tflite_micro::lstm_internal::CalculateLstmGate( - step_info, gate_params, - // Input FC - input, input_weight, input_bias, - // Recurrent FC - recurrent, recurrent_weight, recurrent_bias, - // Output - gate_output, - // Scratch arrays - fc_output_buffer, nonlinear_type); - - ValidateResultGoldens(expected_vals, gate_output, - batch_size * state_dimension, tolerance); -} - -template -void TestCalculateLstmGateInteger( - const TfLiteEvalTensor* input, const TfLiteEvalTensor* input_weight, - const TfLiteEvalTensor* input_bias, - // Recurrent FC - const TfLiteEvalTensor* recurrent, const TfLiteEvalTensor* recurrent_weight, - const TfLiteEvalTensor* recurrent_bias, - // Quantization settings - const NodeQuantizationParameters& node_quantization_settings, - const GateQuantizationParameters& gate_quantization_settings, - // Result comparison - TfLiteFusedActivation nonlinear_type, const float* expected_vals, - float tolerance) { - CellType gate_output[batch_size * state_dimension] = {}; - CellType fc_output_buffer[batch_size * state_dimension] = {}; - - tflite_micro::GateParameters gate_params = CreateGateParams( - node_quantization_settings.input, node_quantization_settings.hidden_state, - gate_quantization_settings, - node_quantization_settings.nonlinear_activation_input_scale); - - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, input->dims, recurrent->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - // only int8 weight is supported now - tflite_micro::lstm_internal::CalculateLstmGate( - step_info, gate_params, - // Input FC - input, input_weight, input_bias, - // Recurrent FC - recurrent, recurrent_weight, recurrent_bias, - // Output - gate_output, - // Scratch arrays - fc_output_buffer, nonlinear_type); - - float gate_output_float[batch_size * state_dimension] = {}; - Dequantize(gate_output, batch_size * state_dimension, - node_quantization_settings.nonlinear_activation_output_scale, 0, - gate_output_float); - - ValidateResultGoldens(expected_vals, gate_output_float, - batch_size * state_dimension, tolerance); -} - -template -void TestUpdateLstmCellFloat( - const GateOutputCheckData& gate_output_data, - LstmNodeContent& node_content, - const float tolerance) { - float buffer[batch_size * state_dimension] = {}; - - auto forget_cell_mul_params = CreateInterGateMulParamsFloat(); - auto input_mul_params = CreateInterGateMulParamsFloat(); - - auto cell_state = node_content.CellStateEvalTensor(); - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, - node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, - node_content.HiddenStateEvalTensor()->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - // copy the data since it will be updated - float forget_gate[batch_size * state_dimension] = {}; - std::memcpy(forget_gate, gate_output_data.expected_forget_gate_output, - batch_size * state_dimension * sizeof(float)); - - CellStateInfo cell_state_info; - cell_state_info.cell_clip = node_content.BuiltinData().cell_clip; - // Call the function to be tested - tflite_micro::lstm_internal::UpdateLstmCell( - step_info, cell_state, forget_gate, - gate_output_data.expected_input_gate_output, - gate_output_data.expected_cell_gate_output, forget_cell_mul_params, - input_mul_params, cell_state_info, buffer); - - ValidateResultGoldens(gate_output_data.expected_updated_cell, - tflite_micro::micro::GetTensorData(cell_state), - batch_size * state_dimension, tolerance); -} - -template -void TestUpdateLstmCellInteger( - const GateOutputCheckData& gate_output_data, - LstmNodeContent& node_content, - const float tolerance) { - const auto& quantization_settings = node_content.QuantizationSettings(); - CellType quantized_forget_gate[batch_size * state_dimension] = {}; - tflite_micro::Quantize(gate_output_data.expected_forget_gate_output, - quantized_forget_gate, batch_size * state_dimension, - quantization_settings.nonlinear_activation_output_scale, 0); - - CellType quantized_input_gate[batch_size * state_dimension] = {}; - tflite_micro::Quantize(gate_output_data.expected_input_gate_output, - quantized_input_gate, batch_size * state_dimension, - quantization_settings.nonlinear_activation_output_scale, 0); - - CellType quantized_cell_gate[batch_size * state_dimension] = {}; - tflite_micro::Quantize(gate_output_data.expected_cell_gate_output, - quantized_cell_gate, batch_size * state_dimension, - quantization_settings.nonlinear_activation_output_scale, 0); - - CellType buffer[batch_size * state_dimension] = {}; - - auto forget_cell_mul_params = CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.cell_state.scale, - quantization_settings.cell_state.scale); - auto input_mul_params = CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.cell_state.scale); - - auto cell_state_info = - CreateLstmCellStateInfo(quantization_settings.cell_state.scale, - node_content.BuiltinData().cell_clip); - - auto cell_state = node_content.CellStateEvalTensor(); - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, - node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, - node_content.HiddenStateEvalTensor()->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - // Call the function to be tested - tflite_micro::lstm_internal::UpdateLstmCell( - step_info, cell_state, quantized_forget_gate, quantized_input_gate, - quantized_cell_gate, forget_cell_mul_params, input_mul_params, - cell_state_info, buffer); - - float cell_state_float[batch_size * state_dimension] = {}; - Dequantize(tflite_micro::micro::GetTensorData(cell_state), - batch_size * state_dimension, - quantization_settings.cell_state.scale, - quantization_settings.cell_state.zero_point, cell_state_float); - - ValidateResultGoldens(gate_output_data.expected_updated_cell, - cell_state_float, batch_size * state_dimension, - tolerance); -} - -template -void TestUpdateLstmHiddenFloat( - const GateOutputCheckData& gate_output_data, - LstmNodeContent& node_content, - const float tolerance) { - float buffer[batch_size * state_dimension] = {}; - - auto mul_params = CreateInterGateMulParamsFloat(); - - int32_t cell_state_scale_power = 0; - - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, - node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, - node_content.HiddenStateEvalTensor()->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - auto cell_state = node_content.CellStateEvalTensor(); - auto hidden_state = node_content.HiddenStateEvalTensor(); - - tflite_micro::lstm_internal::UpdateLstmHidden( - step_info, cell_state, hidden_state, - gate_output_data.expected_output_gate_output, mul_params, - cell_state_scale_power, buffer); - - ValidateResultGoldens(gate_output_data.expected_updated_hidden, - tflite_micro::micro::GetTensorData(hidden_state), - batch_size * state_dimension, tolerance); -} - -template -void TestUpdateLstmHiddenInteger( - const GateOutputCheckData& gate_output_data, - LstmNodeContent& node_content, - const float tolerance) { - const auto& quantization_settings = node_content.QuantizationSettings(); - CellType quantized_output_gate[batch_size * state_dimension] = {}; - tflite_micro::Quantize(gate_output_data.expected_output_gate_output, - quantized_output_gate, batch_size * state_dimension, - quantization_settings.nonlinear_activation_output_scale, 0); - - CellType buffer[batch_size * state_dimension] = {}; - - auto mul_params = CreateInterGateMulParams( - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.nonlinear_activation_output_scale, - quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point); - - int cell_state_scale_power_buffer; - tflite_micro::CheckedLog2(quantization_settings.cell_state.scale, - &cell_state_scale_power_buffer); - int32_t cell_state_scale_power = cell_state_scale_power_buffer; - - // Create step information: only one time step, no need to update - auto size_info = tflite_micro::testing::CreateLstmSizeInfo( - /*time_major*/ false, - node_content.GetEvalTensor(tflite_micro::kLstmInputTensor)->dims, - node_content.HiddenStateEvalTensor()->dims); - // revise time_major = true to enable batch inference - size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&size_info); - - auto cell_state = node_content.CellStateEvalTensor(); - auto hidden_state = node_content.HiddenStateEvalTensor(); - - tflite_micro::lstm_internal::UpdateLstmHidden( - step_info, cell_state, hidden_state, quantized_output_gate, mul_params, - cell_state_scale_power, buffer); - - float hidden_state_float[batch_size * state_dimension] = {}; - Dequantize(tflite_micro::micro::GetTensorData(hidden_state), - batch_size * state_dimension, - quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point, hidden_state_float); - - ValidateResultGoldens(gate_output_data.expected_updated_hidden, - hidden_state_float, batch_size * state_dimension, - tolerance); -} - -template -void TestLstmStepFloat( - const GateOutputCheckData& gate_output_data, - const float hidden_state_tolerance, const float cell_state_tolerance, - /*can not be const, state will be updated*/ - LstmNodeContent& node_contents) { - // Mimicking the kernel preparation phase, node_contents approximate the - LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); - LSTMBuffers buffers; - // Scratch buffers on the stack - float buffer0[batch_size * state_dimension] = {}; - buffers.buffer0 = buffer0; - float buffer1[batch_size * state_dimension] = {}; - buffers.buffer1 = buffer1; - float buffer2[batch_size * state_dimension] = {}; - buffers.buffer2 = buffer2; - float buffer3[batch_size * state_dimension] = {}; - buffers.buffer3 = buffer3; - - OpDataLSTM op_data = CreateLstmOpDataFloat(node_contents); - // set time_major to true to test batch inference - op_data.size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&op_data.size_info); - tflite_micro::lstm_internal::LstmStep( - step_info, op_data, kernel_content, buffers); - - ValidateResultGoldens( - gate_output_data.expected_updated_hidden, - tflite_micro::micro::GetTensorData(kernel_content.HiddenStateTensor()), - batch_size * state_dimension, hidden_state_tolerance); - ValidateResultGoldens( - gate_output_data.expected_updated_cell, - tflite_micro::micro::GetTensorData(kernel_content.CellStateTensor()), - batch_size * state_dimension, cell_state_tolerance); -} - -template -void TestLstmStepInteger( - const GateOutputCheckData& gate_output_data, - const float hidden_state_tolerance, const float cell_state_tolerance, - /*can not be const, state will be updated*/ - LstmNodeContent& - node_contents) { - // Mimicking the kernel preparation phase, node_contents approximate the - LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); - LSTMBuffers buffers; - - // Scratch buffers on the stack - CellType buffer0[batch_size * state_dimension] = {}; - buffers.buffer0 = buffer0; - CellType buffer1[batch_size * state_dimension] = {}; - buffers.buffer1 = buffer1; - CellType buffer2[batch_size * state_dimension] = {}; - buffers.buffer2 = buffer2; - CellType buffer3[batch_size * state_dimension] = {}; - buffers.buffer3 = buffer3; - - OpDataLSTM op_data = CreateLstmOpData(node_contents); - // set time_major to true to test batch inference - op_data.size_info.time_major = true; - tflite_micro::lstm_internal::LstmStepManager step_info(&op_data.size_info); - tflite_micro::lstm_internal::LstmStep(step_info, op_data, kernel_content, - buffers); - - const auto& quantization_settings = node_contents.QuantizationSettings(); - float dequantized_hidden_state[batch_size * state_dimension] = {}; - Dequantize( - tflite_micro::micro::GetTensorData( - kernel_content.HiddenStateTensor()), - batch_size * state_dimension, quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point, dequantized_hidden_state); - - float dequantized_cell_state[batch_size * state_dimension] = {}; - Dequantize( - tflite_micro::micro::GetTensorData(kernel_content.CellStateTensor()), - batch_size * state_dimension, quantization_settings.cell_state.scale, - quantization_settings.cell_state.zero_point, dequantized_cell_state); - - ValidateResultGoldens(gate_output_data.expected_updated_hidden, - dequantized_hidden_state, batch_size * state_dimension, - hidden_state_tolerance); - ValidateResultGoldens(gate_output_data.expected_updated_cell, - dequantized_cell_state, batch_size * state_dimension, - cell_state_tolerance); -} - -template -void TestEvalLstmFloat( - const LstmEvalCheckData< - batch_size * time_steps * input_dimension, batch_size * state_dimension, - batch_size * state_dimension * time_steps>& eval_check_data, - const float hidden_state_tolerance, const float cell_state_tolerance, - LstmNodeContent& node_contents) { - // Mimicking the kernel preparation phase, node_contents approximate the node - LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); - // Scratch buffers on the stack - LSTMBuffers buffers; - float buffer0[batch_size * state_dimension] = {}; - buffers.buffer0 = buffer0; - float buffer1[batch_size * state_dimension] = {}; - buffers.buffer1 = buffer1; - float buffer2[batch_size * state_dimension] = {}; - buffers.buffer2 = buffer2; - float buffer3[batch_size * state_dimension] = {}; - buffers.buffer3 = buffer3; - - OpDataLSTM op_data = CreateLstmOpDataFloat(node_contents); - - tflite_micro::EvalLstm(op_data, kernel_content, - buffers); - - ValidateResultGoldens(eval_check_data.expected_hidden_state, - node_contents.GetHiddenStateData(), - batch_size * state_dimension, hidden_state_tolerance); - - ValidateResultGoldens(eval_check_data.expected_cell_state, - node_contents.GetCellStateData(), - batch_size * state_dimension, cell_state_tolerance); - - ValidateResultGoldens(eval_check_data.expected_output, - node_contents.GetOutputData(), - batch_size * state_dimension, hidden_state_tolerance); -} - -template -void TestEvalLstmInteger( - const LstmEvalCheckData< - batch_size * time_steps * input_dimension, batch_size * state_dimension, - batch_size * state_dimension * time_steps>& eval_check_data, - const float hidden_state_tolerance, const float cell_state_tolerance, - LstmNodeContent& - node_contents) { - // Mimicking the kernel preparation phase, node_contents approximate the node - LSTMKernelContents kernel_content = CreateLSTMKernelContent(node_contents); - // Scratch buffers on the stack - LSTMBuffers buffers; - CellType buffer0[batch_size * state_dimension] = {}; - buffers.buffer0 = buffer0; - CellType buffer1[batch_size * state_dimension] = {}; - buffers.buffer1 = buffer1; - CellType buffer2[batch_size * state_dimension] = {}; - buffers.buffer2 = buffer2; - CellType buffer3[batch_size * state_dimension] = {}; - buffers.buffer3 = buffer3; - - OpDataLSTM op_data = CreateLstmOpData(node_contents); - - tflite_micro::EvalLstm( - op_data, kernel_content, buffers); - - const auto& quantization_settings = node_contents.QuantizationSettings(); - float dequantized_hidden_state[batch_size * state_dimension] = {}; - Dequantize(node_contents.GetHiddenStateData(), batch_size * state_dimension, - quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point, - dequantized_hidden_state); - - ValidateResultGoldens(eval_check_data.expected_hidden_state, - dequantized_hidden_state, batch_size * state_dimension, - hidden_state_tolerance); - - float dequantized_cell_state[batch_size * state_dimension] = {}; - Dequantize(node_contents.GetCellStateData(), batch_size * state_dimension, - quantization_settings.cell_state.scale, - quantization_settings.cell_state.zero_point, - dequantized_cell_state); - ValidateResultGoldens(eval_check_data.expected_cell_state, - dequantized_cell_state, batch_size * state_dimension, - cell_state_tolerance); - - float dequantized_output[batch_size * state_dimension * time_steps] = {}; - Dequantize(node_contents.GetOutputData(), - batch_size * state_dimension * time_steps, - quantization_settings.output.scale, - quantization_settings.output.zero_point, dequantized_output); - ValidateResultGoldens(eval_check_data.expected_output, dequantized_output, - batch_size * state_dimension, hidden_state_tolerance); -} - -} // namespace testing -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h deleted file mode 100644 index d30728b9..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/lstm_shared.h +++ /dev/null @@ -1,150 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -// Input Tensors of size {n_batch, n_input} -constexpr int kLstmInputTensor = 0; - -// Input weight tensors of size: {n_cell, n_input} -constexpr int kLstmInputToInputWeightsTensor = 1; // Optional -constexpr int kLstmInputToForgetWeightsTensor = 2; -constexpr int kLstmInputToCellWeightsTensor = 3; -constexpr int kLstmInputToOutputWeightsTensor = 4; - -// Recurrent weight tensors of size {n_cell, n_output} -constexpr int kLstmRecurrentToInputWeightsTensor = 5; // Optional -constexpr int kLstmRecurrentToForgetWeightsTensor = 6; -constexpr int kLstmRecurrentToCellWeightsTensor = 7; -constexpr int kLstmRecurrentToOutputWeightsTensor = 8; - -// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. -constexpr int kLstmCellToInputWeightsTensor = 9; // Optional -constexpr int kLstmCellToForgetWeightsTensor = 10; // Optional -constexpr int kLstmCellToOutputWeightsTensor = 11; // Optional - -// Gates bias tensors of size {n_cell} -constexpr int kLstmInputGateBiasTensor = 12; // Optional -constexpr int kLstmForgetGateBiasTensor = 13; -constexpr int kLstmCellGateBiasTensor = 14; -constexpr int kLstmOutputGateBiasTensor = 15; - -// Projection weight tensor of size {n_output, n_cell} -constexpr int kLstmProjectionWeightsTensor = 16; // Optional -// Projection bias tensor of size {n_output} -constexpr int kLstmProjectionBiasTensor = 17; // Optional - -// These state tensors are defined as variable tensors, and will be modified by -// this op. -constexpr int kLstmOutputStateTensor = 18; -constexpr int kLstmCellStateTensor = 19; - -// Layer norm coefficient tensors of size {n_cell}, representing a diagonal -// matrix. -constexpr int kLstmInputLayerNormCoefficientsTensor = 20; // Optional -constexpr int kLstmForgetLayerNormCoefficientsTensor = 21; // Optional -constexpr int kLstmCellLayerNormCoefficientsTensor = 22; // Optional -constexpr int kLstmOutputLayerNormCoefficientsTensor = 23; // Optional - -// Output tensors. -constexpr int kLstmOutputTensor = 0; - -// Parameters for the two fully conncted computation inside each gate -struct GateParameters { - FullyConnectedParams input_fc_params; - FullyConnectedParams recurrent_fc_params; -}; - -// Paramaters for the element wise multiplications between gate outputs -struct InterGateParameters { - ArithmeticParams forget_cell_mul_params; - ArithmeticParams input_mul_params; - ArithmeticParams output_mul_params; -}; - -// Size information about the LSTM kernel, which is deduced from tensors stored -// in the flat buffer file. -struct LstmSizeInfo { - bool time_major; - int batch_size; - int time_steps; - int input_dimension; - int state_dimension; -}; - -// Contains information about the cell state tensor -struct CellStateInfo { - float cell_clip; - // clipping range for cell state only 16 bits cell is supported (could be - // generalized through templatation) - int16_t quantized_cell_clip; - // 2^-cell_state_scale_power = cell state scale, required by integer tanh - // computation - int32_t cell_state_scale_power; -}; - -// Contains required computation information for LSTM kernel evaluation. -// Specifically, it includes shape and quantization settings for the LSTM -// internal operations. Formatted to support operations defined in the -// tensorflow/lite/kernels/internal/reference/integer_ops -// Should be constructed during the preparation phase -struct OpDataLSTM { - LstmSizeInfo size_info; - CellStateInfo cell_state_info; - TfLiteFusedActivation cell_gate_nonlinear_type; - GateParameters forget_gate_parameters; - GateParameters input_gate_parameters; - GateParameters cell_gate_parameters; - GateParameters output_gate_parameters; - InterGateParameters inter_gate_parameters; - int buffer_indices[4]; // TFLM only -}; - -// Provide an interface to access the internal tensors and buffers used for LSTM -// invocation. Constructed during the invocation phase -struct LSTMKernelContents { - public: - // Internal tensors, fixed (const). see lstm_shared.h for tensor names - const TfLiteEvalTensor* GetInternalTensor(const int tensor_index) const { - return internal_tensors[tensor_index]; - } - // Variable tensors (will be changed, can not be const) - TfLiteEvalTensor* HiddenStateTensor() { - return internal_tensors[kLstmOutputStateTensor]; - } - TfLiteEvalTensor* CellStateTensor() { - return internal_tensors[kLstmCellStateTensor]; - } - // Node internal tensors with indexes defined at the beginning of the file - TfLiteEvalTensor* internal_tensors[24]; - TfLiteEvalTensor* output_tensor; -}; - -template -struct LSTMBuffers { - // TFLM buffers requires buffer index from LstmOpData. - CellType* buffer0; - CellType* buffer1; - CellType* buffer2; - CellType* buffer3; -}; - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h deleted file mode 100644 index 52798128..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_ops.h +++ /dev/null @@ -1,158 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ - -#include "signal/micro/kernels/irfft.h" -#include "signal/micro/kernels/rfft.h" -#include "tensorflow/lite/c/common.h" - -// Forward declaration of all micro op kernel registration methods. These -// registrations are included with the standard `BuiltinOpResolver`. -// -// This header is particularly useful in cases where only a subset of ops are -// needed. In such cases, the client can selectively add only the registrations -// their model requires, using a custom `(Micro)MutableOpResolver`. Selective -// registration in turn allows the linker to strip unused kernels. - -namespace tflite_micro { - -// TFLM is incrementally moving towards a flat tflite namespace -// (https://abseil.io/tips/130). Any new ops (or cleanup of existing ops should -// have their Register function declarations in the tflite namespace. - -TFLMRegistration Register_ABS(); -TFLMRegistration Register_ADD(); -TFLMRegistration Register_ADD_N(); -TFLMRegistration Register_ARG_MAX(); -TFLMRegistration Register_ARG_MIN(); -TFLMRegistration Register_ASSIGN_VARIABLE(); -TFLMRegistration Register_AVERAGE_POOL_2D(); -TFLMRegistration Register_BATCH_MATMUL(); -TFLMRegistration Register_BATCH_TO_SPACE_ND(); -TFLMRegistration Register_BROADCAST_ARGS(); -TFLMRegistration Register_BROADCAST_TO(); -TFLMRegistration Register_CALL_ONCE(); -TFLMRegistration Register_CAST(); -TFLMRegistration Register_CEIL(); -// TODO(b/160234179): Change custom OPs to also return by value. -TFLMRegistration* Register_CIRCULAR_BUFFER(); -TFLMRegistration Register_CONCATENATION(); -TFLMRegistration Register_CONV_2D(); -TFLMRegistration Register_COS(); -TFLMRegistration Register_CUMSUM(); -TFLMRegistration Register_DEPTH_TO_SPACE(); -TFLMRegistration Register_DEPTHWISE_CONV_2D(); -TFLMRegistration Register_DEQUANTIZE(); -TFLMRegistration Register_DIV(); -TFLMRegistration Register_ELU(); -TFLMRegistration Register_EMBEDDING_LOOKUP(); -TFLMRegistration Register_EQUAL(); -TFLMRegistration* Register_ETHOSU(); -TFLMRegistration Register_EXP(); -TFLMRegistration Register_EXPAND_DIMS(); -TFLMRegistration Register_FILL(); -TFLMRegistration Register_FLOOR(); -TFLMRegistration Register_FLOOR_DIV(); -TFLMRegistration Register_FLOOR_MOD(); -TFLMRegistration Register_FULLY_CONNECTED(); -TFLMRegistration Register_GATHER(); -TFLMRegistration Register_GATHER_ND(); -TFLMRegistration Register_GREATER(); -TFLMRegistration Register_GREATER_EQUAL(); -TFLMRegistration Register_HARD_SWISH(); -TFLMRegistration Register_IF(); -TFLMRegistration Register_L2_NORMALIZATION(); -TFLMRegistration Register_L2_POOL_2D(); -TFLMRegistration Register_LEAKY_RELU(); -TFLMRegistration Register_LESS(); -TFLMRegistration Register_LESS_EQUAL(); -TFLMRegistration Register_LOG(); -TFLMRegistration Register_LOG_SOFTMAX(); -TFLMRegistration Register_LOGICAL_AND(); -TFLMRegistration Register_LOGICAL_NOT(); -TFLMRegistration Register_LOGICAL_OR(); -TFLMRegistration Register_LOGISTIC(); -TFLMRegistration Register_MAX_POOL_2D(); -TFLMRegistration Register_MAXIMUM(); -TFLMRegistration Register_MEAN(); -TFLMRegistration Register_MINIMUM(); -TFLMRegistration Register_MIRROR_PAD(); -TFLMRegistration Register_MUL(); -TFLMRegistration Register_NEG(); -TFLMRegistration Register_NOT_EQUAL(); -TFLMRegistration Register_PACK(); -TFLMRegistration Register_PAD(); -TFLMRegistration Register_PADV2(); -TFLMRegistration Register_PRELU(); -TFLMRegistration Register_QUANTIZE(); -TFLMRegistration Register_READ_VARIABLE(); -TFLMRegistration Register_REDUCE_MAX(); -TFLMRegistration Register_RELU(); -TFLMRegistration Register_RELU6(); -TFLMRegistration Register_RESHAPE(); -TFLMRegistration Register_RESIZE_BILINEAR(); -TFLMRegistration Register_RESIZE_NEAREST_NEIGHBOR(); -TFLMRegistration Register_ROUND(); -TFLMRegistration Register_RSQRT(); -TFLMRegistration Register_SELECT_V2(); -TFLMRegistration Register_SHAPE(); -TFLMRegistration Register_SIN(); -TFLMRegistration Register_SLICE(); -TFLMRegistration Register_SOFTMAX(); -TFLMRegistration Register_SPACE_TO_BATCH_ND(); -TFLMRegistration Register_SPACE_TO_DEPTH(); -TFLMRegistration Register_SPLIT(); -TFLMRegistration Register_SPLIT_V(); -TFLMRegistration Register_SQRT(); -TFLMRegistration Register_SQUARE(); -TFLMRegistration Register_SQUARED_DIFFERENCE(); -TFLMRegistration Register_SQUEEZE(); -TFLMRegistration Register_STRIDED_SLICE(); -TFLMRegistration Register_SUB(); -TFLMRegistration Register_SUM(); -TFLMRegistration Register_SVDF(); -TFLMRegistration Register_TANH(); -TFLMRegistration Register_TRANSPOSE(); -TFLMRegistration Register_TRANSPOSE_CONV(); -// TODO(b/230666079): resolve conflict with xtensa implementation -TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); -TFLMRegistration Register_UNPACK(); -TFLMRegistration Register_VAR_HANDLE(); -TFLMRegistration Register_WHILE(); -TFLMRegistration Register_ZEROS_LIKE(); - -// TODO(b/295174388): Add the rest of inference only registration functions. -TFLMInferenceRegistration RegisterInference_FULLY_CONNECTED(); - -// TODO(b/160234179): Change custom OPs to also return by value. -namespace tflm_signal { -TFLMRegistration* Register_DELAY(); -TFLMRegistration* Register_FFT_AUTO_SCALE(); -TFLMRegistration* Register_FILTER_BANK(); -TFLMRegistration* Register_FILTER_BANK_LOG(); -TFLMRegistration* Register_FILTER_BANK_SPECTRAL_SUBTRACTION(); -TFLMRegistration* Register_FILTER_BANK_SQUARE_ROOT(); -TFLMRegistration* Register_ENERGY(); -TFLMRegistration* Register_FRAMER(); -TFLMRegistration* Register_OVERLAP_ADD(); -TFLMRegistration* Register_PCAN(); -TFLMRegistration* Register_STACKER(); -TFLMRegistration* Register_WINDOW(); -} // namespace tflm_signal - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h deleted file mode 100644 index 6e2d328f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/micro_tensor_utils.h +++ /dev/null @@ -1,56 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This file and the associated .cc file is branched from -// tensorflow/lite/kernels/internal/reference/portable_tensor_utils* -// TFLM needs to create its own because the original files are coupled with -// the tensor_utils module, which we cannot reuse due to its use of the -// Eigen library. - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ - -#include -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" - -#if defined(_MSC_VER) -#define __restrict__ __restrict -#endif - -namespace tflite_micro { - -// Not all backends support CpuBackendContext usage, so forward declare to avoid -// pulling in its implementation. -// TODO(b/230666277): consider removing this since micro does not utilize it -class CpuBackendContext; - -// Apply sigmoid to elements of a vector. -void PortableApplySigmoidToVector(const float* vector, int v_size, - float* result); -// Apply tanh to elements of a vector -void PortableApplyTanhToVector(const float* vector, int v_size, float* result); -// Apply appropriate activation function to elements of a vector. -void PortableApplyActivationToVector(const float* vector, int v_size, - TfLiteFusedActivation activation, - float* result); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ \ No newline at end of file diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h deleted file mode 100644 index a97049e0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/mul.h +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -extern const int kMulInput1Tensor; -extern const int kMulInput2Tensor; -extern const int kMulOutputTensor; - -struct OpDataMul { - int32_t input1_zero_point; - int32_t input2_zero_point; - - int32_t output_activation_min; - int32_t output_activation_max; - int32_t output_zero_point; - int32_t output_multiplier; - int output_shift; - - float output_activation_min_f32; - float output_activation_max_f32; -}; - -void* MulInit(TfLiteContext* context, const char* buffer, size_t length); - -TfLiteStatus CalculateOpDataMul(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, OpDataMul* data); - -TfLiteStatus MulPrepare(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus EvalMulQuantizedReference(TfLiteContext* context, TfLiteNode* node, - const OpDataMul* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output); - -void EvalMulFloatReference(TfLiteContext* context, TfLiteNode* node, - TfLiteMulParams* params, const OpDataMul* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output); - -// Generic must define registration function. -TFLMRegistration Register_MUL(); - -#if defined(CMSIS_NN) -TFLMRegistration Register_MUL_INT8(); -#else -// Fallback registration -inline TFLMRegistration Register_MUL_INT8() { return Register_MUL(); } -#endif -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h deleted file mode 100644 index 265700a0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/pad.h +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -TfLiteStatus PadPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h deleted file mode 100644 index 2c0aa0f0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/pooling.h +++ /dev/null @@ -1,142 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h" -#include "tensorflow/lite/kernels/internal/reference/pooling.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/padding.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/micro_ops.h" -#include "tensorflow/lite/micro/micro_log.h" - -namespace tflite_micro { - -extern const int kPoolingInputTensor; -extern const int kPoolingOutputTensor; - -struct OpDataPooling { - TfLitePaddingValues padding; - int32_t activation_min; - int32_t activation_max; - float activation_min_f32; - float activation_max_f32; -}; - -TfLiteStatus CalculateOpDataPooling(const TfLiteContext* context, - const TfLitePoolParams* params, - const TfLiteTensor* input, - const TfLiteTensor* output, - OpDataPooling* data); - -TfLiteStatus PoolingPrepare(TfLiteContext* context, TfLiteNode* node); - -void AveragePoolingEvalFloat(const TfLiteContext* context, - const TfLiteNode* node, - const TfLitePoolParams* params, - const OpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); - -template -void AveragePoolingEvalQuantized(TfLiteContext* context, const TfLiteNode* node, - const TfLitePoolParams* params, - const OpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - TFLITE_DCHECK(input->type == kTfLiteInt8 || input->type == kTfLiteInt16); - - PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.quantized_activation_min = data->activation_min; - op_params.quantized_activation_max = data->activation_max; - - reference_integer_ops::AveragePool(op_params, - tflite_micro::micro::GetTensorShape(input), - tflite_micro::micro::GetTensorData(input), - tflite_micro::micro::GetTensorShape(output), - tflite_micro::micro::GetTensorData(output)); -} - -void MaxPoolingEvalFloat(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, const OpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); - -template -void MaxPoolingEvalQuantized(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, - const OpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output) { - TFLITE_DCHECK(input->type == kTfLiteInt8 || input->type == kTfLiteInt16); - - tflite_micro::PoolParams op_params; - op_params.stride_height = params->stride_height; - op_params.stride_width = params->stride_width; - op_params.filter_height = params->filter_height; - op_params.filter_width = params->filter_width; - op_params.padding_values.height = data->padding.height; - op_params.padding_values.width = data->padding.width; - op_params.quantized_activation_min = data->activation_min; - op_params.quantized_activation_max = data->activation_max; - - reference_integer_ops::MaxPool(op_params, - tflite_micro::micro::GetTensorShape(input), - tflite_micro::micro::GetTensorData(input), - tflite_micro::micro::GetTensorShape(output), - tflite_micro::micro::GetTensorData(output)); -} - -#if defined(CMSIS_NN) || defined(XTENSA) -TFLMRegistration Register_AVERAGE_POOL_2D_INT8(); - -TFLMRegistration Register_MAX_POOL_2D_INT8(); - -TFLMRegistration Register_AVERAGE_POOL_2D_INT16(); - -TFLMRegistration Register_MAX_POOL_2D_INT16(); -#else -inline TFLMRegistration Register_AVERAGE_POOL_2D_INT8() { - return tflite_micro::Register_AVERAGE_POOL_2D(); -} - -inline TFLMRegistration Register_MAX_POOL_2D_INT8() { - return tflite_micro::Register_MAX_POOL_2D(); -} - -inline TFLMRegistration Register_AVERAGE_POOL_2D_INT16() { - return tflite_micro::Register_AVERAGE_POOL_2D(); -} - -inline TFLMRegistration Register_MAX_POOL_2D_INT16() { - return tflite_micro::Register_MAX_POOL_2D(); -} -#endif -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h deleted file mode 100644 index e7bfbf17..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/prelu.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -TfLiteStatus CalculatePreluParams(const TfLiteTensor* input, - const TfLiteTensor* alpha, - TfLiteTensor* output, PreluParams* params); - -void BroadcastPrelu4DSlowFloat(const RuntimeShape& unextended_input1_shape, - const float* input1_data, - const RuntimeShape& unextended_input2_shape, - const float* input2_data, - const RuntimeShape& unextended_output_shape, - float* output_data); - -TfLiteStatus PreluPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_PRELU_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h deleted file mode 100644 index e61074ac..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/quantize.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -struct OpDataQuantizeReference { - tflite_micro::QuantizationParams quantization_params; - // The scaling factor from input to output (aka the 'real multiplier') can - // be represented as a fixed point multiplier plus a left shift. - int32_t requantize_output_multiplier; - int requantize_output_shift; - - int32_t input_zero_point; -}; - -TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node); -TfLiteStatus PrepareQuantizeReference(TfLiteContext* context, TfLiteNode* node); -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_QUANTIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h deleted file mode 100644 index 9f70bd6c..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/reduce.h +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -extern const int kMaxNumberOfAxis; -extern const int kMaxNumberOfReducedAxis; - -struct OpDataReduce { - int32_t multiplier; - int shift; - int temp_buffer_idx; - int resolved_axis_idx; - int input_zp; - float input_scale; - int output_zp; - float output_scale; - int num_output_elements; - int num_axis; -}; - -TfLiteStatus PrepareMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -TfLiteStatus PrepareMeanOrSumHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -TfLiteStatus EvalMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); -TfLiteStatus EvalMeanHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); -TfLiteStatus EvalSumHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -void ReduceResolveAxis(const int* axis_data, int axis_count, - MeanParams* op_params); - -TFLMRegistration Register_MEAN(); -TFLMRegistration Register_REDUCE_MAX(); -TFLMRegistration Register_SUM(); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h deleted file mode 100644 index bcb3e056..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/reshape.h +++ /dev/null @@ -1,26 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -constexpr int kReshapeInputTensor = 0; -constexpr int kReshapeOutputTensor = 0; - -TfLiteStatus PrepareReshapeReference(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h deleted file mode 100644 index 137158c4..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/softmax.h +++ /dev/null @@ -1,67 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length); - -// Common helper function to SoftmaxPrepare. -TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteTensor* output, - const TfLiteSoftmaxParams* params, - SoftmaxParams* op_data); - -TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node); - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -TFLMRegistration Register_SOFTMAX(); - -#if defined(XTENSA) || defined(CMSIS_NN) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 input and int16 output. -TFLMRegistration Register_SOFTMAX_INT8_INT16(); -#else -inline TFLMRegistration Register_SOFTMAX_INT8_INT16() { - return Register_SOFTMAX(); -} -#endif - -#if defined(CMSIS_NN) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 input/output and uses the latency optimized implementations. -TFLMRegistration Register_SOFTMAX_INT8(); - -// Returns a TFLMRegistration struct for kernel variant that only supports -// int16 input/output and uses the latency optimized implementations. -TFLMRegistration Register_SOFTMAX_INT16(); - -#else -inline TFLMRegistration Register_SOFTMAX_INT8() { return Register_SOFTMAX(); } - -inline TFLMRegistration Register_SOFTMAX_INT16() { return Register_SOFTMAX(); } -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h deleted file mode 100644 index b62cf746..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/strided_slice.h +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -constexpr int kStridedSliceInputTensor = 0; -constexpr int kStridedSliceBeginTensor = 1; -constexpr int kStridedSliceEndTensor = 2; -constexpr int kStridedSliceStridesTensor = 3; -constexpr int kStridedSliceOutputTensor = 0; - -void* StridedSliceInit(TfLiteContext* context, const char* buffer, - size_t length); - -TfLiteStatus StridedSlicePrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_STRIDED_SLICE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h deleted file mode 100644 index 130eddfe..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/sub.h +++ /dev/null @@ -1,60 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -extern const int kSubInputTensor1; -extern const int kSubInputTensor2; -extern const int kSubOutputTensor; - -struct OpDataSub { - bool requires_broadcast; - - // These fields are used in both the general 8-bit -> 8bit quantized path, - // and the special 16-bit -> 16bit quantized path - int input1_shift; - int input2_shift; - int32_t output_activation_min; - int32_t output_activation_max; - - // These fields are used only in the general 8-bit -> 8bit quantized path - int32_t input1_multiplier; - int32_t input2_multiplier; - int32_t output_multiplier; - int output_shift; - int left_shift; - int32_t input1_offset; - int32_t input2_offset; - int32_t output_offset; -}; - -TfLiteStatus CalculateOpDataSub(TfLiteContext* context, TfLiteSubParams* params, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteTensor* output, OpDataSub* data); - -TfLiteStatus SubPrepare(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_SUB_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h deleted file mode 100644 index 469f8c57..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/svdf.h +++ /dev/null @@ -1,100 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/micro/micro_common.h" - -namespace tflite_micro { - -struct OpDataSvdf { - int32_t effective_scale_1_a; - int32_t effective_scale_2_a; - // b versions of each scale are kept at int since the numbers are just the - // shift value - typically between [-32, 32]. - int effective_scale_1_b; - int effective_scale_2_b; - int scratch_tensor_index; - int scratch_output_tensor_index; - - // Cached tensor zero point values for quantized operations. - int input_zero_point; - int output_zero_point; - int activation_state_zero_point; -}; - -// Input tensors. -extern const int kSvdfInputTensor; -extern const int kSvdfWeightsFeatureTensor; -extern const int kSvdfWeightsTimeTensor; -extern const int kSvdfBiasTensor; -// This is a variable tensor, and will be modified by this op. -extern const int kSvdfInputActivationStateTensor; - -// Output tensor. -extern const int kSvdfOutputTensor; - -void EvalInt8SvdfReference(TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input_tensor, - const TfLiteEvalTensor* weights_feature_tensor, - const TfLiteEvalTensor* weights_time_tensor, - const TfLiteEvalTensor* bias_tensor, - const TfLiteSVDFParams* params, - TfLiteEvalTensor* activation_state_tensor, - TfLiteEvalTensor* output_tensor, - const OpDataSvdf& data); - -// TODO(#523): remove 16-bit code when no longer needed. -void EvalInt16SvdfReference(TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input_tensor, - const TfLiteEvalTensor* weights_feature_tensor, - const TfLiteEvalTensor* weights_time_tensor, - const TfLiteEvalTensor* bias_tensor, - const TfLiteSVDFParams* params, - TfLiteEvalTensor* activation_state_tensor, - TfLiteEvalTensor* output_tensor, - const OpDataSvdf& data); - -void EvalFloatSvdfReference( - TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* weights_feature, - const TfLiteEvalTensor* weights_time, const TfLiteEvalTensor* bias, - const TfLiteSVDFParams* params, int scratch_tensor_index, - TfLiteEvalTensor* activation_state, TfLiteEvalTensor* output); - -TfLiteStatus PrepareSvdf(TfLiteContext* context, TfLiteNode* node); - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -TFLMRegistration Register_SVDF(); - -#if defined(HEXAGON) || defined(CMSIS_NN) || defined(XTENSA) - -TFLMRegistration Register_SVDF_INT8(); - -#else -// Note that while this block gets used for both reference and optimized kernels -// that do not have any specialized implementations, the only goal here is to -// define fallback implementation that allow reference kernels to still be used -// from applications that call a more specific kernel variant. - -inline TFLMRegistration Register_SVDF_INT8() { return Register_SVDF(); } - -#endif -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h deleted file mode 100644 index 4f7ef613..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/conv_test_data.h +++ /dev/null @@ -1,37 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { -extern const int8_t kConvInput1x32x32x3[]; -extern const int8_t kConvFilter8x3x3x3[]; -extern const int32_t kConvBiasQuantized8[]; -extern const int8_t kConvGoldenOutput1x16x16x8[]; - -// Kernel Conv Test Cases: Int8Filter1x3x3x1ShouldMatchGolden -extern const int8_t kConvInput1x4x4x1[]; -extern const int8_t kConvInput1x5x5x1[]; -extern const int8_t kConvFilter1x3x3x1[]; -extern const int32_t kConvZeroBias[]; -extern const int8_t kConvGoldenOutput4x4InputPaddingSame2x2[]; -extern const int8_t kConvGoldenOutput5x5InputPaddingSame3x3[]; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h deleted file mode 100644 index 671bbb00..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/testdata/lstm_test_data.h +++ /dev/null @@ -1,579 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" -#include "tensorflow/lite/micro/kernels/lstm_shared.h" -#include "tensorflow/lite/micro/test_helpers.h" - -namespace tflite_micro { -namespace testing { -// Data structure to store all the data used to check output of internal gates -// of one time step -// input_size = batch_size*input_dimension (size of the input array) -// gate_output_size = batch_size*state_dimension (size of the gate output) -template -struct GateOutputCheckData { - float input_data[input_size]; - float hidden_state[gate_output_size]; - float cell_state[gate_output_size]; - float expected_forget_gate_output[gate_output_size]; - float expected_input_gate_output[gate_output_size]; - float expected_output_gate_output[gate_output_size]; - float expected_cell_gate_output[gate_output_size]; - float expected_updated_cell[gate_output_size]; - float expected_updated_hidden[gate_output_size]; -}; - -// Data structure to store all the data used to check the output of the kernel -// of multiple batch, multiple timesteps -// input_size = batch_size*time_steps*input_dimension (size of the input array) -// gate_output_size = batch_size*state_dimension (size of the gate output) -// output_size = time_steps*gate_output_size (size of the output from the -// kernel) -template -struct LstmEvalCheckData { - float input_data[input_size]; - float hidden_state[gate_output_size]; - float expected_output[output_size]; - float expected_hidden_state[gate_output_size]; - float expected_cell_state[gate_output_size]; -}; - -// Struct that holds the weight/bias information for a standard gate (i.e. no -// modification such as layer normalization, peephole, etc.) -// Every gate is defined by the type and size of the weights (bias included) -// inside. -// Specifically, types are weight type and bias type (normally the same -// type of MatMul accumulator). -// activation_weight has shape (hidden state dimension * input tensor dimension) -// recurrent_weight has shape (hidden state dimension * hidden state dimension) -// bias has shape (hidden state dimension, 1) -template -struct GateData { - WeightType activation_weight[state_dimension * input_dimension]; - WeightType recurrent_weight[state_dimension * state_dimension]; - BiasType fused_bias[state_dimension]; - // Quantized model folded the zero point of activations into biases: - // bias + zero_point * weight. - // Note: folded bias is only required for the legacy 8x8->16 pass. Therefore - // the data type is fixed here to avoid compilation errors (the computation of - // folding does not support other types) - int32_t activation_zp_folded_bias[state_dimension]; - int32_t recurrent_zp_folded_bias[state_dimension]; -}; - -// A struct that holds quantization parameters for a LSTM Tensor -struct TensorQuantizationParameters { - double scale; - int zero_point; - bool symmetry; -}; - -// A struct that holds quantization parameters for an internal gate, which is -// defined by activation/recurrent weight and bias (assuming no internal layer -// normalization) -struct GateQuantizationParameters { - TensorQuantizationParameters activation_weight; - TensorQuantizationParameters recurrent_weight; - TensorQuantizationParameters bias; -}; - -// A struct that holds the quantization settings for the LSTM node. Data -// members can be grouped into five parts. -// 1. Data types (activation,weight, cell, bias) -// 2. Non-linear activation (i.e., tanh and sigmoid) fixed point -// calculation settings -// 3. Input/output tensor quantization settings -// 4. Internal state (hidden and cell) quantization settings -// 5. Internal gate (forget, input, cell, output) settings -struct NodeQuantizationParameters { - TfLiteType activation_type; - TfLiteType weight_type; - TfLiteType cell_type; - TfLiteType bias_type; - // Fixed point setting for integer nonlinear activation calculation - double nonlinear_activation_input_scale; - double nonlinear_activation_output_scale; - // Quantization parameters for input/output - TensorQuantizationParameters input; - TensorQuantizationParameters output; - // Quantization parameters for internal states - TensorQuantizationParameters hidden_state; - TensorQuantizationParameters cell_state; - // Quantization parameters for gates - GateQuantizationParameters forget_gate; - GateQuantizationParameters input_gate; - GateQuantizationParameters cell_gate; - GateQuantizationParameters output_gate; -}; - -// Data structure that holds all the information to evaluate a LSTM kernel -// (mimic the LSTM node). -// Tensor Types: -// ActivationType defines the data type of input/output of the layer. The hidden -// state has the ActivationType as well since it is the layer output of the -// previous time. -// WeightType defines the weight data type inside the internal gates. -// BiasType defines the bias data type inside the internal gates. (normally the -// same type of MatMul accumulator). -// Tensor Shapes: -// The input to the layer has shape (batch_size,time_steps,input_dimension). -// Both the hidden state and cell state has shape (state_dimension, 1) -// The output of the layer has shape (batch_size,time_steps,state_dimension) -// Note: state values can change through calls (stateful) -template -class LstmNodeContent { - public: - LstmNodeContent(const LstmNodeContent& other) = default; - LstmNodeContent& operator=(const LstmNodeContent& other) = default; - // Use the general model setting (builtin data) and the four gates data to - // construct the node content. Note the input, hidden state, and cell state - // data is provided later for flexible testing (initialize as zero now) - LstmNodeContent( - const TfLiteUnidirectionalSequenceLSTMParams builtin_data, - const GateData - forget_gate_params, - const GateData - input_gate_params, - const GateData - cell_gate_params, - const GateData - output_gate_params) - : builtin_data_(builtin_data), - forget_gate_data_(forget_gate_params), - input_gate_data_(input_gate_params), - cell_gate_data_(cell_gate_params), - output_gate_data_(output_gate_params) { - InitializeTensors(); - } - - // Add quantization parameters (scale, zero point) to tensors - // Only required for the integer kernel - void AddQuantizationParameters( - const NodeQuantizationParameters& quantization_params) { - quantization_settings_ = quantization_params; - // Input Tensor - SetTensorQuantizationParam(kLstmInputTensor, quantization_params.input); - // Forget Gate Tensors - const auto& forget_gate_quant_param = quantization_params.forget_gate; - SetTensorQuantizationParam(kLstmInputToForgetWeightsTensor, - forget_gate_quant_param.activation_weight); - SetTensorQuantizationParam(kLstmRecurrentToForgetWeightsTensor, - forget_gate_quant_param.recurrent_weight); - SetTensorQuantizationParam(kLstmForgetGateBiasTensor, - forget_gate_quant_param.bias); - // Input Gate Tensors - const auto& input_gate_quant_param = quantization_params.input_gate; - SetTensorQuantizationParam(kLstmInputToInputWeightsTensor, - input_gate_quant_param.activation_weight); - SetTensorQuantizationParam(kLstmRecurrentToInputWeightsTensor, - input_gate_quant_param.recurrent_weight); - SetTensorQuantizationParam(kLstmInputGateBiasTensor, - input_gate_quant_param.bias); - // Cell Gate Tensors - const auto& cell_gate_quant_param = quantization_params.cell_gate; - SetTensorQuantizationParam(kLstmInputToCellWeightsTensor, - cell_gate_quant_param.activation_weight); - SetTensorQuantizationParam(kLstmRecurrentToCellWeightsTensor, - cell_gate_quant_param.recurrent_weight); - SetTensorQuantizationParam(kLstmCellGateBiasTensor, - cell_gate_quant_param.bias); - // Output Gate Tensors - const auto& output_gate_quant_param = quantization_params.output_gate; - SetTensorQuantizationParam(kLstmInputToOutputWeightsTensor, - output_gate_quant_param.activation_weight); - SetTensorQuantizationParam(kLstmRecurrentToOutputWeightsTensor, - output_gate_quant_param.recurrent_weight); - SetTensorQuantizationParam(kLstmOutputGateBiasTensor, - output_gate_quant_param.bias); - // State Tensors - SetTensorQuantizationParam(kLstmOutputStateTensor, - quantization_params.hidden_state); - SetTensorQuantizationParam(kLstmCellStateTensor, - quantization_params.cell_state); - // Output Tensor - SetTensorQuantizationParam(24, quantization_params.output); - } - - // Provide interface to set the input tensor values for flexible testing - void SetInputData(const ActivationType* data) { - std::memcpy( - input_, data, - batch_size * input_dimension * time_steps * sizeof(ActivationType)); - SetTensor(kLstmInputTensor, input_, input_size_); - } - const ActivationType* GetInputData() const { return input_; } - - // Provide interface to set the hidden state tensor values for flexible - // testing - void SetHiddenStateData(const ActivationType* data) { - std::memcpy(hidden_state_, data, - batch_size * state_dimension * sizeof(ActivationType)); - } - ActivationType* GetHiddenStateData() { return hidden_state_; } - - // Provide interface to set the cell state tensor values for flexible - // testing - void SetCellStateData(const CellType* data) { - std::memcpy(cell_state_, data, - batch_size * state_dimension * sizeof(CellType)); - } - CellType* GetCellStateData() { return cell_state_; } - ActivationType* GetOutputData() { return output_; } - - // Internal tensors, see lstm_shared.h for tensor names - TfLiteEvalTensor* GetEvalTensor(const int tensor_index) { - auto valid_index = input_tensor_indices_[tensor_index + 1]; - if (valid_index < 0) { - return nullptr; - } - return &eval_tensors_[tensor_index]; - } - - TfLiteTensor* GetTensors() { return tensors_; } - - // Required by the kernel runner - TfLiteIntArray* KernelInputs() { - return IntArrayFromInts(input_tensor_indices_); - } - // Required by the kernel runner - TfLiteIntArray* KernelOutputs() { - return IntArrayFromInts(output_tensor_indices_); - } - - // Variable tensors (will be changed, can not be const) - TfLiteEvalTensor* HiddenStateEvalTensor() { - return &eval_tensors_[kLstmOutputStateTensor]; - } - TfLiteEvalTensor* CellStateEvalTensor() { - return &eval_tensors_[kLstmCellStateTensor]; - } - TfLiteEvalTensor* OutputEvalTensor() { return &eval_tensors_[24]; } - - const GateData& - ForgetGateData() const { - return forget_gate_data_; - } - const GateData& - InputGateData() const { - return input_gate_data_; - } - const GateData& - CellGateData() const { - return cell_gate_data_; - } - const GateData& - OutputGateData() const { - return output_gate_data_; - } - - const TfLiteUnidirectionalSequenceLSTMParams& BuiltinData() const { - return builtin_data_; - } - - const NodeQuantizationParameters& QuantizationSettings() const { - return quantization_settings_; - } - - private: - void InitializeTensors() { - // Invalid all the input tensors untill we set it - input_tensor_indices_[0] = 24; // tot elements - for (size_t i = 1; i < 25; i++) { - input_tensor_indices_[i] = kTfLiteOptionalTensor; - } - // Input Tensor - SetTensor(kLstmInputTensor, input_, input_size_); - // Forget Gate Tensors - SetTensor(kLstmInputToForgetWeightsTensor, - forget_gate_data_.activation_weight, activation_weight_size_); - SetTensor(kLstmRecurrentToForgetWeightsTensor, - forget_gate_data_.recurrent_weight, recurrent_weight_size_); - SetTensor(kLstmForgetGateBiasTensor, forget_gate_data_.fused_bias, - bias_size_); - // Input Gate Tensors - SetTensor(kLstmInputToInputWeightsTensor, - input_gate_data_.activation_weight, activation_weight_size_); - SetTensor(kLstmRecurrentToInputWeightsTensor, - input_gate_data_.recurrent_weight, recurrent_weight_size_); - SetTensor(kLstmInputGateBiasTensor, input_gate_data_.fused_bias, - bias_size_); - // Cell Gate Tensors - SetTensor(kLstmInputToCellWeightsTensor, cell_gate_data_.activation_weight, - activation_weight_size_); - SetTensor(kLstmRecurrentToCellWeightsTensor, - cell_gate_data_.recurrent_weight, recurrent_weight_size_); - SetTensor(kLstmCellGateBiasTensor, cell_gate_data_.fused_bias, bias_size_); - // Output Gate Tensors - SetTensor(kLstmInputToOutputWeightsTensor, - output_gate_data_.activation_weight, activation_weight_size_); - SetTensor(kLstmRecurrentToOutputWeightsTensor, - output_gate_data_.recurrent_weight, recurrent_weight_size_); - SetTensor(kLstmOutputGateBiasTensor, output_gate_data_.fused_bias, - bias_size_); - // State Tensors - SetTensor(kLstmOutputStateTensor, hidden_state_, state_size_, - /*is_variable=*/true); - SetTensor(kLstmCellStateTensor, cell_state_, state_size_, - /*is_variable=*/true); - // // Output Tensor - SetTensor(24, output_, output_size_, /*is_variable=*/true); - } - - template - void SetTensor(const int index, const T* data, int* dims, - const bool is_variable = false) { - // Lite tensors for kernel level testing - tensors_[index].data.data = const_cast(data); - tensors_[index].dims = IntArrayFromInts(dims); - tensors_[index].type = typeToTfLiteType(); - tensors_[index].is_variable = is_variable; - // Eval tensors for internal computation testing - eval_tensors_[index].data.data = const_cast(data); - eval_tensors_[index].dims = IntArrayFromInts(dims); - eval_tensors_[index].type = typeToTfLiteType(); - // update the index - if (index < 24) { - input_tensor_indices_[index + 1] = index; - } - } - - void SetTensorQuantizationParam( - const int index, const TensorQuantizationParameters& quant_param) { - tensors_[index].params.scale = quant_param.scale; - tensors_[index].params.zero_point = quant_param.zero_point; - } - - const TfLiteUnidirectionalSequenceLSTMParams builtin_data_; - GateData - forget_gate_data_; - GateData - input_gate_data_; - GateData - cell_gate_data_; - GateData - output_gate_data_; - - // Keep to ease the testing process (although all quantization information can - // be obtained from individual tensors, they are well organized here and light - // weighted) - NodeQuantizationParameters quantization_settings_; - - // Not const since IntArrayFromInts takes int *; the first element of the - // array must be the size of the array - int input_size_[4] = {3, batch_size, time_steps, input_dimension}; - int output_size_[4] = {3, batch_size, time_steps, state_dimension}; - // weight tensor has C-style "row-major" memory ordering - int activation_weight_size_[3] = {2, state_dimension, input_dimension}; - int recurrent_weight_size_[3] = {2, state_dimension, state_dimension}; - int bias_size_[2] = {1, state_dimension}; - int state_size_[3] = {2, batch_size, state_dimension}; - - // see lstm_shared.h for tensor names, the last tensor is the output tensor - TfLiteTensor tensors_[24 + 1]; - // Use for internel kernel testing - TfLiteEvalTensor eval_tensors_[24 + 1]; - // indices for the tensors inside the node (required by kernel runner) - int input_tensor_indices_[1 + 24] = {}; - // single output (last in the tensors array) - int output_tensor_indices_[2] = {1, 24}; - - // tennsor data - // states are initialized to zero - ActivationType hidden_state_[batch_size * state_dimension] = {}; - CellType cell_state_[batch_size * state_dimension] = {}; - // input is defined in the ModelContent (const across all derived models) - ActivationType input_[batch_size * input_dimension * time_steps] = {}; - ActivationType output_[batch_size * state_dimension * time_steps] = {}; -}; - -// Converts floating point gate parameters to the corresponding quantized -// version -template -GateData -CreateQuantizedGateData( - const GateData& - gate_parameters, - const TensorQuantizationParameters& input_quantization_params, - const TensorQuantizationParameters& output_quantization_params, - const GateQuantizationParameters& gate_quantization_params, - const bool fold_zero_point) { - GateData - quantized_gate_params; - tflite_micro::SymmetricQuantize(gate_parameters.activation_weight, - quantized_gate_params.activation_weight, - state_dimension * input_dimension, - gate_quantization_params.activation_weight.scale); - tflite_micro::SymmetricQuantize(gate_parameters.recurrent_weight, - quantized_gate_params.recurrent_weight, - state_dimension * state_dimension, - gate_quantization_params.recurrent_weight.scale); - tflite_micro::SymmetricQuantize(gate_parameters.fused_bias, - quantized_gate_params.fused_bias, state_dimension, - gate_quantization_params.bias.scale); - // Note: steps below are not required for the generalized LSTM evaluation - // (e.g., 16bits activation) - if (fold_zero_point) { - // Copy the bias values to prepare zero_point folded - // bias precomputation. bias has same scale as - // input_scale*input_weight_scale) - std::memcpy(quantized_gate_params.activation_zp_folded_bias, - quantized_gate_params.fused_bias, 2 * sizeof(int32_t)); - // Pre-calculate bias - zero_point * weight (a constant). - tflite_micro::tensor_utils::MatrixScalarMultiplyAccumulate( - quantized_gate_params.activation_weight, - -1 * input_quantization_params.zero_point, 2, 2, - quantized_gate_params.activation_zp_folded_bias); - - // Initialize the folded bias to zeros for accumulation - for (size_t i = 0; i < 2; i++) { - quantized_gate_params.recurrent_zp_folded_bias[i] = 0; - } - // Calculate : -zero_point * weight since it is a constant - tflite_micro::tensor_utils::MatrixScalarMultiplyAccumulate( - quantized_gate_params.recurrent_weight, - -1 * output_quantization_params.zero_point, 2, 2, - quantized_gate_params.recurrent_zp_folded_bias); - } - return quantized_gate_params; -} - -// Create integer LSTM node content from the float node contents and -// quantization settings -// Note: fold_zero_point folds the zero point into the bias (precomputation), -// which is not required for the generalized integer inference (16 bits act -// LSTM). -template -LstmNodeContent -CreateIntegerNodeContents( - const NodeQuantizationParameters& quantization_settings, - const bool fold_zero_point, - LstmNodeContent& float_node_contents) { - const auto quantized_forget_gate_data = - CreateQuantizedGateData( - float_node_contents.ForgetGateData(), quantization_settings.input, - quantization_settings.output, quantization_settings.forget_gate, - fold_zero_point); - const auto quantized_input_gate_data = - CreateQuantizedGateData( - float_node_contents.InputGateData(), quantization_settings.input, - quantization_settings.output, quantization_settings.input_gate, - fold_zero_point); - const auto quantized_cell_gate_data = - CreateQuantizedGateData( - float_node_contents.CellGateData(), quantization_settings.input, - quantization_settings.output, quantization_settings.cell_gate, - fold_zero_point); - const auto quantized_output_gate_params = - CreateQuantizedGateData( - float_node_contents.OutputGateData(), quantization_settings.input, - quantization_settings.output, quantization_settings.output_gate, - fold_zero_point); - LstmNodeContent - quantized_node_content( - float_node_contents.BuiltinData(), quantized_forget_gate_data, - quantized_input_gate_data, quantized_cell_gate_data, - quantized_output_gate_params); - - // Quantize the floating point input - ActivationType quantized_input[batch_size * input_dimension * time_steps] = - {}; - Quantize(float_node_contents.GetInputData(), quantized_input, - batch_size * input_dimension * time_steps, - quantization_settings.input.scale, - quantization_settings.input.zero_point); - quantized_node_content.SetInputData(quantized_input); - // Quantize the floating point hidden state - ActivationType quantized_hidden_state[batch_size * state_dimension] = {}; - Quantize(float_node_contents.GetHiddenStateData(), quantized_hidden_state, - batch_size * state_dimension, - quantization_settings.hidden_state.scale, - quantization_settings.hidden_state.zero_point); - quantized_node_content.SetHiddenStateData(quantized_hidden_state); - // Quantize the floating point cell state - CellType quantized_cell_state[batch_size * state_dimension] = {}; - Quantize(float_node_contents.GetCellStateData(), quantized_cell_state, - batch_size * state_dimension, quantization_settings.cell_state.scale, - quantization_settings.cell_state.zero_point); - quantized_node_content.SetCellStateData(quantized_cell_state); - - // Add scale and zero point to tensors - quantized_node_content.AddQuantizationParameters(quantization_settings); - return quantized_node_content; -} - -// Get the gate output data (one time step) for a simple 2X2 model -// batch_size = 2; time_steps = 1; input_dimension = 2; state_dimension = 2 -// input_size = batch_size*time_steps*input_dimension = 4 -// gate_output_size = batch_size*state_dimension = 4 -GateOutputCheckData<4, 4> Get2X2GateOutputCheckData(); - -// Get the kernel output data for a simple 2X2 model -// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 -// input_size = batch_size*time_steps*input_dimension = 12 -// gate_output_size = batch_size*state_dimension = 4 -// output_size = time_steps*gate_output_size = 12 -LstmEvalCheckData<12, 4, 12> Get2X2LstmEvalCheckData(); - -// Create a 2x2 float node content -// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 -LstmNodeContent -Create2x3x2X2FloatNodeContents(const float* input_data = nullptr, - const float* hidden_state = nullptr, - const float* cell_state = nullptr); - -// Get the quantization settings for the 2X2 model -NodeQuantizationParameters Get2X2Int8LstmQuantizationSettings(); - -// Create int8 (activation) x int8 (weight) -> int16 (cell) node -// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 -// input is in float format since the source of truth is always the float -// configuration -LstmNodeContent -Create2x3x2X2Int8NodeContents(const float* input_data = nullptr, - const float* hidden_state = nullptr, - const float* cell_state = nullptr); - -// Create int16 (activation) x int8 (weight) -> int16 (cell) node -// batch_size = 2; time_steps = 3; input_dimension = 2; state_dimension = 2 -// input is in float format since the source of truth is always the float -// configuration -LstmNodeContent -Create2x3x2X2Int16NodeContents(const float* input_data = nullptr, - const float* hidden_state = nullptr, - const float* cell_state = nullptr); - -} // namespace testing -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_TESTDATA_LSTM_TEST_DATA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h deleted file mode 100644 index a66c7566..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.h +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -// This is the most generic TFLMRegistration. The actual supported types -// may still be target dependent. The only requirement is that every -// implementation (reference or optimized) must define this function. -// TODO(b/230666079): resolve conflict with xtensa implementation -TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); - -#if defined(CMSIS_NN) -// Returns a TFLMRegistration struct for kernel variant that only supports -// int8 activations and int8 weights and uses the latency optimized -// implementations. -TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8(); - -#else -inline TFLMRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM_INT8() { - return Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); -} -#endif - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h deleted file mode 100644 index 7d814717..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h +++ /dev/null @@ -1,139 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ - -#if defined(HIFIMINI) -#include - -#include -#include -#include - -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/kernels/xtensa/xtensa.h" - -namespace tflite_micro { - -// INT24 MIN/MAX -#define INT24_MIN -8388608 -#define INT24_MAX 8388607 - -// Multiply 24bit value by a quantized multiplier (w/ shift) and returns a 48bit -// aligned value in the QR register. -inline ae_q56s MultiplyByQuantizedMultiplier(ae_p24x2s x_24x2, - int32_t quantized_multiplier, - int shift) { - // A value with 1 sign bit, N integer bits and M fractional bits is - // represented as QN+1.M since the sign bit is included in the integer bits. - // - // The Q notation in this method explains the values represented in each - // variable, along with an implicit division since the quantized_multiplier - // represents a value between 0.5 and 1.0 (Q1.X-1 where X is the bit precision - // of the type). - // - // Load the quantized multiplier into the PR register. - // NOTE: This method assumes that this param has been calculated for 24bit - // space - not 32bits. - // Q32.0 / 2^23 -> Q24.0 / 2^23 representing a Q1.23 multiplier. - ae_p24x2s quantized_multiplier_24x2 = AE_MOVPA24(quantized_multiplier); - // Shift right by 23 - 16 bits minus the specified shift. This is because we - // keep 16 fractional bits until the end to perform rounding. Subtract shift - // since shift is a left shift, and the 23-16 is a right shift. - int shift_amount = 7 - shift; - - // Find the product of x and the quantized_multiplier. - // Q24.0 / 2^23 * Q24.0 = Q48.0 / 2^23 - // Q48.0 / 2^23 >> 7 = Q48.0 / 2^16 - ae_q56s result_56 = AE_MULP24S_HH(x_24x2, quantized_multiplier_24x2); - - // Shift right if shift amount is positive, left if shift amount is negative. - if (shift_amount >= 0) { - result_56 = AE_Q56S_SRA(result_56, shift_amount); - } else { - result_56 = AE_Q56S_SLA(result_56, -shift_amount); - } - - // Round off the bottom 16 bits. - // Q48.0 / 2^16 -> Q32.0 aligned to 48 bits. - result_56 = AE_ROUNDSQ32SYM(result_56); - return result_56; -} - -// Multiply 32bit value by a quantized multiplier (w/ shift) and returns a 48bit -// aligned value in the QR register. -inline ae_q56s MultiplyByQuantizedMultiplierResult48Bit( - int32_t x, int32_t quantized_multiplier, int shift) { - // Convert x into a 2x24bit PR register file. If x is outside the numerical - // limits of a 24bit integer, the "fractional" or lower 8bits are discarded. - // If x is within the range of a 24 bit integer, the "signed" or upper 8bits - // are discarded. - ae_p24x2s x_24x2; - if (x > INT24_MIN && x < INT24_MAX) { - x_24x2 = AE_MOVPA24(x); - } else { - x_24x2 = static_cast(*reinterpret_cast(&x)); - shift += 8; - } - - return MultiplyByQuantizedMultiplier(x_24x2, quantized_multiplier, shift); -} - -// Calculate quantization params for 24bit runtimes. -inline void QuantizeMultiplierForInt24(float multiplier, - int32_t* quantized_multiplier, - int* shift) { - if (multiplier == 0.0f) { - *quantized_multiplier = 0; - *shift = 0; - return; - } - - // Special cased to 24bit: - const float q = std::frexp(multiplier, shift); - auto q_fixed = static_cast(std::round(q * (1 << 23))); - - TFLITE_CHECK(q_fixed <= (1 << 23)); - if (q_fixed == (1 << 23)) { - q_fixed /= 2; - ++*shift; - } - TFLITE_CHECK_LE(q_fixed, INT24_MAX); - - // Ensure shift does not exceed 24-bit range. - TFLITE_CHECK_LE(*shift, 23); - if (*shift < -23) { - *shift = 0; - q_fixed = 0; - } - *quantized_multiplier = static_cast(q_fixed); -} - -// Convert a floating point number to a Q representation for 24 bit integers. -inline int CreateQConstantForInt24(int integer_bits, float f) { - const float min_bounds = static_cast(INT24_MIN); - const float max_bounds = static_cast(INT24_MAX); - - int fractional_bits = 23 - integer_bits; - float raw = std::round(f * static_cast(1 << fractional_bits)); - raw = std::max(raw, min_bounds); - raw = std::min(raw, max_bounds); - return static_cast(raw); -} - -} // namespace tflite_micro -#endif // defined(HIFIMINI) -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_HIFIMINI_FIXEDPOINT_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h deleted file mode 100644 index ebf9700d..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_eval.h +++ /dev/null @@ -1,216 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ -#define TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" -#include "tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace lstm_eval { - -#if defined(HIFI5) -void calc_cell_state_without_cifg(int16_t* cell_state, - const int16_t* forget_gate, - const int16_t* cell_gate, - const int16_t* input_gate, int shift1, - int shift2, int clip, int num_elms); - -void calc_cell_state_with_cifg(int16_t* cell_state, const int16_t* forget_gate, - const int16_t* cell_gate, int shift1, int shift2, - int clip, int num_elms); - -void xa_nn_elm_mul_16x16_asym8s(int8_t* output, const int16_t* input_1, - const int16_t* input_2, int32_t multiplier, - int32_t shift, int32_t zero_point, - int num_elms); -#endif // defined(HIFI5) - -// Pamameters for integer LSTM. -// Consider split this into two Integer Parameters if more fields are added. -struct IntegerLstmParameter { - int32_t effective_input_to_input_scale_a; - int effective_input_to_input_scale_b; - int32_t effective_recurrent_to_input_scale_a; - int effective_recurrent_to_input_scale_b; - int32_t effective_cell_to_input_scale_a; - int effective_cell_to_input_scale_b; - int32_t effective_input_to_forget_scale_a; - int effective_input_to_forget_scale_b; - int32_t effective_recurrent_to_forget_scale_a; - int effective_recurrent_to_forget_scale_b; - int32_t effective_cell_to_forget_scale_a; - int effective_cell_to_forget_scale_b; - int32_t effective_input_to_cell_scale_a; - int effective_input_to_cell_scale_b; - int32_t effective_recurrent_to_cell_scale_a; - int effective_recurrent_to_cell_scale_b; - int32_t effective_input_to_output_scale_a; - int effective_input_to_output_scale_b; - int32_t effective_recurrent_to_output_scale_a; - int effective_recurrent_to_output_scale_b; - int32_t effective_cell_to_output_scale_a; - int effective_cell_to_output_scale_b; - int32_t effective_proj_scale_a; - int effective_proj_scale_b; - int32_t effective_hidden_scale_a; - int effective_hidden_scale_b; - int32_t layer_norm_input_scale_a; - int layer_norm_input_scale_b; - int32_t layer_norm_forget_scale_a; - int layer_norm_forget_scale_b; - int32_t layer_norm_cell_scale_a; - int layer_norm_cell_scale_b; - int32_t layer_norm_output_scale_a; - int layer_norm_output_scale_b; - // Quantized clip value for cell and projection. Zero value means no clipping. - int16_t quantized_cell_clip; - int8_t quantized_proj_clip; - int32_t hidden_zp; - int32_t cell_scale; - - int32_t input_variance_guard; - int32_t forget_variance_guard; - int32_t cell_variance_guard; - int32_t output_variance_guard; - - // Pre-calculate bias + zero_point * weight. - // Unabled to use temporary tensors since those are used in Prepare() and - // scratch buffer is only allocated after Preapre(). - std::unique_ptr input_to_forget_effective_bias; - std::unique_ptr recurrent_to_forget_effective_bias; - std::unique_ptr input_to_cell_effective_bias; - std::unique_ptr recurrent_to_cell_effective_bias; - std::unique_ptr input_to_output_effective_bias; - std::unique_ptr recurrent_to_output_effective_bias; - std::unique_ptr input_to_input_effective_bias; - std::unique_ptr recurrent_to_input_effective_bias; - std::unique_ptr projection_effective_bias; - - // Scale and zero point for intermediate tensors. - // Used only in the 8x8_8 case. - int32_t intermediate_scale_a[8]; - int32_t intermediate_scale_b[8]; - int32_t intermediate_zp[12]; -}; - -TfLiteStatus EvalFloat(const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* aux_input, - const TfLiteEvalTensor* aux_input_to_input_weights, - const TfLiteEvalTensor* aux_input_to_forget_weights, - const TfLiteEvalTensor* aux_input_to_cell_weights, - const TfLiteEvalTensor* aux_input_to_output_weights, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, - const TfLiteLSTMParams* params, bool forward_sequence, - bool time_major, int output_offset, - TfLiteEvalTensor* scratch_buffer, - TfLiteEvalTensor* output_state, - TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output); - -TfLiteStatus EvalInteger8x8_16( - TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, - const lstm_eval::IntegerLstmParameter* integer_lstm_param, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, TfLiteEvalTensor* scratch0, - TfLiteEvalTensor* scratch1, TfLiteEvalTensor* scratch2, - TfLiteEvalTensor* scratch3, TfLiteEvalTensor* scratch4, - TfLiteEvalTensor* scratch5); - -TfLiteStatus EvalInteger8x8_8( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, - const lstm_eval::IntegerLstmParameter* integer_lstm_param, - TfLiteEvalTensor* scratch0, TfLiteEvalTensor* scratch1, - TfLiteEvalTensor* scratch2, TfLiteEvalTensor* scratch3, - TfLiteEvalTensor* scratch4, TfLiteEvalTensor* scratch5, - TfLiteEvalTensor* scratch6, TfLiteEvalTensor* scratch7); - -} // namespace lstm_eval -} // namespace micro -} // namespace ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_LSTM_EVAL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h deleted file mode 100644 index 4d854c68..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/lstm_shared.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ -#define TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace lstm { -// For full inputs kernel (24-inputs). -// Please note the 20-input full kernel is deprecated and only kept -// here for backward compatibility. -namespace full { - -// Input Tensors of size {n_batch, n_input} -constexpr int kInputTensor = 0; - -// Input weight tensors of size: {n_cell, n_input} -constexpr int kInputToInputWeightsTensor = 1; // Optional -constexpr int kInputToForgetWeightsTensor = 2; -constexpr int kInputToCellWeightsTensor = 3; -constexpr int kInputToOutputWeightsTensor = 4; - -// Recurrent weight tensors of size {n_cell, n_output} -constexpr int kRecurrentToInputWeightsTensor = 5; // Optional -constexpr int kRecurrentToForgetWeightsTensor = 6; -constexpr int kRecurrentToCellWeightsTensor = 7; -constexpr int kRecurrentToOutputWeightsTensor = 8; - -// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. -constexpr int kCellToInputWeightsTensor = 9; // Optional -constexpr int kCellToForgetWeightsTensor = 10; // Optional -constexpr int kCellToOutputWeightsTensor = 11; // Optional - -// Gates bias tensors of size {n_cell} -constexpr int kInputGateBiasTensor = 12; // Optional -constexpr int kForgetGateBiasTensor = 13; -constexpr int kCellGateBiasTensor = 14; -constexpr int kOutputGateBiasTensor = 15; - -// Projection weight tensor of size {n_output, n_cell} -constexpr int kProjectionWeightsTensor = 16; // Optional -// Projection bias tensor of size {n_output} -constexpr int kProjectionBiasTensor = 17; // Optional - -// These state tensors are defined as variable tensors, and will be modified by -// this op. -constexpr int kOutputStateTensor = 18; -constexpr int kCellStateTensor = 19; - -// Layer norm coefficient tensors of size {n_cell}, representing a diagonal -// matrix. -constexpr int kInputLayerNormCoefficientsTensor = 20; // Optional -constexpr int kForgetLayerNormCoefficientsTensor = 21; // Optional -constexpr int kCellLayerNormCoefficientsTensor = 22; // Optional -constexpr int kOutputLayerNormCoefficientsTensor = 23; // Optional - -// Output tensors. -constexpr int kOutputTensor = 0; -} // namespace full - -} // namespace lstm -} // namespace micro -} // namespace ops -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_KERNELS_LSTM_SHARED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h deleted file mode 100644 index 604736dd..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ - -#if defined(HIFIMINI) -#include - -#include "tensorflow/lite/micro/kernels/xtensa/hifimini/fixedpoint_utils.h" -#endif // defined(HIFMINI) - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) -#include "include/nnlib/xa_nnlib_api.h" -#include "include/nnlib/xa_nnlib_standards.h" - -#define ALIGNED_SIZE(x, bytes) (((x) + (bytes - 1)) & (~(bytes - 1))) -#define ALIGN_PTR(x, bytes) ((((unsigned)(x)) + (bytes - 1)) & (~(bytes - 1))) -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) -#include "utils.h" -#include "vision_api.h" -#endif // defined(VISION_P6) - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h deleted file mode 100644 index 20142fa5..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_add.h +++ /dev/null @@ -1,48 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/kernels/add.h" -namespace tflite_micro { - -struct XtensaAddOpData { - OpDataAdd reference_op_data; -#if defined(VISION_P6) - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // VISION_P6 -}; - -#if defined(VISION_P6) - -TfLiteStatus AddPrepareVision(TfLiteContext* context, TfLiteNode* node); -TfLiteStatus AddEvalQuantizedVision(TfLiteContext* context, TfLiteNode* node, - const TfLiteAddParams& params, - const XtensaAddOpData& data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output); - -#endif // VISION_P6 - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_ADD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h deleted file mode 100644 index a7ba3450..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_conv.h +++ /dev/null @@ -1,89 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/conv.h" - -namespace tflite_micro { -struct XtensaConvOpData { - OpDataConv reference_op_data; - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - int scratch_tensor_index; -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) - int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and - // biases. - uint32_t reorder_coefficient_bias_size; - int8_t* per_channel_output_shift_int8; - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; - bool is_per_channel_quantized; -#endif // VISION_P6 -}; - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) -TfLiteStatus ConvPrepareHifi(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus ConvEvalHifiInt8(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams& params, - const XtensaConvOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); - -#if defined(HIFI3) || defined(HIFI4) -TfLiteStatus ConvEvalHifiInt16(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams& params, - const XtensaConvOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); -#endif // defined(HIFI3) || defined(HIFI4) - -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) - -TfLiteStatus ConvPrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus ConvEvalVision(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams& params, - const XtensaConvOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); - -#endif // VISION_P6 - -TfLiteStatus ConvReferenceEvalInt8(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus ConvReferenceEvalInt16(TfLiteContext* context, TfLiteNode* node); - -void* ConvInitXtensa(TfLiteContext* context, const char* buffer, size_t length); -TfLiteStatus ConvPrepareXtensa(TfLiteContext* context, TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h deleted file mode 100644 index 8aca9de3..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_depthwise_conv.h +++ /dev/null @@ -1,74 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/depthwise_conv.h" - -namespace tflite_micro { -struct XtensaDepthwiseConvOpData { - OpDataConv reference_op_data; - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - int scratch_tensor_index; -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) - int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and - // biases. - uint32_t reorder_coefficient_bias_size; - int8_t* per_channel_output_shift_int8; - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // VISION_P6 -}; - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) -TfLiteStatus DepthwiseConvPrepareHifi(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus DepthwiseConvEvalHifi(TfLiteContext* context, TfLiteNode* node, - const TfLiteDepthwiseConvParams& params, - const XtensaDepthwiseConvOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); - -TfLiteStatus DepthwiseConvReferenceEvalInt8(TfLiteContext* context, - TfLiteNode* node); -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) - -TfLiteStatus DepthwiseConvPrepareVision(TfLiteContext* context, - TfLiteNode* node); - -TfLiteStatus DepthwiseConvEvalVision(TfLiteContext* context, TfLiteNode* node, - const TfLiteDepthwiseConvParams& params, - const XtensaDepthwiseConvOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); - -#endif // VISION_P6 - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_DEPTHWISE_CONV_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h deleted file mode 100644 index fcedf829..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_fully_connected.h +++ /dev/null @@ -1,78 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/fully_connected.h" -#include "tensorflow/lite/micro/micro_log.h" - -namespace tflite_micro { -struct XtensaFullyConnectedOpData { - OpDataFullyConnected reference_op_data; - -#if defined(VISION_P6) - int8_t* reorder_coefficient_bias; // buffers used to keep reordered coeff and - // biases. - uint32_t reorder_coefficient_bias_size; - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // VISION_P6 -}; - -#if defined(HIFIMINI) -void FullyConnectedEvalHifimini( - const FullyConnectedParams& params, const RuntimeShape& input_shape, - const int8_t* input_data, const RuntimeShape& filter_shape, - const int8_t* filter_data, const RuntimeShape& bias_shape, - const int32_t* bias_data, const RuntimeShape& output_shape, - int8_t* output_data); -#endif // defined(HIFIMINI) - -#if defined(VISION_P6) -TfLiteStatus FullyConnectedPrepareVision(TfLiteContext* context, - TfLiteNode* node); - -TfLiteStatus FullyConnectedEvalVision(TfLiteContext* context, TfLiteNode* node, - const TfLiteConvParams& params, - const XtensaFullyConnectedOpData& data, - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, - TfLiteEvalTensor* output); -#endif // VISION_P6 - -void* XtensaInitFullyConnected(TfLiteContext* context, const char* buffer, - size_t length); - -TfLiteStatus XtensaEvalFullyConnectedQuantizedInt8( - TfLiteContext* context, TfLiteNode* node, const OpDataFullyConnected& data, - const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter, - const TfLiteEvalTensor* bias, TfLiteEvalTensor* output); - -TfLiteStatus XtensaCalculateOpDataFullyConnected( - TfLiteContext* context, TfLiteFusedActivation activation, - TfLiteType data_type, const TfLiteTensor* input, const TfLiteTensor* filter, - const TfLiteTensor* bias, TfLiteTensor* output, OpDataFullyConnected* data); - -TfLiteStatus XtensaPrepareFullyConnected(TfLiteContext* context, - TfLiteNode* node); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_FULLY_CONNECTED_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h deleted file mode 100644 index 101fc403..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pad.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -namespace tflite_micro { - -struct OpDataPad { - PadParams params; - int32_t output_zero_point; -}; - -struct XtensaPadData { - OpDataPad reference_op_data; - -#if defined(VISION_P6) - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // VISION_P6 -}; - -#if defined(VISION_P6) - -TfLiteStatus PadPrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus PadEvalVision(const XtensaPadData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); -#endif // VISION_P6 - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_PAD_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h deleted file mode 100644 index 1086f716..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h +++ /dev/null @@ -1,76 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/kernels/pooling.h" -namespace tflite_micro { - -struct XtensaOpDataPooling { - OpDataPooling reference_op_data; - -#if defined(VISION_P6) - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // defined(VISION_P6) - -#if defined(HIFI5) - int scratch_tensor_index; -#endif // defined(HIFI5) -}; - -#if defined(VISION_P6) - -TfLiteStatus AvgPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus MaxPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus PoolEvalVision(TfLiteContext* context, TfLiteNode* node, - const TfLitePoolParams& params, - const XtensaOpDataPooling& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); -#endif - -#if defined(HIFI5) - -TfLiteStatus AveragePrepareHifi(TfLiteContext* context, TfLiteNode* node); -TfLiteStatus AverageEvalQuantizedHifi(TfLiteContext* context, - const TfLiteNode* node, - const TfLitePoolParams* params, - const XtensaOpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); - -TfLiteStatus MaxPrepareHifi(TfLiteContext* context, TfLiteNode* node); -TfLiteStatus MaxEvalQuantizedHifi(TfLiteContext* context, TfLiteNode* node, - TfLitePoolParams* params, - const XtensaOpDataPooling* data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); - -#endif // defined(HIFI5) - -void* XtensaPoolingInit(TfLiteContext* context, const char* buffer, - size_t length); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_POOLING_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h deleted file mode 100644 index a1db4fe2..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reduce.h +++ /dev/null @@ -1,47 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/reduce.h" - -namespace tflite_micro { - -struct XtensaReduceOpData { - OpDataReduce reference_op_data; - -#if defined(VISION_P6) - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -#endif // VISION_P6 -}; - -#if defined(VISION_P6) - -TfLiteStatus ReducePrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus ReduceEvalVision(const XtensaReduceOpData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); - -#endif // VISION_P6 - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_REDUCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h deleted file mode 100644 index 9dce2f61..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_reshape.h +++ /dev/null @@ -1,44 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite_micro { - -#if defined(VISION_P6) - -struct XtensaReshapeData { - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -}; -#endif // VISION_P6 - -#if defined(VISION_P6) - -TfLiteStatus ReshapePrepareVision(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus ReshapeEvalVision(const XtensaReshapeData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); -#endif // VISION_P6 - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_RESHAPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h deleted file mode 100644 index 1e8e393a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_softmax.h +++ /dev/null @@ -1,58 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/softmax.h" - -namespace tflite_micro { - -#if defined(HIFI3) || defined(HIFI4) || defined(HIFI5) -struct XtensaSoftmaxOpData { - SoftmaxParams params; - int scratch_tensor_index; -}; -#endif // defined(HIFI3) || defined(HIFI4) || defined(HIFI5) - -#if defined(VISION_P6) -struct XtensaSoftmaxOpData { - SoftmaxParams params; - uint8_t* p_context; // persistent lib context for this instance saved here - uint32_t context_size; -}; -#endif // defined(VISION_P6) - -void* XtensaInitSoftmax(TfLiteContext* context, const char* buffer, - size_t length); - -TfLiteStatus XtensaPrepareSoftmax(TfLiteContext* context, TfLiteNode* node); - -TfLiteStatus XtensaEvalSoftmaxInt8Int16(TfLiteContext* context, - TfLiteNode* node); - -#if defined(VISION_P6) -TfLiteStatus SoftmaxPrepareVision(TfLiteContext* context, TfLiteNode* node); -TfLiteStatus SoftmaxEvalVision(TfLiteContext* context, TfLiteNode* node, - const XtensaSoftmaxOpData& data, - const TfLiteEvalTensor* input, - TfLiteEvalTensor* output); -#endif // defined(VISION_P6) - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SOFTMAX_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h b/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h deleted file mode 100644 index 002f1496..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/kernels/xtensa/xtensa_svdf.h +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/micro/kernels/svdf.h" - -namespace tflite_micro { -#if defined(HIFIMINI) -TfLiteStatus EvalIntegerSvdfHifimini( - TfLiteContext* context, TfLiteNode* node, - const TfLiteEvalTensor* input_tensor, - const TfLiteEvalTensor* weights_feature_tensor, - const TfLiteEvalTensor* weights_time_tensor, - const TfLiteEvalTensor* bias_tensor, const TfLiteSVDFParams* params, - TfLiteEvalTensor* activation_state_tensor, TfLiteEvalTensor* output_tensor, - OpDataSvdf data); -#endif // HIFIMINI - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_XTENSA_XTENSA_SVDF_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h b/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h deleted file mode 100644 index 51186064..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_helpers.h +++ /dev/null @@ -1,64 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// Returns the next pointer address aligned to the given alignment. -uint8_t* AlignPointerUp(uint8_t* data, size_t alignment); - -// Returns the previous pointer address aligned to the given alignment. -uint8_t* AlignPointerDown(uint8_t* data, size_t alignment); - -// Returns an increased size that's a multiple of alignment. -size_t AlignSizeUp(size_t size, size_t alignment); - -// Templated version of AlignSizeUp -// Returns an increased size that's a multiple of alignment. -template -size_t AlignSizeUp(size_t count = 1) { - return AlignSizeUp(sizeof(T) * count, alignof(T)); -} - -// Returns size in bytes for a given TfLiteType. -TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size); - -// How many bytes are needed to hold a tensor's contents. -TfLiteStatus BytesRequiredForTensor(const tflite_micro::Tensor& flatbuffer_tensor, - size_t* bytes, size_t* type_size); - -// How many bytes are used in a TfLiteEvalTensor instance. The byte length is -// returned in out_bytes. -TfLiteStatus TfLiteEvalTensorByteLength(const TfLiteEvalTensor* eval_tensor, - size_t* out_bytes); - -// Deduce output dimensions from input and allocate given size. -// Useful for operators with two inputs where the largest input should equal the -// output dimension. -TfLiteStatus AllocateOutputDimensionsFromInput(TfLiteContext* context, - const TfLiteTensor* input1, - const TfLiteTensor* input2, - TfLiteTensor* output); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_HELPERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h deleted file mode 100644 index 954798c8..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/greedy_memory_planner.h +++ /dev/null @@ -1,172 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" - -namespace tflite_micro { - -constexpr int kOnlinePlannedBuffer = -1; -// We are using this to mark a buffer explicitly as externally offline allocated -constexpr int kXcoreOfflinePlannedBuffer = -111; - -// A memory planner that uses a greedy algorithm to arrange buffers in memory -// to minimize the overall arena size needed. -// -// The algorithm works like this: -// - The client enters the buffer information through AddBuffer(). -// - When a function like GetOffsetForBuffer() is called, the -// CalculateOffsetsIfNeeded() method is invoked. -// - If an up to date plan is not already present, one will be calculated. -// - The buffers are sorted in descending order of size. -// - The largest buffer is placed at offset zero. -// - The rest of the buffers are looped through in descending size order. -// - The other buffers that need to be in memory at the same time are found. -// - The first gap between simultaneously active buffers that the current -// buffer fits into will be used. -// - If no large-enough gap is found, the current buffer is placed after the -// last buffer that's simultaneously active. -// - This continues until all buffers are placed, and the offsets stored. -// -// This is not guaranteed to produce the best placement, since that's an -// NP-Complete problem, but in practice it should produce one that's decent. -class GreedyMemoryPlanner : public MicroMemoryPlanner { - public: - GreedyMemoryPlanner(); - ~GreedyMemoryPlanner() override; - - // You need to pass in an area of memory to be used for planning. The client - // should ensure the validity of the memory when it needs to use this object. - // This memory isn't owned by this object, so management should be handled by - // the client. This is so it can be stack or globally allocated if necessary - // on devices without dynamic memory allocation. How many buffers can be - // planned for will depend on the size of this scratch memory, so you should - // enlarge it if you see an error when calling AddBuffer(). The memory can be - // reused once you're done with the planner, as long as you copy the - // calculated offsets to another location. Each buffer requires about 36 bytes - // of scratch. - TfLiteStatus Init(unsigned char* scratch_buffer, - int scratch_buffer_size) override; - - // Record details of a buffer we want to place. - TfLiteStatus AddBuffer(int size, int first_time_used, - int last_time_used) override; - - // Record details of an offline planned buffer offset we want to place. - // offline_offset is the buffer offset from the start of the arena. - TfLiteStatus AddBuffer(int size, int first_time_used, int last_time_used, - int offline_offset) override; - - // Returns the high-water mark of used memory. This is the minimum size of a - // memory arena you'd need to allocate to hold these buffers. - size_t GetMaximumMemorySize() override; - - // How many buffers have been recorded. - int GetBufferCount() override; - - // Where a given buffer should be placed in the memory arena. - // This information is stored in the memory arena itself, so once the arena - // is used for inference, it will be overwritten. - TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) override; - - // Prints an ascii-art diagram of the buffer layout plan. - void PrintMemoryPlan() override; - - // Debug method to check whether any buffer allocations are overlapping. This - // is an O(N^2) complexity operation, so only use for testing. - bool DoAnyBuffersOverlap(); - - // Used to store a list of buffers ordered by their offset. - struct ListEntry { - int offset; - int requirements_index; - int next_entry_index; - }; - - // Number of bytes required in order to plan a buffer. - static size_t per_buffer_size() { - const int per_buffer_size = - sizeof(BufferRequirements) + // requirements_ - sizeof(int) + // buffer_sizes_sorted_ - sizeof(int) + // buffer_ids_sorted_ - sizeof(ListEntry) + // buffers_sorted_by_offset_ - sizeof(int); // buffer_offsets_; - return per_buffer_size; - } - - // Returns False because the GreedyMemoryPlanner doesn't preserves all tensors - // after invocation. Do to the fact that tensors that tensor data for tensors - // that aren't being used during a phase of invocation are overwritten. - bool preserves_all_tensors() const override { return false; } - - TF_LITE_REMOVE_VIRTUAL_DELETE - - private: - // Whether a buffer is active in a given time range. - bool DoesEntryOverlapInTime(const ListEntry* entry, const int first_time_used, - const int last_time_used) const; - - // Walks the list to return the next buffer that is active in a given time - // range, or a null pointer if there are none. - ListEntry* NextSimultaneouslyActiveBuffer(const ListEntry* start, - const int first_time_used, - const int last_time_used); - - // If there isn't an up to date plan, calculate a new one. - void CalculateOffsetsIfNeeded(); - - // How many buffers we can plan for, based on the arena size we're given in - // the constructor. - int max_buffer_count_; - - // The number of buffers added so far. - int buffer_count_; - - // Records the client-provided information about each buffer. - struct BufferRequirements { - int size; - int offline_offset; - int first_time_used; - int last_time_used; - }; - - // Working arrays used during the layout algorithm. - BufferRequirements* requirements_; - // buffer_sizes_sorted_ and buffer_ids_sorted_ are sorted according to: - // { - // offline planned buffers, - // online planned buffers sorted by size - // } - int* buffer_sizes_sorted_; - int* buffer_ids_sorted_; - ListEntry* buffers_sorted_by_offset_; - int next_free_entry_; // Index of the next free entry of - // buffers_sorted_by_offset_ - int first_entry_index_; // Index of the first entry (smallest offset) of - // buffers_sorted_by_offset_ - - // Stores the outcome of the plan, the location of each buffer in the arena. - int* buffer_offsets_; - - // Whether buffers have been added since the last plan was calculated. - bool need_to_calculate_offsets_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h deleted file mode 100644 index c701ceef..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/linear_memory_planner.h +++ /dev/null @@ -1,53 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" - -namespace tflite_micro { - -// The simplest possible memory planner that just lays out all buffers at -// increasing offsets without trying to reuse memory. -class LinearMemoryPlanner : public MicroMemoryPlanner { - public: - LinearMemoryPlanner(); - ~LinearMemoryPlanner() override; - - TfLiteStatus AddBuffer(int size, int first_time_used, - int last_time_used) override; - - size_t GetMaximumMemorySize() override; - int GetBufferCount() override; - TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) override; - - // Returns True because the LinearMemoryPlanner preserves all tensors after - // invocation. - bool preserves_all_tensors() const override { return true; } - - private: - static constexpr int kMaxBufferCount = 1024; - size_t buffer_offsets_[kMaxBufferCount]; - int current_buffer_count_; - size_t next_free_offset_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h deleted file mode 100644 index 3ca7ab9f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/memory_plan_struct.h +++ /dev/null @@ -1,73 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ - -#include -#include - -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite_micro { - -// This is an experimental feature and subjected to change. -// More description is available at -// tensorflow/lite/micro/docs/offline_memory_plan.md. - -// Describes a buffer's layout inside an arena. This struct should be kept as -// small as possible for memory footprint sensitive applications and should use -// only primitive fields, making it easy to adjust offline. -struct BufferDescriptor { - // Starting offset inside an arena for this buffer. - // Offset is the minimum information needed for the buffer. The user knows - // the model and the size of each buffer in order to lay out a valid buffer - // plan. - int32_t offset; -}; - -// A structure describing the lay out of buffers inside an arena. -struct BufferPlan { - // Number of buffers described in this plan. - int32_t buffer_count; - - // Each element describes one buffer. - // Buffer index is implicit by the order of AddBuffer() call. - // Specifically, indices of activation tensors are 0 … N-1 where N is the - // number of activation tensors. - // The rest are based on the order of OP requests. - // - // This is a flexible array member and should ideally be - // arena_entries[]; However, in order to support a variety - // of compilers (and without needing to add ifdef's), we - // are implementing the flexible array member with an array of - // length 1 as the last member of the struct. When the size of a BufferPlan - // is needed, use the provided SizeOfBufferPlan(buffer_count) that - // accounts for this implemenatation caveat. - BufferDescriptor buffer_plan_entries[1]; -}; - -// Returns size of a BufferPlan given a buffer count. This size is compile time -// known if buffer_count is a compile time constant. -constexpr size_t SizeOfBufferPlan(int32_t buffer_count) { - // Minus 1 because a BufferPlan struct have a BufferDescriptor already. - // Max to provide a lower bound for the corner case of buffer_count = 0. - return sizeof(BufferPlan) + - sizeof(BufferDescriptor) * Max(buffer_count - 1, 0); -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_MEMORY_PLAN_STRUCT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h deleted file mode 100644 index f3e84f6a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/micro_memory_planner.h +++ /dev/null @@ -1,95 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -// Interface class for planning the layout of memory buffers during the -// execution of a graph. -// It's designed to be used by a client that iterates in any order through the -// buffers it wants to lay out, and then calls the getter functions for -// information about the calculated layout. For example: -// -// SomeMemoryPlanner planner; -// planner.AddBuffer(100, 0, 1); // Buffer 0 -// planner.AddBuffer(50, 2, 3); // Buffer 1 -// planner.AddBuffer(50, 2, 3); // Buffer 2 -// -// int offset0; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(0, &offset0)); -// int offset1; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(1, &offset1)); -// int offset2; -// TF_EXPECT_OK(planner.GetOffsetForBuffer(2, &offset2)); -// const int arena_size_needed = planner.GetMaximumMemorySize(); -// -// The goal is for applications to be able to experiment with different layout -// strategies without changing their client code, by swapping out classes that -// implement this interface.= -class MicroMemoryPlanner { - public: - MicroMemoryPlanner() {} - virtual ~MicroMemoryPlanner() {} - - // Pass information about a buffer's size and lifetime to the layout - // algorithm. The order this is called implicitly assigns an index to the - // result, so the buffer information that's passed into the N-th call of - // this method will be used as the buffer_index argument to - // GetOffsetForBuffer(). - virtual TfLiteStatus AddBuffer(int size, int first_time_used, - int last_time_used) = 0; - - // Record details of an offline planned buffer offset we want to place. - // offline_offset is the buffer offset from the start of the arena. - // This is to support offline memory planning from the flatbuffer metadata. - // By default, it returns an error. - virtual TfLiteStatus AddBuffer(int size, int first_time_used, - int last_time_used, int offline_offset) { - return kTfLiteError; - } - - // The largest contiguous block of memory that's needed to hold the layout. - virtual size_t GetMaximumMemorySize() = 0; - // How many buffers have been added to the planner. - virtual int GetBufferCount() = 0; - // Calculated layout offset for the N-th buffer added to the planner. - virtual TfLiteStatus GetOffsetForBuffer(int buffer_index, int* offset) = 0; - - // Provides the scratch buffer in case that the memory planner needs it. - // The lifetime of scratch buffers lifetime lasts until the static memory plan - // is committed. - // The default implementation is for the memory planner that does not need - // scratch buffer and simply returns ok. - virtual TfLiteStatus Init(unsigned char* scratch_buffer, - int scratch_buffer_size) { - return kTfLiteOk; - } - - // Method will return True if the MicroMemoryPlanner preserves all tensors - // after invocation, and False if it doesn't. - virtual bool preserves_all_tensors() const = 0; - - virtual void PrintMemoryPlan() { - // Default does nothing. - } -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_MEMORY_PLANNER_MEMORY_PLANNER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h b/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h deleted file mode 100644 index 4d4eb31a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.h +++ /dev/null @@ -1,133 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ -#define TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/memory_planner/memory_plan_struct.h" -#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" - -namespace tflite_micro { - -/* This is an experimental feature and subjected to change. - * -The NonPersistentMemoryPlannerShim enables TFLM to work with an external tooling -that can plan the offset of each non persistent buffer for the Model within the -TFLM arena. - -If the NonPersistentMemoryPlannerShim is used, then the final binary does not -have any of the symbols associated with the GreedyMemoryPlanner which results in -a reduced memory footprint. - -Additionally, the offline planning of the non-persistent buffers can be used to -have a more efficient utilization compared to the GreedyMemoryPlanner. - -For example, consider the following hypothetical model: - -A1(400) A2(401) -──┬─────────┐ ┌─────────── - │ │ │ - │ │ │ - │ ▼ ▼ - │ ┌────────┐ - │ │ OP1 │ - │ └───┬────┘ A4(201) - │ A3(10) │ │ - │ │ │ - │ │ │ - │ ┌───┴────┐ │ - │ │ OP2 │◄────────┤ - │ └───┬────┘ │ - │ A5(11) │ A6(202) │ - │ │ │ │ - │ ▼ │ │ - │ ┌────────┐ │ │ - │ │ OP3 │◄─┘ │ - │ └───┬────┘ │ - │ │ A8(200) │ - │ A7(12) │ │ │ - │ │ │ │ - │ ┌───┴────┐◄──┘ │ - └──────►│ OP4 │ │ - └───┬────┘◄────────┘ - │ - A9(13) │ - ▼ - -The GreedyMemoryPlanner will give the following memory layout that requires 1012 -bytes of scratch arena size: - -┌─────────────────────────────────────────┬──────────────────────────┬────────┬───────┐ -│ A2(401) │ A1(400) │ A4(201)│ -A3(10)│ -└─────────────────────────────────────────┴──────────────────────────┴────────┴───────┘ - -┌───────────┬──────┬──────┐ -│ A6(202) │A5(11)│A7(12)│ -└───────────┴──────┴──────┘ - -┌──────────┬───────┐ -│ A8(200) │A9(13) │ -└──────────┴───────┘ - -But a more efficient offline memory plan that requires only 826 bytes of scratch -arena size can be - -┌──────────────────────────────────────┬─────────────────────────────┬───────┬──────┐ -│ A1(400) │ A2(401) │ -A3(10)│A5(11)│ -└──────────────────────────────────────┴─────────────────────────────┴───────┴──────┘ - - ┌────────────────┬────────────┬────────┬───────┐ - │A4(201) │ A8(200) │A9(13) -│A7(12) │ └────────────────┴────────────┴────────┴───────┘ - - ┌─────────────┐ - │ A6(202) │ - └─────────────┘ - -*/ -class NonPersistentMemoryPlannerShim : public MicroMemoryPlanner { - public: - // Does not take ownership of buffer_plan, which must refer to a valid - // BufferPlan that outlives this object. - explicit NonPersistentMemoryPlannerShim(const BufferPlan* buffer_plan); - ~NonPersistentMemoryPlannerShim() override; - - TfLiteStatus GetOffsetForBuffer(int buffer_request_index, - int* offset) override; - - TfLiteStatus AddBuffer(int size, int first_time_used, - int last_time_used) override; - size_t GetMaximumMemorySize() override; - int GetBufferCount() override; - - // Returns False because the NonPersistentMemoryPlannerShim doesn't preserves - // all tensors after invocation. - bool preserves_all_tensors() const override { return false; } - - private: - const BufferPlan* buffer_plan_; // not owned, can't be null - - // The number of buffers requested so far. Used for error checking. - int buffer_request_count_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MEMORY_PLANNER_NON_PERSISTENT_MEMORY_PLANNER_SHIM_H__ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h b/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h deleted file mode 100644 index 2cb7c5fa..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_allocation_info.h +++ /dev/null @@ -1,138 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/flatbuffer_utils.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// Used to hold information used during allocation calculations. -struct AllocationInfo { - size_t bytes; - void** output_ptr; - int first_created; - int last_used; - int32_t offline_offset; - bool needs_allocating; -}; - -// Used to hold the allocation info list and related metadata for the entire -// graph (including subgraphs). Since all subgraphs are planned together, the -// allocation info list contains allocations for all subgraphs. Track the offset -// into this list for each subgraph then reserve space to track all allocations. -// -// The AllocationInfo list is a contiguous list of allocations across all -// subgraphs and scratch buffers. Each element here is marked as -// st. The following is a possible -// AllocationInfo list: -// [s0t0, s0t1, s1t0, s2t1, s1t2, s3t0, s3t1, scratch0, scratch1, scratch2] -// -// For this example, the subgraph offsets would be [0, 2, 5] and the scratch -// offset would be 7. -struct GraphAllocationInfo { - AllocationInfo* allocation_info; - size_t allocation_info_count; - size_t* subgraph_offsets; - size_t scratch_offset; - size_t tensor_count; - size_t scratch_buffer_count; -}; - -// A helper class to construct AllocationInfo array. This array contains the -// lifetime of tensors / scratch_buffer and will be used to calculate the memory -// plan. Methods need to be called in order from `Create`, Init`, `Add*`, to -// `Finish`. -class AllocationInfoBuilder { - public: - AllocationInfoBuilder(const Model* model, - INonPersistentBufferAllocator* non_persistent_allocator) - : model_(model), non_persistent_allocator_(non_persistent_allocator) {} - - // Check if model contains offline planned buffer offsets. - // - If there's no metadata available, offline_planner_offsets is not set - // - If there's metadata available, offline_planner_offsets will point to the - // first offset in the metadata buffer list. - TfLiteStatus GetOfflinePlannedOffsets( - const int32_t** offline_planner_offsets); - - // Allocate memory for the allocation info array as well as offsets into that - // array for each subgraph. - TfLiteStatus CreateAllocationInfo(int scratch_buffer_request_count); - - // Release memory used for the allocation info array. - TfLiteStatus FreeAllocationInfo(); - - // Initialize AllocationInfo for all tensors and scratch buffers in the graph. - TfLiteStatus InitializeAllocationInfo(const int32_t* offline_offsets, - SubgraphAllocations* allocations); - - // Mark the scope of each tensor and scratch buffer across the graph. Enter - // all possible subgraphs invoked by each control flow operator. This method - // marks the maximum lifetime of each buffer so that tensors are correctly - // planned for all valid invocation flows. - TfLiteStatus MarkAllocationLifetimes( - int subgraph_idx, internal::ScratchBufferRequest* scratch_buffer_request, - ScratchBufferHandle* scratch_buffer_handles, - SubgraphAllocations* allocations); - - // Identify control flow operators and recursively mark all subgraphs which - // that operator can invoke. The lifetime of all tensors within a subgraph - // can only be extended. The order of subgraph invocation does not matter - // since subgraphs within the same control flow operator are executed - // within their own allocation scope (planned buffers in a subgraph cannot - // persist beyond the end of that subgraph's invocation). - TfLiteStatus MarkSubgraphLifetimesIfNecessary( - const Operator* op, - internal::ScratchBufferRequest* scratch_buffer_requests, - ScratchBufferHandle* scratch_buffer_handles, - SubgraphAllocations* allocations); - - // Returns the number of allocations. - int AllocationCount() const { return info_.allocation_info_count; } - - // Returns a pointer to the built AllocationInfo array. - AllocationInfo* Finish() const { return info_.allocation_info; } - - private: - // Mark the given Allocation info as first created at the specified allocation - // scope count. Only the first creation must be recorded since the allocation - // scope count monotonically increases throughout the lifetime marking - // process. - void UpdateFirstCreated(AllocationInfo* current, int allocation_scope_count); - - // Mark the given AllocationInfo as last used at the specified allocation - // scope - // count. Update the last used marker every time, since the allocation scope - // count monotonically increases through the lifetime marking process. - void UpdateLastUsed(AllocationInfo* current, int allocation_scope_count); - - // Validate if a subgraph satisfies assumptions. - TfLiteStatus ValidateSubgraph(const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors); - - const tflite_micro::Model* model_ = nullptr; - INonPersistentBufferAllocator* non_persistent_allocator_ = nullptr; - GraphAllocationInfo info_; - int allocation_scope_count_ = 0; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATION_INFO_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h deleted file mode 100644 index 9f57f580..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_allocator.h +++ /dev/null @@ -1,351 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/flatbuffer_utils.h" -#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" -#include "tensorflow/lite/micro/micro_common.h" -#include "tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// TODO(b/199402574): rename to tflite_internal or just remove internal -// namespace. -namespace internal { - -// Sets up all of the data structure members for a TfLiteTensor based on the -// contents of a serialized tensor in the flatbuffer. -// TODO(b/162311891): Drop this method when the interpreter has an API for -// returning buffers on TfLiteEvalTensor. -TfLiteStatus InitializeTfLiteTensorFromFlatbuffer( - IPersistentBufferAllocator* persistent_buffer_allocator, - INonPersistentBufferAllocator* non_persistent_buffer_allocator, - bool allocate_temp, const tflite_micro::Tensor& flatbuffer_tensor, - const flatbuffers::Vector>* buffers, - TfLiteTensor* result); - -// Holds placeholder information for a scratch buffer request from a kernel. -// This struct is only used during the model prepare stage. Each request from a -// kernel is stored in the head section. During the prepare stage, the head -// section will at least hold kMaxScratchBuffersPerOp number of requests plus -// any requests from previous kernel requests. -// -// When the memory plan is finalized, these structs are no longer used in favor -// of a sequential, array of ScratchBufferHandle allocations in the tail -// section. These allocations are indexed by the request API defined in the -// TfLiteContext struct. -struct ScratchBufferRequest { - // Number of bytes required by the buffer. The actual allocated size might be - // greater than `bytes` due to buffer alignment. - size_t bytes; - // Node where the buffer is allocated for. This provides useful information to - // determine the lifetime of the buffer. In AllocationInfo, this buffer will - // have `before` = node_idx and `after` = node_idx. - int node_idx; - int subgraph_idx; -}; - -} // namespace internal - -// Enum used to keep track of which MemoryPlanner is being used for -// MicroAllocater::Create(); -enum class MemoryPlannerType { - kGreedy, - kLinear, -}; - -struct NodeAndRegistration { - TfLiteNode node; - const TFLMRegistration* registration; -}; - -// Holds a pointer to a buffer for a scratch buffer requested by a kernel during -// the model prepare stage. This struct is allocated in-place and allows for -// quick pointer-indexed lookup for speed during model inference. -struct ScratchBufferHandle { - // Pointer to location of the scratch buffer: - uint8_t* data; -}; - -// Stores all per-subgraph allocations. This includes the node and registration -// array, and tensor list for each subgraph. -struct SubgraphAllocations { - NodeAndRegistration* node_and_registrations; - TfLiteEvalTensor* tensors; -}; - -// Allocator responsible for allocating memory for all intermediate tensors -// necessary to invoke a model. -// -// The lifetime of the model, tensor arena and error reporter must be at -// least as long as that of the allocator object, since the allocator needs -// them to be accessible during its entire lifetime. -// -// The MicroAllocator simply plans out additional allocations that are required -// to standup a model for inference in TF Micro. This class currently relies on -// an additional allocator - SingleArenaBufferAllocator - for all allocations -// from an arena. These allocations are divided into head (non-persistent) and -// tail (persistent) regions: -// -// Memory layout to help understand how it works -// This information could change in the future version. -// ************** .memory_allocator->GetBuffer() -// Tensors/Scratch buffers (head) -// ************** .head_watermark -// unused memory -// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() -// - ->GetDataSize() -// persistent area (tail) -// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize() -class MicroAllocator { - public: - // Creates a MicroAllocator instance from a given tensor arena. This arena - // will be managed by the created instance. The GreedyMemoryPlanner will - // by default be used and created on the arena. - // Note: Please use alignas(16) to make sure tensor_arena is 16 - // bytes aligned, otherwise some head room will be wasted. - // TODO(b/157615197): Cleanup constructor + factory usage. - static MicroAllocator* Create( - uint8_t* tensor_arena, size_t arena_size, - MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy); - - // Creates a MicroAllocator instance from a given tensor arena and a given - // MemoryPlanner. This arena will be managed by the created instance. Note: - // Please use alignas(16) to make sure tensor_arena is 16 bytes - // aligned, otherwise some head room will be wasted. - static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size, - MicroMemoryPlanner* memory_planner); - - // Creates a MicroAllocator instance using the provided - // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator - // instance will use the SingleArenaBufferAllocator instance to manage - // allocations internally. - static MicroAllocator* Create(SingleArenaBufferAllocator* memory_allocator, - MicroMemoryPlanner* memory_planner); - - // Creates a MicroAllocator instance using the provided - // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator - // instance will use the SingleArenaBufferAllocator instance to manage - // allocations internally. - static MicroAllocator* Create( - uint8_t* persistent_tensor_arena, size_t persistent_arena_size, - uint8_t* non_persistent_tensor_arena, size_t non_persistent_arena_size, - MemoryPlannerType memory_planner_type = MemoryPlannerType::kGreedy); - - // Returns the fixed amount of memory overhead of MicroAllocator. - static size_t GetDefaultTailUsage(bool is_memory_planner_given); - - // Returns True if the MicroAllocator uses a LinearMemoryPlanner(is compatible - // with the PerserveAllTensors flag / feature ) and False otherwise. - bool preserves_all_tensor() const { - return memory_planner_->preserves_all_tensors(); - }; - - // Allocates internal resources required for model inference for each subgraph - // from the arena. - // - // This method will run through the flatbuffer data supplied in the model to - // properly allocate tensor, node, and op registration data. This method is - // expected to be followed with a call to FinishModelAllocation() Returns a - // pointer to an array of SubgraphAllocations (also stored in the tail of the - // arena) where each index corresponds to a different subgraph in the model. - // Return value is nullptr if the allocations failed. - SubgraphAllocations* StartModelAllocation(const Model* model); - - // Finish allocating internal resources required for model inference. - // - // -Plan the memory for activation tensors and scratch buffers. - // -Update eval tensors for each subgraph based on planned offsets. - // -Allocate scratch buffer handles array and update based on planned offsets. - // - // This method should be called after assigning model resources - // in StartModelAllocation(). The subgraph_allocations pointer should be the - // value passed into this class during StartModelAllocation(). Scratch buffer - // handles are stored in the out-param `scratch_buffer_handles` array which is - // allocated in this method. This value will be used in `GetScratchBuffer` - // call to retrieve scratch buffers. - TfLiteStatus FinishModelAllocation( - const Model* model, SubgraphAllocations* subgraph_allocations, - ScratchBufferHandle** scratch_buffer_handles); - - // Allocates a TfLiteTensor struct and populates the returned value with - // properties from the model flatbuffer. This struct is allocated from - // persistent arena memory is only guaranteed for the lifetime of the - // application. The eval_tensors pointer should be the value passed into this - // class during StartModelAllocation() and contains the source-of-truth for - // buffers. - virtual TfLiteTensor* AllocatePersistentTfLiteTensor( - const Model* model, const SubgraphAllocations* subgraph_allocations, - int tensor_index, int subgraph_index); - - // Allocates a TfLiteTensor struct and populates the returned value with - // properties from the model flatbuffer. This struct is allocated from - // temporary arena memory is only guaranteed until a call is made to - // ResetTempAllocations(). Subgraph_allocations contains the array of - // TfLiteEvalTensors. If the newly allocated temp at the specified subgraph - // and tensor index is already present int the TfLiteEvalTensor array, its - // data buffer will be re-used. - virtual TfLiteTensor* AllocateTempTfLiteTensor( - const Model* model, const SubgraphAllocations* subgraph_allocations, - int tensor_index, int subgraph_index); - - virtual void DeallocateTempTfLiteTensor(TfLiteTensor*); - - // Returns a pointer to a buffer from the temporary arena memory and is only - // guaranteed until a call is made to ResetTempAllocations(). - virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment); - - // Signals that the temporary buffer no longer needed. - virtual void DeallocateTempBuffer(uint8_t* buffer); - - // Resets all temporary allocations. This method should be called after a - // chain of temp allocations (e.g. chain of TfLiteTensor objects via - // AllocateTfLiteTensor()). - virtual TfLiteStatus ResetTempAllocations(); - - // Returns true if all temporary buffers including temp TfLiteTensor are - // already deallocated. - virtual bool IsAllTempDeallocated(); - - // Allocates persistent buffer which has the same life time as the allocator. - // The memory is immediately available and is allocated from the tail of the - // arena. - virtual void* AllocatePersistentBuffer(size_t bytes); - - // Register a scratch buffer of size `bytes` for Node with `node_id`. - // This method only requests a buffer with a given size to be used after a - // model has finished allocation via FinishModelAllocation(). All requested - // buffers will be accessible by the out-param in that method. - TfLiteStatus RequestScratchBufferInArena(size_t bytes, int subgraph_idx, - int* buffer_idx); - - // Finish allocating a specific NodeAndRegistration prepare block (kernel - // entry for a model) with a given node ID. This call ensures that any scratch - // buffer requests and temporary allocations are handled and ready for the - // next node prepare block. - TfLiteStatus FinishPrepareNodeAllocations(int node_id); - - // Returns the arena usage in bytes, only available after - // `FinishModelAllocation`. Otherwise, it will return 0. - size_t used_bytes() const; - - TfLiteBridgeBuiltinDataAllocator* GetBuiltinDataAllocator(); - - MicroMemoryPlanner* memory_planner() { return memory_planner_;} - - // Returns the pointer for the array of ScratchBufferRequest allocations in - // the head section. - internal::ScratchBufferRequest* GetScratchBufferRequests(); - - size_t GetScratchBufferRequestCount() { return scratch_buffer_request_count_;} - - protected: - MicroAllocator(SingleArenaBufferAllocator* memory_allocator, - MicroMemoryPlanner* memory_planner); - MicroAllocator(IPersistentBufferAllocator* persistent_buffer_allocator, - INonPersistentBufferAllocator* non_persistent_buffer_allocator, - MicroMemoryPlanner* memory_planner); - virtual ~MicroAllocator(); - - // Allocates an array in the arena to hold pointers to the node and - // registration pointers required to represent the inference graph of the - // model. - virtual TfLiteStatus AllocateNodeAndRegistrations( - const Model* model, SubgraphAllocations* subgraph_allocations); - - // Allocates the list of persistent TfLiteEvalTensors that are used for the - // "eval" phase of model inference. These structs will be the source of truth - // for all tensor buffers. - virtual TfLiteStatus AllocateTfLiteEvalTensors( - const Model* model, SubgraphAllocations* subgraph_allocations); - - // Allocates persistent tensor buffers for variable tensors in the subgraph. - // Online and offline variable tensors are handled differently hence the - // offline_planner_offsets parameter is needed. - virtual TfLiteStatus AllocateVariables( - const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors, - const int32_t* offline_planner_offsets); - - // Allocate and return a persistent TfLiteTensor. - // TODO(b/162311891): Drop this method when the interpreter has an API for - // accessing TfLiteEvalTensor structs. - virtual TfLiteTensor* AllocatePersistentTfLiteTensorInternal(); - - // Populates a TfLiteTensor struct with data from the model flatbuffer. Any - // quantization data is allocated from either the tail (persistent) or temp - // sections of the arena based on the allocation flag. - virtual TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model, - TfLiteTensor* tensor, - int tensor_index, - int subgraph_idx, - bool allocate_temp); - - private: - // Commits a memory plan for all non-persistent buffer allocations in the - // 'head' section of the memory arena. The eval_tensors pointer is the list of - // pre-allocated TfLiteEvalTensor structs that will point to the buffers that - // will be allocated into the head section in this function call. The - // scratch_buffer_handles pointer is the array of pre-allocated - // ScratchBufferHandle structs that will point to allocated buffers also in - // the head section. - virtual TfLiteStatus CommitStaticMemoryPlan( - const Model* model, SubgraphAllocations* allocations, - ScratchBufferHandle* scratch_buffer_handles); - - // Allocates an array of ScratchBufferHandle structs in the tail section for a - // given number of handles. - virtual TfLiteStatus AllocateScratchBufferHandles( - ScratchBufferHandle** scratch_buffer_handles, size_t handle_count); - - // Clears all internal scratch buffer request counts and resets the head to - // prepare for kernels to request scratch buffer data when a model is - // preparing. - TfLiteStatus InitScratchBufferData(); - - // A simple memory allocator that always allocate from the arena tail or head. - INonPersistentBufferAllocator* non_persistent_buffer_allocator_; - IPersistentBufferAllocator* persistent_buffer_allocator_; - - // Allocator used to allocate persistent builtin data. - TfLiteBridgeBuiltinDataAllocator* builtin_data_allocator_; - - // Activation buffer memory planner. - MicroMemoryPlanner* memory_planner_; - - bool model_is_allocating_; - - // Holds the number of ScratchBufferRequest instances stored in the head - // section when a model is allocating. - size_t scratch_buffer_request_count_ = 0; - - // Holds ScratchBufferRequest when a model is allocating - uint8_t* scratch_buffer_head_ = nullptr; - - // Holds the byte length of the memory plan with the largest head usage. Used - // to ensure that multi-tenant allocations can share the head for buffers. - size_t max_head_buffer_usage_ = 0; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h b/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h deleted file mode 100644 index 96c109e8..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_arena_constants.h +++ /dev/null @@ -1,28 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ - -namespace tflite_micro { - -// The default buffer alignment requirement. -// We align tensor buffers to 16-byte boundaries, since this is a common -// requirement for SIMD extensions. -constexpr int MicroArenaBufferAlignment() { return 16; } - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_ARENA_CONSTANTS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_common.h b/new_ai_tools/include/tensorflow/lite/micro/micro_common.h deleted file mode 100644 index 9ab427f5..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_common.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ -#define THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ - -#include "tensorflow/lite/c/common.h" - -// TFLMRegistration defines the API that TFLM kernels need to implement. -// This will be replacing the current TfLiteRegistration_V1 struct with -// something more compatible Embedded enviroment TFLM is used in. -struct TFLMRegistration { - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - void (*free)(TfLiteContext* context, void* buffer); - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - void (*reset)(TfLiteContext* context, void* buffer); - int32_t builtin_code; - const char* custom_name; -}; - -struct TFLMInferenceRegistration { - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - void (*reset)(TfLiteContext* context, void* buffer); -}; - -#endif // THIRD_PARTY_TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_COMMON_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_context.h b/new_ai_tools/include/tensorflow/lite/micro/micro_context.h deleted file mode 100644 index 40490ac0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_context.h +++ /dev/null @@ -1,176 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_graph.h" - -#define XCORE_TFLITE_MICRO_PATCHED - -#ifdef NO_INTERPRETER - -namespace tflite_micro { - const TfLiteStatus kTfLiteAbort = static_cast(15); - - struct MicroContext{ - TfLiteTensor* (*AllocateTempInputTensor)(const TfLiteNode* node, int index); - TfLiteTensor* (*AllocateTempOutputTensor)(const TfLiteNode* node, int index); - void (*DeallocateTempTfLiteTensor)(TfLiteTensor* tensor); - void* (*external_context)(); - MicroGraph& (*graph)(); - }; - static inline MicroContext* GetMicroContext(const struct TfLiteContext* context){ - return reinterpret_cast(context->impl_); - } -} - -#else - -namespace tflite_micro { -// TODO(b/149795762): kTfLiteAbort cannot be part of the tflite TfLiteStatus. -const TfLiteStatus kTfLiteAbort = static_cast(15); - -// MicroContext is eventually going to become the API between TFLM and the -// kernels, replacing all the functions in TfLiteContext. The end state is code -// kernels to have code like: -// -// MicroContext* micro_context = GetMicroContext(context); -// micro_context-> -class MicroContext { - public: - virtual ~MicroContext() = default; - - // Allocate persistent buffer which has the same life time as the interpreter. - // Returns nullptr on failure. - // The memory is allocated from the tail. - // This method is only available in Init or Prepare stage. - virtual void* AllocatePersistentBuffer(size_t bytes) = 0; - - // Request a scratch buffer in the arena through static memory planning. - // This method is only available in Prepare stage and the buffer is allocated - // by the interpreter between Prepare and Eval stage. In Eval stage, - // GetScratchBuffer API can be used to fetch the address. - virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes, - int* buffer_idx) = 0; - - // Get the scratch buffer pointer. - // This method is only available in Eval stage. - virtual void* GetScratchBuffer(int buffer_idx) = 0; - - // Returns a temporary TfLiteTensor struct for a given index. - virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) = 0; - virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx, int sg){return nullptr;} - - // Returns a temporary TfLiteTensor struct for the specified input tensor of a - // given mode. This is the recommended API over the deprecated - // GetInput/GetInputSafe to get a temp input tensor. The returned tensor shall - // be freed via calling DeallocateTempTfLiteTensor. - TfLiteTensor* AllocateTempInputTensor(const TfLiteNode* node, int index); - - // Returns a temporary TfLiteTensor struct for the specified output tensor of - // a given mode. This is the recommended API over the deprecated - // GetOutput/GetOutputSafe to get a temp output tensor. The returned tensor - // shall be freed via calling DeallocateTempTfLiteTensor. - TfLiteTensor* AllocateTempOutputTensor(const TfLiteNode* node, int index); - - // Returns a temporary TfLiteTensor struct for the specified intermediate - // tensor of a given mode. This is the recommended API over the deprecated - // GetIntermediates/GetIntermediatesSafe to get a temp intermediate tensor. - // The returned tensor shall be freed via calling DeallocateTempTfLiteTensor. - TfLiteTensor* AllocateTempIntermediateTensor(const TfLiteNode* node, - int index); - - // Deallocates a temp TfLiteTensor. - virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) = 0; - - // Returns a pointer to a temporary buffer (from the arena). - // This API is only valid from the kernel's Prepare function and - // the buffer's lifetime is also that of the Prepare function. - virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) = 0; - - // Signals that the temporary buffer is no longer needed. - virtual void DeallocateTempBuffer(uint8_t* buffer) = 0; - - // Returns a TfLiteEvalTensor struct for a given index. - virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) = 0; - virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx, int sg){return nullptr;} - - // Does not take ownership of the pointer and the pointer must refer to valid - // an object that outlive this class instance. - // This can only be called once to set one external context. - virtual TfLiteStatus set_external_context(void* external_context_payload) = 0; - - virtual void* external_context() = 0; - - virtual MicroGraph& graph() = 0; - - private: - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -inline MicroContext* GetMicroContext(const struct TfLiteContext* context) { - return reinterpret_cast(context->impl_); -} - -// Deprecated API. Prefer to using the MicroContext API directly from the -// kernels. -// TODO(b/213010668): migrate all existing kernels to use MicroContext, delete -// these functions, and remove corresponding members from the TfLiteContext -// struct for TFLM. -inline void* MicroContextAllocatePersistentBuffer(TfLiteContext* ctx, - size_t bytes) { - return GetMicroContext(ctx)->AllocatePersistentBuffer(bytes); -} -inline TfLiteStatus MicroContextRequestScratchBufferInArena(TfLiteContext* ctx, - size_t bytes, - int* buffer_idx) { - return GetMicroContext(ctx)->RequestScratchBufferInArena(bytes, buffer_idx); -} -inline void* MicroContextGetScratchBuffer(TfLiteContext* ctx, int buffer_idx) { - return GetMicroContext(ctx)->GetScratchBuffer(buffer_idx); -} -inline TfLiteTensor* MicroContextGetTensor(const struct TfLiteContext* context, - int tensor_idx) { - return GetMicroContext(context)->AllocateTempTfLiteTensor(tensor_idx); -} -inline TfLiteTensor* MicroContextGetTensor(const struct TfLiteContext* context, - int tensor_idx, int sg) { - return GetMicroContext(context)->AllocateTempTfLiteTensor(tensor_idx, sg); -} -inline TfLiteEvalTensor* MicroContextGetEvalTensor( - const struct TfLiteContext* context, int tensor_idx) { - return GetMicroContext(context)->GetEvalTensor(tensor_idx); -} -inline TfLiteEvalTensor* MicroContextGetEvalTensor( - const struct TfLiteContext* context, int tensor_idx, int sg) { - return GetMicroContext(context)->GetEvalTensor(tensor_idx, sg); -} -inline TfLiteExternalContext* MicroContextGetExternalContext( - TfLiteContext* context, TfLiteExternalContextType unused) { - return reinterpret_cast( - GetMicroContext(context)->external_context()); -} - -// Requests that an error be reported with format string msg. -void MicroContextReportOpError(struct TfLiteContext* context, - const char* format, ...); - -} // namespace tflite_micro - -#endif // NO_INTERPRETER - -#endif // TENSORFLOW_LITE_MICRO_MICRO_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h b/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h deleted file mode 100644 index 3bd12792..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_graph.h +++ /dev/null @@ -1,79 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_common.h" -#include "tensorflow/lite/micro/micro_resource_variable.h" - -#ifdef NO_INTERPRETER - -namespace tflite_micro { - struct MicroGraph{ - int (*NumSubgraphs)(); - size_t (*NumSubgraphInputs)(int subgraph_idx); - size_t (*NumSubgraphOutputs)(int subgraph_idx); - TfLiteEvalTensor* (*GetSubgraphInput)(int subgraph_idx, int i); - TfLiteEvalTensor* (*GetSubgraphOutput)(int subgraph_idx, int i); - TfLiteStatus (*InvokeSubgraph)(int subgraph_idx); - }; -} - -#else - -namespace tflite_micro { - -// Abstracts the details of interacting with the graph from the kernels -// -// Provides methods to invoke any subgraph in the tflite_micro::Graph. -class MicroGraph { - public: - virtual ~MicroGraph() = default; - - // Calls TFLMRegistration->Invoke for every operator in a single subgraph - // in the model. - virtual TfLiteStatus InvokeSubgraph(int subgraph_idx) = 0; - - // Number of tensor inputs to a specified subgraph in the model. - virtual size_t NumSubgraphInputs(int subgraph_idx) = 0; - - // Get the specified input tensor of a specified subgraph in the model. - virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, - int input_idx) = 0; - - // Number of tensor outputs from a specified subgraph in the model. - virtual size_t NumSubgraphOutputs(int subgraph_idx) = 0; - - // Get the specified output tensor of a specified subgraph in the model. - virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, - int output_idx) = 0; - - // Number of subgraphs in the model. - virtual int NumSubgraphs() = 0; - - // Get the resource variables for this TFLM graph. - virtual MicroResourceVariables* GetResourceVariables() = 0; - - private: - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // NO_INTERPRETER - -#endif // TENSORFLOW_LITE_MICRO_MICRO_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h deleted file mode 100644 index abe0f83f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter.h +++ /dev/null @@ -1,189 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/c_api_types.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_interpreter_context.h" -#include "tensorflow/lite/micro/micro_interpreter_graph.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/micro/micro_profiler_interface.h" -#include "tensorflow/lite/portable_type_to_tflitetype.h" -#include "tensorflow/lite/schema/schema_generated.h" - -/// Copied from tensorflow/lite/version.h to avoid a dependency chain into -// tensorflow/core. -#define TFLITE_SCHEMA_VERSION (3) - -namespace tflite_micro { - -class MicroInterpreter { - public: - // The lifetime of the model, op resolver, tensor arena, error reporter, - // resource variables, and profiler must be at least as long as that of the - // interpreter object, since the interpreter may need to access them at any - // time. This means that you should usually create them with the same scope as - // each other, for example having them all allocated on the stack as local - // variables through a top-level function. The interpreter doesn't do any - // deallocation of any of the pointed-to objects, ownership remains with the - // caller. - MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, - uint8_t* tensor_arena, size_t tensor_arena_size, - MicroResourceVariables* resource_variables = nullptr, - MicroProfilerInterface* profiler = nullptr, - bool preserve_all_tensors = false); - - // Create an interpreter instance using an existing MicroAllocator instance. - // This constructor should be used when creating an allocator that needs to - // have allocation handled in more than one interpreter or for recording - // allocations inside the interpreter. The lifetime of the allocator must be - // as long as that of the interpreter object. - MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver, - MicroAllocator* allocator, - MicroResourceVariables* resource_variables = nullptr, - MicroProfilerInterface* profiler = nullptr); - - ~MicroInterpreter(); - - // Runs through the model and allocates all necessary input, output and - // intermediate tensors. - TfLiteStatus AllocateTensors(); - - // In order to support partial graph runs for strided models, this can return - // values other than kTfLiteOk and kTfLiteError. - // TODO(b/149795762): Add this to the TfLiteStatus enum. - TfLiteStatus Invoke(); - - // This is the recommended API for an application to pass an external payload - // pointer as an external context to kernels. The life time of the payload - // pointer should be at least as long as this interpreter. TFLM supports only - // one external context. - TfLiteStatus SetMicroExternalContext(void* external_context_payload); - - TfLiteTensor* input(size_t index); - size_t inputs_size() const { - return model_->subgraphs()->Get(0)->inputs()->size(); - } - const flatbuffers::Vector& inputs() const { - return *model_->subgraphs()->Get(0)->inputs(); - } - TfLiteTensor* input_tensor(size_t index) { return input(index); } - template - T* typed_input_tensor(int tensor_index) { - if (TfLiteTensor* tensor_ptr = input_tensor(tensor_index)) { - if (tensor_ptr->type == typeToTfLiteType()) { - return GetTensorData(tensor_ptr); - } - } - return nullptr; - } - - TfLiteTensor* output(size_t index); - size_t outputs_size() const { - return model_->subgraphs()->Get(0)->outputs()->size(); - } - const flatbuffers::Vector& outputs() const { - return *model_->subgraphs()->Get(0)->outputs(); - } - TfLiteTensor* output_tensor(size_t index) { return output(index); } - template - T* typed_output_tensor(int tensor_index) { - if (TfLiteTensor* tensor_ptr = output_tensor(tensor_index)) { - if (tensor_ptr->type == typeToTfLiteType()) { - return GetTensorData(tensor_ptr); - } - } - return nullptr; - } - - // Returns a pointer to the tensor for the corresponding tensor_index - TfLiteEvalTensor* GetTensor(int tensor_index, int subgraph_index = 0); - - // Reset the state to be what you would expect when the interpreter is first - // created. i.e. after Init and Prepare is called for the very first time. - TfLiteStatus Reset(); - - TfLiteStatus initialization_status() const { return initialization_status_; } - - // Populates node and registration pointers representing the inference graph - // of the model from values inside the flatbuffer (loaded from the TfLiteModel - // instance). Persistent data (e.g. operator data) is allocated from the - // arena. - TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer(); - - // For debugging only. - // Returns the actual used arena in bytes. This method gives the optimal arena - // size. It's only available after `AllocateTensors` has been called. - // Note that normally `tensor_arena` requires 16 bytes alignment to fully - // utilize the space. If it's not the case, the optimial arena size would be - // arena_used_bytes() + 16. - size_t arena_used_bytes() const { return allocator_.used_bytes(); } - - // Returns True if all Tensors are being preserves - // TODO(b/297106074) : revisit making C++ example or test for - // preserve_all_tesnors - bool preserve_all_tensors() const { - return allocator_.preserves_all_tensor(); - } - - size_t operators_size(int sg) const { return model_->subgraphs()->Get(sg)->operators()->size(); } - - // For debugging only. - const NodeAndRegistration node_and_registration(int node_index, int sg) { - return graph_.GetAllocations()[sg].node_and_registrations[node_index]; - } - - protected: - const MicroAllocator& allocator() const { return allocator_; } - const TfLiteContext& context() const { return context_; } - - private: - // TODO(b/158263161): Consider switching to Create() function to enable better - // error reporting during initialization. - void Init(MicroProfilerInterface* profiler); - - // Gets the current subgraph index used from within context methods. - int get_subgraph_index() { return graph_.GetCurrentSubgraphIndex(); } - - const Model* model_; - const MicroOpResolver& op_resolver_; - TfLiteContext context_ = {}; - MicroAllocator& allocator_; - MicroInterpreterGraph graph_; - bool tensors_allocated_; - - TfLiteStatus initialization_status_; - - ScratchBufferHandle* scratch_buffer_handles_ = nullptr; - - // TODO(b/162311891): Clean these pointers up when this class supports buffers - // from TfLiteEvalTensor. - TfLiteTensor** input_tensors_; - TfLiteTensor** output_tensors_; - - MicroInterpreterContext micro_context_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h deleted file mode 100644 index 0681bba6..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_context.h +++ /dev/null @@ -1,125 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_context.h" -#include "tensorflow/lite/micro/micro_interpreter_graph.h" -#include "tensorflow/lite/micro/micro_log.h" - -namespace tflite_micro { - -// A full implementation of the MicroContext, to be used by the -// MicroInterpreter. Kernels should not depend on this directly. Instead they -// should only depend on the MicroContext. -class MicroInterpreterContext : public MicroContext { - public: - // Enum that allows MicroContext to keep track of the stages different memory - // planning APIs are available to kernels. - enum class InterpreterState { - kInit, - kPrepare, - kMemoryPlanning, - kInvoke, - }; - - // Does not take any ownership, and all pointers must refer to valid objects - // that outlive the one constructed. - MicroInterpreterContext(MicroAllocator* allocator, const Model* model, - MicroInterpreterGraph* graph); - virtual ~MicroInterpreterContext(); - - // Allocate persistent buffer which has the same life time as the interpreter. - // Returns nullptr on failure. - // The memory is allocated from the tail. - // This method is only available in Init or Prepare stage. - // Virtual so that it can be faked for kernel tests. - virtual void* AllocatePersistentBuffer(size_t bytes) override; - - // Request a scratch buffer in the arena through static memory planning. - // This method is only available in Prepare stage and the buffer is allocated - // by the interpreter between Prepare and Eval stage. In Eval stage, - // GetScratchBuffer API can be used to fetch the address. - // Virtual so that it can be faked for kernel tests. - virtual TfLiteStatus RequestScratchBufferInArena(size_t bytes, - int* buffer_idx) override; - - // Get the scratch buffer pointer. - // This method is only available in Eval stage. - // Virtual so that it can be faked for kernel tests. - virtual void* GetScratchBuffer(int buffer_idx) override; - - // Returns a temporary TfLiteTensor struct for a given index. - // Virtual so that it can be faked for kernel tests. - virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx) override; - virtual TfLiteTensor* AllocateTempTfLiteTensor(int tensor_idx, int sg) override; - - // Deallocates a temp TfLiteTensor. - // Virtual so that it can be faked for kernel tests. - virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor) override; - - // Returns a pointer to a temporary buffer (from the arena). - // This API is only valid from the kernel's Prepare function and - // the buffer's lifetime is also that of the Prepare function. - // Virtual so that it can be faked for kernel tests. - virtual uint8_t* AllocateTempBuffer(size_t size, size_t alignment) override; - - // Signals that the temporary buffer is no longer needed. - // Virtual so that it can be faked for kernel tests. - virtual void DeallocateTempBuffer(uint8_t* buffer) override; - - // Returns a TfLiteEvalTensor struct for a given index. - // Virtual so that it can be faked for kernel tests. - virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx) override; - virtual TfLiteEvalTensor* GetEvalTensor(int tensor_idx, int sg) override; - - // Sets the State of MemoryPlanning MicroInterpreterContext - void SetInterpreterState(InterpreterState state); - - // Sets the State of MemoryPlanning MicroInterpreterContext - InterpreterState GetInterpreterState() const; - - // Does not take ownership of the pointer and the pointer must refer to valid - // an object that outlive this class instance. - // This can only be called once to set one external context. - TfLiteStatus set_external_context(void* external_context_payload) override; - - void* external_context() override { return external_context_payload_; } - - MicroGraph& graph() override { return graph_; } - - // Sets the pointer to a list of ScratchBufferHandle instances. - // Not API between TFLM and kernels. Primarily used by the framework for - // housekeeping in MicroInterpreterContext. - void SetScratchBufferHandles(ScratchBufferHandle* scratch_buffer_handles); - - private: - MicroAllocator& allocator_; - MicroInterpreterGraph& graph_; - const Model* model_; - InterpreterState state_; - - ScratchBufferHandle* scratch_buffer_handles_ = nullptr; - void* external_context_payload_ = nullptr; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_CONTEXT_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h b/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h deleted file mode 100644 index b3de4c97..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_interpreter_graph.h +++ /dev/null @@ -1,110 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ - -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_common.h" -#include "tensorflow/lite/micro/micro_graph.h" -#include "tensorflow/lite/micro/micro_resource_variable.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// Abstracts the details of interacting with the tflite_micro::Model. -// -// Provides methods to access, initialize, prepare, invoke and free any -// subgraph in the tflite_micro::Graph. -class MicroInterpreterGraph : public MicroGraph { - public: - // The lifetime of the context, model, allocator and resource_variables must - // be at least as long as that of the graph object, since the this class may - // need to access them at any time. If resource_variables is a nullptr, - // GetResourceVariables will return a nullptr. - MicroInterpreterGraph(TfLiteContext* context, const Model* model, - MicroAllocator* allocator, - MicroResourceVariables* resource_variables); - virtual ~MicroInterpreterGraph(); - - // Sets up builtin data and calls TFLMRegistration->Init for every - // operator in every subgraph in the model. - virtual TfLiteStatus InitSubgraphs(); - - // Calls TFLMRegistration->Prepare for every operator in every subgraph - // in the model. - virtual TfLiteStatus PrepareSubgraphs(); - - // Calls TFLMRegistration->Reset for every operator in every subgraph in - // the model. - virtual TfLiteStatus ResetSubgraphs(); - - // Calls TFLMRegistration->Free for every operator in every subgraph in - // the model. - virtual TfLiteStatus FreeSubgraphs(); - - // Calls TFLMRegistration->Invoke for every operator in a single subgraph - // in the model. - virtual TfLiteStatus InvokeSubgraph(int subgraph_idx); - - // Zeros out all variable tensors in all subgraphs in the model. - virtual TfLiteStatus ResetVariableTensors(); - - // Number of tensor inputs to a specified subgraph in the model. - virtual size_t NumSubgraphInputs(int subgraph_idx); - - // Get the specified input tensor of a specified subgraph in the model. - virtual TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int input_idx); - - // Number of tensor outputs from a specified subgraph in the model. - virtual size_t NumSubgraphOutputs(int subgraph_idx); - - // Get the specified output tensor of a specified subgraph in the model. - virtual TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, int output_idx); - - // Number of subgraphs in the model. - virtual int NumSubgraphs(); - - // Hook to pass in subgraph allocations tracked within the interpreter, - // allowing MicroInterpreterGraph to init / prepare / invoke subgraphs in the - // model. - void SetSubgraphAllocations(SubgraphAllocations* subgraph_allocations); - - // Get the current subgraph index. Within an on operator, this is guaranteed - // to be the subgraph of that operator. - int GetCurrentSubgraphIndex() { return current_subgraph_index_; } - - // Gets the list of alloctions for each subgraph. This is the source of truth - // for all per-subgraph allocation data. - SubgraphAllocations* GetAllocations() { return subgraph_allocations_; } - - // Get the resource variables for this TFLM graph. - MicroResourceVariables* GetResourceVariables() { return resource_variables_; } - - private: - TfLiteContext* context_; - const Model* model_; - MicroAllocator* allocator_; - SubgraphAllocations* subgraph_allocations_ = nullptr; - int current_subgraph_index_; - MicroResourceVariables* resource_variables_; - const flatbuffers::Vector>* subgraphs_; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_log.h b/new_ai_tools/include/tensorflow/lite/micro/micro_log.h deleted file mode 100644 index c9db1e44..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_log.h +++ /dev/null @@ -1,42 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ - -#if !defined(TF_LITE_STRIP_ERROR_STRINGS) -#include -// These functions can be used independent of the MicroErrorReporter to get -// printf-like functionalitys and are common to all target platforms. -void MicroPrintf(const char* format, ...); -void VMicroPrintf(const char* format, va_list args); -#else -// We use a #define to ensure that the strings are completely stripped, to -// prevent an unnecessary increase in the binary size. -#define MicroPrintf(...) tflite_micro::Unused(__VA_ARGS__) -#define VMicroPrintf(...) tflite_micro::Unused(__VA_ARGS__) -#endif - -namespace tflite_micro { - -// From -// https://stackoverflow.com/questions/23235910/variadic-unused-function-macro -template -void Unused(Args&&... args) { - (void)(sizeof...(args)); -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_LOG_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h deleted file mode 100644 index 2790bba6..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_mutable_op_resolver.h +++ /dev/null @@ -1,708 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/kernels/add.h" -#include "tensorflow/lite/micro/kernels/conv.h" -#include "tensorflow/lite/micro/kernels/depthwise_conv.h" -#include "tensorflow/lite/micro/kernels/ethosu.h" -#include "tensorflow/lite/micro/kernels/fully_connected.h" -#include "tensorflow/lite/micro/kernels/micro_ops.h" -#include "tensorflow/lite/micro/kernels/pooling.h" -#include "tensorflow/lite/micro/kernels/reduce.h" -#include "tensorflow/lite/micro/kernels/softmax.h" -#include "tensorflow/lite/micro/micro_log.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { -TFLMRegistration* Register_DETECTION_POSTPROCESS(); - -template -class MicroMutableOpResolver : public MicroOpResolver { - public: - TF_LITE_REMOVE_VIRTUAL_DELETE - - explicit MicroMutableOpResolver() {} - - const TFLMRegistration* FindOp(tflite_micro::BuiltinOperator op) const override { - if (op == BuiltinOperator_CUSTOM) return nullptr; - - for (unsigned int i = 0; i < registrations_len_; ++i) { - const TFLMRegistration& registration = registrations_[i]; - if (registration.builtin_code == op) { - return ®istration; - } - } - return nullptr; - } - - const TFLMRegistration* FindOp(const char* op) const override { - for (unsigned int i = 0; i < registrations_len_; ++i) { - const TFLMRegistration& registration = registrations_[i]; - if ((registration.builtin_code == BuiltinOperator_CUSTOM) && - (strcmp(registration.custom_name, op) == 0)) { - return ®istration; - } - } - return nullptr; - } - - TfLiteBridgeBuiltinParseFunction GetOpDataParser( - BuiltinOperator op) const override { - TFLITE_DCHECK(num_buitin_ops_ <= tOpCount); - for (unsigned int i = 0; i < num_buitin_ops_; ++i) { - if (builtin_codes_[i] == op) return builtin_parsers_[i]; - } - return nullptr; - } - - // Registers a Custom Operator with the MicroOpResolver. - // - // Only the first call for a given name will be successful. i.e. if this - // function is called again for a previously added Custom Operator, the - // MicroOpResolver will be unchanged and this function will return - // kTfLiteError. - TfLiteStatus AddCustom(const char* name, - const TFLMRegistration* registration) { - if (registrations_len_ >= tOpCount) { - MicroPrintf( - "Couldn't register custom op '%s', resolver size is too" - "small (%d)", - name, tOpCount); - return kTfLiteError; - } - - if (FindOp(name) != nullptr) { - MicroPrintf("Calling AddCustom for the same op more than once "); - MicroPrintf("is not supported (Op: %s).", name); - return kTfLiteError; - } - - TFLMRegistration* new_registration = ®istrations_[registrations_len_]; - registrations_len_ += 1; - - *new_registration = *registration; - new_registration->builtin_code = BuiltinOperator_CUSTOM; - new_registration->custom_name = name; - return kTfLiteOk; - } - - // The Add* functions below add the various Builtin operators to the - // MicroMutableOpResolver object. - - TfLiteStatus AddAbs() { - return AddBuiltin(BuiltinOperator_ABS, Register_ABS(), ParseAbs); - } - - TfLiteStatus AddAdd(const TFLMRegistration& registration = Register_ADD()) { - return AddBuiltin(BuiltinOperator_ADD, registration, ParseAdd); - } - - TfLiteStatus AddAddN() { - return AddBuiltin(BuiltinOperator_ADD_N, tflite_micro::Register_ADD_N(), - ParseAddN); - } - - TfLiteStatus AddArgMax() { - return AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX(), ParseArgMax); - } - - TfLiteStatus AddArgMin() { - return AddBuiltin(BuiltinOperator_ARG_MIN, Register_ARG_MIN(), ParseArgMin); - } - - TfLiteStatus AddAssignVariable() { - return AddBuiltin(BuiltinOperator_ASSIGN_VARIABLE, - tflite_micro::Register_ASSIGN_VARIABLE(), ParseAssignVariable); - } - - TfLiteStatus AddAveragePool2D( - const TFLMRegistration& registration = Register_AVERAGE_POOL_2D()) { - return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, registration, ParsePool); - } - - TfLiteStatus AddBatchMatMul() { - return AddBuiltin(BuiltinOperator_BATCH_MATMUL, - tflite_micro::Register_BATCH_MATMUL(), ParseBatchMatMul); - } - - TfLiteStatus AddBatchToSpaceNd() { - return AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, - Register_BATCH_TO_SPACE_ND(), ParseBatchToSpaceNd); - } - - TfLiteStatus AddBroadcastArgs() { - return AddBuiltin(BuiltinOperator_BROADCAST_ARGS, Register_BROADCAST_ARGS(), - ParseBroadcastArgs); - } - - TfLiteStatus AddBroadcastTo() { - return AddBuiltin(BuiltinOperator_BROADCAST_TO, Register_BROADCAST_TO(), - ParseBroadcastTo); - } - - TfLiteStatus AddCallOnce() { - return AddBuiltin(BuiltinOperator_CALL_ONCE, Register_CALL_ONCE(), - ParseCallOnce); - } - - TfLiteStatus AddCast() { - return AddBuiltin(BuiltinOperator_CAST, Register_CAST(), ParseCast); - } - - TfLiteStatus AddCeil() { - return AddBuiltin(BuiltinOperator_CEIL, Register_CEIL(), ParseCeil); - } - - TfLiteStatus AddCircularBuffer() { - return AddCustom("CIRCULAR_BUFFER", tflite_micro::Register_CIRCULAR_BUFFER()); - } - - TfLiteStatus AddConcatenation() { - return AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION(), - ParseConcatenation); - } - - TfLiteStatus AddConv2D( - const TFLMRegistration& registration = Register_CONV_2D()) { - return AddBuiltin(BuiltinOperator_CONV_2D, registration, ParseConv2D); - } - - TfLiteStatus AddCos() { - return AddBuiltin(BuiltinOperator_COS, tflite_micro::Register_COS(), ParseCos); - } - - TfLiteStatus AddCumSum() { - return AddBuiltin(BuiltinOperator_CUMSUM, tflite_micro::Register_CUMSUM(), - ParseCumsum); - } - - TfLiteStatus AddDelay() { - // TODO(b/286250473): change back name to "Delay" and remove namespace - return AddCustom("SignalDelay", tflite_micro::tflm_signal::Register_DELAY()); - } - - TfLiteStatus AddDepthToSpace() { - return AddBuiltin(BuiltinOperator_DEPTH_TO_SPACE, - tflite_micro::Register_DEPTH_TO_SPACE(), ParseDepthToSpace); - } - - TfLiteStatus AddDepthwiseConv2D( - const TFLMRegistration& registration = Register_DEPTHWISE_CONV_2D()) { - return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, registration, - ParseDepthwiseConv2D); - } - - TfLiteStatus AddDequantize() { - return AddBuiltin(BuiltinOperator_DEQUANTIZE, tflite_micro::Register_DEQUANTIZE(), - ParseDequantize); - } - - TfLiteStatus AddDetectionPostprocess() { - return AddCustom("TFLite_Detection_PostProcess", - tflite_micro::Register_DETECTION_POSTPROCESS()); - } - - TfLiteStatus AddDiv() { - return AddBuiltin(BuiltinOperator_DIV, tflite_micro::Register_DIV(), ParseDiv); - } - - TfLiteStatus AddEmbeddingLookup() { - return AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, - Register_EMBEDDING_LOOKUP(), ParseEmbeddingLookup); - } - - TfLiteStatus AddEnergy() { - // TODO(b/286250473): change back name to "Energy" and remove namespace - return AddCustom("SignalEnergy", tflite_micro::tflm_signal::Register_ENERGY()); - } - - TfLiteStatus AddElu() { - return AddBuiltin(BuiltinOperator_ELU, tflite_micro::Register_ELU(), ParseElu); - } - - TfLiteStatus AddEqual() { - return AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL(), ParseEqual); - } - - TfLiteStatus AddEthosU() { - TFLMRegistration* registration = tflite_micro::Register_ETHOSU(); - if (registration) { - return AddCustom(tflite_micro::GetString_ETHOSU(), registration); - } - return kTfLiteOk; - } - - TfLiteStatus AddExp() { - return AddBuiltin(BuiltinOperator_EXP, Register_EXP(), ParseExp); - } - - TfLiteStatus AddExpandDims() { - return AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS(), - ParseExpandDims); - } - - TfLiteStatus AddFftAutoScale() { - // TODO(b/286250473): change back name and remove namespace - return AddCustom("SignalFftAutoScale", - tflite_micro::tflm_signal::Register_FFT_AUTO_SCALE()); - } - - TfLiteStatus AddFill() { - return AddBuiltin(BuiltinOperator_FILL, tflite_micro::Register_FILL(), ParseFill); - } - - TfLiteStatus AddFilterBank() { - // TODO(b/286250473): change back name to "FilterBank" and remove namespace - return AddCustom("SignalFilterBank", - tflite_micro::tflm_signal::Register_FILTER_BANK()); - } - TfLiteStatus AddFilterBankLog() { - // TODO(b/286250473): change back name to "FilterBankLog" and remove - // namespace - return AddCustom("SignalFilterBankLog", - tflite_micro::tflm_signal::Register_FILTER_BANK_LOG()); - } - TfLiteStatus AddFilterBankSquareRoot() { - // TODO(b/286250473): change back name to "FilterBankSquareRoot" and remove - // namespace - return AddCustom("SignalFilterBankSquareRoot", - tflite_micro::tflm_signal::Register_FILTER_BANK_SQUARE_ROOT()); - } - TfLiteStatus AddFilterBankSpectralSubtraction() { - // TODO(b/286250473): change back name to "FilterBankSpectralSubtraction" - // and remove namespace - return AddCustom( - "SignalFilterBankSpectralSubtraction", - tflite_micro::tflm_signal::Register_FILTER_BANK_SPECTRAL_SUBTRACTION()); - } - - TfLiteStatus AddFloor() { - return AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR(), ParseFloor); - } - - TfLiteStatus AddFloorDiv() { - return AddBuiltin(BuiltinOperator_FLOOR_DIV, tflite_micro::Register_FLOOR_DIV(), - ParseFloorDiv); - } - - TfLiteStatus AddFloorMod() { - return AddBuiltin(BuiltinOperator_FLOOR_MOD, tflite_micro::Register_FLOOR_MOD(), - ParseFloorMod); - } - - TfLiteStatus AddFramer() { - // TODO(b/286250473): change back name to "Framer" and remove namespace - return AddCustom("SignalFramer", tflite_micro::tflm_signal::Register_FRAMER()); - } - - TfLiteStatus AddFullyConnected( - const TFLMRegistration& registration = Register_FULLY_CONNECTED()) { - return AddBuiltin(BuiltinOperator_FULLY_CONNECTED, registration, - ParseFullyConnected); - } - - TfLiteStatus AddGather() { - return AddBuiltin(BuiltinOperator_GATHER, tflite_micro::Register_GATHER(), - ParseGather); - } - - TfLiteStatus AddGatherNd() { - return AddBuiltin(BuiltinOperator_GATHER_ND, tflite_micro::Register_GATHER_ND(), - ParseGatherNd); - } - - TfLiteStatus AddGreater() { - return AddBuiltin(BuiltinOperator_GREATER, Register_GREATER(), - ParseGreater); - } - - TfLiteStatus AddGreaterEqual() { - return AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL(), - ParseGreaterEqual); - } - - TfLiteStatus AddHardSwish() { - return AddBuiltin(BuiltinOperator_HARD_SWISH, tflite_micro::Register_HARD_SWISH(), - ParseHardSwish); - } - - TfLiteStatus AddIf() { - return AddBuiltin(BuiltinOperator_IF, tflite_micro::Register_IF(), ParseIf); - } - - TfLiteStatus AddIrfft(const TFLMRegistration* registration = - tflite::tflm_signal::Register_IRFFT()) { - // TODO(b/286250473): change back name and remove namespace - return AddCustom("SignalIrfft", registration); - } - - TfLiteStatus AddL2Normalization() { - return AddBuiltin(BuiltinOperator_L2_NORMALIZATION, - Register_L2_NORMALIZATION(), ParseL2Normalization); - } - - TfLiteStatus AddL2Pool2D() { - return AddBuiltin(BuiltinOperator_L2_POOL_2D, tflite_micro::Register_L2_POOL_2D(), - ParsePool); - } - - TfLiteStatus AddLeakyRelu() { - return AddBuiltin(BuiltinOperator_LEAKY_RELU, tflite_micro::Register_LEAKY_RELU(), - ParseLeakyRelu); - } - - TfLiteStatus AddLess() { - return AddBuiltin(BuiltinOperator_LESS, Register_LESS(), ParseLess); - } - - TfLiteStatus AddLessEqual() { - return AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL(), - ParseLessEqual); - } - - TfLiteStatus AddLog() { - return AddBuiltin(BuiltinOperator_LOG, Register_LOG(), ParseLog); - } - - TfLiteStatus AddLogicalAnd() { - return AddBuiltin(BuiltinOperator_LOGICAL_AND, - tflite_micro::Register_LOGICAL_AND(), ParseLogicalAnd); - } - - TfLiteStatus AddLogicalNot() { - return AddBuiltin(BuiltinOperator_LOGICAL_NOT, Register_LOGICAL_NOT(), - ParseLogicalNot); - } - - TfLiteStatus AddLogicalOr() { - return AddBuiltin(BuiltinOperator_LOGICAL_OR, tflite_micro::Register_LOGICAL_OR(), - ParseLogicalOr); - } - - TfLiteStatus AddLogistic() { - return AddBuiltin(BuiltinOperator_LOGISTIC, tflite_micro::Register_LOGISTIC(), - ParseLogistic); - } - - TfLiteStatus AddLogSoftmax() { - return AddBuiltin(BuiltinOperator_LOG_SOFTMAX, - tflite_micro::Register_LOG_SOFTMAX(), ParseLogSoftmax); - } - - TfLiteStatus AddMaximum() { - return AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM(), - ParseMaximum); - } - - TfLiteStatus AddMaxPool2D( - const TFLMRegistration& registration = Register_MAX_POOL_2D()) { - return AddBuiltin(BuiltinOperator_MAX_POOL_2D, registration, ParsePool); - } - - TfLiteStatus AddMirrorPad() { - return AddBuiltin(BuiltinOperator_MIRROR_PAD, tflite_micro::Register_MIRROR_PAD(), - ParseMirrorPad); - } - - TfLiteStatus AddMean() { - return AddBuiltin(BuiltinOperator_MEAN, Register_MEAN(), ParseReducer); - } - - TfLiteStatus AddMinimum() { - return AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM(), - ParseMinimum); - } - - TfLiteStatus AddMul(const TFLMRegistration& registration = Register_MUL()) { - return AddBuiltin(BuiltinOperator_MUL, registration, ParseMul); - } - - TfLiteStatus AddNeg() { - return AddBuiltin(BuiltinOperator_NEG, Register_NEG(), ParseNeg); - } - - TfLiteStatus AddNotEqual() { - return AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL(), - ParseNotEqual); - } - - TfLiteStatus AddOverlapAdd() { - // TODO(b/286250473): change back name to "OverlapAdd" and remove namespace - return AddCustom("SignalOverlapAdd", - tflite_micro::tflm_signal::Register_OVERLAP_ADD()); - } - - TfLiteStatus AddPack() { - return AddBuiltin(BuiltinOperator_PACK, Register_PACK(), ParsePack); - } - - TfLiteStatus AddPad(const TFLMRegistration& registration = Register_PAD()) { - return AddBuiltin(BuiltinOperator_PAD, registration, ParsePad); - } - - TfLiteStatus AddPadV2() { - return AddBuiltin(BuiltinOperator_PADV2, Register_PADV2(), ParsePadV2); - } - - TfLiteStatus AddPCAN() { - // TODO(b/286250473): change back name to "PCAN" and remove namespace - return AddCustom("SignalPCAN", tflite_micro::tflm_signal::Register_PCAN()); - } - - TfLiteStatus AddPrelu() { - return AddBuiltin(BuiltinOperator_PRELU, tflite_micro::Register_PRELU(), - ParsePrelu); - } - - TfLiteStatus AddQuantize() { - return AddBuiltin(BuiltinOperator_QUANTIZE, Register_QUANTIZE(), - ParseQuantize); - } - - TfLiteStatus AddReadVariable() { - return AddBuiltin(BuiltinOperator_READ_VARIABLE, - tflite_micro::Register_READ_VARIABLE(), ParseReadVariable); - } - - TfLiteStatus AddReduceMax() { - return AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX(), - ParseReducer); - } - - TfLiteStatus AddRelu() { - return AddBuiltin(BuiltinOperator_RELU, tflite_micro::Register_RELU(), ParseRelu); - } - - TfLiteStatus AddRelu6() { - return AddBuiltin(BuiltinOperator_RELU6, tflite_micro::Register_RELU6(), - ParseRelu6); - } - - TfLiteStatus AddReshape() { - return AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE(), - ParseReshape); - } - - TfLiteStatus AddResizeBilinear() { - return AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, - Register_RESIZE_BILINEAR(), ParseResizeBilinear); - } - - TfLiteStatus AddResizeNearestNeighbor() { - return AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - Register_RESIZE_NEAREST_NEIGHBOR(), - ParseResizeNearestNeighbor); - } - - TfLiteStatus AddRfft(const TFLMRegistration* registration = - tflite::tflm_signal::Register_RFFT()) { - // TODO(b/286250473): change back name and remove namespace - return AddCustom("SignalRfft", registration); - } - - TfLiteStatus AddRound() { - return AddBuiltin(BuiltinOperator_ROUND, Register_ROUND(), ParseRound); - } - - TfLiteStatus AddRsqrt() { - return AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT(), ParseRsqrt); - } - - TfLiteStatus AddSelectV2() { - return AddBuiltin(BuiltinOperator_SELECT_V2, Register_SELECT_V2(), - ParseSelectV2); - } - - TfLiteStatus AddShape() { - return AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE(), ParseShape); - } - - TfLiteStatus AddSin() { - return AddBuiltin(BuiltinOperator_SIN, Register_SIN(), ParseSin); - } - - TfLiteStatus AddSlice() { - return AddBuiltin(BuiltinOperator_SLICE, Register_SLICE(), ParseSlice); - } - - TfLiteStatus AddSoftmax( - const TFLMRegistration& registration = Register_SOFTMAX()) { - return AddBuiltin(BuiltinOperator_SOFTMAX, registration, ParseSoftmax); - } - - TfLiteStatus AddSpaceToBatchNd() { - return AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, - Register_SPACE_TO_BATCH_ND(), ParseSpaceToBatchNd); - } - - TfLiteStatus AddSpaceToDepth() { - return AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH(), - ParseSpaceToDepth); - } - - TfLiteStatus AddSplit() { - return AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT(), ParseSplit); - } - - TfLiteStatus AddSplitV() { - return AddBuiltin(BuiltinOperator_SPLIT_V, Register_SPLIT_V(), ParseSplitV); - } - - TfLiteStatus AddSqueeze() { - return AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE(), - ParseSqueeze); - } - - TfLiteStatus AddSqrt() { - return AddBuiltin(BuiltinOperator_SQRT, Register_SQRT(), ParseSqrt); - } - - TfLiteStatus AddSquare() { - return AddBuiltin(BuiltinOperator_SQUARE, Register_SQUARE(), ParseSquare); - } - - TfLiteStatus AddSquaredDifference() { - return AddBuiltin(BuiltinOperator_SQUARED_DIFFERENCE, - tflite_micro::Register_SQUARED_DIFFERENCE(), - ParseSquaredDifference); - } - - TfLiteStatus AddStridedSlice() { - return AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE(), - ParseStridedSlice); - } - - TfLiteStatus AddStacker() { - // TODO(b/286250473): change back name to "Stacker" and remove namespace - return AddCustom("SignalStacker", tflite_micro::tflm_signal::Register_STACKER()); - } - - TfLiteStatus AddSub() { - return AddBuiltin(BuiltinOperator_SUB, tflite_micro::Register_SUB(), ParseSub); - } - - TfLiteStatus AddSum() { - return AddBuiltin(BuiltinOperator_SUM, Register_SUM(), ParseReducer); - } - - TfLiteStatus AddSvdf(const TFLMRegistration& registration = Register_SVDF()) { - return AddBuiltin(BuiltinOperator_SVDF, registration, ParseSvdf); - } - - TfLiteStatus AddTanh() { - return AddBuiltin(BuiltinOperator_TANH, Register_TANH(), ParseTanh); - } - - TfLiteStatus AddTransposeConv() { - return AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, - tflite_micro::Register_TRANSPOSE_CONV(), ParseTransposeConv); - } - - TfLiteStatus AddTranspose() { - return AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE(), - ParseTranspose); - } - - TfLiteStatus AddUnpack() { - return AddBuiltin(BuiltinOperator_UNPACK, Register_UNPACK(), ParseUnpack); - } - - TfLiteStatus AddUnidirectionalSequenceLSTM( - const TFLMRegistration& registration = - Register_UNIDIRECTIONAL_SEQUENCE_LSTM()) { - return AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - registration, ParseUnidirectionalSequenceLSTM); - } - - TfLiteStatus AddVarHandle() { - return AddBuiltin(BuiltinOperator_VAR_HANDLE, Register_VAR_HANDLE(), - ParseVarHandle); - } - - TfLiteStatus AddWhile() { - return AddBuiltin(BuiltinOperator_WHILE, Register_WHILE(), ParseWhile); - } - - TfLiteStatus AddWindow() { - // TODO(b/286250473): change back name to "Window" and remove namespace - return AddCustom("SignalWindow", tflite_micro::tflm_signal::Register_WINDOW()); - } - - TfLiteStatus AddZerosLike() { - return AddBuiltin(BuiltinOperator_ZEROS_LIKE, Register_ZEROS_LIKE(), - ParseZerosLike); - } - - unsigned int GetRegistrationLength() { return registrations_len_; } - - private: - TfLiteStatus AddBuiltin(tflite_micro::BuiltinOperator op, - const TFLMRegistration& registration, - TfLiteBridgeBuiltinParseFunction parser) { - if (op == BuiltinOperator_CUSTOM) { - MicroPrintf("Invalid parameter BuiltinOperator_CUSTOM to the "); - MicroPrintf("AddBuiltin function."); - return kTfLiteError; - } - - if (FindOp(op) != nullptr) { - MicroPrintf("Calling AddBuiltin with the same op more than "); - MicroPrintf("once is not supported (Op: #%d).", op); - return kTfLiteError; - } - - if (registrations_len_ >= tOpCount) { - MicroPrintf("Couldn't register builtin op #%d, resolver size ", op); - MicroPrintf("is too small (%d).", tOpCount); - return kTfLiteError; - } - - registrations_[registrations_len_] = registration; - // Strictly speaking, the builtin_code is not necessary for TFLM but filling - // it in regardless. - registrations_[registrations_len_].builtin_code = op; - registrations_len_++; - - builtin_codes_[num_buitin_ops_] = op; - builtin_parsers_[num_buitin_ops_] = parser; - num_buitin_ops_++; - - return kTfLiteOk; - } - - TFLMRegistration registrations_[tOpCount]; - unsigned int registrations_len_ = 0; - - // Arrays (and counter) to store the builtin codes and their corresponding - // parse functions as these are registered with the Op Resolver. - BuiltinOperator builtin_codes_[tOpCount]; - TfLiteBridgeBuiltinParseFunction builtin_parsers_[tOpCount]; - unsigned int num_buitin_ops_ = 0; -}; - -}; // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h deleted file mode 100644 index af4e4d26..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_op_resolver.h +++ /dev/null @@ -1,62 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_common.h" -#include "tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// This is an interface for the OpResolver for TFLiteMicro. The differences from -// the TFLite OpResolver base class are to: -// * explicitly remove support for Op versions -// * allow for finer grained registration of the Builtin Ops to reduce code -// size for TFLiteMicro. -// -// We need an interface class instead of directly using MicroMutableOpResolver -// because MicroMutableOpResolver is a class template with the number of -// registered Ops as the template parameter. -class MicroOpResolver { - public: - // Returns the Op registration struct corresponding to the enum code from the - // flatbuffer schema. Returns nullptr if the op is not found or if op == - // BuiltinOperator_CUSTOM. - virtual const TFLMRegistration* FindOp(BuiltinOperator op) const = 0; - - // Returns the Op registration struct corresponding to the custom operator by - // name. - virtual const TFLMRegistration* FindOp(const char* op) const = 0; - - // Returns the operator specific parsing function for the OpData for a - // BuiltinOperator (if registered), else nullptr. - virtual TfLiteBridgeBuiltinParseFunction GetOpDataParser( - BuiltinOperator op) const = 0; - - virtual ~MicroOpResolver() {} -}; - -// Handles the logic for converting between an OperatorCode structure extracted -// from a flatbuffer and information about a registered operator -// implementation. -TfLiteStatus GetRegistrationFromOpCode(const OperatorCode* opcode, - const MicroOpResolver& op_resolver, - const TFLMRegistration** registration); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h deleted file mode 100644 index fe8e586e..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler.h +++ /dev/null @@ -1,140 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ - -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_profiler_interface.h" - -namespace tflite_micro { - -// MicroProfiler creates a common way to gain fine-grained insight into runtime -// performance. Bottleck operators can be identified along with slow code -// sections. This can be used in conjunction with running the relevant micro -// benchmark to evaluate end-to-end performance. -class MicroProfiler : public MicroProfilerInterface { - public: - MicroProfiler() = default; - virtual ~MicroProfiler() = default; - - // Marks the start of a new event and returns an event handle that can be used - // to mark the end of the event via EndEvent. The lifetime of the tag - // parameter must exceed that of the MicroProfiler. - virtual uint32_t BeginEvent(const char* tag) override; - - // Marks the end of an event associated with event_handle. It is the - // responsibility of the caller to ensure than EndEvent is called once and - // only once per event_handle. - // - // If EndEvent is called more than once for the same event_handle, the last - // call will be used as the end of event marker.If EndEvent is called 0 times - // for a particular event_handle, the duration of that event will be 0 ticks. - virtual void EndEvent(uint32_t event_handle) override; - - // Clears all the events that have been currently profiled. - void ClearEvents() { num_events_ = 0; } - - // Returns the sum of the ticks taken across all the events. This number - // is only meaningful if all of the events are disjoint (the end time of - // event[i] <= start time of event[i+1]). - uint32_t GetTotalTicks() const; - - // Prints the profiling information of each of the events in human readable - // form. - void Log() const; - - // Prints the profiling information of each of the events in CSV (Comma - // Separated Value) form. - void LogCsv() const; - - // Prints total ticks for each unique tag in CSV format. - // Output will have one row for each unique tag along with the - // total ticks summed across all events with that particular tag. - void LogTicksPerTagCsv(); - - private: - // Maximum number of events that this class can keep track of. If we call - // AddEvent more than kMaxEvents number of times, then the oldest event's - // profiling information will be overwritten. - static constexpr int kMaxEvents = 4096; - - const char* tags_[kMaxEvents]; - uint32_t start_ticks_[kMaxEvents]; - uint32_t end_ticks_[kMaxEvents]; - int num_events_ = 0; - - struct TicksPerTag { - const char* tag; - uint32_t ticks; - }; - // In practice, the number of tags will be much lower than the number of - // events. But it is theoretically possible that each event to be unique and - // hence we allow total_ticks_per_tag to have kMaxEvents entries. - TicksPerTag total_ticks_per_tag[kMaxEvents] = {}; - - int FindExistingOrNextPosition(const char* tag_name); - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -#if defined(TF_LITE_STRIP_ERROR_STRINGS) -// For release builds, the ScopedMicroProfiler is a noop. -// -// This is done because the ScipedProfiler is used as part of the -// MicroInterpreter and we want to ensure zero overhead for the release builds. -class ScopedMicroProfiler { - public: - explicit ScopedMicroProfiler(const char* tag, - MicroProfilerInterface* profiler) {} -}; - -#else - -// This class can be used to add events to a MicroProfiler object that span the -// lifetime of the ScopedMicroProfiler object. -// Usage example: -// -// MicroProfiler profiler(); -// ... -// { -// ScopedMicroProfiler scoped_profiler("custom_tag", profiler); -// work_to_profile(); -// } -class ScopedMicroProfiler { - public: - explicit ScopedMicroProfiler(const char* tag, - MicroProfilerInterface* profiler) - : profiler_(profiler) { - if (profiler_ != nullptr) { - event_handle_ = profiler_->BeginEvent(tag); - } - } - - ~ScopedMicroProfiler() { - if (profiler_ != nullptr) { - profiler_->EndEvent(event_handle_); - } - } - - private: - uint32_t event_handle_ = 0; - MicroProfilerInterface* profiler_ = nullptr; -}; -#endif // !defined(TF_LITE_STRIP_ERROR_STRINGS) - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h b/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h deleted file mode 100644 index a879501b..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_profiler_interface.h +++ /dev/null @@ -1,38 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ - -#include - -namespace tflite_micro { - -// Interface class that the TFLM framework relies on for profiling. -class MicroProfilerInterface { - public: - virtual ~MicroProfilerInterface() {} - - // Marks the start of a new event and returns an event handle that can be used - // to mark the end of the event via EndEvent. - virtual uint32_t BeginEvent(const char* tag) = 0; - - // Marks the end of an event associated with event_handle. - virtual void EndEvent(uint32_t event_handle) = 0; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_INTERFACE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h b/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h deleted file mode 100644 index 44bf92d0..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_resource_variable.h +++ /dev/null @@ -1,89 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ -#define TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ - -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_allocator.h" - -namespace tflite_micro { - -class MicroResourceVariables { - public: - // Create - static MicroResourceVariables* Create(MicroAllocator* allocator, - int num_variables); - - // Creates a resource variable if none is available for the given container - // and shared name pair. Returns the resource ID corresponding to the - // container and shared name pair. If allocation fails, the returned resource - // ID will be negative. The the container and shared_name must outlive this - // class. - int CreateIdIfNoneFound(const char* container, const char* shared_name); - - // Read the resource buffer associated with the given ID into the given - // tensor. - TfLiteStatus Read(int id, const TfLiteEvalTensor* tensor); - - // Allocates the resource buffer if none has been allocated, based on the - // length of the input tensor. Copies input tensor contents to the resource - // buffer. - TfLiteStatus Allocate(int id, TfLiteContext* context, - const TfLiteTensor* tensor); - - // Copies input tensor contents to the resource buffer. - // AllocateResourceVariable with a TFLite tensor must have been called first - // in order to allocate the resource buffer. - TfLiteStatus Assign(int id, const TfLiteEvalTensor* tensor); - - // Zeros out all resource buffers. - TfLiteStatus ResetAll(); - - private: - int FindId(const char* container, const char* shared_name); - - // Micro resource contains the mapping between resource container/name strings - // and resouce IDs. Each resource ID corresponds to a resource buffer pointer. - // The resouce ID is created during the VAR_HANDLE operator preparation stage. - // The resource buffer pointer is created during ASSIGN_VARIABLE preparation - // stage based on the size of the TFLiteTensor being assigned. - struct MicroResourceVariable { - const char* container; - const char* shared_name; - void* resource_buffer; - - // This is only for verifying read size. - size_t bytes; - // Initialization default value - int8_t default_value; - }; - - MicroResourceVariables(MicroResourceVariable* variables, - int max_variable_count) - : resource_variables_(variables), - max_variable_count_(max_variable_count), - num_resource_variables_(0) {} - - MicroResourceVariable* resource_variables_; - int max_variable_count_; - int num_resource_variables_; -}; - -} // namespace tflite_micro - -#endif // TFLITE_MICRO_TENSORFLOW_LITE_MICRO_MICRO_RESOURCE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_time.h b/new_ai_tools/include/tensorflow/lite/micro/micro_time.h deleted file mode 100644 index 27e7292d..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_time.h +++ /dev/null @@ -1,36 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ - -#include - -namespace tflite_micro { - -// These functions should be implemented by each target platform, and provide an -// accurate tick count along with how many ticks there are per second. -uint32_t ticks_per_second(); - -// Return time in ticks. The meaning of a tick varies per platform. -uint32_t GetCurrentTimeTicks(); - -inline uint32_t TicksToMs(int32_t ticks) { - return static_cast(1000.0f * static_cast(ticks) / - static_cast(ticks_per_second())); -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_TIME_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h b/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h deleted file mode 100644 index ad53ffdb..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/micro_utils.h +++ /dev/null @@ -1,162 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ - -#include -#include -#include -#include - -#include "tensorflow/lite/c/common.h" - -namespace tflite_micro { - -// Returns number of elements in the shape array. - -int ElementCount(const TfLiteIntArray& dims); - -size_t EvalTensorBytes(const TfLiteEvalTensor* tensor); - -// C++11 does not support constexpr max; hence, use ternary conditional to -// create our own constexpr Max function. -constexpr int Max(int a, int b) { return a >= b ? a : b; } - -// Converts a float value into a quantized value. Note that large values (close -// to max int and min int) may see significant error due to a lack of floating -// point granularity for large values. -template -T FloatToQuantizedType(const float value, const float scale, int zero_point) { - int32_t result = round(value / scale) + zero_point; - result = - std::max(static_cast(std::numeric_limits::min()), result); - result = - std::min(static_cast(std::numeric_limits::max()), result); - return result; -} - -template -T FloatToSymmetricQuantizedType(const float value, const float scale) { - // 64-bit values are required since 8x16 conv accumulates to int64, meaning - // an int64 bias is required. - std::int64_t result = round(value / scale); - result = std::max( - static_cast(std::numeric_limits::min() + 1), result); - result = std::min(static_cast(std::numeric_limits::max()), - result); - return result; -} - -// Helper methods to quantize arrays of floats to the desired format. -// -// There are several key flavors of quantization in TfLite: -// asymmetric symmetric per channel -// int8_t | X | X | X | -// uint8_t | X | X | | -// int16_t | X | | | -// int32_t | | X | X | -// -// The per-op quantization spec can be found here: -// https://www.tensorflow.org/lite/performance/quantization_spec -template -void Quantize(const float* input, T* output, int num_elements, float scale, - int zero_point) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToQuantizedType(input[i], scale, zero_point); - } -} - -template -void SymmetricQuantize(const float* input, T* output, int num_elements, - float scale) { - for (int i = 0; i < num_elements; i++) { - output[i] = FloatToSymmetricQuantizedType(input[i], scale); - } -} - -template -void SymmetricPerChannelQuantize(const float* input, T* output, - int num_elements, int num_channels, - float* scales) { - int elements_per_channel = num_elements / num_channels; - for (int i = 0; i < num_channels; i++) { - for (int j = 0; j < elements_per_channel; j++) { - output[i * elements_per_channel + j] = FloatToSymmetricQuantizedType( - input[i * elements_per_channel + j], scales[i]); - } - } -} - -void SignedSymmetricPerChannelQuantize(const float* values, - TfLiteIntArray* dims, - int quantized_dimension, - int8_t* quantized_values, - float* scaling_factor, - TfLiteType type = kTfLiteNoType); - -// Quantizes inputs based on the values provided, choosing the smallest range -// which includes all input values. -template -void SymmetricQuantizeCalculateScales(const float* values, TfLiteIntArray* dims, - T* output, float* scale) { - int input_size = ElementCount(*dims); - - float min = 0; - float max = 0; - for (int i = 0; i < input_size; i++) { - min = fminf(min, values[i]); - max = fmaxf(max, values[i]); - } - *scale = fmaxf(std::abs(min), std::abs(max)) / std::numeric_limits::max(); - for (int i = 0; i < input_size; i++) { - const int32_t quantized_value = - static_cast(roundf(values[i] / *scale)); - // Clamp: just in case some odd numeric offset. - quantized_value = fminf(std::numeric_limits::max(), quantized_value); - quantized_value = fmaxf(std::numeric_limits::min() + 1, quantized_value); - output[i] = quantized_value; - } -} - -template -void Dequantize(const T* values, const int size, const float scale, - int zero_point, float* dequantized_values) { - for (int i = 0; i < size; ++i) { - dequantized_values[i] = (values[i] - zero_point) * scale; - } -} - -// based on TfLiteType passed in to these functions the corresponding max / min -// int for that type are returned -inline int QMinFromTfLiteType(TfLiteType type) { - if (type == kTfLiteInt4) { - return -8; - } else { - return std::numeric_limits::min(); - } -} - -inline int QMaxFromTfLiteType(TfLiteType type) { - if (type == kTfLiteInt4) { - return 7; - } else { - return std::numeric_limits::max(); - } -} - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MICRO_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h b/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h deleted file mode 100644 index 219b6b4b..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/mock_micro_graph.h +++ /dev/null @@ -1,60 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ -#define TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_allocator.h" -#include "tensorflow/lite/micro/micro_graph.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// MockMicroGraph stubs out all MicroGraph methods used during invoke. A count -// of the number of calls to invoke for each subgraph is maintained for -// validation of control flow operators. -class MockMicroGraph : public MicroGraph { - public: - explicit MockMicroGraph(SingleArenaBufferAllocator* allocator); - TfLiteStatus InvokeSubgraph(int subgraph_idx) override; - size_t NumSubgraphInputs(int subgraph_idx) override; - TfLiteEvalTensor* GetSubgraphInput(int subgraph_idx, int tensor_idx) override; - size_t NumSubgraphOutputs(int subgraph_idx) override; - TfLiteEvalTensor* GetSubgraphOutput(int subgraph_idx, - int tensor_idx) override; - int NumSubgraphs() override; - MicroResourceVariables* GetResourceVariables() override; - int get_init_count() const { return init_count_; } - int get_prepare_count() const { return prepare_count_; } - int get_free_count() const { return free_count_; } - int get_invoke_count(int subgraph_idx) const { - return invoke_counts_[subgraph_idx]; - } - - private: - static constexpr int kMaxSubgraphs = 10; - SingleArenaBufferAllocator* allocator_; - TfLiteEvalTensor* mock_tensor_; - int init_count_; - int prepare_count_; - int free_count_; - int invoke_counts_[kMaxSubgraphs]; - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_MOCK_MICRO_GRAPH_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h deleted file mode 100644 index 8d27aee9..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/python/interpreter/src/python_ops_resolver.h +++ /dev/null @@ -1,21 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ -#define TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ - -// TODO(b/286456378): remove once this shim is no longer needed. -#include "python/tflite_micro/python_ops_resolver.h" - -#endif // TENSORFLOW_LITE_MICRO_PYTHON_INTERPRETER_SRC_PYTHON_OPS_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h deleted file mode 100644 index f65d8bb6..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size.h +++ /dev/null @@ -1,30 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ -#define TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ - -#include - -#include "flatbuffers/flatbuffers.h" -#include "flatbuffers/util.h" - -namespace tflite_micro { - -std::string FlatBufferSizeToJsonString( - const uint8_t* buffer, const flatbuffers::TypeTable* type_table); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h b/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h deleted file mode 100644 index 96df3291..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/python/tflite_size/src/flatbuffer_size_wrapper.h +++ /dev/null @@ -1,33 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ -#define TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ - -#include - -#include - -namespace tflite_micro { - -class FlatbufferSizeWrapper { - public: - FlatbufferSizeWrapper(); - ~FlatbufferSizeWrapper(); - - std::string ConvertToJsonString(const char* in_flatbuffer); -}; - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_MICRO_PYTHON_TFLITE_SIZE_SRC_FLATBUFFERS_SIZE_WRAPPER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h deleted file mode 100644 index e585f0e8..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_allocator.h +++ /dev/null @@ -1,125 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ - -#include "tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" -#include "tensorflow/lite/micro/micro_allocator.h" - -namespace tflite_micro { - -// List of buckets currently recorded by this class. Each type keeps a list of -// allocated information during model initialization. -// TODO(b/169834511): Add tracking for scratch buffer allocations. -enum class RecordedAllocationType { - kTfLiteEvalTensorData, - kPersistentTfLiteTensorData, - kPersistentTfLiteTensorQuantizationData, - kPersistentBufferData, - kTfLiteTensorVariableBufferData, - kNodeAndRegistrationArray, - kOpData, -}; - -// Container for holding information about allocation recordings by a given -// type. Each recording contains the number of bytes requested, the actual bytes -// allocated (can defer from requested by alignment), and the number of items -// allocated. -struct RecordedAllocation { - size_t requested_bytes; - size_t used_bytes; - size_t count; -}; - -// Utility subclass of MicroAllocator that records all allocations -// inside the arena. A summary of allocations can be logged through the -// ErrorReporter by invoking LogAllocations(). This special allocator requires -// an instance of RecordingSingleArenaBufferAllocator to capture allocations in -// the head and tail. Arena allocation recording can be retrieved by type -// through the GetRecordedAllocation() function. This class should only be used -// for auditing memory usage or integration testing. -class RecordingMicroAllocator : public MicroAllocator { - public: - static RecordingMicroAllocator* Create(uint8_t* tensor_arena, - size_t arena_size); - - // Returns the fixed amount of memory overhead of RecordingMicroAllocator. - static size_t GetDefaultTailUsage(); - - // Returns the recorded allocations information for a given allocation type. - RecordedAllocation GetRecordedAllocation( - RecordedAllocationType allocation_type) const; - - const RecordingSingleArenaBufferAllocator* GetSimpleMemoryAllocator() const; - - // Logs out through the ErrorReporter all allocation recordings by type - // defined in RecordedAllocationType. - void PrintAllocations() const; - - void* AllocatePersistentBuffer(size_t bytes) override; - - protected: - TfLiteStatus AllocateNodeAndRegistrations( - const Model* model, SubgraphAllocations* subgraph_allocations) override; - TfLiteStatus AllocateTfLiteEvalTensors( - const Model* model, SubgraphAllocations* subgraph_allocations) override; - TfLiteStatus AllocateVariables( - const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors, - const int32_t* offline_planner_offsets) override; - // TODO(b/162311891): Once all kernels have been updated to the new API drop - // this method. It is only used to record TfLiteTensor persistent allocations. - TfLiteTensor* AllocatePersistentTfLiteTensorInternal() override; - - // TODO(b/162311891): Once all kernels have been updated to the new API drop - // this function since all allocations for quantized data will take place in - // the temp section. - TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model, - TfLiteTensor* tensor, - int tensor_index, - int subgraph_index, - bool allocate_temp) override; - - private: - RecordingMicroAllocator(RecordingSingleArenaBufferAllocator* memory_allocator, - MicroMemoryPlanner* memory_planner); - - void PrintRecordedAllocation(RecordedAllocationType allocation_type, - const char* allocation_name, - const char* allocation_description) const; - - RecordedAllocation SnapshotAllocationUsage() const; - void RecordAllocationUsage(const RecordedAllocation& snapshotted_allocation, - RecordedAllocation& recorded_allocation); - - const RecordingSingleArenaBufferAllocator* recording_memory_allocator_; - - RecordedAllocation recorded_tflite_eval_tensor_data_ = {}; - RecordedAllocation recorded_persistent_tflite_tensor_data_ = {}; - RecordedAllocation recorded_persistent_tflite_tensor_quantization_data_ = {}; - RecordedAllocation recorded_persistent_buffer_data_ = {}; - RecordedAllocation recorded_tflite_tensor_variable_buffer_data_ = {}; - RecordedAllocation recorded_node_and_registration_array_data_ = {}; - - // TODO(b/187993291): Re-enable OpData allocating tracking. - RecordedAllocation recorded_op_data_ = {}; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h b/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h deleted file mode 100644 index f53d0335..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/recording_micro_interpreter.h +++ /dev/null @@ -1,69 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ -#define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ - -#include "tensorflow/lite/micro/micro_interpreter.h" -#include "tensorflow/lite/micro/micro_profiler_interface.h" -#include "tensorflow/lite/micro/recording_micro_allocator.h" - -namespace tflite_micro { - -// Utility subclass that enables internal recordings of the MicroInterpreter. -// This class should be used to audit and analyze memory arena usage for a given -// model and interpreter. -// -// After construction and the first Invoke() or AllocateTensors() call - the -// memory usage is recorded and available through the GetMicroAllocator() -// function. See RecordingMicroAlloctor for more details on what is currently -// recorded from arena allocations. -// -// It is recommended for users to increase the tensor arena size by at least 1kb -// to ensure enough additional memory is available for internal recordings. -class RecordingMicroInterpreter : public MicroInterpreter { - public: - RecordingMicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - uint8_t* tensor_arena, size_t tensor_arena_size, - MicroResourceVariables* resource_variable = nullptr, - MicroProfilerInterface* profiler = nullptr) - : MicroInterpreter( - model, op_resolver, - RecordingMicroAllocator::Create(tensor_arena, tensor_arena_size), - resource_variable, profiler), - recording_micro_allocator_( - static_cast(allocator())) {} - - RecordingMicroInterpreter(const Model* model, - const MicroOpResolver& op_resolver, - RecordingMicroAllocator* allocator, - MicroResourceVariables* resource_variable = nullptr, - MicroProfilerInterface* profiler = nullptr) - : MicroInterpreter(model, op_resolver, allocator, resource_variable, - profiler), - recording_micro_allocator_(*allocator) {} - - const RecordingMicroAllocator& GetMicroAllocator() const { - return recording_micro_allocator_; - } - - private: - const RecordingMicroAllocator& recording_micro_allocator_; -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_RECORDING_MICRO_INTERPRETER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/system_setup.h b/new_ai_tools/include/tensorflow/lite/micro/system_setup.h deleted file mode 100644 index f01d27e9..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/system_setup.h +++ /dev/null @@ -1,27 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ -#define TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ - -namespace tflite_micro { - -// This should called during initialization of TFLM binaries and tests. It can -// be specialized if there is a need for custom target-specific intialization. -// For more information, see tensorflow/lite/micro/system_setup.cc. -void InitializeTarget(); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_SYSTEM_SETUP_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h b/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h deleted file mode 100644 index aab4aca1..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/test_helper_custom_ops.h +++ /dev/null @@ -1,49 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ -#define TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ - -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/micro_common.h" -#include "tensorflow/lite/micro/micro_utils.h" -#include "tensorflow/lite/portable_type_to_tflitetype.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { -namespace testing { - -class PackerOp { - public: - static const TFLMRegistration* getRegistration(); - static TFLMRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static void Free(TfLiteContext* context, void* buffer); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); - - private: - static bool freed_; -}; - -} // namespace testing -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_TEST_HELPER_CUSTOM_OPS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h b/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h deleted file mode 100644 index 58888614..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/test_helpers.h +++ /dev/null @@ -1,334 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ -#define TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ - -#include -#include -#include -#include - -#include "flatbuffers/flatbuffers.h" // from @flatbuffers -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" -#include "tensorflow/lite/micro/micro_utils.h" -#include "tensorflow/lite/portable_type_to_tflitetype.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { -namespace testing { - -constexpr int kOfflinePlannerHeaderSize = 3; -using TestingOpResolver = tflite_micro::MicroMutableOpResolver<10>; - -struct NodeConnection_ { - std::initializer_list input; - std::initializer_list output; -}; -typedef struct NodeConnection_ NodeConnection; - -// A simple operator that returns the median of the input with the number of -// times the kernel was invoked. The implementation below is deliberately -// complicated, just to demonstrate how kernel memory planning works. -class SimpleStatefulOp { - static constexpr int kBufferNotAllocated = 0; - // Inputs: - static constexpr int kInputTensor = 0; - // Outputs: - static constexpr int kMedianTensor = 0; - static constexpr int kInvokeCount = 1; - struct OpData { - int* invoke_count = nullptr; - int sorting_buffer = kBufferNotAllocated; - }; - - public: - static const TFLMRegistration* getRegistration(); - static TFLMRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); -}; - -class MockCustom { - public: - static const TFLMRegistration* getRegistration(); - static TFLMRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static void Free(TfLiteContext* context, void* buffer); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); - - static bool freed_; -}; - -// A simple operator with the purpose of testing multiple inputs. It returns -// the sum of the inputs. -class MultipleInputs { - public: - static const TFLMRegistration* getRegistration(); - static TFLMRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static void Free(TfLiteContext* context, void* buffer); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); - - static bool freed_; -}; - -// A simple no-op operator. -class NoOp { - public: - static const TFLMRegistration* getRegistration(); - static TFLMRegistration* GetMutableRegistration(); - static void* Init(TfLiteContext* context, const char* buffer, size_t length); - static void Free(TfLiteContext* context, void* buffer); - static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node); - static TfLiteStatus Invoke(TfLiteContext* context, TfLiteNode* node); - - static bool freed_; -}; - -// Returns an Op Resolver that can be used in the testing code. -TfLiteStatus GetTestingOpResolver(TestingOpResolver& op_resolver); - -// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input, -// 1 layer of weights, 1 output Tensor, and 1 operator. -const Model* GetSimpleMockModel(); - -// Returns a flatbuffer TensorFlow Lite model with more inputs, variable -// tensors, and operators. -const Model* GetComplexMockModel(); - -// Returns a simple example flatbuffer TensorFlow Lite model. Contains 1 input, -// 1 layer of weights, 1 output Tensor, and 1 operator. -// The size of all three tensors is 256 x 256, which is larger than what other -// models provide from this test helper. -const Model* GetModelWith256x256Tensor(); - -// Returns a simple flatbuffer model with two branches. -const Model* GetSimpleModelWithBranch(); - -// Returns a simple example flatbuffer TensorFlow Lite model. Contains 3 inputs, -// 1 output Tensor, and 1 operator. -const Model* GetSimpleMultipleInputsModel(); - -// Returns a simple flatbuffer model with offline planned tensors -// @param[in] num_tensors Number of tensors in the model. -// @param[in] metadata_buffer Metadata for offline planner. -// @param[in] node_con List of connections, i.e. operators -// in the model. -// @param[in] num_conns Number of connections. -// @param[in] num_subgraph_inputs How many of the input tensors are in -// the subgraph inputs. The default value -// of 0 means all of the input tensors -// are in the subgraph input list. There -// must be at least 1 input tensor in the -// subgraph input list. -const Model* GetModelWithOfflinePlanning(int num_tensors, - const int32_t* metadata_buffer, - NodeConnection* node_conn, - int num_conns, - int num_subgraph_inputs = 0); - -// Returns a flatbuffer with a single operator, two inputs (one unused) and one -// output. -const Model* GetModelWithUnusedInputs(); - -// Returns a flatbuffer with a single operator, zero inputs and two outputs -// (one unused). -const Model* GetModelWithUnusedOperatorOutputs(); - -// Returns a flatbuffer model with `simple_stateful_op` -const Model* GetSimpleStatefulModel(); - -// Returns a flatbuffer model with "if" and two subgraphs. -const Model* GetSimpleModelWithSubgraphsAndIf(); - -// Returns a flatbuffer model with "if" and two subgraphs one of which is empty. -const Model* GetSimpleModelWithIfAndEmptySubgraph(); - -// Returns a flatbuffer model with "while" and three subgraphs. -const Model* GetSimpleModelWithSubgraphsAndWhile(); - -// Returns a flatbuffer model with "if" and two subgraphs and the input tensor 1 -// of "if" subgraph overlaps with the input tensor 2 of subgraph 1. -const Model* GetModelWithIfAndSubgraphInputTensorOverlap(); - -// Returns a flatbuffer model with null subgraph/operator inputs and outputs. -const Model* GetSimpleModelWithNullInputsAndOutputs(); - -// Builds a one-dimensional flatbuffer tensor of the given size. -const Tensor* Create1dFlatbufferTensor(int size, bool is_variable = false); - -// Builds a one-dimensional flatbuffer tensor of the given size with -// quantization metadata. -const Tensor* CreateQuantizedFlatbufferTensor(int size); - -// Creates a one-dimensional tensor with no quantization metadata. -const Tensor* CreateMissingQuantizationFlatbufferTensor(int size); - -// Creates a vector of flatbuffer buffers. -const flatbuffers::Vector>* -CreateFlatbufferBuffers(); - -// Performs a simple string comparison without requiring standard C library. -int TestStrcmp(const char* a, const char* b); - -void PopulateContext(TfLiteTensor* tensors, int tensors_size, - TfLiteContext* context); - -// Create a TfLiteIntArray from an array of ints. The first element in the -// supplied array must be the size of the array expressed as an int. -TfLiteIntArray* IntArrayFromInts(const int* int_array); - -// Create a TfLiteFloatArray from an array of floats. The first element in the -// supplied array must be the size of the array expressed as a float. -TfLiteFloatArray* FloatArrayFromFloats(const float* floats); - -// Assumes that `src_tensor` is a buffer where each element is a 4-bit value -// stored in 8-bit. -// Returns a new buffer that is packed densely with 2 4-bit values in a byte. -// The packing format is low-bits-first, i.e. the lower nibble of a byte is -// filled first, followed by the upper nibble. -void PackInt4ValuesDenselyInPlace(uint8_t* src_buffer, int buffer_size); - -template -TfLiteTensor CreateTensor(const T* data, TfLiteIntArray* dims, - const bool is_variable = false, - TfLiteType type = kTfLiteNoType) { - TfLiteTensor result; - result.dims = dims; - result.params = {}; - result.quantization = {kTfLiteNoQuantization, nullptr}; - result.is_variable = is_variable; - result.allocation_type = kTfLiteMemNone; - result.data.data = const_cast(data); - result.bytes = ElementCount(*dims) * sizeof(T); - result.data.data = const_cast(data); - - if (type == kTfLiteInt4) { - result.type = kTfLiteInt4; - PackInt4ValuesDenselyInPlace(tflite_micro::GetTensorData(&result), - ElementCount(*dims)); - result.bytes = ((ElementCount(*dims) + 1) / 2); - } else { - // Const cast is used to allow passing in const and non-const arrays within - // a single CreateTensor method. A Const array should be used for immutable - // input tensors and non-const array should be used for mutable and output - // tensors. - result.type = typeToTfLiteType(); - } - return result; -} - -template -TfLiteTensor CreateQuantizedTensor(const T* data, TfLiteIntArray* dims, - const float scale, const int zero_point = 0, - const bool is_variable = false, - TfLiteType type = kTfLiteNoType) { - TfLiteTensor result = CreateTensor(data, dims, is_variable, type); - result.params = {scale, zero_point}; - result.quantization = {kTfLiteAffineQuantization, nullptr}; - return result; -} - -template -TfLiteTensor CreateQuantizedTensor(const float* input, T* quantized, - TfLiteIntArray* dims, float scale, - int zero_point, bool is_variable = false, - TfLiteType type = kTfLiteNoType) { - int input_size = ElementCount(*dims); - tflite_micro::Quantize(input, quantized, input_size, scale, zero_point); - return CreateQuantizedTensor(quantized, dims, scale, zero_point, is_variable, - type); -} - -TfLiteTensor CreateQuantizedBiasTensor(const float* data, int16_t* quantized, - TfLiteIntArray* dims, float input_scale, - float weights_scale, - bool is_variable = false); - -TfLiteTensor CreateQuantizedBiasTensor(const float* data, int32_t* quantized, - TfLiteIntArray* dims, float input_scale, - float weights_scale, - bool is_variable = false); - -TfLiteTensor CreateQuantizedBiasTensor(const float* data, - std::int64_t* quantized, - TfLiteIntArray* dims, float input_scale, - float weights_scale, - bool is_variable = false); - -// Quantizes int32_t bias tensor with per-channel weights determined by input -// scale multiplied by weight scale for each channel. -TfLiteTensor CreatePerChannelQuantizedBiasTensor( - const float* input, int32_t* quantized, TfLiteIntArray* dims, - float input_scale, float* weight_scales, float* scales, int* zero_points, - TfLiteAffineQuantization* affine_quant, int quantized_dimension, - bool is_variable = false); - -// Quantizes int64_t bias tensor with per-channel weights determined by input -// scale multiplied by weight scale for each channel. -TfLiteTensor CreatePerChannelQuantizedBiasTensor( - const float* input, std::int64_t* quantized, TfLiteIntArray* dims, - float input_scale, float* weight_scales, float* scales, int* zero_points, - TfLiteAffineQuantization* affine_quant, int quantized_dimension, - bool is_variable = false); - -TfLiteTensor CreateSymmetricPerChannelQuantizedTensor( - const float* input, int8_t* quantized, TfLiteIntArray* dims, float* scales, - int* zero_points, TfLiteAffineQuantization* affine_quant, - int quantized_dimension, bool is_variable = false, - TfLiteType tensor_weight_type = kTfLiteNoType); - -// Returns the number of tensors in the default subgraph for a tflite_micro::Model. -size_t GetModelTensorCount(const Model* model); - -// Derives the asymmetric quantization scaling factor from a min and max range. -template -inline float ScaleFromMinMax(const float min, const float max) { - return (max - min) / - static_cast((std::numeric_limits::max() * 1.0) - - std::numeric_limits::min()); -} - -// Derives the symmetric quantization scaling factor from a min and max range. -template -inline float SymmetricScaleFromMinMax(const float min, const float max) { - const int32_t kScale = - std::numeric_limits::type>::max(); - const float range = std::max(std::abs(min), std::abs(max)); - if (range == 0) { - return 1.0f; - } else { - return range / kScale; - } -} - -// Derives the quantization zero point from a min and max range. -template -inline int ZeroPointFromMinMax(const float min, const float max) { - return static_cast(std::numeric_limits::min()) + - static_cast(-min / ScaleFromMinMax(min, max) + 0.5f); -} - -} // namespace testing -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_TEST_HELPERS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h b/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h deleted file mode 100644 index a5595fe5..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/testing/micro_test.h +++ /dev/null @@ -1,267 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// An ultra-lightweight testing framework designed for use with microcontroller -// applications. This is designed to be usable even -// when no standard C or C++ libraries are available, and without any dynamic -// memory allocation or reliance on global constructors. -// -// To build a test, you use syntax similar to gunit, but with some extra -// decoration to create a hidden 'main' function containing each of the tests to -// be run. Your code should look something like: -// ---------------------------------------------------------------------------- -// #include "path/to/this/header" -// -// TF_LITE_MICRO_TESTS_BEGIN -// -// TF_LITE_MICRO_TEST(SomeTest) { -// TF_LITE_LOG_EXPECT_EQ(true, true); -// } -// -// TF_LITE_MICRO_TESTS_END -// ---------------------------------------------------------------------------- -// If you compile this for your platform, you'll get a normal binary that you -// should be able to run. Executing it will output logging information like this -// to stderr: -// ---------------------------------------------------------------------------- -// Testing SomeTest -// 1/1 tests passed -// ~~~ALL TESTS PASSED~~~ -// ---------------------------------------------------------------------------- -// This is designed to be human-readable, so you can just run tests manually, -// but the string "~~~ALL TESTS PASSED~~~" should only appear if all of the -// tests do pass. This makes it possible to integrate with automated test -// systems by scanning the output logs and looking for that magic value. -// -// This framework is intended to be a rudimentary alternative to no testing at -// all on systems that struggle to run more conventional approaches, so use with -// caution! - -#ifndef TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ -#define TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/micro/micro_log.h" -#include "tensorflow/lite/micro/system_setup.h" - -namespace micro_test { -extern int tests_passed; -extern int tests_failed; -extern bool is_test_complete; -extern bool did_test_fail; -} // namespace micro_test - -namespace tflite_micro { - -// This additional helper function is used (instead of directly calling -// tflite_micro::InitializeTarget from the TF_LITE_MICRO_TESTS_BEGIN macro) to avoid -// adding a dependency from every bazel test target to micro:system_setp (which -// is the target that implements InitializeTarget(). -// -// The underlying issue here is that the use of the macros results in -// dependencies that can be containted within the micro/testing:micro_test -// target bleeding on to all the tests. -inline void InitializeTest() { InitializeTarget(); } -} // namespace tflite_micro - -#define TF_LITE_MICRO_TESTS_BEGIN \ - namespace micro_test { \ - int tests_passed; \ - int tests_failed; \ - bool is_test_complete; \ - bool did_test_fail; \ - } \ - \ - int main(int argc, char** argv) { \ - micro_test::tests_passed = 0; \ - micro_test::tests_failed = 0; \ - tflite_micro::InitializeTest(); - -#define TF_LITE_MICRO_TESTS_END \ - MicroPrintf("%d/%d tests passed", micro_test::tests_passed, \ - (micro_test::tests_failed + micro_test::tests_passed)); \ - if (micro_test::tests_failed == 0) { \ - MicroPrintf("~~~ALL TESTS PASSED~~~\n"); \ - return kTfLiteOk; \ - } else { \ - MicroPrintf("~~~SOME TESTS FAILED~~~\n"); \ - return kTfLiteError; \ - } \ - } - -// TODO(petewarden): I'm going to hell for what I'm doing to this poor for loop. -#define TF_LITE_MICRO_TEST(name) \ - MicroPrintf("Testing " #name); \ - for (micro_test::is_test_complete = false, \ - micro_test::did_test_fail = false; \ - !micro_test::is_test_complete; micro_test::is_test_complete = true, \ - micro_test::tests_passed += (micro_test::did_test_fail) ? 0 : 1, \ - micro_test::tests_failed += (micro_test::did_test_fail) ? 1 : 0) - -#define TF_LITE_MICRO_EXPECT(x) \ - do { \ - if (!(x)) { \ - MicroPrintf(#x " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_EQ(x, y) \ - do { \ - auto vx = x; \ - auto vy = y; \ - bool isFloatingX = (std::is_floating_point::value); \ - bool isFloatingY = (std::is_floating_point::value); \ - if (isFloatingX && isFloatingY) { \ - auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ - if (delta > std::numeric_limits::epsilon()) { \ - MicroPrintf(#x " == " #y " failed at %s:%d (%f vs %f)", __FILE__, \ - __LINE__, static_cast(vx), \ - static_cast(vy)); \ - micro_test::did_test_fail = true; \ - } \ - } else if ((vx) != (vy)) { \ - MicroPrintf(#x " == " #y " failed at %s:%d (%d vs %d)", __FILE__, \ - __LINE__, static_cast(vx), static_cast(vy)); \ - if (isFloatingX || isFloatingY) { \ - MicroPrintf("-----------WARNING-----------"); \ - MicroPrintf("Only one of the values is floating point value."); \ - } \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_NE(x, y) \ - do { \ - auto vx = x; \ - auto vy = y; \ - bool isFloatingX = (std::is_floating_point::value); \ - bool isFloatingY = (std::is_floating_point::value); \ - if (isFloatingX && isFloatingY) { \ - auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ - if (delta <= std::numeric_limits::epsilon()) { \ - MicroPrintf(#x " != " #y " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } else if ((vx) == (vy)) { \ - MicroPrintf(#x " != " #y " failed at %s:%d", __FILE__, __LINE__); \ - if (isFloatingX || isFloatingY) { \ - MicroPrintf("-----------WARNING-----------"); \ - MicroPrintf("Only one of the values is floating point value."); \ - } \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -// TODO(wangtz): Making it more generic once needed. -#define TF_LITE_MICRO_ARRAY_ELEMENT_EXPECT_NEAR(arr1, idx1, arr2, idx2, \ - epsilon) \ - do { \ - auto delta = ((arr1)[(idx1)] > (arr2)[(idx2)]) \ - ? ((arr1)[(idx1)] - (arr2)[(idx2)]) \ - : ((arr2)[(idx2)] - (arr1)[(idx1)]); \ - if (delta > epsilon) { \ - MicroPrintf(#arr1 "[%d] (%f) near " #arr2 "[%d] (%f) failed at %s:%d", \ - static_cast(idx1), static_cast((arr1)[(idx1)]), \ - static_cast(idx2), static_cast((arr2)[(idx2)]), \ - __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -// The check vx != vy is needed to properly handle the case where both -// x and y evaluate to infinity. See #46960 for more details. -#define TF_LITE_MICRO_EXPECT_NEAR(x, y, epsilon) \ - do { \ - auto vx = (x); \ - auto vy = (y); \ - auto delta = ((vx) > (vy)) ? ((vx) - (vy)) : ((vy) - (vx)); \ - if (vx != vy && delta > epsilon) { \ - MicroPrintf(#x " (%f) near " #y " (%f) failed at %s:%d", \ - static_cast(vx), static_cast(vy), __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_GT(x, y) \ - do { \ - if ((x) <= (y)) { \ - MicroPrintf(#x " > " #y " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_LT(x, y) \ - do { \ - if ((x) >= (y)) { \ - MicroPrintf(#x " < " #y " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_GE(x, y) \ - do { \ - if ((x) < (y)) { \ - MicroPrintf(#x " >= " #y " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_LE(x, y) \ - do { \ - if ((x) > (y)) { \ - MicroPrintf(#x " <= " #y " failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_TRUE(x) \ - do { \ - if (!(x)) { \ - MicroPrintf(#x " was not true failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_FALSE(x) \ - do { \ - if (x) { \ - MicroPrintf(#x " was not false failed at %s:%d", __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } \ - } while (false) - -#define TF_LITE_MICRO_FAIL(msg) \ - do { \ - MicroPrintf("FAIL: %s", msg, __FILE__, __LINE__); \ - micro_test::did_test_fail = true; \ - } while (false) - -#define TF_LITE_MICRO_EXPECT_STRING_EQ(string1, string2) \ - do { \ - for (int i = 0; string1[i] != '\0' && string2[i] != '\0'; i++) { \ - if (string1[i] != string2[i]) { \ - MicroPrintf("FAIL: %s did not match %s", string1, string2, __FILE__, \ - __LINE__); \ - micro_test::did_test_fail = true; \ - break; \ - } \ - } \ - } while (false) - -#endif // TENSORFLOW_LITE_MICRO_TESTING_MICRO_TEST_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h b/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h deleted file mode 100644 index 2103196e..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/testing/test_conv_model.h +++ /dev/null @@ -1,23 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ -#define TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ - -// See generate_test_models.py for updating the contents of this model: -extern const unsigned char kTestConvModelData[]; -extern const unsigned int kTestConvModelDataSize; - -#endif // TENSORFLOW_LITE_MICRO_TESTING_TEST_CONV_MODEL_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h deleted file mode 100644 index 6580ad16..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.h +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ -#define TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ - -#include "tensorflow/lite/c/c_api_types.h" -#include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// Forward declaration of the ErrorReporter class to hide it from the TFLM code. -class ErrorReporter; - -using TfLiteBridgeBuiltinDataAllocator = BuiltinDataAllocator; - -using TfLiteBridgeBuiltinParseFunction = - TfLiteStatus (*)(const Operator* op, ErrorReporter* error_reporter, - BuiltinDataAllocator* allocator, void** builtin_data); - -// Converts the tensor data type used in the flatbuffer to the representation -// used by the runtime. -TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type); - -// CallBuiltinParseFunction is a wrapper function to wrap the parser function -// calls to Call parser(op, allocator, builtin_data) -TfLiteStatus CallBuiltinParseFunction(TfLiteBridgeBuiltinParseFunction parser, - const Operator* op, - BuiltinDataAllocator* allocator, - void** builtin_data); -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_FLATBUFFER_CONVERSIONS_BRIDGE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h b/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h deleted file mode 100644 index 23793558..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.h +++ /dev/null @@ -1,36 +0,0 @@ -/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ -#define TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ - -#include - -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite_micro { -// Get a pointer to a singleton global error reporter. -ErrorReporter* GetMicroErrorReporter(); -class MicroErrorReporter : public ErrorReporter { - public: - ~MicroErrorReporter() override {} - int Report(const char* format, va_list args) override; - - TF_LITE_REMOVE_VIRTUAL_DELETE -}; - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_MICRO_TFLITE_BRIDGE_MICRO_ERROR_REPORTER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h deleted file mode 100644 index 405d007f..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/log_utils.h +++ /dev/null @@ -1,273 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ -#define TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ - -#include -#include -#include -#include - -#include "tensorflow/lite/micro/micro_log.h" - -namespace tflite_micro { - -// The maxmimum length of a string. -static constexpr int kMaxStringLength = 32; - -// The maximum length of a table row, applies to the header as well. -static constexpr int kMaxRowLength = 100; - -// The default padding between columns in a table. -static constexpr int kDefaultColumnPadding = 4; - -// Defines how formatted data is printed to stdout. -enum class PrettyPrintType { - // Prints as a CSV file. - kCsv, - // Prints as a formatted table. - kTable, -}; - -// Returns the length of the longest string in an array. -// Args: -// - strings: An array of strings. -// - count: The number of strings in the array. -int GetLongestStringLength(const char strings[][kMaxStringLength], int count); - -// Adds padding between two columns in a table. -// ex) "hello" is being inserted into a column. The largest value in that column -// is 10, and there's a global padding of 4 spaces. Therefore, 9 spaces (10 -// - 5 + 4) are added as padding. -// Args: -// - string: The input padding string. -// - size: The size of the string that's being inserted into a column. -// - max_size: The size of the largest string in the column. -// - padding: The amount of padding to add to each column regardless of its -// size. -void FillColumnPadding(char* string, int size, int max_size, - int padding = kDefaultColumnPadding); - -// Fills a string with a specified value. -// Args: -// - string: The input string. This is filled in with the specified value. -// - size: The size of the string after being filled in. This must be less than -// the allocated space for the string. -// - buffer_size: The size of the string's buffer. -// - value: The value to insert into the string. Defaults to a space. -void FillString(char* string, int size, int buffer_size, char value = ' '); - -// Concatenates the input string onto the first. -// Args: -// - output: The destination string for where to append input. -// - input: The input string to concatenate. -// - size: The number of characters to concatenate from the first string. If -// negative, the whole input string will be concatenated. -void MicroStrcat(char* output, const char* input, int size = -1); - -// Copies the input string into the output. -void MicroStrcpy(char* output, const char* input); - -// Formats a division operation to have a specified number of decimal places. -// Args: -// - output: The output string to be formatted. -// - numerator: The numerator in the division operation. -// - denominator: The denominator in the division operation. -// - decimal places: The number of decimal places to print to. -void FormatIntegerDivide(char* output, int64_t numerator, int64_t denominator, - int decimal_places); - -// Formats a division operation as a percentage. -// Args: -// - output: The output string to be formatted. -// - numerator: The numerator in the division operation. -// - denominator: The denominator in the division operation. -// - decimal places: The number of decimal places to print to. -void FormatAsPercentage(char* output, int64_t numerator, int64_t denominator, - int decimal_places); - -void PrettyPrintTableHeader(PrettyPrintType type, const char* table_name); - -// Formats a number as a string. -// Args: -// - output: The location of where to write the formatted number. -// - value: The value to write to a string. -template -void FormatNumber(char* output, T value); - -// Pretty prints a table to stdout. -// Note: kMaxRows and kColumns should describe the allocated size of the table, -// not the amount of data that is populated. It is required that all -// columns are filled out, but not all rows. -// -// ex) PrintTable<3, 25>(headers, data, 4); -// This will print a table with 3 columns and 4 rows. In this example, it -// is required that data is defined as char[3][25][kMaxStringLength] to -// properly print. -// -// op cycles cpu % -// ------------------------- -// foo | 1000 | 10 -// bar | 2500 | 25 -// baz | 1000 | 10 -// lorem | 2000 | 20 -// -// Args: -// - headers: A 1D array of strings containing the headers of the table. This -// must be equal in size to kColumns. -// - data: A 2D array of string data organized in [columns, rows]. As stated -// above, it is required that all columns are populated, but not all rows. -// - rows: The number of populated rows in `data`. -template -void PrintTable(const char headers[kColumns][kMaxStringLength], - const char data[kColumns][kMaxRows][kMaxStringLength], - const int rows) { - // Get the maximum width for each column in the table. - int max_column_width[kColumns]; - for (int i = 0; i < kColumns; ++i) { - max_column_width[i] = std::max(GetLongestStringLength(data[i], rows), - static_cast(strlen(headers[i]))); - } - - // Add padding between each item in the header so it can be printed on one - // line. - char header_spaces[kColumns][kMaxStringLength]; - for (int i = 0; i < kColumns; ++i) { - FillColumnPadding(header_spaces[i], strlen(headers[i]), max_column_width[i], - kDefaultColumnPadding + 2); - } - - // Print the header. - char header[kMaxRowLength]; - memset(header, 0, kMaxRowLength); - for (int i = 0; i < kColumns; ++i) { - MicroStrcat(header, headers[i]); - MicroStrcat(header, header_spaces[i]); - } - MicroPrintf("%s", header); - - // Print a separator to separate the header from the data. - char separator[kMaxRowLength]; - FillString(separator, strlen(header) - 1, kMaxRowLength, '-'); - MicroPrintf("%s", separator); - - for (int i = 0; i < rows; ++i) { - char spaces[kColumns][kMaxStringLength]; - for (int j = 0; j < kColumns; ++j) { - FillColumnPadding(spaces[j], strlen(data[j][i]), max_column_width[j]); - } - - char row[kMaxRowLength]; - memset(row, 0, kMaxRowLength); - - // Concatenate each column in a row with the format "[data][padding]| " - for (int j = 0; j < kColumns; ++j) { - MicroStrcat(row, data[j][i]); - MicroStrcat(row, spaces[j]); - MicroStrcat(row, "| "); - } - - MicroPrintf("%s", row); - } - - MicroPrintf(separator); - MicroPrintf(""); -} - -// Pretty prints a csv to stdout. -// Note: kMaxRows and kColumns should describe the allocated size of the table, -// not the amount of data that is populated. It is required that all -// columns are filled out, but not all rows. -// -// ex) -// op,cycles,%cpu -// foo,1000,10 -// bar,2500,25 -// baz,1000,10 -// -// Args: -// - headers: A 1D array of strings containing the headers of the table. This -// must be equal in size to kColumns. -// - data: A 2D array of string data organized in [columns, rows]. As stated -// above, it is required that all columns are populated, but not all rows. -// - rows: The number of populated rows in `data`. -template -void PrintCsv(const char headers[kColumns][kMaxStringLength], - const char data[kColumns][kMaxRows][kMaxStringLength], - const int rows) { - char header[kMaxRowLength]; - memset(header, 0, kMaxRowLength); - for (int i = 0; i < kColumns; ++i) { - MicroStrcat(header, headers[i]); - if (i < kColumns - 1) { - MicroStrcat(header, ","); - } - } - - MicroPrintf("%s", header); - - char row[kMaxRowLength]; - for (int i = 0; i < rows; ++i) { - memset(row, 0, kMaxRowLength); - for (int j = 0; j < kColumns; ++j) { - MicroStrcat(row, data[j][i]); - if (j < kColumns - 1) { - MicroStrcat(row, ","); - } - } - - MicroPrintf("%s", row); - } - - MicroPrintf(""); // Serves as a new line. -} - -// Prints a 2D array of strings in a formatted manner along with a table name -// that includes the table type. -// -// Note: kMaxRows and kColumns should describe the allocated size of the table, -// not the amount of data that is populated. It is required that all -// columns are filled out, but not all rows. -// -// ex) PrettyPrint::kCsv will print a csv with a [[ CSV ]]: table_name header. -// -// Args: -// - headers: A 1D array of strings containing the headers of the table. This -// must be equal in size to kColumns. -// - data: A 2D array of string data organized in [columns, rows]. As stated -// above, it is required that all columns are populated, but not all rows. -// - rows: The number of populated rows in `data`. -// - type: The format type that should be used to pretty print. -// - table_name: The name of the table to be printed alongside the format type. -template -void PrintFormattedData(const char headers[kColumns][kMaxStringLength], - const char data[kColumns][kMaxRows][kMaxStringLength], - const int rows, const PrettyPrintType type, - const char* table_name) { - PrettyPrintTableHeader(type, table_name); - switch (type) { - case PrettyPrintType::kCsv: - PrintCsv(headers, data, rows); - break; - case PrettyPrintType::kTable: - PrintTable(headers, data, rows); - break; - } -} - -} // namespace tflite_micro - -#endif // TFLM_BENCHMARK_INTERNAL_LOG_UTILS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h deleted file mode 100644 index 103c1385..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/metrics.h +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TFLM_BENCHMARK_INTERNAL_METRICS_H_ -#define TFLM_BENCHMARK_INTERNAL_METRICS_H_ - -#include - -#include -#include - -#include "tensorflow/lite/micro/micro_profiler.h" -#include "tensorflow/lite/micro/recording_micro_allocator.h" -#include "tensorflow/lite/micro/tools/benchmarking/log_utils.h" - -namespace tflite_micro { - -// Logs the allocation events. Prints out two tables, one for the arena -// allocations, and one for each type of TFLM allocation type. -// Args: -// - allocator: The recording micro allocator used during the invocation -// process. -// - type: Which print format should be used to output the allocation data to -// stdout. -void LogAllocatorEvents(const tflite_micro::RecordingMicroAllocator& allocator, - PrettyPrintType type); -} // namespace tflite_micro - -#endif // TFLM_BENCHMARK_INTERNAL_METRICS_H_ diff --git a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h b/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h deleted file mode 100644 index 4f8f6b3a..00000000 --- a/new_ai_tools/include/tensorflow/lite/micro/tools/benchmarking/op_resolver.h +++ /dev/null @@ -1,127 +0,0 @@ -/* Copyright 2023 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TFLM_BENCHMARK_OP_RESOLVER_H_ -#define TFLM_BENCHMARK_OP_RESOLVER_H_ - -#include - -#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" -#include "tensorflow/lite/micro/micro_op_resolver.h" - -namespace tflite_micro { - -inline TfLiteStatus CreateOpResolver( - tflite_micro::MicroMutableOpResolver<96>& op_resolver) { - TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected()); - TF_LITE_ENSURE_STATUS(op_resolver.AddAdd()); - TF_LITE_ENSURE_STATUS(op_resolver.AddAbs()); - TF_LITE_ENSURE_STATUS(op_resolver.AddAddN()); - TF_LITE_ENSURE_STATUS(op_resolver.AddArgMax()); - TF_LITE_ENSURE_STATUS(op_resolver.AddArgMin()); - TF_LITE_ENSURE_STATUS(op_resolver.AddAssignVariable()); - TF_LITE_ENSURE_STATUS(op_resolver.AddBatchToSpaceNd()); - TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastArgs()); - TF_LITE_ENSURE_STATUS(op_resolver.AddBroadcastTo()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCallOnce()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCast()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCeil()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCircularBuffer()); - TF_LITE_ENSURE_STATUS(op_resolver.AddConcatenation()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCos()); - TF_LITE_ENSURE_STATUS(op_resolver.AddCumSum()); - TF_LITE_ENSURE_STATUS(op_resolver.AddDepthToSpace()); - TF_LITE_ENSURE_STATUS(op_resolver.AddDequantize()); - TF_LITE_ENSURE_STATUS(op_resolver.AddDiv()); - TF_LITE_ENSURE_STATUS(op_resolver.AddElu()); - TF_LITE_ENSURE_STATUS(op_resolver.AddEqual()); - TF_LITE_ENSURE_STATUS(op_resolver.AddEthosU()); - TF_LITE_ENSURE_STATUS(op_resolver.AddExp()); - TF_LITE_ENSURE_STATUS(op_resolver.AddExpandDims()); - TF_LITE_ENSURE_STATUS(op_resolver.AddFill()); - TF_LITE_ENSURE_STATUS(op_resolver.AddFloor()); - TF_LITE_ENSURE_STATUS(op_resolver.AddFloorDiv()); - TF_LITE_ENSURE_STATUS(op_resolver.AddFloorMod()); - TF_LITE_ENSURE_STATUS(op_resolver.AddGather()); - TF_LITE_ENSURE_STATUS(op_resolver.AddGatherNd()); - TF_LITE_ENSURE_STATUS(op_resolver.AddGreater()); - TF_LITE_ENSURE_STATUS(op_resolver.AddGreaterEqual()); - TF_LITE_ENSURE_STATUS(op_resolver.AddHardSwish()); - TF_LITE_ENSURE_STATUS(op_resolver.AddIf()); - TF_LITE_ENSURE_STATUS(op_resolver.AddL2Normalization()); - TF_LITE_ENSURE_STATUS(op_resolver.AddL2Pool2D()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLeakyRelu()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLess()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLessEqual()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLog()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalAnd()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalNot()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLogicalOr()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLogistic()); - TF_LITE_ENSURE_STATUS(op_resolver.AddLogSoftmax()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMaximum()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMirrorPad()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMean()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMinimum()); - TF_LITE_ENSURE_STATUS(op_resolver.AddNeg()); - TF_LITE_ENSURE_STATUS(op_resolver.AddNotEqual()); - TF_LITE_ENSURE_STATUS(op_resolver.AddPack()); - TF_LITE_ENSURE_STATUS(op_resolver.AddPadV2()); - TF_LITE_ENSURE_STATUS(op_resolver.AddPrelu()); - TF_LITE_ENSURE_STATUS(op_resolver.AddQuantize()); - TF_LITE_ENSURE_STATUS(op_resolver.AddReadVariable()); - TF_LITE_ENSURE_STATUS(op_resolver.AddReduceMax()); - TF_LITE_ENSURE_STATUS(op_resolver.AddRelu()); - TF_LITE_ENSURE_STATUS(op_resolver.AddRelu6()); - TF_LITE_ENSURE_STATUS(op_resolver.AddReshape()); - TF_LITE_ENSURE_STATUS(op_resolver.AddResizeBilinear()); - TF_LITE_ENSURE_STATUS(op_resolver.AddResizeNearestNeighbor()); - TF_LITE_ENSURE_STATUS(op_resolver.AddRound()); - TF_LITE_ENSURE_STATUS(op_resolver.AddRsqrt()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSelectV2()); - TF_LITE_ENSURE_STATUS(op_resolver.AddShape()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSin()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSlice()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSoftmax()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSpaceToBatchNd()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSpaceToDepth()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSplit()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSplitV()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSqueeze()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSqrt()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSquare()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSquaredDifference()); - TF_LITE_ENSURE_STATUS(op_resolver.AddStridedSlice()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSub()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSum()); - TF_LITE_ENSURE_STATUS(op_resolver.AddSvdf()); - TF_LITE_ENSURE_STATUS(op_resolver.AddTanh()); - TF_LITE_ENSURE_STATUS(op_resolver.AddTransposeConv()); - TF_LITE_ENSURE_STATUS(op_resolver.AddTranspose()); - TF_LITE_ENSURE_STATUS(op_resolver.AddUnpack()); - TF_LITE_ENSURE_STATUS(op_resolver.AddUnidirectionalSequenceLSTM()); - TF_LITE_ENSURE_STATUS(op_resolver.AddVarHandle()); - TF_LITE_ENSURE_STATUS(op_resolver.AddWhile()); - TF_LITE_ENSURE_STATUS(op_resolver.AddZerosLike()); - TF_LITE_ENSURE_STATUS(op_resolver.AddDepthwiseConv2D()); - TF_LITE_ENSURE_STATUS(op_resolver.AddConv2D()); - TF_LITE_ENSURE_STATUS(op_resolver.AddAveragePool2D()); - TF_LITE_ENSURE_STATUS(op_resolver.AddPad()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMaxPool2D()); - TF_LITE_ENSURE_STATUS(op_resolver.AddMul()); - return kTfLiteOk; -} -} // namespace tflite_micro -#endif // TFLM_BENCHMARK_OP_RESOLVER_H_ diff --git a/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h b/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h deleted file mode 100644 index d0ca67b0..00000000 --- a/new_ai_tools/include/tensorflow/lite/portable_type_to_tflitetype.h +++ /dev/null @@ -1,75 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ -#define TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ - -// Most of the definitions have been moved to this subheader so that Micro -// can include it without relying on and , which isn't -// available on all platforms. - -// Arduino build defines abs as a macro here. That is invalid C++, and breaks -// libc++'s header, undefine it. -#ifdef abs -#undef abs -#endif - -#include - -#include "tensorflow/lite/core/c/common.h" - -namespace tflite_micro { - -// Map statically from a C++ type to a TfLiteType. Used in interpreter for -// safe casts. -// Example: -// typeToTfLiteType() -> kTfLiteBool -template -constexpr TfLiteType typeToTfLiteType() { - return kTfLiteNoType; -} -// Map from TfLiteType to the corresponding C++ type. -// Example: -// TfLiteTypeToType::Type -> bool -template -struct TfLiteTypeToType {}; // Specializations below - -// Template specialization for both typeToTfLiteType and TfLiteTypeToType. -#define MATCH_TYPE_AND_TFLITE_TYPE(CPP_TYPE, TFLITE_TYPE_ENUM) \ - template <> \ - constexpr TfLiteType typeToTfLiteType() { \ - return TFLITE_TYPE_ENUM; \ - } \ - template <> \ - struct TfLiteTypeToType { \ - using Type = CPP_TYPE; \ - } - -// No string mapping is included here, since the TF Lite packed representation -// doesn't correspond to a C++ type well. -MATCH_TYPE_AND_TFLITE_TYPE(int32_t, kTfLiteInt32); -MATCH_TYPE_AND_TFLITE_TYPE(uint32_t, kTfLiteUInt32); -MATCH_TYPE_AND_TFLITE_TYPE(int16_t, kTfLiteInt16); -MATCH_TYPE_AND_TFLITE_TYPE(uint16_t, kTfLiteUInt16); -MATCH_TYPE_AND_TFLITE_TYPE(int64_t, kTfLiteInt64); -MATCH_TYPE_AND_TFLITE_TYPE(float, kTfLiteFloat32); -MATCH_TYPE_AND_TFLITE_TYPE(unsigned char, kTfLiteUInt8); -MATCH_TYPE_AND_TFLITE_TYPE(int8_t, kTfLiteInt8); -MATCH_TYPE_AND_TFLITE_TYPE(bool, kTfLiteBool); -MATCH_TYPE_AND_TFLITE_TYPE(TfLiteFloat16, kTfLiteFloat16); -MATCH_TYPE_AND_TFLITE_TYPE(double, kTfLiteFloat64); -MATCH_TYPE_AND_TFLITE_TYPE(uint64_t, kTfLiteUInt64); - -} // namespace tflite_micro -#endif // TENSORFLOW_LITE_PORTABLE_TYPE_TO_TFLITETYPE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h b/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h deleted file mode 100644 index 2446f9e0..00000000 --- a/new_ai_tools/include/tensorflow/lite/schema/schema_generated.h +++ /dev/null @@ -1,24644 +0,0 @@ -// automatically generated by the FlatBuffers compiler, do not modify - - -#ifndef FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ -#define FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ - -#include "flatbuffers/flatbuffers.h" - -// Ensure the included flatbuffers.h is the same version as when this file was -// generated, otherwise it may not be compatible. -static_assert(FLATBUFFERS_VERSION_MAJOR == 2 && - FLATBUFFERS_VERSION_MINOR == 0 && - FLATBUFFERS_VERSION_REVISION == 6, - "Non-compatible flatbuffers version included"); - -namespace tflite_micro { - -struct CustomQuantization; -struct CustomQuantizationBuilder; -struct CustomQuantizationT; - -struct QuantizationParameters; -struct QuantizationParametersBuilder; -struct QuantizationParametersT; - -struct Int32Vector; -struct Int32VectorBuilder; -struct Int32VectorT; - -struct Uint16Vector; -struct Uint16VectorBuilder; -struct Uint16VectorT; - -struct Uint8Vector; -struct Uint8VectorBuilder; -struct Uint8VectorT; - -struct DimensionMetadata; -struct DimensionMetadataBuilder; -struct DimensionMetadataT; - -struct SparsityParameters; -struct SparsityParametersBuilder; -struct SparsityParametersT; - -struct VariantSubType; -struct VariantSubTypeBuilder; -struct VariantSubTypeT; - -struct Tensor; -struct TensorBuilder; -struct TensorT; - -struct StablehloGatherOptions; -struct StablehloGatherOptionsBuilder; -struct StablehloGatherOptionsT; - -struct StablehloTransposeOptions; -struct StablehloTransposeOptionsBuilder; -struct StablehloTransposeOptionsT; - -struct StablehloDotGeneralOptions; -struct StablehloDotGeneralOptionsBuilder; -struct StablehloDotGeneralOptionsT; - -struct StablehloReduceWindowOptions; -struct StablehloReduceWindowOptionsBuilder; -struct StablehloReduceWindowOptionsT; - -struct StablehloWhileOptions; -struct StablehloWhileOptionsBuilder; -struct StablehloWhileOptionsT; - -struct StablehloSortOptions; -struct StablehloSortOptionsBuilder; -struct StablehloSortOptionsT; - -struct StablehloConcatenateOptions; -struct StablehloConcatenateOptionsBuilder; -struct StablehloConcatenateOptionsT; - -struct StablehloBroadcastInDimOptions; -struct StablehloBroadcastInDimOptionsBuilder; -struct StablehloBroadcastInDimOptionsT; - -struct StablehloCompareOptions; -struct StablehloCompareOptionsBuilder; -struct StablehloCompareOptionsT; - -struct StablehloDynamicSliceOptions; -struct StablehloDynamicSliceOptionsBuilder; -struct StablehloDynamicSliceOptionsT; - -struct StablehloPadOptions; -struct StablehloPadOptionsBuilder; -struct StablehloPadOptionsT; - -struct StablehloIotaOptions; -struct StablehloIotaOptionsBuilder; -struct StablehloIotaOptionsT; - -struct StablehloCustomCallOptions; -struct StablehloCustomCallOptionsBuilder; -struct StablehloCustomCallOptionsT; - -struct StablehloReduceOptions; -struct StablehloReduceOptionsBuilder; -struct StablehloReduceOptionsT; - -struct StablehloSliceOptions; -struct StablehloSliceOptionsBuilder; -struct StablehloSliceOptionsT; - -struct StablehloConvolutionOptions; -struct StablehloConvolutionOptionsBuilder; -struct StablehloConvolutionOptionsT; - -struct StablehloScatterOptions; -struct StablehloScatterOptionsBuilder; -struct StablehloScatterOptionsT; - -struct StablehloRngBitGeneratorOptions; -struct StablehloRngBitGeneratorOptionsBuilder; -struct StablehloRngBitGeneratorOptionsT; - -struct Conv2DOptions; -struct Conv2DOptionsBuilder; -struct Conv2DOptionsT; - -struct Conv3DOptions; -struct Conv3DOptionsBuilder; -struct Conv3DOptionsT; - -struct Pool2DOptions; -struct Pool2DOptionsBuilder; -struct Pool2DOptionsT; - -struct DepthwiseConv2DOptions; -struct DepthwiseConv2DOptionsBuilder; -struct DepthwiseConv2DOptionsT; - -struct ConcatEmbeddingsOptions; -struct ConcatEmbeddingsOptionsBuilder; -struct ConcatEmbeddingsOptionsT; - -struct LSHProjectionOptions; -struct LSHProjectionOptionsBuilder; -struct LSHProjectionOptionsT; - -struct SVDFOptions; -struct SVDFOptionsBuilder; -struct SVDFOptionsT; - -struct RNNOptions; -struct RNNOptionsBuilder; -struct RNNOptionsT; - -struct SequenceRNNOptions; -struct SequenceRNNOptionsBuilder; -struct SequenceRNNOptionsT; - -struct BidirectionalSequenceRNNOptions; -struct BidirectionalSequenceRNNOptionsBuilder; -struct BidirectionalSequenceRNNOptionsT; - -struct FullyConnectedOptions; -struct FullyConnectedOptionsBuilder; -struct FullyConnectedOptionsT; - -struct SoftmaxOptions; -struct SoftmaxOptionsBuilder; -struct SoftmaxOptionsT; - -struct ConcatenationOptions; -struct ConcatenationOptionsBuilder; -struct ConcatenationOptionsT; - -struct AddOptions; -struct AddOptionsBuilder; -struct AddOptionsT; - -struct MulOptions; -struct MulOptionsBuilder; -struct MulOptionsT; - -struct L2NormOptions; -struct L2NormOptionsBuilder; -struct L2NormOptionsT; - -struct LocalResponseNormalizationOptions; -struct LocalResponseNormalizationOptionsBuilder; -struct LocalResponseNormalizationOptionsT; - -struct LSTMOptions; -struct LSTMOptionsBuilder; -struct LSTMOptionsT; - -struct UnidirectionalSequenceLSTMOptions; -struct UnidirectionalSequenceLSTMOptionsBuilder; -struct UnidirectionalSequenceLSTMOptionsT; - -struct BidirectionalSequenceLSTMOptions; -struct BidirectionalSequenceLSTMOptionsBuilder; -struct BidirectionalSequenceLSTMOptionsT; - -struct ResizeBilinearOptions; -struct ResizeBilinearOptionsBuilder; -struct ResizeBilinearOptionsT; - -struct ResizeNearestNeighborOptions; -struct ResizeNearestNeighborOptionsBuilder; -struct ResizeNearestNeighborOptionsT; - -struct CallOptions; -struct CallOptionsBuilder; -struct CallOptionsT; - -struct PadOptions; -struct PadOptionsBuilder; -struct PadOptionsT; - -struct PadV2Options; -struct PadV2OptionsBuilder; -struct PadV2OptionsT; - -struct ReshapeOptions; -struct ReshapeOptionsBuilder; -struct ReshapeOptionsT; - -struct SpaceToBatchNDOptions; -struct SpaceToBatchNDOptionsBuilder; -struct SpaceToBatchNDOptionsT; - -struct BatchToSpaceNDOptions; -struct BatchToSpaceNDOptionsBuilder; -struct BatchToSpaceNDOptionsT; - -struct SkipGramOptions; -struct SkipGramOptionsBuilder; -struct SkipGramOptionsT; - -struct SpaceToDepthOptions; -struct SpaceToDepthOptionsBuilder; -struct SpaceToDepthOptionsT; - -struct DepthToSpaceOptions; -struct DepthToSpaceOptionsBuilder; -struct DepthToSpaceOptionsT; - -struct SubOptions; -struct SubOptionsBuilder; -struct SubOptionsT; - -struct DivOptions; -struct DivOptionsBuilder; -struct DivOptionsT; - -struct TopKV2Options; -struct TopKV2OptionsBuilder; -struct TopKV2OptionsT; - -struct EmbeddingLookupSparseOptions; -struct EmbeddingLookupSparseOptionsBuilder; -struct EmbeddingLookupSparseOptionsT; - -struct GatherOptions; -struct GatherOptionsBuilder; -struct GatherOptionsT; - -struct TransposeOptions; -struct TransposeOptionsBuilder; -struct TransposeOptionsT; - -struct ExpOptions; -struct ExpOptionsBuilder; -struct ExpOptionsT; - -struct CosOptions; -struct CosOptionsBuilder; -struct CosOptionsT; - -struct ReducerOptions; -struct ReducerOptionsBuilder; -struct ReducerOptionsT; - -struct SqueezeOptions; -struct SqueezeOptionsBuilder; -struct SqueezeOptionsT; - -struct SplitOptions; -struct SplitOptionsBuilder; -struct SplitOptionsT; - -struct SplitVOptions; -struct SplitVOptionsBuilder; -struct SplitVOptionsT; - -struct StridedSliceOptions; -struct StridedSliceOptionsBuilder; -struct StridedSliceOptionsT; - -struct LogSoftmaxOptions; -struct LogSoftmaxOptionsBuilder; -struct LogSoftmaxOptionsT; - -struct CastOptions; -struct CastOptionsBuilder; -struct CastOptionsT; - -struct DequantizeOptions; -struct DequantizeOptionsBuilder; -struct DequantizeOptionsT; - -struct MaximumMinimumOptions; -struct MaximumMinimumOptionsBuilder; -struct MaximumMinimumOptionsT; - -struct TileOptions; -struct TileOptionsBuilder; -struct TileOptionsT; - -struct ArgMaxOptions; -struct ArgMaxOptionsBuilder; -struct ArgMaxOptionsT; - -struct ArgMinOptions; -struct ArgMinOptionsBuilder; -struct ArgMinOptionsT; - -struct GreaterOptions; -struct GreaterOptionsBuilder; -struct GreaterOptionsT; - -struct GreaterEqualOptions; -struct GreaterEqualOptionsBuilder; -struct GreaterEqualOptionsT; - -struct LessOptions; -struct LessOptionsBuilder; -struct LessOptionsT; - -struct LessEqualOptions; -struct LessEqualOptionsBuilder; -struct LessEqualOptionsT; - -struct NegOptions; -struct NegOptionsBuilder; -struct NegOptionsT; - -struct SelectOptions; -struct SelectOptionsBuilder; -struct SelectOptionsT; - -struct SliceOptions; -struct SliceOptionsBuilder; -struct SliceOptionsT; - -struct TransposeConvOptions; -struct TransposeConvOptionsBuilder; -struct TransposeConvOptionsT; - -struct ExpandDimsOptions; -struct ExpandDimsOptionsBuilder; -struct ExpandDimsOptionsT; - -struct SparseToDenseOptions; -struct SparseToDenseOptionsBuilder; -struct SparseToDenseOptionsT; - -struct EqualOptions; -struct EqualOptionsBuilder; -struct EqualOptionsT; - -struct NotEqualOptions; -struct NotEqualOptionsBuilder; -struct NotEqualOptionsT; - -struct ShapeOptions; -struct ShapeOptionsBuilder; -struct ShapeOptionsT; - -struct RankOptions; -struct RankOptionsBuilder; -struct RankOptionsT; - -struct PowOptions; -struct PowOptionsBuilder; -struct PowOptionsT; - -struct FakeQuantOptions; -struct FakeQuantOptionsBuilder; -struct FakeQuantOptionsT; - -struct PackOptions; -struct PackOptionsBuilder; -struct PackOptionsT; - -struct LogicalOrOptions; -struct LogicalOrOptionsBuilder; -struct LogicalOrOptionsT; - -struct OneHotOptions; -struct OneHotOptionsBuilder; -struct OneHotOptionsT; - -struct AbsOptions; -struct AbsOptionsBuilder; -struct AbsOptionsT; - -struct HardSwishOptions; -struct HardSwishOptionsBuilder; -struct HardSwishOptionsT; - -struct LogicalAndOptions; -struct LogicalAndOptionsBuilder; -struct LogicalAndOptionsT; - -struct LogicalNotOptions; -struct LogicalNotOptionsBuilder; -struct LogicalNotOptionsT; - -struct UnpackOptions; -struct UnpackOptionsBuilder; -struct UnpackOptionsT; - -struct FloorDivOptions; -struct FloorDivOptionsBuilder; -struct FloorDivOptionsT; - -struct SquareOptions; -struct SquareOptionsBuilder; -struct SquareOptionsT; - -struct ZerosLikeOptions; -struct ZerosLikeOptionsBuilder; -struct ZerosLikeOptionsT; - -struct FillOptions; -struct FillOptionsBuilder; -struct FillOptionsT; - -struct FloorModOptions; -struct FloorModOptionsBuilder; -struct FloorModOptionsT; - -struct RangeOptions; -struct RangeOptionsBuilder; -struct RangeOptionsT; - -struct LeakyReluOptions; -struct LeakyReluOptionsBuilder; -struct LeakyReluOptionsT; - -struct SquaredDifferenceOptions; -struct SquaredDifferenceOptionsBuilder; -struct SquaredDifferenceOptionsT; - -struct MirrorPadOptions; -struct MirrorPadOptionsBuilder; -struct MirrorPadOptionsT; - -struct UniqueOptions; -struct UniqueOptionsBuilder; -struct UniqueOptionsT; - -struct ReverseV2Options; -struct ReverseV2OptionsBuilder; -struct ReverseV2OptionsT; - -struct AddNOptions; -struct AddNOptionsBuilder; -struct AddNOptionsT; - -struct GatherNdOptions; -struct GatherNdOptionsBuilder; -struct GatherNdOptionsT; - -struct WhereOptions; -struct WhereOptionsBuilder; -struct WhereOptionsT; - -struct ReverseSequenceOptions; -struct ReverseSequenceOptionsBuilder; -struct ReverseSequenceOptionsT; - -struct MatrixDiagOptions; -struct MatrixDiagOptionsBuilder; -struct MatrixDiagOptionsT; - -struct QuantizeOptions; -struct QuantizeOptionsBuilder; -struct QuantizeOptionsT; - -struct MatrixSetDiagOptions; -struct MatrixSetDiagOptionsBuilder; -struct MatrixSetDiagOptionsT; - -struct IfOptions; -struct IfOptionsBuilder; -struct IfOptionsT; - -struct CallOnceOptions; -struct CallOnceOptionsBuilder; -struct CallOnceOptionsT; - -struct WhileOptions; -struct WhileOptionsBuilder; -struct WhileOptionsT; - -struct NonMaxSuppressionV4Options; -struct NonMaxSuppressionV4OptionsBuilder; -struct NonMaxSuppressionV4OptionsT; - -struct NonMaxSuppressionV5Options; -struct NonMaxSuppressionV5OptionsBuilder; -struct NonMaxSuppressionV5OptionsT; - -struct ScatterNdOptions; -struct ScatterNdOptionsBuilder; -struct ScatterNdOptionsT; - -struct SelectV2Options; -struct SelectV2OptionsBuilder; -struct SelectV2OptionsT; - -struct DensifyOptions; -struct DensifyOptionsBuilder; -struct DensifyOptionsT; - -struct SegmentSumOptions; -struct SegmentSumOptionsBuilder; -struct SegmentSumOptionsT; - -struct BatchMatMulOptions; -struct BatchMatMulOptionsBuilder; -struct BatchMatMulOptionsT; - -struct CumsumOptions; -struct CumsumOptionsBuilder; -struct CumsumOptionsT; - -struct BroadcastToOptions; -struct BroadcastToOptionsBuilder; -struct BroadcastToOptionsT; - -struct Rfft2dOptions; -struct Rfft2dOptionsBuilder; -struct Rfft2dOptionsT; - -struct HashtableOptions; -struct HashtableOptionsBuilder; -struct HashtableOptionsT; - -struct HashtableFindOptions; -struct HashtableFindOptionsBuilder; -struct HashtableFindOptionsT; - -struct HashtableImportOptions; -struct HashtableImportOptionsBuilder; -struct HashtableImportOptionsT; - -struct HashtableSizeOptions; -struct HashtableSizeOptionsBuilder; -struct HashtableSizeOptionsT; - -struct VarHandleOptions; -struct VarHandleOptionsBuilder; -struct VarHandleOptionsT; - -struct ReadVariableOptions; -struct ReadVariableOptionsBuilder; -struct ReadVariableOptionsT; - -struct AssignVariableOptions; -struct AssignVariableOptionsBuilder; -struct AssignVariableOptionsT; - -struct RandomOptions; -struct RandomOptionsBuilder; -struct RandomOptionsT; - -struct BucketizeOptions; -struct BucketizeOptionsBuilder; -struct BucketizeOptionsT; - -struct GeluOptions; -struct GeluOptionsBuilder; -struct GeluOptionsT; - -struct DynamicUpdateSliceOptions; -struct DynamicUpdateSliceOptionsBuilder; -struct DynamicUpdateSliceOptionsT; - -struct UnsortedSegmentProdOptions; -struct UnsortedSegmentProdOptionsBuilder; -struct UnsortedSegmentProdOptionsT; - -struct UnsortedSegmentMaxOptions; -struct UnsortedSegmentMaxOptionsBuilder; -struct UnsortedSegmentMaxOptionsT; - -struct UnsortedSegmentSumOptions; -struct UnsortedSegmentSumOptionsBuilder; -struct UnsortedSegmentSumOptionsT; - -struct ATan2Options; -struct ATan2OptionsBuilder; -struct ATan2OptionsT; - -struct UnsortedSegmentMinOptions; -struct UnsortedSegmentMinOptionsBuilder; -struct UnsortedSegmentMinOptionsT; - -struct SignOptions; -struct SignOptionsBuilder; -struct SignOptionsT; - -struct BitcastOptions; -struct BitcastOptionsBuilder; -struct BitcastOptionsT; - -struct BitwiseXorOptions; -struct BitwiseXorOptionsBuilder; -struct BitwiseXorOptionsT; - -struct RightShiftOptions; -struct RightShiftOptionsBuilder; -struct RightShiftOptionsT; - -struct DilateOptions; -struct DilateOptionsBuilder; -struct DilateOptionsT; - -struct ReduceWindowOptions; -struct ReduceWindowOptionsBuilder; -struct ReduceWindowOptionsT; - -struct OperatorCode; -struct OperatorCodeBuilder; -struct OperatorCodeT; - -struct Operator; -struct OperatorBuilder; -struct OperatorT; - -struct SubGraph; -struct SubGraphBuilder; -struct SubGraphT; - -struct Buffer; -struct BufferBuilder; -struct BufferT; - -struct Metadata; -struct MetadataBuilder; -struct MetadataT; - -struct TensorMap; -struct TensorMapBuilder; -struct TensorMapT; - -struct SignatureDef; -struct SignatureDefBuilder; -struct SignatureDefT; - -struct Model; -struct ModelBuilder; -struct ModelT; - -enum TensorType : int8_t { - TensorType_FLOAT32 = 0, - TensorType_FLOAT16 = 1, - TensorType_INT32 = 2, - TensorType_UINT8 = 3, - TensorType_INT64 = 4, - TensorType_STRING = 5, - TensorType_BOOL = 6, - TensorType_INT16 = 7, - TensorType_COMPLEX64 = 8, - TensorType_INT8 = 9, - TensorType_FLOAT64 = 10, - TensorType_COMPLEX128 = 11, - TensorType_UINT64 = 12, - TensorType_RESOURCE = 13, - TensorType_VARIANT = 14, - TensorType_UINT32 = 15, - TensorType_UINT16 = 16, - TensorType_INT4 = 17, - TensorType_MIN = TensorType_FLOAT32, - TensorType_MAX = TensorType_INT4 -}; - -inline const TensorType (&EnumValuesTensorType())[18] { - static const TensorType values[] = { - TensorType_FLOAT32, - TensorType_FLOAT16, - TensorType_INT32, - TensorType_UINT8, - TensorType_INT64, - TensorType_STRING, - TensorType_BOOL, - TensorType_INT16, - TensorType_COMPLEX64, - TensorType_INT8, - TensorType_FLOAT64, - TensorType_COMPLEX128, - TensorType_UINT64, - TensorType_RESOURCE, - TensorType_VARIANT, - TensorType_UINT32, - TensorType_UINT16, - TensorType_INT4 - }; - return values; -} - -inline const char * const *EnumNamesTensorType() { - static const char * const names[19] = { - "FLOAT32", - "FLOAT16", - "INT32", - "UINT8", - "INT64", - "STRING", - "BOOL", - "INT16", - "COMPLEX64", - "INT8", - "FLOAT64", - "COMPLEX128", - "UINT64", - "RESOURCE", - "VARIANT", - "UINT32", - "UINT16", - "INT4", - nullptr - }; - return names; -} - -inline const char *EnumNameTensorType(TensorType e) { - if (flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_INT4)) return ""; - const size_t index = static_cast(e); - return EnumNamesTensorType()[index]; -} - -enum QuantizationDetails : uint8_t { - QuantizationDetails_NONE = 0, - QuantizationDetails_CustomQuantization = 1, - QuantizationDetails_MIN = QuantizationDetails_NONE, - QuantizationDetails_MAX = QuantizationDetails_CustomQuantization -}; - -inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] { - static const QuantizationDetails values[] = { - QuantizationDetails_NONE, - QuantizationDetails_CustomQuantization - }; - return values; -} - -inline const char * const *EnumNamesQuantizationDetails() { - static const char * const names[3] = { - "NONE", - "CustomQuantization", - nullptr - }; - return names; -} - -inline const char *EnumNameQuantizationDetails(QuantizationDetails e) { - if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return ""; - const size_t index = static_cast(e); - return EnumNamesQuantizationDetails()[index]; -} - -template struct QuantizationDetailsTraits { - static const QuantizationDetails enum_value = QuantizationDetails_NONE; -}; - -template<> struct QuantizationDetailsTraits { - static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; -}; - -template struct QuantizationDetailsUnionTraits { - static const QuantizationDetails enum_value = QuantizationDetails_NONE; -}; - -template<> struct QuantizationDetailsUnionTraits { - static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization; -}; - -struct QuantizationDetailsUnion { - QuantizationDetails type; - void *value; - - QuantizationDetailsUnion() : type(QuantizationDetails_NONE), value(nullptr) {} - QuantizationDetailsUnion(QuantizationDetailsUnion&& u) FLATBUFFERS_NOEXCEPT : - type(QuantizationDetails_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - QuantizationDetailsUnion(const QuantizationDetailsUnion &); - QuantizationDetailsUnion &operator=(const QuantizationDetailsUnion &u) - { QuantizationDetailsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - QuantizationDetailsUnion &operator=(QuantizationDetailsUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~QuantizationDetailsUnion() { Reset(); } - - void Reset(); - - template - void Set(T&& val) { - typedef typename std::remove_reference::type RT; - Reset(); - type = QuantizationDetailsUnionTraits::enum_value; - if (type != QuantizationDetails_NONE) { - value = new RT(std::forward(val)); - } - } - - static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite_micro::CustomQuantizationT *AsCustomQuantization() { - return type == QuantizationDetails_CustomQuantization ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CustomQuantizationT *AsCustomQuantization() const { - return type == QuantizationDetails_CustomQuantization ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type); -bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum DimensionType : int8_t { - DimensionType_DENSE = 0, - DimensionType_SPARSE_CSR = 1, - DimensionType_MIN = DimensionType_DENSE, - DimensionType_MAX = DimensionType_SPARSE_CSR -}; - -inline const DimensionType (&EnumValuesDimensionType())[2] { - static const DimensionType values[] = { - DimensionType_DENSE, - DimensionType_SPARSE_CSR - }; - return values; -} - -inline const char * const *EnumNamesDimensionType() { - static const char * const names[3] = { - "DENSE", - "SPARSE_CSR", - nullptr - }; - return names; -} - -inline const char *EnumNameDimensionType(DimensionType e) { - if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return ""; - const size_t index = static_cast(e); - return EnumNamesDimensionType()[index]; -} - -enum SparseIndexVector : uint8_t { - SparseIndexVector_NONE = 0, - SparseIndexVector_Int32Vector = 1, - SparseIndexVector_Uint16Vector = 2, - SparseIndexVector_Uint8Vector = 3, - SparseIndexVector_MIN = SparseIndexVector_NONE, - SparseIndexVector_MAX = SparseIndexVector_Uint8Vector -}; - -inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] { - static const SparseIndexVector values[] = { - SparseIndexVector_NONE, - SparseIndexVector_Int32Vector, - SparseIndexVector_Uint16Vector, - SparseIndexVector_Uint8Vector - }; - return values; -} - -inline const char * const *EnumNamesSparseIndexVector() { - static const char * const names[5] = { - "NONE", - "Int32Vector", - "Uint16Vector", - "Uint8Vector", - nullptr - }; - return names; -} - -inline const char *EnumNameSparseIndexVector(SparseIndexVector e) { - if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return ""; - const size_t index = static_cast(e); - return EnumNamesSparseIndexVector()[index]; -} - -template struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_NONE; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; -}; - -template<> struct SparseIndexVectorTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; -}; - -template struct SparseIndexVectorUnionTraits { - static const SparseIndexVector enum_value = SparseIndexVector_NONE; -}; - -template<> struct SparseIndexVectorUnionTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector; -}; - -template<> struct SparseIndexVectorUnionTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector; -}; - -template<> struct SparseIndexVectorUnionTraits { - static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector; -}; - -struct SparseIndexVectorUnion { - SparseIndexVector type; - void *value; - - SparseIndexVectorUnion() : type(SparseIndexVector_NONE), value(nullptr) {} - SparseIndexVectorUnion(SparseIndexVectorUnion&& u) FLATBUFFERS_NOEXCEPT : - type(SparseIndexVector_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - SparseIndexVectorUnion(const SparseIndexVectorUnion &); - SparseIndexVectorUnion &operator=(const SparseIndexVectorUnion &u) - { SparseIndexVectorUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - SparseIndexVectorUnion &operator=(SparseIndexVectorUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~SparseIndexVectorUnion() { Reset(); } - - void Reset(); - - template - void Set(T&& val) { - typedef typename std::remove_reference::type RT; - Reset(); - type = SparseIndexVectorUnionTraits::enum_value; - if (type != SparseIndexVector_NONE) { - value = new RT(std::forward(val)); - } - } - - static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite_micro::Int32VectorT *AsInt32Vector() { - return type == SparseIndexVector_Int32Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Int32VectorT *AsInt32Vector() const { - return type == SparseIndexVector_Int32Vector ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::Uint16VectorT *AsUint16Vector() { - return type == SparseIndexVector_Uint16Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Uint16VectorT *AsUint16Vector() const { - return type == SparseIndexVector_Uint16Vector ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::Uint8VectorT *AsUint8Vector() { - return type == SparseIndexVector_Uint8Vector ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Uint8VectorT *AsUint8Vector() const { - return type == SparseIndexVector_Uint8Vector ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type); -bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum BuiltinOperator : int32_t { - BuiltinOperator_ADD = 0, - BuiltinOperator_AVERAGE_POOL_2D = 1, - BuiltinOperator_CONCATENATION = 2, - BuiltinOperator_CONV_2D = 3, - BuiltinOperator_DEPTHWISE_CONV_2D = 4, - BuiltinOperator_DEPTH_TO_SPACE = 5, - BuiltinOperator_DEQUANTIZE = 6, - BuiltinOperator_EMBEDDING_LOOKUP = 7, - BuiltinOperator_FLOOR = 8, - BuiltinOperator_FULLY_CONNECTED = 9, - BuiltinOperator_HASHTABLE_LOOKUP = 10, - BuiltinOperator_L2_NORMALIZATION = 11, - BuiltinOperator_L2_POOL_2D = 12, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, - BuiltinOperator_LOGISTIC = 14, - BuiltinOperator_LSH_PROJECTION = 15, - BuiltinOperator_LSTM = 16, - BuiltinOperator_MAX_POOL_2D = 17, - BuiltinOperator_MUL = 18, - BuiltinOperator_RELU = 19, - BuiltinOperator_RELU_N1_TO_1 = 20, - BuiltinOperator_RELU6 = 21, - BuiltinOperator_RESHAPE = 22, - BuiltinOperator_RESIZE_BILINEAR = 23, - BuiltinOperator_RNN = 24, - BuiltinOperator_SOFTMAX = 25, - BuiltinOperator_SPACE_TO_DEPTH = 26, - BuiltinOperator_SVDF = 27, - BuiltinOperator_TANH = 28, - BuiltinOperator_CONCAT_EMBEDDINGS = 29, - BuiltinOperator_SKIP_GRAM = 30, - BuiltinOperator_CALL = 31, - BuiltinOperator_CUSTOM = 32, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, - BuiltinOperator_PAD = 34, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, - BuiltinOperator_GATHER = 36, - BuiltinOperator_BATCH_TO_SPACE_ND = 37, - BuiltinOperator_SPACE_TO_BATCH_ND = 38, - BuiltinOperator_TRANSPOSE = 39, - BuiltinOperator_MEAN = 40, - BuiltinOperator_SUB = 41, - BuiltinOperator_DIV = 42, - BuiltinOperator_SQUEEZE = 43, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - BuiltinOperator_STRIDED_SLICE = 45, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, - BuiltinOperator_EXP = 47, - BuiltinOperator_TOPK_V2 = 48, - BuiltinOperator_SPLIT = 49, - BuiltinOperator_LOG_SOFTMAX = 50, - BuiltinOperator_DELEGATE = 51, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, - BuiltinOperator_CAST = 53, - BuiltinOperator_PRELU = 54, - BuiltinOperator_MAXIMUM = 55, - BuiltinOperator_ARG_MAX = 56, - BuiltinOperator_MINIMUM = 57, - BuiltinOperator_LESS = 58, - BuiltinOperator_NEG = 59, - BuiltinOperator_PADV2 = 60, - BuiltinOperator_GREATER = 61, - BuiltinOperator_GREATER_EQUAL = 62, - BuiltinOperator_LESS_EQUAL = 63, - BuiltinOperator_SELECT = 64, - BuiltinOperator_SLICE = 65, - BuiltinOperator_SIN = 66, - BuiltinOperator_TRANSPOSE_CONV = 67, - BuiltinOperator_SPARSE_TO_DENSE = 68, - BuiltinOperator_TILE = 69, - BuiltinOperator_EXPAND_DIMS = 70, - BuiltinOperator_EQUAL = 71, - BuiltinOperator_NOT_EQUAL = 72, - BuiltinOperator_LOG = 73, - BuiltinOperator_SUM = 74, - BuiltinOperator_SQRT = 75, - BuiltinOperator_RSQRT = 76, - BuiltinOperator_SHAPE = 77, - BuiltinOperator_POW = 78, - BuiltinOperator_ARG_MIN = 79, - BuiltinOperator_FAKE_QUANT = 80, - BuiltinOperator_REDUCE_PROD = 81, - BuiltinOperator_REDUCE_MAX = 82, - BuiltinOperator_PACK = 83, - BuiltinOperator_LOGICAL_OR = 84, - BuiltinOperator_ONE_HOT = 85, - BuiltinOperator_LOGICAL_AND = 86, - BuiltinOperator_LOGICAL_NOT = 87, - BuiltinOperator_UNPACK = 88, - BuiltinOperator_REDUCE_MIN = 89, - BuiltinOperator_FLOOR_DIV = 90, - BuiltinOperator_REDUCE_ANY = 91, - BuiltinOperator_SQUARE = 92, - BuiltinOperator_ZEROS_LIKE = 93, - BuiltinOperator_FILL = 94, - BuiltinOperator_FLOOR_MOD = 95, - BuiltinOperator_RANGE = 96, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, - BuiltinOperator_LEAKY_RELU = 98, - BuiltinOperator_SQUARED_DIFFERENCE = 99, - BuiltinOperator_MIRROR_PAD = 100, - BuiltinOperator_ABS = 101, - BuiltinOperator_SPLIT_V = 102, - BuiltinOperator_UNIQUE = 103, - BuiltinOperator_CEIL = 104, - BuiltinOperator_REVERSE_V2 = 105, - BuiltinOperator_ADD_N = 106, - BuiltinOperator_GATHER_ND = 107, - BuiltinOperator_COS = 108, - BuiltinOperator_WHERE = 109, - BuiltinOperator_RANK = 110, - BuiltinOperator_ELU = 111, - BuiltinOperator_REVERSE_SEQUENCE = 112, - BuiltinOperator_MATRIX_DIAG = 113, - BuiltinOperator_QUANTIZE = 114, - BuiltinOperator_MATRIX_SET_DIAG = 115, - BuiltinOperator_ROUND = 116, - BuiltinOperator_HARD_SWISH = 117, - BuiltinOperator_IF = 118, - BuiltinOperator_WHILE = 119, - BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, - BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, - BuiltinOperator_SCATTER_ND = 122, - BuiltinOperator_SELECT_V2 = 123, - BuiltinOperator_DENSIFY = 124, - BuiltinOperator_SEGMENT_SUM = 125, - BuiltinOperator_BATCH_MATMUL = 126, - BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES = 127, - BuiltinOperator_CUMSUM = 128, - BuiltinOperator_CALL_ONCE = 129, - BuiltinOperator_BROADCAST_TO = 130, - BuiltinOperator_RFFT2D = 131, - BuiltinOperator_CONV_3D = 132, - BuiltinOperator_IMAG = 133, - BuiltinOperator_REAL = 134, - BuiltinOperator_COMPLEX_ABS = 135, - BuiltinOperator_HASHTABLE = 136, - BuiltinOperator_HASHTABLE_FIND = 137, - BuiltinOperator_HASHTABLE_IMPORT = 138, - BuiltinOperator_HASHTABLE_SIZE = 139, - BuiltinOperator_REDUCE_ALL = 140, - BuiltinOperator_CONV_3D_TRANSPOSE = 141, - BuiltinOperator_VAR_HANDLE = 142, - BuiltinOperator_READ_VARIABLE = 143, - BuiltinOperator_ASSIGN_VARIABLE = 144, - BuiltinOperator_BROADCAST_ARGS = 145, - BuiltinOperator_RANDOM_STANDARD_NORMAL = 146, - BuiltinOperator_BUCKETIZE = 147, - BuiltinOperator_RANDOM_UNIFORM = 148, - BuiltinOperator_MULTINOMIAL = 149, - BuiltinOperator_GELU = 150, - BuiltinOperator_DYNAMIC_UPDATE_SLICE = 151, - BuiltinOperator_RELU_0_TO_1 = 152, - BuiltinOperator_UNSORTED_SEGMENT_PROD = 153, - BuiltinOperator_UNSORTED_SEGMENT_MAX = 154, - BuiltinOperator_UNSORTED_SEGMENT_SUM = 155, - BuiltinOperator_ATAN2 = 156, - BuiltinOperator_UNSORTED_SEGMENT_MIN = 157, - BuiltinOperator_SIGN = 158, - BuiltinOperator_BITCAST = 159, - BuiltinOperator_BITWISE_XOR = 160, - BuiltinOperator_RIGHT_SHIFT = 161, - BuiltinOperator_STABLEHLO_LOGISTIC = 162, - BuiltinOperator_STABLEHLO_ADD = 163, - BuiltinOperator_STABLEHLO_DIVIDE = 164, - BuiltinOperator_STABLEHLO_MULTIPLY = 165, - BuiltinOperator_STABLEHLO_MAXIMUM = 166, - BuiltinOperator_STABLEHLO_RESHAPE = 167, - BuiltinOperator_STABLEHLO_CLAMP = 168, - BuiltinOperator_STABLEHLO_CONCATENATE = 169, - BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM = 170, - BuiltinOperator_STABLEHLO_CONVOLUTION = 171, - BuiltinOperator_STABLEHLO_SLICE = 172, - BuiltinOperator_STABLEHLO_CUSTOM_CALL = 173, - BuiltinOperator_STABLEHLO_REDUCE = 174, - BuiltinOperator_STABLEHLO_ABS = 175, - BuiltinOperator_STABLEHLO_AND = 176, - BuiltinOperator_STABLEHLO_COSINE = 177, - BuiltinOperator_STABLEHLO_EXPONENTIAL = 178, - BuiltinOperator_STABLEHLO_FLOOR = 179, - BuiltinOperator_STABLEHLO_LOG = 180, - BuiltinOperator_STABLEHLO_MINIMUM = 181, - BuiltinOperator_STABLEHLO_NEGATE = 182, - BuiltinOperator_STABLEHLO_OR = 183, - BuiltinOperator_STABLEHLO_POWER = 184, - BuiltinOperator_STABLEHLO_REMAINDER = 185, - BuiltinOperator_STABLEHLO_RSQRT = 186, - BuiltinOperator_STABLEHLO_SELECT = 187, - BuiltinOperator_STABLEHLO_SUBTRACT = 188, - BuiltinOperator_STABLEHLO_TANH = 189, - BuiltinOperator_STABLEHLO_SCATTER = 190, - BuiltinOperator_STABLEHLO_COMPARE = 191, - BuiltinOperator_STABLEHLO_CONVERT = 192, - BuiltinOperator_STABLEHLO_DYNAMIC_SLICE = 193, - BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE = 194, - BuiltinOperator_STABLEHLO_PAD = 195, - BuiltinOperator_STABLEHLO_IOTA = 196, - BuiltinOperator_STABLEHLO_DOT_GENERAL = 197, - BuiltinOperator_STABLEHLO_REDUCE_WINDOW = 198, - BuiltinOperator_STABLEHLO_SORT = 199, - BuiltinOperator_STABLEHLO_WHILE = 200, - BuiltinOperator_STABLEHLO_GATHER = 201, - BuiltinOperator_STABLEHLO_TRANSPOSE = 202, - BuiltinOperator_DILATE = 203, - BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR = 204, - BuiltinOperator_REDUCE_WINDOW = 205, - BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_REDUCE_WINDOW -}; - -inline const BuiltinOperator (&EnumValuesBuiltinOperator())[206] { - static const BuiltinOperator values[] = { - BuiltinOperator_ADD, - BuiltinOperator_AVERAGE_POOL_2D, - BuiltinOperator_CONCATENATION, - BuiltinOperator_CONV_2D, - BuiltinOperator_DEPTHWISE_CONV_2D, - BuiltinOperator_DEPTH_TO_SPACE, - BuiltinOperator_DEQUANTIZE, - BuiltinOperator_EMBEDDING_LOOKUP, - BuiltinOperator_FLOOR, - BuiltinOperator_FULLY_CONNECTED, - BuiltinOperator_HASHTABLE_LOOKUP, - BuiltinOperator_L2_NORMALIZATION, - BuiltinOperator_L2_POOL_2D, - BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - BuiltinOperator_LOGISTIC, - BuiltinOperator_LSH_PROJECTION, - BuiltinOperator_LSTM, - BuiltinOperator_MAX_POOL_2D, - BuiltinOperator_MUL, - BuiltinOperator_RELU, - BuiltinOperator_RELU_N1_TO_1, - BuiltinOperator_RELU6, - BuiltinOperator_RESHAPE, - BuiltinOperator_RESIZE_BILINEAR, - BuiltinOperator_RNN, - BuiltinOperator_SOFTMAX, - BuiltinOperator_SPACE_TO_DEPTH, - BuiltinOperator_SVDF, - BuiltinOperator_TANH, - BuiltinOperator_CONCAT_EMBEDDINGS, - BuiltinOperator_SKIP_GRAM, - BuiltinOperator_CALL, - BuiltinOperator_CUSTOM, - BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - BuiltinOperator_PAD, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_GATHER, - BuiltinOperator_BATCH_TO_SPACE_ND, - BuiltinOperator_SPACE_TO_BATCH_ND, - BuiltinOperator_TRANSPOSE, - BuiltinOperator_MEAN, - BuiltinOperator_SUB, - BuiltinOperator_DIV, - BuiltinOperator_SQUEEZE, - BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_STRIDED_SLICE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, - BuiltinOperator_EXP, - BuiltinOperator_TOPK_V2, - BuiltinOperator_SPLIT, - BuiltinOperator_LOG_SOFTMAX, - BuiltinOperator_DELEGATE, - BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, - BuiltinOperator_CAST, - BuiltinOperator_PRELU, - BuiltinOperator_MAXIMUM, - BuiltinOperator_ARG_MAX, - BuiltinOperator_MINIMUM, - BuiltinOperator_LESS, - BuiltinOperator_NEG, - BuiltinOperator_PADV2, - BuiltinOperator_GREATER, - BuiltinOperator_GREATER_EQUAL, - BuiltinOperator_LESS_EQUAL, - BuiltinOperator_SELECT, - BuiltinOperator_SLICE, - BuiltinOperator_SIN, - BuiltinOperator_TRANSPOSE_CONV, - BuiltinOperator_SPARSE_TO_DENSE, - BuiltinOperator_TILE, - BuiltinOperator_EXPAND_DIMS, - BuiltinOperator_EQUAL, - BuiltinOperator_NOT_EQUAL, - BuiltinOperator_LOG, - BuiltinOperator_SUM, - BuiltinOperator_SQRT, - BuiltinOperator_RSQRT, - BuiltinOperator_SHAPE, - BuiltinOperator_POW, - BuiltinOperator_ARG_MIN, - BuiltinOperator_FAKE_QUANT, - BuiltinOperator_REDUCE_PROD, - BuiltinOperator_REDUCE_MAX, - BuiltinOperator_PACK, - BuiltinOperator_LOGICAL_OR, - BuiltinOperator_ONE_HOT, - BuiltinOperator_LOGICAL_AND, - BuiltinOperator_LOGICAL_NOT, - BuiltinOperator_UNPACK, - BuiltinOperator_REDUCE_MIN, - BuiltinOperator_FLOOR_DIV, - BuiltinOperator_REDUCE_ANY, - BuiltinOperator_SQUARE, - BuiltinOperator_ZEROS_LIKE, - BuiltinOperator_FILL, - BuiltinOperator_FLOOR_MOD, - BuiltinOperator_RANGE, - BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - BuiltinOperator_LEAKY_RELU, - BuiltinOperator_SQUARED_DIFFERENCE, - BuiltinOperator_MIRROR_PAD, - BuiltinOperator_ABS, - BuiltinOperator_SPLIT_V, - BuiltinOperator_UNIQUE, - BuiltinOperator_CEIL, - BuiltinOperator_REVERSE_V2, - BuiltinOperator_ADD_N, - BuiltinOperator_GATHER_ND, - BuiltinOperator_COS, - BuiltinOperator_WHERE, - BuiltinOperator_RANK, - BuiltinOperator_ELU, - BuiltinOperator_REVERSE_SEQUENCE, - BuiltinOperator_MATRIX_DIAG, - BuiltinOperator_QUANTIZE, - BuiltinOperator_MATRIX_SET_DIAG, - BuiltinOperator_ROUND, - BuiltinOperator_HARD_SWISH, - BuiltinOperator_IF, - BuiltinOperator_WHILE, - BuiltinOperator_NON_MAX_SUPPRESSION_V4, - BuiltinOperator_NON_MAX_SUPPRESSION_V5, - BuiltinOperator_SCATTER_ND, - BuiltinOperator_SELECT_V2, - BuiltinOperator_DENSIFY, - BuiltinOperator_SEGMENT_SUM, - BuiltinOperator_BATCH_MATMUL, - BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES, - BuiltinOperator_CUMSUM, - BuiltinOperator_CALL_ONCE, - BuiltinOperator_BROADCAST_TO, - BuiltinOperator_RFFT2D, - BuiltinOperator_CONV_3D, - BuiltinOperator_IMAG, - BuiltinOperator_REAL, - BuiltinOperator_COMPLEX_ABS, - BuiltinOperator_HASHTABLE, - BuiltinOperator_HASHTABLE_FIND, - BuiltinOperator_HASHTABLE_IMPORT, - BuiltinOperator_HASHTABLE_SIZE, - BuiltinOperator_REDUCE_ALL, - BuiltinOperator_CONV_3D_TRANSPOSE, - BuiltinOperator_VAR_HANDLE, - BuiltinOperator_READ_VARIABLE, - BuiltinOperator_ASSIGN_VARIABLE, - BuiltinOperator_BROADCAST_ARGS, - BuiltinOperator_RANDOM_STANDARD_NORMAL, - BuiltinOperator_BUCKETIZE, - BuiltinOperator_RANDOM_UNIFORM, - BuiltinOperator_MULTINOMIAL, - BuiltinOperator_GELU, - BuiltinOperator_DYNAMIC_UPDATE_SLICE, - BuiltinOperator_RELU_0_TO_1, - BuiltinOperator_UNSORTED_SEGMENT_PROD, - BuiltinOperator_UNSORTED_SEGMENT_MAX, - BuiltinOperator_UNSORTED_SEGMENT_SUM, - BuiltinOperator_ATAN2, - BuiltinOperator_UNSORTED_SEGMENT_MIN, - BuiltinOperator_SIGN, - BuiltinOperator_BITCAST, - BuiltinOperator_BITWISE_XOR, - BuiltinOperator_RIGHT_SHIFT, - BuiltinOperator_STABLEHLO_LOGISTIC, - BuiltinOperator_STABLEHLO_ADD, - BuiltinOperator_STABLEHLO_DIVIDE, - BuiltinOperator_STABLEHLO_MULTIPLY, - BuiltinOperator_STABLEHLO_MAXIMUM, - BuiltinOperator_STABLEHLO_RESHAPE, - BuiltinOperator_STABLEHLO_CLAMP, - BuiltinOperator_STABLEHLO_CONCATENATE, - BuiltinOperator_STABLEHLO_BROADCAST_IN_DIM, - BuiltinOperator_STABLEHLO_CONVOLUTION, - BuiltinOperator_STABLEHLO_SLICE, - BuiltinOperator_STABLEHLO_CUSTOM_CALL, - BuiltinOperator_STABLEHLO_REDUCE, - BuiltinOperator_STABLEHLO_ABS, - BuiltinOperator_STABLEHLO_AND, - BuiltinOperator_STABLEHLO_COSINE, - BuiltinOperator_STABLEHLO_EXPONENTIAL, - BuiltinOperator_STABLEHLO_FLOOR, - BuiltinOperator_STABLEHLO_LOG, - BuiltinOperator_STABLEHLO_MINIMUM, - BuiltinOperator_STABLEHLO_NEGATE, - BuiltinOperator_STABLEHLO_OR, - BuiltinOperator_STABLEHLO_POWER, - BuiltinOperator_STABLEHLO_REMAINDER, - BuiltinOperator_STABLEHLO_RSQRT, - BuiltinOperator_STABLEHLO_SELECT, - BuiltinOperator_STABLEHLO_SUBTRACT, - BuiltinOperator_STABLEHLO_TANH, - BuiltinOperator_STABLEHLO_SCATTER, - BuiltinOperator_STABLEHLO_COMPARE, - BuiltinOperator_STABLEHLO_CONVERT, - BuiltinOperator_STABLEHLO_DYNAMIC_SLICE, - BuiltinOperator_STABLEHLO_DYNAMIC_UPDATE_SLICE, - BuiltinOperator_STABLEHLO_PAD, - BuiltinOperator_STABLEHLO_IOTA, - BuiltinOperator_STABLEHLO_DOT_GENERAL, - BuiltinOperator_STABLEHLO_REDUCE_WINDOW, - BuiltinOperator_STABLEHLO_SORT, - BuiltinOperator_STABLEHLO_WHILE, - BuiltinOperator_STABLEHLO_GATHER, - BuiltinOperator_STABLEHLO_TRANSPOSE, - BuiltinOperator_DILATE, - BuiltinOperator_STABLEHLO_RNG_BIT_GENERATOR, - BuiltinOperator_REDUCE_WINDOW - }; - return values; -} - -inline const char * const *EnumNamesBuiltinOperator() { - static const char * const names[207] = { - "ADD", - "AVERAGE_POOL_2D", - "CONCATENATION", - "CONV_2D", - "DEPTHWISE_CONV_2D", - "DEPTH_TO_SPACE", - "DEQUANTIZE", - "EMBEDDING_LOOKUP", - "FLOOR", - "FULLY_CONNECTED", - "HASHTABLE_LOOKUP", - "L2_NORMALIZATION", - "L2_POOL_2D", - "LOCAL_RESPONSE_NORMALIZATION", - "LOGISTIC", - "LSH_PROJECTION", - "LSTM", - "MAX_POOL_2D", - "MUL", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "RESHAPE", - "RESIZE_BILINEAR", - "RNN", - "SOFTMAX", - "SPACE_TO_DEPTH", - "SVDF", - "TANH", - "CONCAT_EMBEDDINGS", - "SKIP_GRAM", - "CALL", - "CUSTOM", - "EMBEDDING_LOOKUP_SPARSE", - "PAD", - "UNIDIRECTIONAL_SEQUENCE_RNN", - "GATHER", - "BATCH_TO_SPACE_ND", - "SPACE_TO_BATCH_ND", - "TRANSPOSE", - "MEAN", - "SUB", - "DIV", - "SQUEEZE", - "UNIDIRECTIONAL_SEQUENCE_LSTM", - "STRIDED_SLICE", - "BIDIRECTIONAL_SEQUENCE_RNN", - "EXP", - "TOPK_V2", - "SPLIT", - "LOG_SOFTMAX", - "DELEGATE", - "BIDIRECTIONAL_SEQUENCE_LSTM", - "CAST", - "PRELU", - "MAXIMUM", - "ARG_MAX", - "MINIMUM", - "LESS", - "NEG", - "PADV2", - "GREATER", - "GREATER_EQUAL", - "LESS_EQUAL", - "SELECT", - "SLICE", - "SIN", - "TRANSPOSE_CONV", - "SPARSE_TO_DENSE", - "TILE", - "EXPAND_DIMS", - "EQUAL", - "NOT_EQUAL", - "LOG", - "SUM", - "SQRT", - "RSQRT", - "SHAPE", - "POW", - "ARG_MIN", - "FAKE_QUANT", - "REDUCE_PROD", - "REDUCE_MAX", - "PACK", - "LOGICAL_OR", - "ONE_HOT", - "LOGICAL_AND", - "LOGICAL_NOT", - "UNPACK", - "REDUCE_MIN", - "FLOOR_DIV", - "REDUCE_ANY", - "SQUARE", - "ZEROS_LIKE", - "FILL", - "FLOOR_MOD", - "RANGE", - "RESIZE_NEAREST_NEIGHBOR", - "LEAKY_RELU", - "SQUARED_DIFFERENCE", - "MIRROR_PAD", - "ABS", - "SPLIT_V", - "UNIQUE", - "CEIL", - "REVERSE_V2", - "ADD_N", - "GATHER_ND", - "COS", - "WHERE", - "RANK", - "ELU", - "REVERSE_SEQUENCE", - "MATRIX_DIAG", - "QUANTIZE", - "MATRIX_SET_DIAG", - "ROUND", - "HARD_SWISH", - "IF", - "WHILE", - "NON_MAX_SUPPRESSION_V4", - "NON_MAX_SUPPRESSION_V5", - "SCATTER_ND", - "SELECT_V2", - "DENSIFY", - "SEGMENT_SUM", - "BATCH_MATMUL", - "PLACEHOLDER_FOR_GREATER_OP_CODES", - "CUMSUM", - "CALL_ONCE", - "BROADCAST_TO", - "RFFT2D", - "CONV_3D", - "IMAG", - "REAL", - "COMPLEX_ABS", - "HASHTABLE", - "HASHTABLE_FIND", - "HASHTABLE_IMPORT", - "HASHTABLE_SIZE", - "REDUCE_ALL", - "CONV_3D_TRANSPOSE", - "VAR_HANDLE", - "READ_VARIABLE", - "ASSIGN_VARIABLE", - "BROADCAST_ARGS", - "RANDOM_STANDARD_NORMAL", - "BUCKETIZE", - "RANDOM_UNIFORM", - "MULTINOMIAL", - "GELU", - "DYNAMIC_UPDATE_SLICE", - "RELU_0_TO_1", - "UNSORTED_SEGMENT_PROD", - "UNSORTED_SEGMENT_MAX", - "UNSORTED_SEGMENT_SUM", - "ATAN2", - "UNSORTED_SEGMENT_MIN", - "SIGN", - "BITCAST", - "BITWISE_XOR", - "RIGHT_SHIFT", - "STABLEHLO_LOGISTIC", - "STABLEHLO_ADD", - "STABLEHLO_DIVIDE", - "STABLEHLO_MULTIPLY", - "STABLEHLO_MAXIMUM", - "STABLEHLO_RESHAPE", - "STABLEHLO_CLAMP", - "STABLEHLO_CONCATENATE", - "STABLEHLO_BROADCAST_IN_DIM", - "STABLEHLO_CONVOLUTION", - "STABLEHLO_SLICE", - "STABLEHLO_CUSTOM_CALL", - "STABLEHLO_REDUCE", - "STABLEHLO_ABS", - "STABLEHLO_AND", - "STABLEHLO_COSINE", - "STABLEHLO_EXPONENTIAL", - "STABLEHLO_FLOOR", - "STABLEHLO_LOG", - "STABLEHLO_MINIMUM", - "STABLEHLO_NEGATE", - "STABLEHLO_OR", - "STABLEHLO_POWER", - "STABLEHLO_REMAINDER", - "STABLEHLO_RSQRT", - "STABLEHLO_SELECT", - "STABLEHLO_SUBTRACT", - "STABLEHLO_TANH", - "STABLEHLO_SCATTER", - "STABLEHLO_COMPARE", - "STABLEHLO_CONVERT", - "STABLEHLO_DYNAMIC_SLICE", - "STABLEHLO_DYNAMIC_UPDATE_SLICE", - "STABLEHLO_PAD", - "STABLEHLO_IOTA", - "STABLEHLO_DOT_GENERAL", - "STABLEHLO_REDUCE_WINDOW", - "STABLEHLO_SORT", - "STABLEHLO_WHILE", - "STABLEHLO_GATHER", - "STABLEHLO_TRANSPOSE", - "DILATE", - "STABLEHLO_RNG_BIT_GENERATOR", - "REDUCE_WINDOW", - nullptr - }; - return names; -} - -inline const char *EnumNameBuiltinOperator(BuiltinOperator e) { - if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_REDUCE_WINDOW)) return ""; - const size_t index = static_cast(e); - return EnumNamesBuiltinOperator()[index]; -} - -enum BuiltinOptions : uint8_t { - BuiltinOptions_NONE = 0, - BuiltinOptions_Conv2DOptions = 1, - BuiltinOptions_DepthwiseConv2DOptions = 2, - BuiltinOptions_ConcatEmbeddingsOptions = 3, - BuiltinOptions_LSHProjectionOptions = 4, - BuiltinOptions_Pool2DOptions = 5, - BuiltinOptions_SVDFOptions = 6, - BuiltinOptions_RNNOptions = 7, - BuiltinOptions_FullyConnectedOptions = 8, - BuiltinOptions_SoftmaxOptions = 9, - BuiltinOptions_ConcatenationOptions = 10, - BuiltinOptions_AddOptions = 11, - BuiltinOptions_L2NormOptions = 12, - BuiltinOptions_LocalResponseNormalizationOptions = 13, - BuiltinOptions_LSTMOptions = 14, - BuiltinOptions_ResizeBilinearOptions = 15, - BuiltinOptions_CallOptions = 16, - BuiltinOptions_ReshapeOptions = 17, - BuiltinOptions_SkipGramOptions = 18, - BuiltinOptions_SpaceToDepthOptions = 19, - BuiltinOptions_EmbeddingLookupSparseOptions = 20, - BuiltinOptions_MulOptions = 21, - BuiltinOptions_PadOptions = 22, - BuiltinOptions_GatherOptions = 23, - BuiltinOptions_BatchToSpaceNDOptions = 24, - BuiltinOptions_SpaceToBatchNDOptions = 25, - BuiltinOptions_TransposeOptions = 26, - BuiltinOptions_ReducerOptions = 27, - BuiltinOptions_SubOptions = 28, - BuiltinOptions_DivOptions = 29, - BuiltinOptions_SqueezeOptions = 30, - BuiltinOptions_SequenceRNNOptions = 31, - BuiltinOptions_StridedSliceOptions = 32, - BuiltinOptions_ExpOptions = 33, - BuiltinOptions_TopKV2Options = 34, - BuiltinOptions_SplitOptions = 35, - BuiltinOptions_LogSoftmaxOptions = 36, - BuiltinOptions_CastOptions = 37, - BuiltinOptions_DequantizeOptions = 38, - BuiltinOptions_MaximumMinimumOptions = 39, - BuiltinOptions_ArgMaxOptions = 40, - BuiltinOptions_LessOptions = 41, - BuiltinOptions_NegOptions = 42, - BuiltinOptions_PadV2Options = 43, - BuiltinOptions_GreaterOptions = 44, - BuiltinOptions_GreaterEqualOptions = 45, - BuiltinOptions_LessEqualOptions = 46, - BuiltinOptions_SelectOptions = 47, - BuiltinOptions_SliceOptions = 48, - BuiltinOptions_TransposeConvOptions = 49, - BuiltinOptions_SparseToDenseOptions = 50, - BuiltinOptions_TileOptions = 51, - BuiltinOptions_ExpandDimsOptions = 52, - BuiltinOptions_EqualOptions = 53, - BuiltinOptions_NotEqualOptions = 54, - BuiltinOptions_ShapeOptions = 55, - BuiltinOptions_PowOptions = 56, - BuiltinOptions_ArgMinOptions = 57, - BuiltinOptions_FakeQuantOptions = 58, - BuiltinOptions_PackOptions = 59, - BuiltinOptions_LogicalOrOptions = 60, - BuiltinOptions_OneHotOptions = 61, - BuiltinOptions_LogicalAndOptions = 62, - BuiltinOptions_LogicalNotOptions = 63, - BuiltinOptions_UnpackOptions = 64, - BuiltinOptions_FloorDivOptions = 65, - BuiltinOptions_SquareOptions = 66, - BuiltinOptions_ZerosLikeOptions = 67, - BuiltinOptions_FillOptions = 68, - BuiltinOptions_BidirectionalSequenceLSTMOptions = 69, - BuiltinOptions_BidirectionalSequenceRNNOptions = 70, - BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71, - BuiltinOptions_FloorModOptions = 72, - BuiltinOptions_RangeOptions = 73, - BuiltinOptions_ResizeNearestNeighborOptions = 74, - BuiltinOptions_LeakyReluOptions = 75, - BuiltinOptions_SquaredDifferenceOptions = 76, - BuiltinOptions_MirrorPadOptions = 77, - BuiltinOptions_AbsOptions = 78, - BuiltinOptions_SplitVOptions = 79, - BuiltinOptions_UniqueOptions = 80, - BuiltinOptions_ReverseV2Options = 81, - BuiltinOptions_AddNOptions = 82, - BuiltinOptions_GatherNdOptions = 83, - BuiltinOptions_CosOptions = 84, - BuiltinOptions_WhereOptions = 85, - BuiltinOptions_RankOptions = 86, - BuiltinOptions_ReverseSequenceOptions = 87, - BuiltinOptions_MatrixDiagOptions = 88, - BuiltinOptions_QuantizeOptions = 89, - BuiltinOptions_MatrixSetDiagOptions = 90, - BuiltinOptions_HardSwishOptions = 91, - BuiltinOptions_IfOptions = 92, - BuiltinOptions_WhileOptions = 93, - BuiltinOptions_DepthToSpaceOptions = 94, - BuiltinOptions_NonMaxSuppressionV4Options = 95, - BuiltinOptions_NonMaxSuppressionV5Options = 96, - BuiltinOptions_ScatterNdOptions = 97, - BuiltinOptions_SelectV2Options = 98, - BuiltinOptions_DensifyOptions = 99, - BuiltinOptions_SegmentSumOptions = 100, - BuiltinOptions_BatchMatMulOptions = 101, - BuiltinOptions_CumsumOptions = 102, - BuiltinOptions_CallOnceOptions = 103, - BuiltinOptions_BroadcastToOptions = 104, - BuiltinOptions_Rfft2dOptions = 105, - BuiltinOptions_Conv3DOptions = 106, - BuiltinOptions_HashtableOptions = 107, - BuiltinOptions_HashtableFindOptions = 108, - BuiltinOptions_HashtableImportOptions = 109, - BuiltinOptions_HashtableSizeOptions = 110, - BuiltinOptions_VarHandleOptions = 111, - BuiltinOptions_ReadVariableOptions = 112, - BuiltinOptions_AssignVariableOptions = 113, - BuiltinOptions_RandomOptions = 114, - BuiltinOptions_BucketizeOptions = 115, - BuiltinOptions_GeluOptions = 116, - BuiltinOptions_DynamicUpdateSliceOptions = 117, - BuiltinOptions_UnsortedSegmentProdOptions = 118, - BuiltinOptions_UnsortedSegmentMaxOptions = 119, - BuiltinOptions_UnsortedSegmentMinOptions = 120, - BuiltinOptions_UnsortedSegmentSumOptions = 121, - BuiltinOptions_ATan2Options = 122, - BuiltinOptions_SignOptions = 123, - BuiltinOptions_BitcastOptions = 124, - BuiltinOptions_BitwiseXorOptions = 125, - BuiltinOptions_RightShiftOptions = 126, - BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_RightShiftOptions -}; - -inline const BuiltinOptions (&EnumValuesBuiltinOptions())[127] { - static const BuiltinOptions values[] = { - BuiltinOptions_NONE, - BuiltinOptions_Conv2DOptions, - BuiltinOptions_DepthwiseConv2DOptions, - BuiltinOptions_ConcatEmbeddingsOptions, - BuiltinOptions_LSHProjectionOptions, - BuiltinOptions_Pool2DOptions, - BuiltinOptions_SVDFOptions, - BuiltinOptions_RNNOptions, - BuiltinOptions_FullyConnectedOptions, - BuiltinOptions_SoftmaxOptions, - BuiltinOptions_ConcatenationOptions, - BuiltinOptions_AddOptions, - BuiltinOptions_L2NormOptions, - BuiltinOptions_LocalResponseNormalizationOptions, - BuiltinOptions_LSTMOptions, - BuiltinOptions_ResizeBilinearOptions, - BuiltinOptions_CallOptions, - BuiltinOptions_ReshapeOptions, - BuiltinOptions_SkipGramOptions, - BuiltinOptions_SpaceToDepthOptions, - BuiltinOptions_EmbeddingLookupSparseOptions, - BuiltinOptions_MulOptions, - BuiltinOptions_PadOptions, - BuiltinOptions_GatherOptions, - BuiltinOptions_BatchToSpaceNDOptions, - BuiltinOptions_SpaceToBatchNDOptions, - BuiltinOptions_TransposeOptions, - BuiltinOptions_ReducerOptions, - BuiltinOptions_SubOptions, - BuiltinOptions_DivOptions, - BuiltinOptions_SqueezeOptions, - BuiltinOptions_SequenceRNNOptions, - BuiltinOptions_StridedSliceOptions, - BuiltinOptions_ExpOptions, - BuiltinOptions_TopKV2Options, - BuiltinOptions_SplitOptions, - BuiltinOptions_LogSoftmaxOptions, - BuiltinOptions_CastOptions, - BuiltinOptions_DequantizeOptions, - BuiltinOptions_MaximumMinimumOptions, - BuiltinOptions_ArgMaxOptions, - BuiltinOptions_LessOptions, - BuiltinOptions_NegOptions, - BuiltinOptions_PadV2Options, - BuiltinOptions_GreaterOptions, - BuiltinOptions_GreaterEqualOptions, - BuiltinOptions_LessEqualOptions, - BuiltinOptions_SelectOptions, - BuiltinOptions_SliceOptions, - BuiltinOptions_TransposeConvOptions, - BuiltinOptions_SparseToDenseOptions, - BuiltinOptions_TileOptions, - BuiltinOptions_ExpandDimsOptions, - BuiltinOptions_EqualOptions, - BuiltinOptions_NotEqualOptions, - BuiltinOptions_ShapeOptions, - BuiltinOptions_PowOptions, - BuiltinOptions_ArgMinOptions, - BuiltinOptions_FakeQuantOptions, - BuiltinOptions_PackOptions, - BuiltinOptions_LogicalOrOptions, - BuiltinOptions_OneHotOptions, - BuiltinOptions_LogicalAndOptions, - BuiltinOptions_LogicalNotOptions, - BuiltinOptions_UnpackOptions, - BuiltinOptions_FloorDivOptions, - BuiltinOptions_SquareOptions, - BuiltinOptions_ZerosLikeOptions, - BuiltinOptions_FillOptions, - BuiltinOptions_BidirectionalSequenceLSTMOptions, - BuiltinOptions_BidirectionalSequenceRNNOptions, - BuiltinOptions_UnidirectionalSequenceLSTMOptions, - BuiltinOptions_FloorModOptions, - BuiltinOptions_RangeOptions, - BuiltinOptions_ResizeNearestNeighborOptions, - BuiltinOptions_LeakyReluOptions, - BuiltinOptions_SquaredDifferenceOptions, - BuiltinOptions_MirrorPadOptions, - BuiltinOptions_AbsOptions, - BuiltinOptions_SplitVOptions, - BuiltinOptions_UniqueOptions, - BuiltinOptions_ReverseV2Options, - BuiltinOptions_AddNOptions, - BuiltinOptions_GatherNdOptions, - BuiltinOptions_CosOptions, - BuiltinOptions_WhereOptions, - BuiltinOptions_RankOptions, - BuiltinOptions_ReverseSequenceOptions, - BuiltinOptions_MatrixDiagOptions, - BuiltinOptions_QuantizeOptions, - BuiltinOptions_MatrixSetDiagOptions, - BuiltinOptions_HardSwishOptions, - BuiltinOptions_IfOptions, - BuiltinOptions_WhileOptions, - BuiltinOptions_DepthToSpaceOptions, - BuiltinOptions_NonMaxSuppressionV4Options, - BuiltinOptions_NonMaxSuppressionV5Options, - BuiltinOptions_ScatterNdOptions, - BuiltinOptions_SelectV2Options, - BuiltinOptions_DensifyOptions, - BuiltinOptions_SegmentSumOptions, - BuiltinOptions_BatchMatMulOptions, - BuiltinOptions_CumsumOptions, - BuiltinOptions_CallOnceOptions, - BuiltinOptions_BroadcastToOptions, - BuiltinOptions_Rfft2dOptions, - BuiltinOptions_Conv3DOptions, - BuiltinOptions_HashtableOptions, - BuiltinOptions_HashtableFindOptions, - BuiltinOptions_HashtableImportOptions, - BuiltinOptions_HashtableSizeOptions, - BuiltinOptions_VarHandleOptions, - BuiltinOptions_ReadVariableOptions, - BuiltinOptions_AssignVariableOptions, - BuiltinOptions_RandomOptions, - BuiltinOptions_BucketizeOptions, - BuiltinOptions_GeluOptions, - BuiltinOptions_DynamicUpdateSliceOptions, - BuiltinOptions_UnsortedSegmentProdOptions, - BuiltinOptions_UnsortedSegmentMaxOptions, - BuiltinOptions_UnsortedSegmentMinOptions, - BuiltinOptions_UnsortedSegmentSumOptions, - BuiltinOptions_ATan2Options, - BuiltinOptions_SignOptions, - BuiltinOptions_BitcastOptions, - BuiltinOptions_BitwiseXorOptions, - BuiltinOptions_RightShiftOptions - }; - return values; -} - -inline const char * const *EnumNamesBuiltinOptions() { - static const char * const names[128] = { - "NONE", - "Conv2DOptions", - "DepthwiseConv2DOptions", - "ConcatEmbeddingsOptions", - "LSHProjectionOptions", - "Pool2DOptions", - "SVDFOptions", - "RNNOptions", - "FullyConnectedOptions", - "SoftmaxOptions", - "ConcatenationOptions", - "AddOptions", - "L2NormOptions", - "LocalResponseNormalizationOptions", - "LSTMOptions", - "ResizeBilinearOptions", - "CallOptions", - "ReshapeOptions", - "SkipGramOptions", - "SpaceToDepthOptions", - "EmbeddingLookupSparseOptions", - "MulOptions", - "PadOptions", - "GatherOptions", - "BatchToSpaceNDOptions", - "SpaceToBatchNDOptions", - "TransposeOptions", - "ReducerOptions", - "SubOptions", - "DivOptions", - "SqueezeOptions", - "SequenceRNNOptions", - "StridedSliceOptions", - "ExpOptions", - "TopKV2Options", - "SplitOptions", - "LogSoftmaxOptions", - "CastOptions", - "DequantizeOptions", - "MaximumMinimumOptions", - "ArgMaxOptions", - "LessOptions", - "NegOptions", - "PadV2Options", - "GreaterOptions", - "GreaterEqualOptions", - "LessEqualOptions", - "SelectOptions", - "SliceOptions", - "TransposeConvOptions", - "SparseToDenseOptions", - "TileOptions", - "ExpandDimsOptions", - "EqualOptions", - "NotEqualOptions", - "ShapeOptions", - "PowOptions", - "ArgMinOptions", - "FakeQuantOptions", - "PackOptions", - "LogicalOrOptions", - "OneHotOptions", - "LogicalAndOptions", - "LogicalNotOptions", - "UnpackOptions", - "FloorDivOptions", - "SquareOptions", - "ZerosLikeOptions", - "FillOptions", - "BidirectionalSequenceLSTMOptions", - "BidirectionalSequenceRNNOptions", - "UnidirectionalSequenceLSTMOptions", - "FloorModOptions", - "RangeOptions", - "ResizeNearestNeighborOptions", - "LeakyReluOptions", - "SquaredDifferenceOptions", - "MirrorPadOptions", - "AbsOptions", - "SplitVOptions", - "UniqueOptions", - "ReverseV2Options", - "AddNOptions", - "GatherNdOptions", - "CosOptions", - "WhereOptions", - "RankOptions", - "ReverseSequenceOptions", - "MatrixDiagOptions", - "QuantizeOptions", - "MatrixSetDiagOptions", - "HardSwishOptions", - "IfOptions", - "WhileOptions", - "DepthToSpaceOptions", - "NonMaxSuppressionV4Options", - "NonMaxSuppressionV5Options", - "ScatterNdOptions", - "SelectV2Options", - "DensifyOptions", - "SegmentSumOptions", - "BatchMatMulOptions", - "CumsumOptions", - "CallOnceOptions", - "BroadcastToOptions", - "Rfft2dOptions", - "Conv3DOptions", - "HashtableOptions", - "HashtableFindOptions", - "HashtableImportOptions", - "HashtableSizeOptions", - "VarHandleOptions", - "ReadVariableOptions", - "AssignVariableOptions", - "RandomOptions", - "BucketizeOptions", - "GeluOptions", - "DynamicUpdateSliceOptions", - "UnsortedSegmentProdOptions", - "UnsortedSegmentMaxOptions", - "UnsortedSegmentMinOptions", - "UnsortedSegmentSumOptions", - "ATan2Options", - "SignOptions", - "BitcastOptions", - "BitwiseXorOptions", - "RightShiftOptions", - nullptr - }; - return names; -} - -inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { - if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_RightShiftOptions)) return ""; - const size_t index = static_cast(e); - return EnumNamesBuiltinOptions()[index]; -} - -template struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NONE; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SignOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BitcastOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BitwiseXorOptions; -}; - -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RightShiftOptions; -}; - -template struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NONE; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CallOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MulOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SubOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DivOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CastOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NegOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_TileOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PowOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FillOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CosOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RankOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_IfOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_SignOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BitcastOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_BitwiseXorOptions; -}; - -template<> struct BuiltinOptionsUnionTraits { - static const BuiltinOptions enum_value = BuiltinOptions_RightShiftOptions; -}; - -struct BuiltinOptionsUnion { - BuiltinOptions type; - void *value; - - BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {} - BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT : - type(BuiltinOptions_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - BuiltinOptionsUnion(const BuiltinOptionsUnion &); - BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u) - { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~BuiltinOptionsUnion() { Reset(); } - - void Reset(); - - template - void Set(T&& val) { - typedef typename std::remove_reference::type RT; - Reset(); - type = BuiltinOptionsUnionTraits::enum_value; - if (type != BuiltinOptions_NONE) { - value = new RT(std::forward(val)); - } - } - - static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite_micro::Conv2DOptionsT *AsConv2DOptions() { - return type == BuiltinOptions_Conv2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Conv2DOptionsT *AsConv2DOptions() const { - return type == BuiltinOptions_Conv2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() { - return type == BuiltinOptions_DepthwiseConv2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const { - return type == BuiltinOptions_DepthwiseConv2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() { - return type == BuiltinOptions_ConcatEmbeddingsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const { - return type == BuiltinOptions_ConcatEmbeddingsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LSHProjectionOptionsT *AsLSHProjectionOptions() { - return type == BuiltinOptions_LSHProjectionOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LSHProjectionOptionsT *AsLSHProjectionOptions() const { - return type == BuiltinOptions_LSHProjectionOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::Pool2DOptionsT *AsPool2DOptions() { - return type == BuiltinOptions_Pool2DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Pool2DOptionsT *AsPool2DOptions() const { - return type == BuiltinOptions_Pool2DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SVDFOptionsT *AsSVDFOptions() { - return type == BuiltinOptions_SVDFOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SVDFOptionsT *AsSVDFOptions() const { - return type == BuiltinOptions_SVDFOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::RNNOptionsT *AsRNNOptions() { - return type == BuiltinOptions_RNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::RNNOptionsT *AsRNNOptions() const { - return type == BuiltinOptions_RNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::FullyConnectedOptionsT *AsFullyConnectedOptions() { - return type == BuiltinOptions_FullyConnectedOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::FullyConnectedOptionsT *AsFullyConnectedOptions() const { - return type == BuiltinOptions_FullyConnectedOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SoftmaxOptionsT *AsSoftmaxOptions() { - return type == BuiltinOptions_SoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SoftmaxOptionsT *AsSoftmaxOptions() const { - return type == BuiltinOptions_SoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ConcatenationOptionsT *AsConcatenationOptions() { - return type == BuiltinOptions_ConcatenationOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ConcatenationOptionsT *AsConcatenationOptions() const { - return type == BuiltinOptions_ConcatenationOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::AddOptionsT *AsAddOptions() { - return type == BuiltinOptions_AddOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::AddOptionsT *AsAddOptions() const { - return type == BuiltinOptions_AddOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::L2NormOptionsT *AsL2NormOptions() { - return type == BuiltinOptions_L2NormOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::L2NormOptionsT *AsL2NormOptions() const { - return type == BuiltinOptions_L2NormOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() { - return type == BuiltinOptions_LocalResponseNormalizationOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const { - return type == BuiltinOptions_LocalResponseNormalizationOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LSTMOptionsT *AsLSTMOptions() { - return type == BuiltinOptions_LSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LSTMOptionsT *AsLSTMOptions() const { - return type == BuiltinOptions_LSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ResizeBilinearOptionsT *AsResizeBilinearOptions() { - return type == BuiltinOptions_ResizeBilinearOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ResizeBilinearOptionsT *AsResizeBilinearOptions() const { - return type == BuiltinOptions_ResizeBilinearOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::CallOptionsT *AsCallOptions() { - return type == BuiltinOptions_CallOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CallOptionsT *AsCallOptions() const { - return type == BuiltinOptions_CallOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReshapeOptionsT *AsReshapeOptions() { - return type == BuiltinOptions_ReshapeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReshapeOptionsT *AsReshapeOptions() const { - return type == BuiltinOptions_ReshapeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SkipGramOptionsT *AsSkipGramOptions() { - return type == BuiltinOptions_SkipGramOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SkipGramOptionsT *AsSkipGramOptions() const { - return type == BuiltinOptions_SkipGramOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SpaceToDepthOptionsT *AsSpaceToDepthOptions() { - return type == BuiltinOptions_SpaceToDepthOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SpaceToDepthOptionsT *AsSpaceToDepthOptions() const { - return type == BuiltinOptions_SpaceToDepthOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() { - return type == BuiltinOptions_EmbeddingLookupSparseOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const { - return type == BuiltinOptions_EmbeddingLookupSparseOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::MulOptionsT *AsMulOptions() { - return type == BuiltinOptions_MulOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::MulOptionsT *AsMulOptions() const { - return type == BuiltinOptions_MulOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::PadOptionsT *AsPadOptions() { - return type == BuiltinOptions_PadOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::PadOptionsT *AsPadOptions() const { - return type == BuiltinOptions_PadOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::GatherOptionsT *AsGatherOptions() { - return type == BuiltinOptions_GatherOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::GatherOptionsT *AsGatherOptions() const { - return type == BuiltinOptions_GatherOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() { - return type == BuiltinOptions_BatchToSpaceNDOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const { - return type == BuiltinOptions_BatchToSpaceNDOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() { - return type == BuiltinOptions_SpaceToBatchNDOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const { - return type == BuiltinOptions_SpaceToBatchNDOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::TransposeOptionsT *AsTransposeOptions() { - return type == BuiltinOptions_TransposeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::TransposeOptionsT *AsTransposeOptions() const { - return type == BuiltinOptions_TransposeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReducerOptionsT *AsReducerOptions() { - return type == BuiltinOptions_ReducerOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReducerOptionsT *AsReducerOptions() const { - return type == BuiltinOptions_ReducerOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SubOptionsT *AsSubOptions() { - return type == BuiltinOptions_SubOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SubOptionsT *AsSubOptions() const { - return type == BuiltinOptions_SubOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DivOptionsT *AsDivOptions() { - return type == BuiltinOptions_DivOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DivOptionsT *AsDivOptions() const { - return type == BuiltinOptions_DivOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SqueezeOptionsT *AsSqueezeOptions() { - return type == BuiltinOptions_SqueezeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SqueezeOptionsT *AsSqueezeOptions() const { - return type == BuiltinOptions_SqueezeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SequenceRNNOptionsT *AsSequenceRNNOptions() { - return type == BuiltinOptions_SequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SequenceRNNOptionsT *AsSequenceRNNOptions() const { - return type == BuiltinOptions_SequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StridedSliceOptionsT *AsStridedSliceOptions() { - return type == BuiltinOptions_StridedSliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StridedSliceOptionsT *AsStridedSliceOptions() const { - return type == BuiltinOptions_StridedSliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ExpOptionsT *AsExpOptions() { - return type == BuiltinOptions_ExpOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ExpOptionsT *AsExpOptions() const { - return type == BuiltinOptions_ExpOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::TopKV2OptionsT *AsTopKV2Options() { - return type == BuiltinOptions_TopKV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::TopKV2OptionsT *AsTopKV2Options() const { - return type == BuiltinOptions_TopKV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SplitOptionsT *AsSplitOptions() { - return type == BuiltinOptions_SplitOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SplitOptionsT *AsSplitOptions() const { - return type == BuiltinOptions_SplitOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LogSoftmaxOptionsT *AsLogSoftmaxOptions() { - return type == BuiltinOptions_LogSoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LogSoftmaxOptionsT *AsLogSoftmaxOptions() const { - return type == BuiltinOptions_LogSoftmaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::CastOptionsT *AsCastOptions() { - return type == BuiltinOptions_CastOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CastOptionsT *AsCastOptions() const { - return type == BuiltinOptions_CastOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DequantizeOptionsT *AsDequantizeOptions() { - return type == BuiltinOptions_DequantizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DequantizeOptionsT *AsDequantizeOptions() const { - return type == BuiltinOptions_DequantizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::MaximumMinimumOptionsT *AsMaximumMinimumOptions() { - return type == BuiltinOptions_MaximumMinimumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::MaximumMinimumOptionsT *AsMaximumMinimumOptions() const { - return type == BuiltinOptions_MaximumMinimumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ArgMaxOptionsT *AsArgMaxOptions() { - return type == BuiltinOptions_ArgMaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ArgMaxOptionsT *AsArgMaxOptions() const { - return type == BuiltinOptions_ArgMaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LessOptionsT *AsLessOptions() { - return type == BuiltinOptions_LessOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LessOptionsT *AsLessOptions() const { - return type == BuiltinOptions_LessOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::NegOptionsT *AsNegOptions() { - return type == BuiltinOptions_NegOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::NegOptionsT *AsNegOptions() const { - return type == BuiltinOptions_NegOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::PadV2OptionsT *AsPadV2Options() { - return type == BuiltinOptions_PadV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::PadV2OptionsT *AsPadV2Options() const { - return type == BuiltinOptions_PadV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::GreaterOptionsT *AsGreaterOptions() { - return type == BuiltinOptions_GreaterOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::GreaterOptionsT *AsGreaterOptions() const { - return type == BuiltinOptions_GreaterOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::GreaterEqualOptionsT *AsGreaterEqualOptions() { - return type == BuiltinOptions_GreaterEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::GreaterEqualOptionsT *AsGreaterEqualOptions() const { - return type == BuiltinOptions_GreaterEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LessEqualOptionsT *AsLessEqualOptions() { - return type == BuiltinOptions_LessEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LessEqualOptionsT *AsLessEqualOptions() const { - return type == BuiltinOptions_LessEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SelectOptionsT *AsSelectOptions() { - return type == BuiltinOptions_SelectOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SelectOptionsT *AsSelectOptions() const { - return type == BuiltinOptions_SelectOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SliceOptionsT *AsSliceOptions() { - return type == BuiltinOptions_SliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SliceOptionsT *AsSliceOptions() const { - return type == BuiltinOptions_SliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::TransposeConvOptionsT *AsTransposeConvOptions() { - return type == BuiltinOptions_TransposeConvOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::TransposeConvOptionsT *AsTransposeConvOptions() const { - return type == BuiltinOptions_TransposeConvOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SparseToDenseOptionsT *AsSparseToDenseOptions() { - return type == BuiltinOptions_SparseToDenseOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SparseToDenseOptionsT *AsSparseToDenseOptions() const { - return type == BuiltinOptions_SparseToDenseOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::TileOptionsT *AsTileOptions() { - return type == BuiltinOptions_TileOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::TileOptionsT *AsTileOptions() const { - return type == BuiltinOptions_TileOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ExpandDimsOptionsT *AsExpandDimsOptions() { - return type == BuiltinOptions_ExpandDimsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ExpandDimsOptionsT *AsExpandDimsOptions() const { - return type == BuiltinOptions_ExpandDimsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::EqualOptionsT *AsEqualOptions() { - return type == BuiltinOptions_EqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::EqualOptionsT *AsEqualOptions() const { - return type == BuiltinOptions_EqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::NotEqualOptionsT *AsNotEqualOptions() { - return type == BuiltinOptions_NotEqualOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::NotEqualOptionsT *AsNotEqualOptions() const { - return type == BuiltinOptions_NotEqualOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ShapeOptionsT *AsShapeOptions() { - return type == BuiltinOptions_ShapeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ShapeOptionsT *AsShapeOptions() const { - return type == BuiltinOptions_ShapeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::PowOptionsT *AsPowOptions() { - return type == BuiltinOptions_PowOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::PowOptionsT *AsPowOptions() const { - return type == BuiltinOptions_PowOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ArgMinOptionsT *AsArgMinOptions() { - return type == BuiltinOptions_ArgMinOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ArgMinOptionsT *AsArgMinOptions() const { - return type == BuiltinOptions_ArgMinOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::FakeQuantOptionsT *AsFakeQuantOptions() { - return type == BuiltinOptions_FakeQuantOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::FakeQuantOptionsT *AsFakeQuantOptions() const { - return type == BuiltinOptions_FakeQuantOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::PackOptionsT *AsPackOptions() { - return type == BuiltinOptions_PackOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::PackOptionsT *AsPackOptions() const { - return type == BuiltinOptions_PackOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LogicalOrOptionsT *AsLogicalOrOptions() { - return type == BuiltinOptions_LogicalOrOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LogicalOrOptionsT *AsLogicalOrOptions() const { - return type == BuiltinOptions_LogicalOrOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::OneHotOptionsT *AsOneHotOptions() { - return type == BuiltinOptions_OneHotOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::OneHotOptionsT *AsOneHotOptions() const { - return type == BuiltinOptions_OneHotOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LogicalAndOptionsT *AsLogicalAndOptions() { - return type == BuiltinOptions_LogicalAndOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LogicalAndOptionsT *AsLogicalAndOptions() const { - return type == BuiltinOptions_LogicalAndOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LogicalNotOptionsT *AsLogicalNotOptions() { - return type == BuiltinOptions_LogicalNotOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LogicalNotOptionsT *AsLogicalNotOptions() const { - return type == BuiltinOptions_LogicalNotOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnpackOptionsT *AsUnpackOptions() { - return type == BuiltinOptions_UnpackOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnpackOptionsT *AsUnpackOptions() const { - return type == BuiltinOptions_UnpackOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::FloorDivOptionsT *AsFloorDivOptions() { - return type == BuiltinOptions_FloorDivOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::FloorDivOptionsT *AsFloorDivOptions() const { - return type == BuiltinOptions_FloorDivOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SquareOptionsT *AsSquareOptions() { - return type == BuiltinOptions_SquareOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SquareOptionsT *AsSquareOptions() const { - return type == BuiltinOptions_SquareOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ZerosLikeOptionsT *AsZerosLikeOptions() { - return type == BuiltinOptions_ZerosLikeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ZerosLikeOptionsT *AsZerosLikeOptions() const { - return type == BuiltinOptions_ZerosLikeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::FillOptionsT *AsFillOptions() { - return type == BuiltinOptions_FillOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::FillOptionsT *AsFillOptions() const { - return type == BuiltinOptions_FillOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() { - return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() const { - return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() { - return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() const { - return type == BuiltinOptions_BidirectionalSequenceRNNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() { - return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() const { - return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::FloorModOptionsT *AsFloorModOptions() { - return type == BuiltinOptions_FloorModOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::FloorModOptionsT *AsFloorModOptions() const { - return type == BuiltinOptions_FloorModOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::RangeOptionsT *AsRangeOptions() { - return type == BuiltinOptions_RangeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::RangeOptionsT *AsRangeOptions() const { - return type == BuiltinOptions_RangeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() { - return type == BuiltinOptions_ResizeNearestNeighborOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() const { - return type == BuiltinOptions_ResizeNearestNeighborOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::LeakyReluOptionsT *AsLeakyReluOptions() { - return type == BuiltinOptions_LeakyReluOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::LeakyReluOptionsT *AsLeakyReluOptions() const { - return type == BuiltinOptions_LeakyReluOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() { - return type == BuiltinOptions_SquaredDifferenceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() const { - return type == BuiltinOptions_SquaredDifferenceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::MirrorPadOptionsT *AsMirrorPadOptions() { - return type == BuiltinOptions_MirrorPadOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::MirrorPadOptionsT *AsMirrorPadOptions() const { - return type == BuiltinOptions_MirrorPadOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::AbsOptionsT *AsAbsOptions() { - return type == BuiltinOptions_AbsOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::AbsOptionsT *AsAbsOptions() const { - return type == BuiltinOptions_AbsOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SplitVOptionsT *AsSplitVOptions() { - return type == BuiltinOptions_SplitVOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SplitVOptionsT *AsSplitVOptions() const { - return type == BuiltinOptions_SplitVOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UniqueOptionsT *AsUniqueOptions() { - return type == BuiltinOptions_UniqueOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UniqueOptionsT *AsUniqueOptions() const { - return type == BuiltinOptions_UniqueOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReverseV2OptionsT *AsReverseV2Options() { - return type == BuiltinOptions_ReverseV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReverseV2OptionsT *AsReverseV2Options() const { - return type == BuiltinOptions_ReverseV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::AddNOptionsT *AsAddNOptions() { - return type == BuiltinOptions_AddNOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::AddNOptionsT *AsAddNOptions() const { - return type == BuiltinOptions_AddNOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::GatherNdOptionsT *AsGatherNdOptions() { - return type == BuiltinOptions_GatherNdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::GatherNdOptionsT *AsGatherNdOptions() const { - return type == BuiltinOptions_GatherNdOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::CosOptionsT *AsCosOptions() { - return type == BuiltinOptions_CosOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CosOptionsT *AsCosOptions() const { - return type == BuiltinOptions_CosOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::WhereOptionsT *AsWhereOptions() { - return type == BuiltinOptions_WhereOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::WhereOptionsT *AsWhereOptions() const { - return type == BuiltinOptions_WhereOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::RankOptionsT *AsRankOptions() { - return type == BuiltinOptions_RankOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::RankOptionsT *AsRankOptions() const { - return type == BuiltinOptions_RankOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReverseSequenceOptionsT *AsReverseSequenceOptions() { - return type == BuiltinOptions_ReverseSequenceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReverseSequenceOptionsT *AsReverseSequenceOptions() const { - return type == BuiltinOptions_ReverseSequenceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::MatrixDiagOptionsT *AsMatrixDiagOptions() { - return type == BuiltinOptions_MatrixDiagOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::MatrixDiagOptionsT *AsMatrixDiagOptions() const { - return type == BuiltinOptions_MatrixDiagOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::QuantizeOptionsT *AsQuantizeOptions() { - return type == BuiltinOptions_QuantizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::QuantizeOptionsT *AsQuantizeOptions() const { - return type == BuiltinOptions_QuantizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() { - return type == BuiltinOptions_MatrixSetDiagOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() const { - return type == BuiltinOptions_MatrixSetDiagOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::HardSwishOptionsT *AsHardSwishOptions() { - return type == BuiltinOptions_HardSwishOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::HardSwishOptionsT *AsHardSwishOptions() const { - return type == BuiltinOptions_HardSwishOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::IfOptionsT *AsIfOptions() { - return type == BuiltinOptions_IfOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::IfOptionsT *AsIfOptions() const { - return type == BuiltinOptions_IfOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::WhileOptionsT *AsWhileOptions() { - return type == BuiltinOptions_WhileOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::WhileOptionsT *AsWhileOptions() const { - return type == BuiltinOptions_WhileOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DepthToSpaceOptionsT *AsDepthToSpaceOptions() { - return type == BuiltinOptions_DepthToSpaceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DepthToSpaceOptionsT *AsDepthToSpaceOptions() const { - return type == BuiltinOptions_DepthToSpaceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() { - return type == BuiltinOptions_NonMaxSuppressionV4Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() const { - return type == BuiltinOptions_NonMaxSuppressionV4Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() { - return type == BuiltinOptions_NonMaxSuppressionV5Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() const { - return type == BuiltinOptions_NonMaxSuppressionV5Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ScatterNdOptionsT *AsScatterNdOptions() { - return type == BuiltinOptions_ScatterNdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ScatterNdOptionsT *AsScatterNdOptions() const { - return type == BuiltinOptions_ScatterNdOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SelectV2OptionsT *AsSelectV2Options() { - return type == BuiltinOptions_SelectV2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SelectV2OptionsT *AsSelectV2Options() const { - return type == BuiltinOptions_SelectV2Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DensifyOptionsT *AsDensifyOptions() { - return type == BuiltinOptions_DensifyOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DensifyOptionsT *AsDensifyOptions() const { - return type == BuiltinOptions_DensifyOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SegmentSumOptionsT *AsSegmentSumOptions() { - return type == BuiltinOptions_SegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SegmentSumOptionsT *AsSegmentSumOptions() const { - return type == BuiltinOptions_SegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BatchMatMulOptionsT *AsBatchMatMulOptions() { - return type == BuiltinOptions_BatchMatMulOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BatchMatMulOptionsT *AsBatchMatMulOptions() const { - return type == BuiltinOptions_BatchMatMulOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::CumsumOptionsT *AsCumsumOptions() { - return type == BuiltinOptions_CumsumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CumsumOptionsT *AsCumsumOptions() const { - return type == BuiltinOptions_CumsumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::CallOnceOptionsT *AsCallOnceOptions() { - return type == BuiltinOptions_CallOnceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::CallOnceOptionsT *AsCallOnceOptions() const { - return type == BuiltinOptions_CallOnceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BroadcastToOptionsT *AsBroadcastToOptions() { - return type == BuiltinOptions_BroadcastToOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BroadcastToOptionsT *AsBroadcastToOptions() const { - return type == BuiltinOptions_BroadcastToOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::Rfft2dOptionsT *AsRfft2dOptions() { - return type == BuiltinOptions_Rfft2dOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Rfft2dOptionsT *AsRfft2dOptions() const { - return type == BuiltinOptions_Rfft2dOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::Conv3DOptionsT *AsConv3DOptions() { - return type == BuiltinOptions_Conv3DOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::Conv3DOptionsT *AsConv3DOptions() const { - return type == BuiltinOptions_Conv3DOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::HashtableOptionsT *AsHashtableOptions() { - return type == BuiltinOptions_HashtableOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::HashtableOptionsT *AsHashtableOptions() const { - return type == BuiltinOptions_HashtableOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::HashtableFindOptionsT *AsHashtableFindOptions() { - return type == BuiltinOptions_HashtableFindOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::HashtableFindOptionsT *AsHashtableFindOptions() const { - return type == BuiltinOptions_HashtableFindOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::HashtableImportOptionsT *AsHashtableImportOptions() { - return type == BuiltinOptions_HashtableImportOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::HashtableImportOptionsT *AsHashtableImportOptions() const { - return type == BuiltinOptions_HashtableImportOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::HashtableSizeOptionsT *AsHashtableSizeOptions() { - return type == BuiltinOptions_HashtableSizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::HashtableSizeOptionsT *AsHashtableSizeOptions() const { - return type == BuiltinOptions_HashtableSizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::VarHandleOptionsT *AsVarHandleOptions() { - return type == BuiltinOptions_VarHandleOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::VarHandleOptionsT *AsVarHandleOptions() const { - return type == BuiltinOptions_VarHandleOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReadVariableOptionsT *AsReadVariableOptions() { - return type == BuiltinOptions_ReadVariableOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReadVariableOptionsT *AsReadVariableOptions() const { - return type == BuiltinOptions_ReadVariableOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::AssignVariableOptionsT *AsAssignVariableOptions() { - return type == BuiltinOptions_AssignVariableOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::AssignVariableOptionsT *AsAssignVariableOptions() const { - return type == BuiltinOptions_AssignVariableOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::RandomOptionsT *AsRandomOptions() { - return type == BuiltinOptions_RandomOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::RandomOptionsT *AsRandomOptions() const { - return type == BuiltinOptions_RandomOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BucketizeOptionsT *AsBucketizeOptions() { - return type == BuiltinOptions_BucketizeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BucketizeOptionsT *AsBucketizeOptions() const { - return type == BuiltinOptions_BucketizeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::GeluOptionsT *AsGeluOptions() { - return type == BuiltinOptions_GeluOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::GeluOptionsT *AsGeluOptions() const { - return type == BuiltinOptions_GeluOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() { - return type == BuiltinOptions_DynamicUpdateSliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() const { - return type == BuiltinOptions_DynamicUpdateSliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() { - return type == BuiltinOptions_UnsortedSegmentProdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() const { - return type == BuiltinOptions_UnsortedSegmentProdOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() { - return type == BuiltinOptions_UnsortedSegmentMaxOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() const { - return type == BuiltinOptions_UnsortedSegmentMaxOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() { - return type == BuiltinOptions_UnsortedSegmentMinOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() const { - return type == BuiltinOptions_UnsortedSegmentMinOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() { - return type == BuiltinOptions_UnsortedSegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() const { - return type == BuiltinOptions_UnsortedSegmentSumOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ATan2OptionsT *AsATan2Options() { - return type == BuiltinOptions_ATan2Options ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ATan2OptionsT *AsATan2Options() const { - return type == BuiltinOptions_ATan2Options ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::SignOptionsT *AsSignOptions() { - return type == BuiltinOptions_SignOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::SignOptionsT *AsSignOptions() const { - return type == BuiltinOptions_SignOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BitcastOptionsT *AsBitcastOptions() { - return type == BuiltinOptions_BitcastOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BitcastOptionsT *AsBitcastOptions() const { - return type == BuiltinOptions_BitcastOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::BitwiseXorOptionsT *AsBitwiseXorOptions() { - return type == BuiltinOptions_BitwiseXorOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::BitwiseXorOptionsT *AsBitwiseXorOptions() const { - return type == BuiltinOptions_BitwiseXorOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::RightShiftOptionsT *AsRightShiftOptions() { - return type == BuiltinOptions_RightShiftOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::RightShiftOptionsT *AsRightShiftOptions() const { - return type == BuiltinOptions_RightShiftOptions ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); -bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum BuiltinOptions2 : uint8_t { - BuiltinOptions2_NONE = 0, - BuiltinOptions2_StablehloConcatenateOptions = 1, - BuiltinOptions2_StablehloBroadcastInDimOptions = 2, - BuiltinOptions2_StablehloSliceOptions = 3, - BuiltinOptions2_StablehloConvolutionOptions = 4, - BuiltinOptions2_StablehloCustomCallOptions = 5, - BuiltinOptions2_StablehloReduceOptions = 6, - BuiltinOptions2_StablehloScatterOptions = 7, - BuiltinOptions2_StablehloCompareOptions = 8, - BuiltinOptions2_StablehloDynamicSliceOptions = 9, - BuiltinOptions2_StablehloPadOptions = 10, - BuiltinOptions2_StablehloIotaOptions = 11, - BuiltinOptions2_StablehloDotGeneralOptions = 12, - BuiltinOptions2_StablehloReduceWindowOptions = 13, - BuiltinOptions2_StablehloSortOptions = 14, - BuiltinOptions2_StablehloWhileOptions = 15, - BuiltinOptions2_StablehloGatherOptions = 16, - BuiltinOptions2_StablehloTransposeOptions = 17, - BuiltinOptions2_DilateOptions = 18, - BuiltinOptions2_StablehloRngBitGeneratorOptions = 19, - BuiltinOptions2_ReduceWindowOptions = 20, - BuiltinOptions2_MIN = BuiltinOptions2_NONE, - BuiltinOptions2_MAX = BuiltinOptions2_ReduceWindowOptions -}; - -inline const BuiltinOptions2 (&EnumValuesBuiltinOptions2())[21] { - static const BuiltinOptions2 values[] = { - BuiltinOptions2_NONE, - BuiltinOptions2_StablehloConcatenateOptions, - BuiltinOptions2_StablehloBroadcastInDimOptions, - BuiltinOptions2_StablehloSliceOptions, - BuiltinOptions2_StablehloConvolutionOptions, - BuiltinOptions2_StablehloCustomCallOptions, - BuiltinOptions2_StablehloReduceOptions, - BuiltinOptions2_StablehloScatterOptions, - BuiltinOptions2_StablehloCompareOptions, - BuiltinOptions2_StablehloDynamicSliceOptions, - BuiltinOptions2_StablehloPadOptions, - BuiltinOptions2_StablehloIotaOptions, - BuiltinOptions2_StablehloDotGeneralOptions, - BuiltinOptions2_StablehloReduceWindowOptions, - BuiltinOptions2_StablehloSortOptions, - BuiltinOptions2_StablehloWhileOptions, - BuiltinOptions2_StablehloGatherOptions, - BuiltinOptions2_StablehloTransposeOptions, - BuiltinOptions2_DilateOptions, - BuiltinOptions2_StablehloRngBitGeneratorOptions, - BuiltinOptions2_ReduceWindowOptions - }; - return values; -} - -inline const char * const *EnumNamesBuiltinOptions2() { - static const char * const names[22] = { - "NONE", - "StablehloConcatenateOptions", - "StablehloBroadcastInDimOptions", - "StablehloSliceOptions", - "StablehloConvolutionOptions", - "StablehloCustomCallOptions", - "StablehloReduceOptions", - "StablehloScatterOptions", - "StablehloCompareOptions", - "StablehloDynamicSliceOptions", - "StablehloPadOptions", - "StablehloIotaOptions", - "StablehloDotGeneralOptions", - "StablehloReduceWindowOptions", - "StablehloSortOptions", - "StablehloWhileOptions", - "StablehloGatherOptions", - "StablehloTransposeOptions", - "DilateOptions", - "StablehloRngBitGeneratorOptions", - "ReduceWindowOptions", - nullptr - }; - return names; -} - -inline const char *EnumNameBuiltinOptions2(BuiltinOptions2 e) { - if (flatbuffers::IsOutRange(e, BuiltinOptions2_NONE, BuiltinOptions2_ReduceWindowOptions)) return ""; - const size_t index = static_cast(e); - return EnumNamesBuiltinOptions2()[index]; -} - -template struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions; -}; - -template<> struct BuiltinOptions2Traits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions; -}; - -template struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_NONE; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConcatenateOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloBroadcastInDimOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSliceOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloConvolutionOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCustomCallOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloScatterOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloCompareOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDynamicSliceOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloPadOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloIotaOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloDotGeneralOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloReduceWindowOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloSortOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloWhileOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloGatherOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloTransposeOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_DilateOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_StablehloRngBitGeneratorOptions; -}; - -template<> struct BuiltinOptions2UnionTraits { - static const BuiltinOptions2 enum_value = BuiltinOptions2_ReduceWindowOptions; -}; - -struct BuiltinOptions2Union { - BuiltinOptions2 type; - void *value; - - BuiltinOptions2Union() : type(BuiltinOptions2_NONE), value(nullptr) {} - BuiltinOptions2Union(BuiltinOptions2Union&& u) FLATBUFFERS_NOEXCEPT : - type(BuiltinOptions2_NONE), value(nullptr) - { std::swap(type, u.type); std::swap(value, u.value); } - BuiltinOptions2Union(const BuiltinOptions2Union &); - BuiltinOptions2Union &operator=(const BuiltinOptions2Union &u) - { BuiltinOptions2Union t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; } - BuiltinOptions2Union &operator=(BuiltinOptions2Union &&u) FLATBUFFERS_NOEXCEPT - { std::swap(type, u.type); std::swap(value, u.value); return *this; } - ~BuiltinOptions2Union() { Reset(); } - - void Reset(); - - template - void Set(T&& val) { - typedef typename std::remove_reference::type RT; - Reset(); - type = BuiltinOptions2UnionTraits::enum_value; - if (type != BuiltinOptions2_NONE) { - value = new RT(std::forward(val)); - } - } - - static void *UnPack(const void *obj, BuiltinOptions2 type, const flatbuffers::resolver_function_t *resolver); - flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const; - - tflite_micro::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() { - return type == BuiltinOptions2_StablehloConcatenateOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloConcatenateOptionsT *AsStablehloConcatenateOptions() const { - return type == BuiltinOptions2_StablehloConcatenateOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() { - return type == BuiltinOptions2_StablehloBroadcastInDimOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloBroadcastInDimOptionsT *AsStablehloBroadcastInDimOptions() const { - return type == BuiltinOptions2_StablehloBroadcastInDimOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloSliceOptionsT *AsStablehloSliceOptions() { - return type == BuiltinOptions2_StablehloSliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloSliceOptionsT *AsStablehloSliceOptions() const { - return type == BuiltinOptions2_StablehloSliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() { - return type == BuiltinOptions2_StablehloConvolutionOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloConvolutionOptionsT *AsStablehloConvolutionOptions() const { - return type == BuiltinOptions2_StablehloConvolutionOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() { - return type == BuiltinOptions2_StablehloCustomCallOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloCustomCallOptionsT *AsStablehloCustomCallOptions() const { - return type == BuiltinOptions2_StablehloCustomCallOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloReduceOptionsT *AsStablehloReduceOptions() { - return type == BuiltinOptions2_StablehloReduceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloReduceOptionsT *AsStablehloReduceOptions() const { - return type == BuiltinOptions2_StablehloReduceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloScatterOptionsT *AsStablehloScatterOptions() { - return type == BuiltinOptions2_StablehloScatterOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloScatterOptionsT *AsStablehloScatterOptions() const { - return type == BuiltinOptions2_StablehloScatterOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloCompareOptionsT *AsStablehloCompareOptions() { - return type == BuiltinOptions2_StablehloCompareOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloCompareOptionsT *AsStablehloCompareOptions() const { - return type == BuiltinOptions2_StablehloCompareOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() { - return type == BuiltinOptions2_StablehloDynamicSliceOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloDynamicSliceOptionsT *AsStablehloDynamicSliceOptions() const { - return type == BuiltinOptions2_StablehloDynamicSliceOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloPadOptionsT *AsStablehloPadOptions() { - return type == BuiltinOptions2_StablehloPadOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloPadOptionsT *AsStablehloPadOptions() const { - return type == BuiltinOptions2_StablehloPadOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloIotaOptionsT *AsStablehloIotaOptions() { - return type == BuiltinOptions2_StablehloIotaOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloIotaOptionsT *AsStablehloIotaOptions() const { - return type == BuiltinOptions2_StablehloIotaOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() { - return type == BuiltinOptions2_StablehloDotGeneralOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloDotGeneralOptionsT *AsStablehloDotGeneralOptions() const { - return type == BuiltinOptions2_StablehloDotGeneralOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() { - return type == BuiltinOptions2_StablehloReduceWindowOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloReduceWindowOptionsT *AsStablehloReduceWindowOptions() const { - return type == BuiltinOptions2_StablehloReduceWindowOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloSortOptionsT *AsStablehloSortOptions() { - return type == BuiltinOptions2_StablehloSortOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloSortOptionsT *AsStablehloSortOptions() const { - return type == BuiltinOptions2_StablehloSortOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloWhileOptionsT *AsStablehloWhileOptions() { - return type == BuiltinOptions2_StablehloWhileOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloWhileOptionsT *AsStablehloWhileOptions() const { - return type == BuiltinOptions2_StablehloWhileOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloGatherOptionsT *AsStablehloGatherOptions() { - return type == BuiltinOptions2_StablehloGatherOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloGatherOptionsT *AsStablehloGatherOptions() const { - return type == BuiltinOptions2_StablehloGatherOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloTransposeOptionsT *AsStablehloTransposeOptions() { - return type == BuiltinOptions2_StablehloTransposeOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloTransposeOptionsT *AsStablehloTransposeOptions() const { - return type == BuiltinOptions2_StablehloTransposeOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::DilateOptionsT *AsDilateOptions() { - return type == BuiltinOptions2_DilateOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::DilateOptionsT *AsDilateOptions() const { - return type == BuiltinOptions2_DilateOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() { - return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::StablehloRngBitGeneratorOptionsT *AsStablehloRngBitGeneratorOptions() const { - return type == BuiltinOptions2_StablehloRngBitGeneratorOptions ? - reinterpret_cast(value) : nullptr; - } - tflite_micro::ReduceWindowOptionsT *AsReduceWindowOptions() { - return type == BuiltinOptions2_ReduceWindowOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite_micro::ReduceWindowOptionsT *AsReduceWindowOptions() const { - return type == BuiltinOptions2_ReduceWindowOptions ? - reinterpret_cast(value) : nullptr; - } -}; - -bool VerifyBuiltinOptions2(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type); -bool VerifyBuiltinOptions2Vector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types); - -enum StablehloPrecisionConfig : uint32_t { - StablehloPrecisionConfig_DEFAULT = 0, - StablehloPrecisionConfig_HIGH = 1, - StablehloPrecisionConfig_HIGHEST = 2, - StablehloPrecisionConfig_MIN = StablehloPrecisionConfig_DEFAULT, - StablehloPrecisionConfig_MAX = StablehloPrecisionConfig_HIGHEST -}; - -inline const StablehloPrecisionConfig (&EnumValuesStablehloPrecisionConfig())[3] { - static const StablehloPrecisionConfig values[] = { - StablehloPrecisionConfig_DEFAULT, - StablehloPrecisionConfig_HIGH, - StablehloPrecisionConfig_HIGHEST - }; - return values; -} - -inline const char * const *EnumNamesStablehloPrecisionConfig() { - static const char * const names[4] = { - "DEFAULT", - "HIGH", - "HIGHEST", - nullptr - }; - return names; -} - -inline const char *EnumNameStablehloPrecisionConfig(StablehloPrecisionConfig e) { - if (flatbuffers::IsOutRange(e, StablehloPrecisionConfig_DEFAULT, StablehloPrecisionConfig_HIGHEST)) return ""; - const size_t index = static_cast(e); - return EnumNamesStablehloPrecisionConfig()[index]; -} - -enum StablehloComparisonDirection : uint32_t { - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ = 0, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE = 1, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE = 2, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT = 3, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE = 4, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT = 5, - StablehloComparisonDirection_MIN = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, - StablehloComparisonDirection_MAX = StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT -}; - -inline const StablehloComparisonDirection (&EnumValuesStablehloComparisonDirection())[6] { - static const StablehloComparisonDirection values[] = { - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_NE, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GE, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_GT, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LE, - StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT - }; - return values; -} - -inline const char * const *EnumNamesStablehloComparisonDirection() { - static const char * const names[7] = { - "STABLEHLO_COMPARISON_DIRECTION_EQ", - "STABLEHLO_COMPARISON_DIRECTION_NE", - "STABLEHLO_COMPARISON_DIRECTION_GE", - "STABLEHLO_COMPARISON_DIRECTION_GT", - "STABLEHLO_COMPARISON_DIRECTION_LE", - "STABLEHLO_COMPARISON_DIRECTION_LT", - nullptr - }; - return names; -} - -inline const char *EnumNameStablehloComparisonDirection(StablehloComparisonDirection e) { - if (flatbuffers::IsOutRange(e, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_LT)) return ""; - const size_t index = static_cast(e); - return EnumNamesStablehloComparisonDirection()[index]; -} - -enum StablehloComparisonType : uint32_t { - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE = 0, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT = 1, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER = 2, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED = 3, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED = 4, - StablehloComparisonType_MIN = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, - StablehloComparisonType_MAX = StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED -}; - -inline const StablehloComparisonType (&EnumValuesStablehloComparisonType())[5] { - static const StablehloComparisonType values[] = { - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_SIGNED, - StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED - }; - return values; -} - -inline const char * const *EnumNamesStablehloComparisonType() { - static const char * const names[6] = { - "STABLEHLO_COMPARISON_TYPE_NOTYPE", - "STABLEHLO_COMPARISON_TYPE_FLOAT", - "STABLEHLO_COMPARISON_TYPE_FLOAT_TOTAL_ORDER", - "STABLEHLO_COMPARISON_TYPE_SIGNED", - "STABLEHLO_COMPARISON_TYPE_UNSIGNED", - nullptr - }; - return names; -} - -inline const char *EnumNameStablehloComparisonType(StablehloComparisonType e) { - if (flatbuffers::IsOutRange(e, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE, StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_UNSIGNED)) return ""; - const size_t index = static_cast(e); - return EnumNamesStablehloComparisonType()[index]; -} - -enum RngAlgorithm : int8_t { - RngAlgorithm_DEFAULT = 0, - RngAlgorithm_PHILOX = 1, - RngAlgorithm_THREEFRY = 2, - RngAlgorithm_MIN = RngAlgorithm_DEFAULT, - RngAlgorithm_MAX = RngAlgorithm_THREEFRY -}; - -inline const RngAlgorithm (&EnumValuesRngAlgorithm())[3] { - static const RngAlgorithm values[] = { - RngAlgorithm_DEFAULT, - RngAlgorithm_PHILOX, - RngAlgorithm_THREEFRY - }; - return values; -} - -inline const char * const *EnumNamesRngAlgorithm() { - static const char * const names[4] = { - "DEFAULT", - "PHILOX", - "THREEFRY", - nullptr - }; - return names; -} - -inline const char *EnumNameRngAlgorithm(RngAlgorithm e) { - if (flatbuffers::IsOutRange(e, RngAlgorithm_DEFAULT, RngAlgorithm_THREEFRY)) return ""; - const size_t index = static_cast(e); - return EnumNamesRngAlgorithm()[index]; -} - -enum Padding : int8_t { - Padding_SAME = 0, - Padding_VALID = 1, - Padding_MIN = Padding_SAME, - Padding_MAX = Padding_VALID -}; - -inline const Padding (&EnumValuesPadding())[2] { - static const Padding values[] = { - Padding_SAME, - Padding_VALID - }; - return values; -} - -inline const char * const *EnumNamesPadding() { - static const char * const names[3] = { - "SAME", - "VALID", - nullptr - }; - return names; -} - -inline const char *EnumNamePadding(Padding e) { - if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return ""; - const size_t index = static_cast(e); - return EnumNamesPadding()[index]; -} - -enum ActivationFunctionType : int8_t { - ActivationFunctionType_NONE = 0, - ActivationFunctionType_RELU = 1, - ActivationFunctionType_RELU_N1_TO_1 = 2, - ActivationFunctionType_RELU6 = 3, - ActivationFunctionType_TANH = 4, - ActivationFunctionType_SIGN_BIT = 5, - ActivationFunctionType_MIN = ActivationFunctionType_NONE, - ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT -}; - -inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] { - static const ActivationFunctionType values[] = { - ActivationFunctionType_NONE, - ActivationFunctionType_RELU, - ActivationFunctionType_RELU_N1_TO_1, - ActivationFunctionType_RELU6, - ActivationFunctionType_TANH, - ActivationFunctionType_SIGN_BIT - }; - return values; -} - -inline const char * const *EnumNamesActivationFunctionType() { - static const char * const names[7] = { - "NONE", - "RELU", - "RELU_N1_TO_1", - "RELU6", - "TANH", - "SIGN_BIT", - nullptr - }; - return names; -} - -inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) { - if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return ""; - const size_t index = static_cast(e); - return EnumNamesActivationFunctionType()[index]; -} - -enum LSHProjectionType : int8_t { - LSHProjectionType_UNKNOWN = 0, - LSHProjectionType_SPARSE = 1, - LSHProjectionType_DENSE = 2, - LSHProjectionType_MIN = LSHProjectionType_UNKNOWN, - LSHProjectionType_MAX = LSHProjectionType_DENSE -}; - -inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] { - static const LSHProjectionType values[] = { - LSHProjectionType_UNKNOWN, - LSHProjectionType_SPARSE, - LSHProjectionType_DENSE - }; - return values; -} - -inline const char * const *EnumNamesLSHProjectionType() { - static const char * const names[4] = { - "UNKNOWN", - "SPARSE", - "DENSE", - nullptr - }; - return names; -} - -inline const char *EnumNameLSHProjectionType(LSHProjectionType e) { - if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return ""; - const size_t index = static_cast(e); - return EnumNamesLSHProjectionType()[index]; -} - -enum FullyConnectedOptionsWeightsFormat : int8_t { - FullyConnectedOptionsWeightsFormat_DEFAULT = 0, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1, - FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 -}; - -inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] { - static const FullyConnectedOptionsWeightsFormat values[] = { - FullyConnectedOptionsWeightsFormat_DEFAULT, - FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 - }; - return values; -} - -inline const char * const *EnumNamesFullyConnectedOptionsWeightsFormat() { - static const char * const names[3] = { - "DEFAULT", - "SHUFFLED4x16INT8", - nullptr - }; - return names; -} - -inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) { - if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return ""; - const size_t index = static_cast(e); - return EnumNamesFullyConnectedOptionsWeightsFormat()[index]; -} - -enum LSTMKernelType : int8_t { - LSTMKernelType_FULL = 0, - LSTMKernelType_BASIC = 1, - LSTMKernelType_MIN = LSTMKernelType_FULL, - LSTMKernelType_MAX = LSTMKernelType_BASIC -}; - -inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] { - static const LSTMKernelType values[] = { - LSTMKernelType_FULL, - LSTMKernelType_BASIC - }; - return values; -} - -inline const char * const *EnumNamesLSTMKernelType() { - static const char * const names[3] = { - "FULL", - "BASIC", - nullptr - }; - return names; -} - -inline const char *EnumNameLSTMKernelType(LSTMKernelType e) { - if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return ""; - const size_t index = static_cast(e); - return EnumNamesLSTMKernelType()[index]; -} - -enum CombinerType : int8_t { - CombinerType_SUM = 0, - CombinerType_MEAN = 1, - CombinerType_SQRTN = 2, - CombinerType_MIN = CombinerType_SUM, - CombinerType_MAX = CombinerType_SQRTN -}; - -inline const CombinerType (&EnumValuesCombinerType())[3] { - static const CombinerType values[] = { - CombinerType_SUM, - CombinerType_MEAN, - CombinerType_SQRTN - }; - return values; -} - -inline const char * const *EnumNamesCombinerType() { - static const char * const names[4] = { - "SUM", - "MEAN", - "SQRTN", - nullptr - }; - return names; -} - -inline const char *EnumNameCombinerType(CombinerType e) { - if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return ""; - const size_t index = static_cast(e); - return EnumNamesCombinerType()[index]; -} - -enum MirrorPadMode : int8_t { - MirrorPadMode_REFLECT = 0, - MirrorPadMode_SYMMETRIC = 1, - MirrorPadMode_MIN = MirrorPadMode_REFLECT, - MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC -}; - -inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] { - static const MirrorPadMode values[] = { - MirrorPadMode_REFLECT, - MirrorPadMode_SYMMETRIC - }; - return values; -} - -inline const char * const *EnumNamesMirrorPadMode() { - static const char * const names[3] = { - "REFLECT", - "SYMMETRIC", - nullptr - }; - return names; -} - -inline const char *EnumNameMirrorPadMode(MirrorPadMode e) { - if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return ""; - const size_t index = static_cast(e); - return EnumNamesMirrorPadMode()[index]; -} - -enum ReduceWindowFunction : int32_t { - ReduceWindowFunction_UNSUPPORTED = 0, - ReduceWindowFunction_ADD = 1, - ReduceWindowFunction_MUL = 2, - ReduceWindowFunction_MINIMUM = 3, - ReduceWindowFunction_MAXIMUM = 4, - ReduceWindowFunction_ALL = 5, - ReduceWindowFunction_ANY = 6, - ReduceWindowFunction_MIN = ReduceWindowFunction_UNSUPPORTED, - ReduceWindowFunction_MAX = ReduceWindowFunction_ANY -}; - -inline const ReduceWindowFunction (&EnumValuesReduceWindowFunction())[7] { - static const ReduceWindowFunction values[] = { - ReduceWindowFunction_UNSUPPORTED, - ReduceWindowFunction_ADD, - ReduceWindowFunction_MUL, - ReduceWindowFunction_MINIMUM, - ReduceWindowFunction_MAXIMUM, - ReduceWindowFunction_ALL, - ReduceWindowFunction_ANY - }; - return values; -} - -inline const char * const *EnumNamesReduceWindowFunction() { - static const char * const names[8] = { - "UNSUPPORTED", - "ADD", - "MUL", - "MINIMUM", - "MAXIMUM", - "ALL", - "ANY", - nullptr - }; - return names; -} - -inline const char *EnumNameReduceWindowFunction(ReduceWindowFunction e) { - if (flatbuffers::IsOutRange(e, ReduceWindowFunction_UNSUPPORTED, ReduceWindowFunction_ANY)) return ""; - const size_t index = static_cast(e); - return EnumNamesReduceWindowFunction()[index]; -} - -enum CustomOptionsFormat : int8_t { - CustomOptionsFormat_FLEXBUFFERS = 0, - CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS, - CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS -}; - -inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] { - static const CustomOptionsFormat values[] = { - CustomOptionsFormat_FLEXBUFFERS - }; - return values; -} - -inline const char * const *EnumNamesCustomOptionsFormat() { - static const char * const names[2] = { - "FLEXBUFFERS", - nullptr - }; - return names; -} - -inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) { - if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return ""; - const size_t index = static_cast(e); - return EnumNamesCustomOptionsFormat()[index]; -} - -struct CustomQuantizationT : public flatbuffers::NativeTable { - typedef CustomQuantization TableType; - std::vector custom{}; -}; - -struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CustomQuantizationT NativeTableType; - typedef CustomQuantizationBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_CUSTOM = 4 - }; - const flatbuffers::Vector *custom() const { - return GetPointer *>(VT_CUSTOM); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_CUSTOM) && - verifier.VerifyVector(custom()) && - verifier.EndTable(); - } - CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CustomQuantizationBuilder { - typedef CustomQuantization Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_custom(flatbuffers::Offset> custom) { - fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom); - } - explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCustomQuantization( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> custom = 0) { - CustomQuantizationBuilder builder_(_fbb); - builder_.add_custom(custom); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateCustomQuantizationDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *custom = nullptr) { - if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); } - auto custom__ = custom ? _fbb.CreateVector(*custom) : 0; - return tflite_micro::CreateCustomQuantization( - _fbb, - custom__); -} - -flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct QuantizationParametersT : public flatbuffers::NativeTable { - typedef QuantizationParameters TableType; - std::vector min{}; - std::vector max{}; - std::vector scale{}; - std::vector zero_point{}; - tflite_micro::QuantizationDetailsUnion details{}; - int32_t quantized_dimension = 0; -}; - -struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef QuantizationParametersT NativeTableType; - typedef QuantizationParametersBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MIN = 4, - VT_MAX = 6, - VT_SCALE = 8, - VT_ZERO_POINT = 10, - VT_DETAILS_TYPE = 12, - VT_DETAILS = 14, - VT_QUANTIZED_DIMENSION = 16 - }; - const flatbuffers::Vector *min() const { - return GetPointer *>(VT_MIN); - } - const flatbuffers::Vector *max() const { - return GetPointer *>(VT_MAX); - } - const flatbuffers::Vector *scale() const { - return GetPointer *>(VT_SCALE); - } - const flatbuffers::Vector *zero_point() const { - return GetPointer *>(VT_ZERO_POINT); - } - tflite_micro::QuantizationDetails details_type() const { - return static_cast(GetField(VT_DETAILS_TYPE, 0)); - } - const void *details() const { - return GetPointer(VT_DETAILS); - } - template const T *details_as() const; - const tflite_micro::CustomQuantization *details_as_CustomQuantization() const { - return details_type() == tflite_micro::QuantizationDetails_CustomQuantization ? static_cast(details()) : nullptr; - } - int32_t quantized_dimension() const { - return GetField(VT_QUANTIZED_DIMENSION, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_MIN) && - verifier.VerifyVector(min()) && - VerifyOffset(verifier, VT_MAX) && - verifier.VerifyVector(max()) && - VerifyOffset(verifier, VT_SCALE) && - verifier.VerifyVector(scale()) && - VerifyOffset(verifier, VT_ZERO_POINT) && - verifier.VerifyVector(zero_point()) && - VerifyField(verifier, VT_DETAILS_TYPE, 1) && - VerifyOffset(verifier, VT_DETAILS) && - VerifyQuantizationDetails(verifier, details(), details_type()) && - VerifyField(verifier, VT_QUANTIZED_DIMENSION, 4) && - verifier.EndTable(); - } - QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite_micro::CustomQuantization *QuantizationParameters::details_as() const { - return details_as_CustomQuantization(); -} - -struct QuantizationParametersBuilder { - typedef QuantizationParameters Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(flatbuffers::Offset> min) { - fbb_.AddOffset(QuantizationParameters::VT_MIN, min); - } - void add_max(flatbuffers::Offset> max) { - fbb_.AddOffset(QuantizationParameters::VT_MAX, max); - } - void add_scale(flatbuffers::Offset> scale) { - fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale); - } - void add_zero_point(flatbuffers::Offset> zero_point) { - fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point); - } - void add_details_type(tflite_micro::QuantizationDetails details_type) { - fbb_.AddElement(QuantizationParameters::VT_DETAILS_TYPE, static_cast(details_type), 0); - } - void add_details(flatbuffers::Offset details) { - fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details); - } - void add_quantized_dimension(int32_t quantized_dimension) { - fbb_.AddElement(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0); - } - explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateQuantizationParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> min = 0, - flatbuffers::Offset> max = 0, - flatbuffers::Offset> scale = 0, - flatbuffers::Offset> zero_point = 0, - tflite_micro::QuantizationDetails details_type = tflite_micro::QuantizationDetails_NONE, - flatbuffers::Offset details = 0, - int32_t quantized_dimension = 0) { - QuantizationParametersBuilder builder_(_fbb); - builder_.add_quantized_dimension(quantized_dimension); - builder_.add_details(details); - builder_.add_zero_point(zero_point); - builder_.add_scale(scale); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_details_type(details_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateQuantizationParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *min = nullptr, - const std::vector *max = nullptr, - const std::vector *scale = nullptr, - const std::vector *zero_point = nullptr, - tflite_micro::QuantizationDetails details_type = tflite_micro::QuantizationDetails_NONE, - flatbuffers::Offset details = 0, - int32_t quantized_dimension = 0) { - auto min__ = min ? _fbb.CreateVector(*min) : 0; - auto max__ = max ? _fbb.CreateVector(*max) : 0; - auto scale__ = scale ? _fbb.CreateVector(*scale) : 0; - auto zero_point__ = zero_point ? _fbb.CreateVector(*zero_point) : 0; - return tflite_micro::CreateQuantizationParameters( - _fbb, - min__, - max__, - scale__, - zero_point__, - details_type, - details, - quantized_dimension); -} - -flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Int32VectorT : public flatbuffers::NativeTable { - typedef Int32Vector TableType; - std::vector values{}; -}; - -struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Int32VectorT NativeTableType; - typedef Int32VectorBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Int32VectorBuilder { - typedef Int32Vector Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Int32Vector::VT_VALUES, values); - } - explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateInt32Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Int32VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateInt32VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite_micro::CreateInt32Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Uint16VectorT : public flatbuffers::NativeTable { - typedef Uint16Vector TableType; - std::vector values{}; -}; - -struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Uint16VectorT NativeTableType; - typedef Uint16VectorBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Uint16VectorBuilder { - typedef Uint16Vector Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Uint16Vector::VT_VALUES, values); - } - explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUint16Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Uint16VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateUint16VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); } - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite_micro::CreateUint16Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Uint8VectorT : public flatbuffers::NativeTable { - typedef Uint8Vector TableType; - std::vector values{}; -}; - -struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Uint8VectorT NativeTableType; - typedef Uint8VectorBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES = 4 - }; - const flatbuffers::Vector *values() const { - return GetPointer *>(VT_VALUES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_VALUES) && - verifier.VerifyVector(values()) && - verifier.EndTable(); - } - Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Uint8VectorBuilder { - typedef Uint8Vector Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values(flatbuffers::Offset> values) { - fbb_.AddOffset(Uint8Vector::VT_VALUES, values); - } - explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUint8Vector( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> values = 0) { - Uint8VectorBuilder builder_(_fbb); - builder_.add_values(values); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateUint8VectorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *values = nullptr) { - if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); } - auto values__ = values ? _fbb.CreateVector(*values) : 0; - return tflite_micro::CreateUint8Vector( - _fbb, - values__); -} - -flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DimensionMetadataT : public flatbuffers::NativeTable { - typedef DimensionMetadata TableType; - tflite_micro::DimensionType format = tflite_micro::DimensionType_DENSE; - int32_t dense_size = 0; - tflite_micro::SparseIndexVectorUnion array_segments{}; - tflite_micro::SparseIndexVectorUnion array_indices{}; -}; - -struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DimensionMetadataT NativeTableType; - typedef DimensionMetadataBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FORMAT = 4, - VT_DENSE_SIZE = 6, - VT_ARRAY_SEGMENTS_TYPE = 8, - VT_ARRAY_SEGMENTS = 10, - VT_ARRAY_INDICES_TYPE = 12, - VT_ARRAY_INDICES = 14 - }; - tflite_micro::DimensionType format() const { - return static_cast(GetField(VT_FORMAT, 0)); - } - int32_t dense_size() const { - return GetField(VT_DENSE_SIZE, 0); - } - tflite_micro::SparseIndexVector array_segments_type() const { - return static_cast(GetField(VT_ARRAY_SEGMENTS_TYPE, 0)); - } - const void *array_segments() const { - return GetPointer(VT_ARRAY_SEGMENTS); - } - template const T *array_segments_as() const; - const tflite_micro::Int32Vector *array_segments_as_Int32Vector() const { - return array_segments_type() == tflite_micro::SparseIndexVector_Int32Vector ? static_cast(array_segments()) : nullptr; - } - const tflite_micro::Uint16Vector *array_segments_as_Uint16Vector() const { - return array_segments_type() == tflite_micro::SparseIndexVector_Uint16Vector ? static_cast(array_segments()) : nullptr; - } - const tflite_micro::Uint8Vector *array_segments_as_Uint8Vector() const { - return array_segments_type() == tflite_micro::SparseIndexVector_Uint8Vector ? static_cast(array_segments()) : nullptr; - } - tflite_micro::SparseIndexVector array_indices_type() const { - return static_cast(GetField(VT_ARRAY_INDICES_TYPE, 0)); - } - const void *array_indices() const { - return GetPointer(VT_ARRAY_INDICES); - } - template const T *array_indices_as() const; - const tflite_micro::Int32Vector *array_indices_as_Int32Vector() const { - return array_indices_type() == tflite_micro::SparseIndexVector_Int32Vector ? static_cast(array_indices()) : nullptr; - } - const tflite_micro::Uint16Vector *array_indices_as_Uint16Vector() const { - return array_indices_type() == tflite_micro::SparseIndexVector_Uint16Vector ? static_cast(array_indices()) : nullptr; - } - const tflite_micro::Uint8Vector *array_indices_as_Uint8Vector() const { - return array_indices_type() == tflite_micro::SparseIndexVector_Uint8Vector ? static_cast(array_indices()) : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FORMAT, 1) && - VerifyField(verifier, VT_DENSE_SIZE, 4) && - VerifyField(verifier, VT_ARRAY_SEGMENTS_TYPE, 1) && - VerifyOffset(verifier, VT_ARRAY_SEGMENTS) && - VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) && - VerifyField(verifier, VT_ARRAY_INDICES_TYPE, 1) && - VerifyOffset(verifier, VT_ARRAY_INDICES) && - VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) && - verifier.EndTable(); - } - DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite_micro::Int32Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Int32Vector(); -} - -template<> inline const tflite_micro::Uint16Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Uint16Vector(); -} - -template<> inline const tflite_micro::Uint8Vector *DimensionMetadata::array_segments_as() const { - return array_segments_as_Uint8Vector(); -} - -template<> inline const tflite_micro::Int32Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Int32Vector(); -} - -template<> inline const tflite_micro::Uint16Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Uint16Vector(); -} - -template<> inline const tflite_micro::Uint8Vector *DimensionMetadata::array_indices_as() const { - return array_indices_as_Uint8Vector(); -} - -struct DimensionMetadataBuilder { - typedef DimensionMetadata Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_format(tflite_micro::DimensionType format) { - fbb_.AddElement(DimensionMetadata::VT_FORMAT, static_cast(format), 0); - } - void add_dense_size(int32_t dense_size) { - fbb_.AddElement(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0); - } - void add_array_segments_type(tflite_micro::SparseIndexVector array_segments_type) { - fbb_.AddElement(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast(array_segments_type), 0); - } - void add_array_segments(flatbuffers::Offset array_segments) { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments); - } - void add_array_indices_type(tflite_micro::SparseIndexVector array_indices_type) { - fbb_.AddElement(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast(array_indices_type), 0); - } - void add_array_indices(flatbuffers::Offset array_indices) { - fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices); - } - explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDimensionMetadata( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::DimensionType format = tflite_micro::DimensionType_DENSE, - int32_t dense_size = 0, - tflite_micro::SparseIndexVector array_segments_type = tflite_micro::SparseIndexVector_NONE, - flatbuffers::Offset array_segments = 0, - tflite_micro::SparseIndexVector array_indices_type = tflite_micro::SparseIndexVector_NONE, - flatbuffers::Offset array_indices = 0) { - DimensionMetadataBuilder builder_(_fbb); - builder_.add_array_indices(array_indices); - builder_.add_array_segments(array_segments); - builder_.add_dense_size(dense_size); - builder_.add_array_indices_type(array_indices_type); - builder_.add_array_segments_type(array_segments_type); - builder_.add_format(format); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SparsityParametersT : public flatbuffers::NativeTable { - typedef SparsityParameters TableType; - std::vector traversal_order{}; - std::vector block_map{}; - std::vector> dim_metadata{}; - SparsityParametersT() = default; - SparsityParametersT(const SparsityParametersT &o); - SparsityParametersT(SparsityParametersT&&) FLATBUFFERS_NOEXCEPT = default; - SparsityParametersT &operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT; -}; - -struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SparsityParametersT NativeTableType; - typedef SparsityParametersBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TRAVERSAL_ORDER = 4, - VT_BLOCK_MAP = 6, - VT_DIM_METADATA = 8 - }; - const flatbuffers::Vector *traversal_order() const { - return GetPointer *>(VT_TRAVERSAL_ORDER); - } - const flatbuffers::Vector *block_map() const { - return GetPointer *>(VT_BLOCK_MAP); - } - const flatbuffers::Vector> *dim_metadata() const { - return GetPointer> *>(VT_DIM_METADATA); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_TRAVERSAL_ORDER) && - verifier.VerifyVector(traversal_order()) && - VerifyOffset(verifier, VT_BLOCK_MAP) && - verifier.VerifyVector(block_map()) && - VerifyOffset(verifier, VT_DIM_METADATA) && - verifier.VerifyVector(dim_metadata()) && - verifier.VerifyVectorOfTables(dim_metadata()) && - verifier.EndTable(); - } - SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SparsityParametersBuilder { - typedef SparsityParameters Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_traversal_order(flatbuffers::Offset> traversal_order) { - fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order); - } - void add_block_map(flatbuffers::Offset> block_map) { - fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map); - } - void add_dim_metadata(flatbuffers::Offset>> dim_metadata) { - fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata); - } - explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSparsityParameters( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> traversal_order = 0, - flatbuffers::Offset> block_map = 0, - flatbuffers::Offset>> dim_metadata = 0) { - SparsityParametersBuilder builder_(_fbb); - builder_.add_dim_metadata(dim_metadata); - builder_.add_block_map(block_map); - builder_.add_traversal_order(traversal_order); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSparsityParametersDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *traversal_order = nullptr, - const std::vector *block_map = nullptr, - const std::vector> *dim_metadata = nullptr) { - auto traversal_order__ = traversal_order ? _fbb.CreateVector(*traversal_order) : 0; - auto block_map__ = block_map ? _fbb.CreateVector(*block_map) : 0; - auto dim_metadata__ = dim_metadata ? _fbb.CreateVector>(*dim_metadata) : 0; - return tflite_micro::CreateSparsityParameters( - _fbb, - traversal_order__, - block_map__, - dim_metadata__); -} - -flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct VariantSubTypeT : public flatbuffers::NativeTable { - typedef VariantSubType TableType; - std::vector shape{}; - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32; - bool has_rank = false; -}; - -struct VariantSubType FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef VariantSubTypeT NativeTableType; - typedef VariantSubTypeBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SHAPE = 4, - VT_TYPE = 6, - VT_HAS_RANK = 8 - }; - const flatbuffers::Vector *shape() const { - return GetPointer *>(VT_SHAPE); - } - tflite_micro::TensorType type() const { - return static_cast(GetField(VT_TYPE, 0)); - } - bool has_rank() const { - return GetField(VT_HAS_RANK, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && - VerifyField(verifier, VT_TYPE, 1) && - VerifyField(verifier, VT_HAS_RANK, 1) && - verifier.EndTable(); - } - VariantSubTypeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct VariantSubTypeBuilder { - typedef VariantSubType Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset> shape) { - fbb_.AddOffset(VariantSubType::VT_SHAPE, shape); - } - void add_type(tflite_micro::TensorType type) { - fbb_.AddElement(VariantSubType::VT_TYPE, static_cast(type), 0); - } - void add_has_rank(bool has_rank) { - fbb_.AddElement(VariantSubType::VT_HAS_RANK, static_cast(has_rank), 0); - } - explicit VariantSubTypeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateVariantSubType( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> shape = 0, - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, - bool has_rank = false) { - VariantSubTypeBuilder builder_(_fbb); - builder_.add_shape(shape); - builder_.add_has_rank(has_rank); - builder_.add_type(type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateVariantSubTypeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *shape = nullptr, - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, - bool has_rank = false) { - auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; - return tflite_micro::CreateVariantSubType( - _fbb, - shape__, - type, - has_rank); -} - -flatbuffers::Offset CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TensorT : public flatbuffers::NativeTable { - typedef Tensor TableType; - std::vector shape{}; - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32; - uint32_t buffer = 0; - std::string name{}; - std::unique_ptr quantization{}; - bool is_variable = false; - std::unique_ptr sparsity{}; - std::vector shape_signature{}; - bool has_rank = false; - std::vector> variant_tensors{}; - TensorT() = default; - TensorT(const TensorT &o); - TensorT(TensorT&&) FLATBUFFERS_NOEXCEPT = default; - TensorT &operator=(TensorT o) FLATBUFFERS_NOEXCEPT; -}; - -struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TensorT NativeTableType; - typedef TensorBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SHAPE = 4, - VT_TYPE = 6, - VT_BUFFER = 8, - VT_NAME = 10, - VT_QUANTIZATION = 12, - VT_IS_VARIABLE = 14, - VT_SPARSITY = 16, - VT_SHAPE_SIGNATURE = 18, - VT_HAS_RANK = 20, - VT_VARIANT_TENSORS = 22 - }; - const flatbuffers::Vector *shape() const { - return GetPointer *>(VT_SHAPE); - } - tflite_micro::TensorType type() const { - return static_cast(GetField(VT_TYPE, 0)); - } - uint32_t buffer() const { - return GetField(VT_BUFFER, 0); - } - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - const tflite_micro::QuantizationParameters *quantization() const { - return GetPointer(VT_QUANTIZATION); - } - bool is_variable() const { - return GetField(VT_IS_VARIABLE, 0) != 0; - } - const tflite_micro::SparsityParameters *sparsity() const { - return GetPointer(VT_SPARSITY); - } - const flatbuffers::Vector *shape_signature() const { - return GetPointer *>(VT_SHAPE_SIGNATURE); - } - bool has_rank() const { - return GetField(VT_HAS_RANK, 0) != 0; - } - const flatbuffers::Vector> *variant_tensors() const { - return GetPointer> *>(VT_VARIANT_TENSORS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SHAPE) && - verifier.VerifyVector(shape()) && - VerifyField(verifier, VT_TYPE, 1) && - VerifyField(verifier, VT_BUFFER, 4) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyOffset(verifier, VT_QUANTIZATION) && - verifier.VerifyTable(quantization()) && - VerifyField(verifier, VT_IS_VARIABLE, 1) && - VerifyOffset(verifier, VT_SPARSITY) && - verifier.VerifyTable(sparsity()) && - VerifyOffset(verifier, VT_SHAPE_SIGNATURE) && - verifier.VerifyVector(shape_signature()) && - VerifyField(verifier, VT_HAS_RANK, 1) && - VerifyOffset(verifier, VT_VARIANT_TENSORS) && - verifier.VerifyVector(variant_tensors()) && - verifier.VerifyVectorOfTables(variant_tensors()) && - verifier.EndTable(); - } - TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TensorBuilder { - typedef Tensor Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_shape(flatbuffers::Offset> shape) { - fbb_.AddOffset(Tensor::VT_SHAPE, shape); - } - void add_type(tflite_micro::TensorType type) { - fbb_.AddElement(Tensor::VT_TYPE, static_cast(type), 0); - } - void add_buffer(uint32_t buffer) { - fbb_.AddElement(Tensor::VT_BUFFER, buffer, 0); - } - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Tensor::VT_NAME, name); - } - void add_quantization(flatbuffers::Offset quantization) { - fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization); - } - void add_is_variable(bool is_variable) { - fbb_.AddElement(Tensor::VT_IS_VARIABLE, static_cast(is_variable), 0); - } - void add_sparsity(flatbuffers::Offset sparsity) { - fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity); - } - void add_shape_signature(flatbuffers::Offset> shape_signature) { - fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature); - } - void add_has_rank(bool has_rank) { - fbb_.AddElement(Tensor::VT_HAS_RANK, static_cast(has_rank), 0); - } - void add_variant_tensors(flatbuffers::Offset>> variant_tensors) { - fbb_.AddOffset(Tensor::VT_VARIANT_TENSORS, variant_tensors); - } - explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTensor( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> shape = 0, - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, - uint32_t buffer = 0, - flatbuffers::Offset name = 0, - flatbuffers::Offset quantization = 0, - bool is_variable = false, - flatbuffers::Offset sparsity = 0, - flatbuffers::Offset> shape_signature = 0, - bool has_rank = false, - flatbuffers::Offset>> variant_tensors = 0) { - TensorBuilder builder_(_fbb); - builder_.add_variant_tensors(variant_tensors); - builder_.add_shape_signature(shape_signature); - builder_.add_sparsity(sparsity); - builder_.add_quantization(quantization); - builder_.add_name(name); - builder_.add_buffer(buffer); - builder_.add_shape(shape); - builder_.add_has_rank(has_rank); - builder_.add_is_variable(is_variable); - builder_.add_type(type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateTensorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *shape = nullptr, - tflite_micro::TensorType type = tflite_micro::TensorType_FLOAT32, - uint32_t buffer = 0, - const char *name = nullptr, - flatbuffers::Offset quantization = 0, - bool is_variable = false, - flatbuffers::Offset sparsity = 0, - const std::vector *shape_signature = nullptr, - bool has_rank = false, - const std::vector> *variant_tensors = nullptr) { - auto shape__ = shape ? _fbb.CreateVector(*shape) : 0; - auto name__ = name ? _fbb.CreateString(name) : 0; - auto shape_signature__ = shape_signature ? _fbb.CreateVector(*shape_signature) : 0; - auto variant_tensors__ = variant_tensors ? _fbb.CreateVector>(*variant_tensors) : 0; - return tflite_micro::CreateTensor( - _fbb, - shape__, - type, - buffer, - name__, - quantization, - is_variable, - sparsity, - shape_signature__, - has_rank, - variant_tensors__); -} - -flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloGatherOptionsT : public flatbuffers::NativeTable { - typedef StablehloGatherOptions TableType; - std::vector offset_dims{}; - std::vector collapsed_slice_dims{}; - std::vector start_index_map{}; - int64_t index_vector_dim = 0; - std::vector slice_sizes{}; - bool indices_are_sorted = false; -}; - -struct StablehloGatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloGatherOptionsT NativeTableType; - typedef StablehloGatherOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OFFSET_DIMS = 4, - VT_COLLAPSED_SLICE_DIMS = 6, - VT_START_INDEX_MAP = 8, - VT_INDEX_VECTOR_DIM = 10, - VT_SLICE_SIZES = 12, - VT_INDICES_ARE_SORTED = 14 - }; - const flatbuffers::Vector *offset_dims() const { - return GetPointer *>(VT_OFFSET_DIMS); - } - const flatbuffers::Vector *collapsed_slice_dims() const { - return GetPointer *>(VT_COLLAPSED_SLICE_DIMS); - } - const flatbuffers::Vector *start_index_map() const { - return GetPointer *>(VT_START_INDEX_MAP); - } - int64_t index_vector_dim() const { - return GetField(VT_INDEX_VECTOR_DIM, 0); - } - const flatbuffers::Vector *slice_sizes() const { - return GetPointer *>(VT_SLICE_SIZES); - } - bool indices_are_sorted() const { - return GetField(VT_INDICES_ARE_SORTED, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_OFFSET_DIMS) && - verifier.VerifyVector(offset_dims()) && - VerifyOffset(verifier, VT_COLLAPSED_SLICE_DIMS) && - verifier.VerifyVector(collapsed_slice_dims()) && - VerifyOffset(verifier, VT_START_INDEX_MAP) && - verifier.VerifyVector(start_index_map()) && - VerifyField(verifier, VT_INDEX_VECTOR_DIM, 8) && - VerifyOffset(verifier, VT_SLICE_SIZES) && - verifier.VerifyVector(slice_sizes()) && - VerifyField(verifier, VT_INDICES_ARE_SORTED, 1) && - verifier.EndTable(); - } - StablehloGatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloGatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloGatherOptionsBuilder { - typedef StablehloGatherOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_offset_dims(flatbuffers::Offset> offset_dims) { - fbb_.AddOffset(StablehloGatherOptions::VT_OFFSET_DIMS, offset_dims); - } - void add_collapsed_slice_dims(flatbuffers::Offset> collapsed_slice_dims) { - fbb_.AddOffset(StablehloGatherOptions::VT_COLLAPSED_SLICE_DIMS, collapsed_slice_dims); - } - void add_start_index_map(flatbuffers::Offset> start_index_map) { - fbb_.AddOffset(StablehloGatherOptions::VT_START_INDEX_MAP, start_index_map); - } - void add_index_vector_dim(int64_t index_vector_dim) { - fbb_.AddElement(StablehloGatherOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0); - } - void add_slice_sizes(flatbuffers::Offset> slice_sizes) { - fbb_.AddOffset(StablehloGatherOptions::VT_SLICE_SIZES, slice_sizes); - } - void add_indices_are_sorted(bool indices_are_sorted) { - fbb_.AddElement(StablehloGatherOptions::VT_INDICES_ARE_SORTED, static_cast(indices_are_sorted), 0); - } - explicit StablehloGatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> offset_dims = 0, - flatbuffers::Offset> collapsed_slice_dims = 0, - flatbuffers::Offset> start_index_map = 0, - int64_t index_vector_dim = 0, - flatbuffers::Offset> slice_sizes = 0, - bool indices_are_sorted = false) { - StablehloGatherOptionsBuilder builder_(_fbb); - builder_.add_index_vector_dim(index_vector_dim); - builder_.add_slice_sizes(slice_sizes); - builder_.add_start_index_map(start_index_map); - builder_.add_collapsed_slice_dims(collapsed_slice_dims); - builder_.add_offset_dims(offset_dims); - builder_.add_indices_are_sorted(indices_are_sorted); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloGatherOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *offset_dims = nullptr, - const std::vector *collapsed_slice_dims = nullptr, - const std::vector *start_index_map = nullptr, - int64_t index_vector_dim = 0, - const std::vector *slice_sizes = nullptr, - bool indices_are_sorted = false) { - auto offset_dims__ = offset_dims ? _fbb.CreateVector(*offset_dims) : 0; - auto collapsed_slice_dims__ = collapsed_slice_dims ? _fbb.CreateVector(*collapsed_slice_dims) : 0; - auto start_index_map__ = start_index_map ? _fbb.CreateVector(*start_index_map) : 0; - auto slice_sizes__ = slice_sizes ? _fbb.CreateVector(*slice_sizes) : 0; - return tflite_micro::CreateStablehloGatherOptions( - _fbb, - offset_dims__, - collapsed_slice_dims__, - start_index_map__, - index_vector_dim, - slice_sizes__, - indices_are_sorted); -} - -flatbuffers::Offset CreateStablehloGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloTransposeOptionsT : public flatbuffers::NativeTable { - typedef StablehloTransposeOptions TableType; - std::vector permutation{}; -}; - -struct StablehloTransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloTransposeOptionsT NativeTableType; - typedef StablehloTransposeOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PERMUTATION = 4 - }; - const flatbuffers::Vector *permutation() const { - return GetPointer *>(VT_PERMUTATION); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_PERMUTATION) && - verifier.VerifyVector(permutation()) && - verifier.EndTable(); - } - StablehloTransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloTransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloTransposeOptionsBuilder { - typedef StablehloTransposeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_permutation(flatbuffers::Offset> permutation) { - fbb_.AddOffset(StablehloTransposeOptions::VT_PERMUTATION, permutation); - } - explicit StablehloTransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> permutation = 0) { - StablehloTransposeOptionsBuilder builder_(_fbb); - builder_.add_permutation(permutation); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloTransposeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *permutation = nullptr) { - auto permutation__ = permutation ? _fbb.CreateVector(*permutation) : 0; - return tflite_micro::CreateStablehloTransposeOptions( - _fbb, - permutation__); -} - -flatbuffers::Offset CreateStablehloTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloDotGeneralOptionsT : public flatbuffers::NativeTable { - typedef StablehloDotGeneralOptions TableType; - std::vector lhs_batching_dimensions{}; - std::vector rhs_batching_dimensions{}; - std::vector lhs_contracting_dimensions{}; - std::vector rhs_contracting_dimensions{}; - std::vector precision_config{}; -}; - -struct StablehloDotGeneralOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloDotGeneralOptionsT NativeTableType; - typedef StablehloDotGeneralOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_LHS_BATCHING_DIMENSIONS = 4, - VT_RHS_BATCHING_DIMENSIONS = 6, - VT_LHS_CONTRACTING_DIMENSIONS = 8, - VT_RHS_CONTRACTING_DIMENSIONS = 10, - VT_PRECISION_CONFIG = 12 - }; - const flatbuffers::Vector *lhs_batching_dimensions() const { - return GetPointer *>(VT_LHS_BATCHING_DIMENSIONS); - } - const flatbuffers::Vector *rhs_batching_dimensions() const { - return GetPointer *>(VT_RHS_BATCHING_DIMENSIONS); - } - const flatbuffers::Vector *lhs_contracting_dimensions() const { - return GetPointer *>(VT_LHS_CONTRACTING_DIMENSIONS); - } - const flatbuffers::Vector *rhs_contracting_dimensions() const { - return GetPointer *>(VT_RHS_CONTRACTING_DIMENSIONS); - } - const flatbuffers::Vector *precision_config() const { - return GetPointer *>(VT_PRECISION_CONFIG); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_LHS_BATCHING_DIMENSIONS) && - verifier.VerifyVector(lhs_batching_dimensions()) && - VerifyOffset(verifier, VT_RHS_BATCHING_DIMENSIONS) && - verifier.VerifyVector(rhs_batching_dimensions()) && - VerifyOffset(verifier, VT_LHS_CONTRACTING_DIMENSIONS) && - verifier.VerifyVector(lhs_contracting_dimensions()) && - VerifyOffset(verifier, VT_RHS_CONTRACTING_DIMENSIONS) && - verifier.VerifyVector(rhs_contracting_dimensions()) && - VerifyOffset(verifier, VT_PRECISION_CONFIG) && - verifier.VerifyVector(precision_config()) && - verifier.EndTable(); - } - StablehloDotGeneralOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloDotGeneralOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloDotGeneralOptionsBuilder { - typedef StablehloDotGeneralOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_lhs_batching_dimensions(flatbuffers::Offset> lhs_batching_dimensions) { - fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_BATCHING_DIMENSIONS, lhs_batching_dimensions); - } - void add_rhs_batching_dimensions(flatbuffers::Offset> rhs_batching_dimensions) { - fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_BATCHING_DIMENSIONS, rhs_batching_dimensions); - } - void add_lhs_contracting_dimensions(flatbuffers::Offset> lhs_contracting_dimensions) { - fbb_.AddOffset(StablehloDotGeneralOptions::VT_LHS_CONTRACTING_DIMENSIONS, lhs_contracting_dimensions); - } - void add_rhs_contracting_dimensions(flatbuffers::Offset> rhs_contracting_dimensions) { - fbb_.AddOffset(StablehloDotGeneralOptions::VT_RHS_CONTRACTING_DIMENSIONS, rhs_contracting_dimensions); - } - void add_precision_config(flatbuffers::Offset> precision_config) { - fbb_.AddOffset(StablehloDotGeneralOptions::VT_PRECISION_CONFIG, precision_config); - } - explicit StablehloDotGeneralOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloDotGeneralOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> lhs_batching_dimensions = 0, - flatbuffers::Offset> rhs_batching_dimensions = 0, - flatbuffers::Offset> lhs_contracting_dimensions = 0, - flatbuffers::Offset> rhs_contracting_dimensions = 0, - flatbuffers::Offset> precision_config = 0) { - StablehloDotGeneralOptionsBuilder builder_(_fbb); - builder_.add_precision_config(precision_config); - builder_.add_rhs_contracting_dimensions(rhs_contracting_dimensions); - builder_.add_lhs_contracting_dimensions(lhs_contracting_dimensions); - builder_.add_rhs_batching_dimensions(rhs_batching_dimensions); - builder_.add_lhs_batching_dimensions(lhs_batching_dimensions); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloDotGeneralOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *lhs_batching_dimensions = nullptr, - const std::vector *rhs_batching_dimensions = nullptr, - const std::vector *lhs_contracting_dimensions = nullptr, - const std::vector *rhs_contracting_dimensions = nullptr, - const std::vector *precision_config = nullptr) { - auto lhs_batching_dimensions__ = lhs_batching_dimensions ? _fbb.CreateVector(*lhs_batching_dimensions) : 0; - auto rhs_batching_dimensions__ = rhs_batching_dimensions ? _fbb.CreateVector(*rhs_batching_dimensions) : 0; - auto lhs_contracting_dimensions__ = lhs_contracting_dimensions ? _fbb.CreateVector(*lhs_contracting_dimensions) : 0; - auto rhs_contracting_dimensions__ = rhs_contracting_dimensions ? _fbb.CreateVector(*rhs_contracting_dimensions) : 0; - auto precision_config__ = precision_config ? _fbb.CreateVector(*precision_config) : 0; - return tflite_micro::CreateStablehloDotGeneralOptions( - _fbb, - lhs_batching_dimensions__, - rhs_batching_dimensions__, - lhs_contracting_dimensions__, - rhs_contracting_dimensions__, - precision_config__); -} - -flatbuffers::Offset CreateStablehloDotGeneralOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloReduceWindowOptionsT : public flatbuffers::NativeTable { - typedef StablehloReduceWindowOptions TableType; - std::vector window_dimensions{}; - std::vector window_strides{}; - std::vector base_dilations{}; - std::vector window_dilations{}; - std::vector padding{}; - int32_t body_subgraph_index = 0; -}; - -struct StablehloReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloReduceWindowOptionsT NativeTableType; - typedef StablehloReduceWindowOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_WINDOW_DIMENSIONS = 4, - VT_WINDOW_STRIDES = 6, - VT_BASE_DILATIONS = 8, - VT_WINDOW_DILATIONS = 10, - VT_PADDING = 12, - VT_BODY_SUBGRAPH_INDEX = 14 - }; - const flatbuffers::Vector *window_dimensions() const { - return GetPointer *>(VT_WINDOW_DIMENSIONS); - } - const flatbuffers::Vector *window_strides() const { - return GetPointer *>(VT_WINDOW_STRIDES); - } - const flatbuffers::Vector *base_dilations() const { - return GetPointer *>(VT_BASE_DILATIONS); - } - const flatbuffers::Vector *window_dilations() const { - return GetPointer *>(VT_WINDOW_DILATIONS); - } - const flatbuffers::Vector *padding() const { - return GetPointer *>(VT_PADDING); - } - int32_t body_subgraph_index() const { - return GetField(VT_BODY_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_WINDOW_DIMENSIONS) && - verifier.VerifyVector(window_dimensions()) && - VerifyOffset(verifier, VT_WINDOW_STRIDES) && - verifier.VerifyVector(window_strides()) && - VerifyOffset(verifier, VT_BASE_DILATIONS) && - verifier.VerifyVector(base_dilations()) && - VerifyOffset(verifier, VT_WINDOW_DILATIONS) && - verifier.VerifyVector(window_dilations()) && - VerifyOffset(verifier, VT_PADDING) && - verifier.VerifyVector(padding()) && - VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - StablehloReduceWindowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloReduceWindowOptionsBuilder { - typedef StablehloReduceWindowOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_window_dimensions(flatbuffers::Offset> window_dimensions) { - fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DIMENSIONS, window_dimensions); - } - void add_window_strides(flatbuffers::Offset> window_strides) { - fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_STRIDES, window_strides); - } - void add_base_dilations(flatbuffers::Offset> base_dilations) { - fbb_.AddOffset(StablehloReduceWindowOptions::VT_BASE_DILATIONS, base_dilations); - } - void add_window_dilations(flatbuffers::Offset> window_dilations) { - fbb_.AddOffset(StablehloReduceWindowOptions::VT_WINDOW_DILATIONS, window_dilations); - } - void add_padding(flatbuffers::Offset> padding) { - fbb_.AddOffset(StablehloReduceWindowOptions::VT_PADDING, padding); - } - void add_body_subgraph_index(int32_t body_subgraph_index) { - fbb_.AddElement(StablehloReduceWindowOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit StablehloReduceWindowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloReduceWindowOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> window_dimensions = 0, - flatbuffers::Offset> window_strides = 0, - flatbuffers::Offset> base_dilations = 0, - flatbuffers::Offset> window_dilations = 0, - flatbuffers::Offset> padding = 0, - int32_t body_subgraph_index = 0) { - StablehloReduceWindowOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_padding(padding); - builder_.add_window_dilations(window_dilations); - builder_.add_base_dilations(base_dilations); - builder_.add_window_strides(window_strides); - builder_.add_window_dimensions(window_dimensions); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloReduceWindowOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *window_dimensions = nullptr, - const std::vector *window_strides = nullptr, - const std::vector *base_dilations = nullptr, - const std::vector *window_dilations = nullptr, - const std::vector *padding = nullptr, - int32_t body_subgraph_index = 0) { - auto window_dimensions__ = window_dimensions ? _fbb.CreateVector(*window_dimensions) : 0; - auto window_strides__ = window_strides ? _fbb.CreateVector(*window_strides) : 0; - auto base_dilations__ = base_dilations ? _fbb.CreateVector(*base_dilations) : 0; - auto window_dilations__ = window_dilations ? _fbb.CreateVector(*window_dilations) : 0; - auto padding__ = padding ? _fbb.CreateVector(*padding) : 0; - return tflite_micro::CreateStablehloReduceWindowOptions( - _fbb, - window_dimensions__, - window_strides__, - base_dilations__, - window_dilations__, - padding__, - body_subgraph_index); -} - -flatbuffers::Offset CreateStablehloReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloWhileOptionsT : public flatbuffers::NativeTable { - typedef StablehloWhileOptions TableType; - int32_t cond_subgraph_index = 0; - int32_t body_subgraph_index = 0; -}; - -struct StablehloWhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloWhileOptionsT NativeTableType; - typedef StablehloWhileOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COND_SUBGRAPH_INDEX = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - int32_t cond_subgraph_index() const { - return GetField(VT_COND_SUBGRAPH_INDEX, 0); - } - int32_t body_subgraph_index() const { - return GetField(VT_BODY_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COND_SUBGRAPH_INDEX, 4) && - VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - StablehloWhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloWhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloWhileOptionsBuilder { - typedef StablehloWhileOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_subgraph_index(int32_t cond_subgraph_index) { - fbb_.AddElement(StablehloWhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); - } - void add_body_subgraph_index(int32_t body_subgraph_index) { - fbb_.AddElement(StablehloWhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit StablehloWhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloWhileOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t cond_subgraph_index = 0, - int32_t body_subgraph_index = 0) { - StablehloWhileOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_cond_subgraph_index(cond_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloSortOptionsT : public flatbuffers::NativeTable { - typedef StablehloSortOptions TableType; - int64_t dimension = 0; - bool is_stable = false; - int32_t comparator_subgraph_index = 0; -}; - -struct StablehloSortOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloSortOptionsT NativeTableType; - typedef StablehloSortOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DIMENSION = 4, - VT_IS_STABLE = 6, - VT_COMPARATOR_SUBGRAPH_INDEX = 8 - }; - int64_t dimension() const { - return GetField(VT_DIMENSION, 0); - } - bool is_stable() const { - return GetField(VT_IS_STABLE, 0) != 0; - } - int32_t comparator_subgraph_index() const { - return GetField(VT_COMPARATOR_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_DIMENSION, 8) && - VerifyField(verifier, VT_IS_STABLE, 1) && - VerifyField(verifier, VT_COMPARATOR_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - StablehloSortOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloSortOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloSortOptionsBuilder { - typedef StablehloSortOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_dimension(int64_t dimension) { - fbb_.AddElement(StablehloSortOptions::VT_DIMENSION, dimension, 0); - } - void add_is_stable(bool is_stable) { - fbb_.AddElement(StablehloSortOptions::VT_IS_STABLE, static_cast(is_stable), 0); - } - void add_comparator_subgraph_index(int32_t comparator_subgraph_index) { - fbb_.AddElement(StablehloSortOptions::VT_COMPARATOR_SUBGRAPH_INDEX, comparator_subgraph_index, 0); - } - explicit StablehloSortOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloSortOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int64_t dimension = 0, - bool is_stable = false, - int32_t comparator_subgraph_index = 0) { - StablehloSortOptionsBuilder builder_(_fbb); - builder_.add_dimension(dimension); - builder_.add_comparator_subgraph_index(comparator_subgraph_index); - builder_.add_is_stable(is_stable); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloSortOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloConcatenateOptionsT : public flatbuffers::NativeTable { - typedef StablehloConcatenateOptions TableType; - int64_t dimension = 0; -}; - -struct StablehloConcatenateOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloConcatenateOptionsT NativeTableType; - typedef StablehloConcatenateOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DIMENSION = 4 - }; - int64_t dimension() const { - return GetField(VT_DIMENSION, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_DIMENSION, 8) && - verifier.EndTable(); - } - StablehloConcatenateOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloConcatenateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloConcatenateOptionsBuilder { - typedef StablehloConcatenateOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_dimension(int64_t dimension) { - fbb_.AddElement(StablehloConcatenateOptions::VT_DIMENSION, dimension, 0); - } - explicit StablehloConcatenateOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloConcatenateOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int64_t dimension = 0) { - StablehloConcatenateOptionsBuilder builder_(_fbb); - builder_.add_dimension(dimension); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloConcatenateOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloBroadcastInDimOptionsT : public flatbuffers::NativeTable { - typedef StablehloBroadcastInDimOptions TableType; - std::vector broadcast_dimensions{}; -}; - -struct StablehloBroadcastInDimOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloBroadcastInDimOptionsT NativeTableType; - typedef StablehloBroadcastInDimOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BROADCAST_DIMENSIONS = 4 - }; - const flatbuffers::Vector *broadcast_dimensions() const { - return GetPointer *>(VT_BROADCAST_DIMENSIONS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_BROADCAST_DIMENSIONS) && - verifier.VerifyVector(broadcast_dimensions()) && - verifier.EndTable(); - } - StablehloBroadcastInDimOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloBroadcastInDimOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloBroadcastInDimOptionsBuilder { - typedef StablehloBroadcastInDimOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_broadcast_dimensions(flatbuffers::Offset> broadcast_dimensions) { - fbb_.AddOffset(StablehloBroadcastInDimOptions::VT_BROADCAST_DIMENSIONS, broadcast_dimensions); - } - explicit StablehloBroadcastInDimOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloBroadcastInDimOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> broadcast_dimensions = 0) { - StablehloBroadcastInDimOptionsBuilder builder_(_fbb); - builder_.add_broadcast_dimensions(broadcast_dimensions); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloBroadcastInDimOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *broadcast_dimensions = nullptr) { - auto broadcast_dimensions__ = broadcast_dimensions ? _fbb.CreateVector(*broadcast_dimensions) : 0; - return tflite_micro::CreateStablehloBroadcastInDimOptions( - _fbb, - broadcast_dimensions__); -} - -flatbuffers::Offset CreateStablehloBroadcastInDimOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloCompareOptionsT : public flatbuffers::NativeTable { - typedef StablehloCompareOptions TableType; - tflite_micro::StablehloComparisonDirection comparison_direction = tflite_micro::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ; - tflite_micro::StablehloComparisonType compare_type = tflite_micro::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE; -}; - -struct StablehloCompareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloCompareOptionsT NativeTableType; - typedef StablehloCompareOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COMPARISON_DIRECTION = 4, - VT_COMPARE_TYPE = 6 - }; - tflite_micro::StablehloComparisonDirection comparison_direction() const { - return static_cast(GetField(VT_COMPARISON_DIRECTION, 0)); - } - tflite_micro::StablehloComparisonType compare_type() const { - return static_cast(GetField(VT_COMPARE_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COMPARISON_DIRECTION, 4) && - VerifyField(verifier, VT_COMPARE_TYPE, 4) && - verifier.EndTable(); - } - StablehloCompareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloCompareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloCompareOptionsBuilder { - typedef StablehloCompareOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_comparison_direction(tflite_micro::StablehloComparisonDirection comparison_direction) { - fbb_.AddElement(StablehloCompareOptions::VT_COMPARISON_DIRECTION, static_cast(comparison_direction), 0); - } - void add_compare_type(tflite_micro::StablehloComparisonType compare_type) { - fbb_.AddElement(StablehloCompareOptions::VT_COMPARE_TYPE, static_cast(compare_type), 0); - } - explicit StablehloCompareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloCompareOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::StablehloComparisonDirection comparison_direction = tflite_micro::StablehloComparisonDirection_STABLEHLO_COMPARISON_DIRECTION_EQ, - tflite_micro::StablehloComparisonType compare_type = tflite_micro::StablehloComparisonType_STABLEHLO_COMPARISON_TYPE_NOTYPE) { - StablehloCompareOptionsBuilder builder_(_fbb); - builder_.add_compare_type(compare_type); - builder_.add_comparison_direction(comparison_direction); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloCompareOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloDynamicSliceOptionsT : public flatbuffers::NativeTable { - typedef StablehloDynamicSliceOptions TableType; - std::vector slice_sizes{}; -}; - -struct StablehloDynamicSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloDynamicSliceOptionsT NativeTableType; - typedef StablehloDynamicSliceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SLICE_SIZES = 4 - }; - const flatbuffers::Vector *slice_sizes() const { - return GetPointer *>(VT_SLICE_SIZES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SLICE_SIZES) && - verifier.VerifyVector(slice_sizes()) && - verifier.EndTable(); - } - StablehloDynamicSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloDynamicSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloDynamicSliceOptionsBuilder { - typedef StablehloDynamicSliceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_slice_sizes(flatbuffers::Offset> slice_sizes) { - fbb_.AddOffset(StablehloDynamicSliceOptions::VT_SLICE_SIZES, slice_sizes); - } - explicit StablehloDynamicSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloDynamicSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> slice_sizes = 0) { - StablehloDynamicSliceOptionsBuilder builder_(_fbb); - builder_.add_slice_sizes(slice_sizes); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloDynamicSliceOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *slice_sizes = nullptr) { - auto slice_sizes__ = slice_sizes ? _fbb.CreateVector(*slice_sizes) : 0; - return tflite_micro::CreateStablehloDynamicSliceOptions( - _fbb, - slice_sizes__); -} - -flatbuffers::Offset CreateStablehloDynamicSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloPadOptionsT : public flatbuffers::NativeTable { - typedef StablehloPadOptions TableType; - std::vector edge_padding_low{}; - std::vector edge_padding_high{}; - std::vector interior_padding{}; -}; - -struct StablehloPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloPadOptionsT NativeTableType; - typedef StablehloPadOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_EDGE_PADDING_LOW = 4, - VT_EDGE_PADDING_HIGH = 6, - VT_INTERIOR_PADDING = 8 - }; - const flatbuffers::Vector *edge_padding_low() const { - return GetPointer *>(VT_EDGE_PADDING_LOW); - } - const flatbuffers::Vector *edge_padding_high() const { - return GetPointer *>(VT_EDGE_PADDING_HIGH); - } - const flatbuffers::Vector *interior_padding() const { - return GetPointer *>(VT_INTERIOR_PADDING); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_EDGE_PADDING_LOW) && - verifier.VerifyVector(edge_padding_low()) && - VerifyOffset(verifier, VT_EDGE_PADDING_HIGH) && - verifier.VerifyVector(edge_padding_high()) && - VerifyOffset(verifier, VT_INTERIOR_PADDING) && - verifier.VerifyVector(interior_padding()) && - verifier.EndTable(); - } - StablehloPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloPadOptionsBuilder { - typedef StablehloPadOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_edge_padding_low(flatbuffers::Offset> edge_padding_low) { - fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_LOW, edge_padding_low); - } - void add_edge_padding_high(flatbuffers::Offset> edge_padding_high) { - fbb_.AddOffset(StablehloPadOptions::VT_EDGE_PADDING_HIGH, edge_padding_high); - } - void add_interior_padding(flatbuffers::Offset> interior_padding) { - fbb_.AddOffset(StablehloPadOptions::VT_INTERIOR_PADDING, interior_padding); - } - explicit StablehloPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloPadOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> edge_padding_low = 0, - flatbuffers::Offset> edge_padding_high = 0, - flatbuffers::Offset> interior_padding = 0) { - StablehloPadOptionsBuilder builder_(_fbb); - builder_.add_interior_padding(interior_padding); - builder_.add_edge_padding_high(edge_padding_high); - builder_.add_edge_padding_low(edge_padding_low); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloPadOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *edge_padding_low = nullptr, - const std::vector *edge_padding_high = nullptr, - const std::vector *interior_padding = nullptr) { - auto edge_padding_low__ = edge_padding_low ? _fbb.CreateVector(*edge_padding_low) : 0; - auto edge_padding_high__ = edge_padding_high ? _fbb.CreateVector(*edge_padding_high) : 0; - auto interior_padding__ = interior_padding ? _fbb.CreateVector(*interior_padding) : 0; - return tflite_micro::CreateStablehloPadOptions( - _fbb, - edge_padding_low__, - edge_padding_high__, - interior_padding__); -} - -flatbuffers::Offset CreateStablehloPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloIotaOptionsT : public flatbuffers::NativeTable { - typedef StablehloIotaOptions TableType; - int64_t iota_dimension = 0; -}; - -struct StablehloIotaOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloIotaOptionsT NativeTableType; - typedef StablehloIotaOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_IOTA_DIMENSION = 4 - }; - int64_t iota_dimension() const { - return GetField(VT_IOTA_DIMENSION, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_IOTA_DIMENSION, 8) && - verifier.EndTable(); - } - StablehloIotaOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloIotaOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloIotaOptionsBuilder { - typedef StablehloIotaOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_iota_dimension(int64_t iota_dimension) { - fbb_.AddElement(StablehloIotaOptions::VT_IOTA_DIMENSION, iota_dimension, 0); - } - explicit StablehloIotaOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloIotaOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int64_t iota_dimension = 0) { - StablehloIotaOptionsBuilder builder_(_fbb); - builder_.add_iota_dimension(iota_dimension); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloIotaOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloCustomCallOptionsT : public flatbuffers::NativeTable { - typedef StablehloCustomCallOptions TableType; - std::string call_target_name{}; - bool has_side_effect = false; - std::string backend_config{}; - int32_t api_version = 0; - std::vector called_computations{}; - std::vector custom_attributes{}; -}; - -struct StablehloCustomCallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloCustomCallOptionsT NativeTableType; - typedef StablehloCustomCallOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_CALL_TARGET_NAME = 4, - VT_HAS_SIDE_EFFECT = 6, - VT_BACKEND_CONFIG = 8, - VT_API_VERSION = 10, - VT_CALLED_COMPUTATIONS = 12, - VT_CUSTOM_ATTRIBUTES = 14 - }; - const flatbuffers::String *call_target_name() const { - return GetPointer(VT_CALL_TARGET_NAME); - } - bool has_side_effect() const { - return GetField(VT_HAS_SIDE_EFFECT, 0) != 0; - } - const flatbuffers::String *backend_config() const { - return GetPointer(VT_BACKEND_CONFIG); - } - int32_t api_version() const { - return GetField(VT_API_VERSION, 0); - } - const flatbuffers::Vector *called_computations() const { - return GetPointer *>(VT_CALLED_COMPUTATIONS); - } - const flatbuffers::Vector *custom_attributes() const { - return GetPointer *>(VT_CUSTOM_ATTRIBUTES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_CALL_TARGET_NAME) && - verifier.VerifyString(call_target_name()) && - VerifyField(verifier, VT_HAS_SIDE_EFFECT, 1) && - VerifyOffset(verifier, VT_BACKEND_CONFIG) && - verifier.VerifyString(backend_config()) && - VerifyField(verifier, VT_API_VERSION, 4) && - VerifyOffset(verifier, VT_CALLED_COMPUTATIONS) && - verifier.VerifyVector(called_computations()) && - VerifyOffset(verifier, VT_CUSTOM_ATTRIBUTES) && - verifier.VerifyVector(custom_attributes()) && - verifier.EndTable(); - } - StablehloCustomCallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloCustomCallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloCustomCallOptionsBuilder { - typedef StablehloCustomCallOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_call_target_name(flatbuffers::Offset call_target_name) { - fbb_.AddOffset(StablehloCustomCallOptions::VT_CALL_TARGET_NAME, call_target_name); - } - void add_has_side_effect(bool has_side_effect) { - fbb_.AddElement(StablehloCustomCallOptions::VT_HAS_SIDE_EFFECT, static_cast(has_side_effect), 0); - } - void add_backend_config(flatbuffers::Offset backend_config) { - fbb_.AddOffset(StablehloCustomCallOptions::VT_BACKEND_CONFIG, backend_config); - } - void add_api_version(int32_t api_version) { - fbb_.AddElement(StablehloCustomCallOptions::VT_API_VERSION, api_version, 0); - } - void add_called_computations(flatbuffers::Offset> called_computations) { - fbb_.AddOffset(StablehloCustomCallOptions::VT_CALLED_COMPUTATIONS, called_computations); - } - void add_custom_attributes(flatbuffers::Offset> custom_attributes) { - fbb_.AddOffset(StablehloCustomCallOptions::VT_CUSTOM_ATTRIBUTES, custom_attributes); - } - explicit StablehloCustomCallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloCustomCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset call_target_name = 0, - bool has_side_effect = false, - flatbuffers::Offset backend_config = 0, - int32_t api_version = 0, - flatbuffers::Offset> called_computations = 0, - flatbuffers::Offset> custom_attributes = 0) { - StablehloCustomCallOptionsBuilder builder_(_fbb); - builder_.add_custom_attributes(custom_attributes); - builder_.add_called_computations(called_computations); - builder_.add_api_version(api_version); - builder_.add_backend_config(backend_config); - builder_.add_call_target_name(call_target_name); - builder_.add_has_side_effect(has_side_effect); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloCustomCallOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *call_target_name = nullptr, - bool has_side_effect = false, - const char *backend_config = nullptr, - int32_t api_version = 0, - const std::vector *called_computations = nullptr, - const std::vector *custom_attributes = nullptr) { - auto call_target_name__ = call_target_name ? _fbb.CreateString(call_target_name) : 0; - auto backend_config__ = backend_config ? _fbb.CreateString(backend_config) : 0; - auto called_computations__ = called_computations ? _fbb.CreateVector(*called_computations) : 0; - auto custom_attributes__ = custom_attributes ? _fbb.CreateVector(*custom_attributes) : 0; - return tflite_micro::CreateStablehloCustomCallOptions( - _fbb, - call_target_name__, - has_side_effect, - backend_config__, - api_version, - called_computations__, - custom_attributes__); -} - -flatbuffers::Offset CreateStablehloCustomCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloReduceOptionsT : public flatbuffers::NativeTable { - typedef StablehloReduceOptions TableType; - std::vector dimensions{}; - int32_t body_subgraph_index = 0; -}; - -struct StablehloReduceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloReduceOptionsT NativeTableType; - typedef StablehloReduceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DIMENSIONS = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - const flatbuffers::Vector *dimensions() const { - return GetPointer *>(VT_DIMENSIONS); - } - int32_t body_subgraph_index() const { - return GetField(VT_BODY_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_DIMENSIONS) && - verifier.VerifyVector(dimensions()) && - VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - StablehloReduceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloReduceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloReduceOptionsBuilder { - typedef StablehloReduceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_dimensions(flatbuffers::Offset> dimensions) { - fbb_.AddOffset(StablehloReduceOptions::VT_DIMENSIONS, dimensions); - } - void add_body_subgraph_index(int32_t body_subgraph_index) { - fbb_.AddElement(StablehloReduceOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit StablehloReduceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloReduceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> dimensions = 0, - int32_t body_subgraph_index = 0) { - StablehloReduceOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_dimensions(dimensions); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloReduceOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *dimensions = nullptr, - int32_t body_subgraph_index = 0) { - auto dimensions__ = dimensions ? _fbb.CreateVector(*dimensions) : 0; - return tflite_micro::CreateStablehloReduceOptions( - _fbb, - dimensions__, - body_subgraph_index); -} - -flatbuffers::Offset CreateStablehloReduceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloSliceOptionsT : public flatbuffers::NativeTable { - typedef StablehloSliceOptions TableType; - std::vector start_indices{}; - std::vector limit_indices{}; - std::vector strides{}; -}; - -struct StablehloSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloSliceOptionsT NativeTableType; - typedef StablehloSliceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_START_INDICES = 4, - VT_LIMIT_INDICES = 6, - VT_STRIDES = 8 - }; - const flatbuffers::Vector *start_indices() const { - return GetPointer *>(VT_START_INDICES); - } - const flatbuffers::Vector *limit_indices() const { - return GetPointer *>(VT_LIMIT_INDICES); - } - const flatbuffers::Vector *strides() const { - return GetPointer *>(VT_STRIDES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_START_INDICES) && - verifier.VerifyVector(start_indices()) && - VerifyOffset(verifier, VT_LIMIT_INDICES) && - verifier.VerifyVector(limit_indices()) && - VerifyOffset(verifier, VT_STRIDES) && - verifier.VerifyVector(strides()) && - verifier.EndTable(); - } - StablehloSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloSliceOptionsBuilder { - typedef StablehloSliceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_start_indices(flatbuffers::Offset> start_indices) { - fbb_.AddOffset(StablehloSliceOptions::VT_START_INDICES, start_indices); - } - void add_limit_indices(flatbuffers::Offset> limit_indices) { - fbb_.AddOffset(StablehloSliceOptions::VT_LIMIT_INDICES, limit_indices); - } - void add_strides(flatbuffers::Offset> strides) { - fbb_.AddOffset(StablehloSliceOptions::VT_STRIDES, strides); - } - explicit StablehloSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> start_indices = 0, - flatbuffers::Offset> limit_indices = 0, - flatbuffers::Offset> strides = 0) { - StablehloSliceOptionsBuilder builder_(_fbb); - builder_.add_strides(strides); - builder_.add_limit_indices(limit_indices); - builder_.add_start_indices(start_indices); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloSliceOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *start_indices = nullptr, - const std::vector *limit_indices = nullptr, - const std::vector *strides = nullptr) { - auto start_indices__ = start_indices ? _fbb.CreateVector(*start_indices) : 0; - auto limit_indices__ = limit_indices ? _fbb.CreateVector(*limit_indices) : 0; - auto strides__ = strides ? _fbb.CreateVector(*strides) : 0; - return tflite_micro::CreateStablehloSliceOptions( - _fbb, - start_indices__, - limit_indices__, - strides__); -} - -flatbuffers::Offset CreateStablehloSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloConvolutionOptionsT : public flatbuffers::NativeTable { - typedef StablehloConvolutionOptions TableType; - std::vector window_strides{}; - std::vector padding{}; - std::vector lhs_dilation{}; - std::vector rhs_dilation{}; - std::vector window_reversal{}; - int64_t input_batch_dimension = 0; - int64_t input_feature_dimension = 0; - std::vector input_spatial_dimensions{}; - int64_t kernel_input_feature_dimension = 0; - int64_t kernel_output_feature_dimension = 0; - std::vector kernel_spatial_dimensions{}; - int64_t output_batch_dimension = 0; - int64_t output_feature_dimension = 0; - std::vector output_spatial_dimensions{}; - int64_t feature_group_count = 0; - int64_t batch_group_count = 0; - std::vector precision_config{}; -}; - -struct StablehloConvolutionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloConvolutionOptionsT NativeTableType; - typedef StablehloConvolutionOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_WINDOW_STRIDES = 4, - VT_PADDING = 6, - VT_LHS_DILATION = 8, - VT_RHS_DILATION = 10, - VT_WINDOW_REVERSAL = 12, - VT_INPUT_BATCH_DIMENSION = 14, - VT_INPUT_FEATURE_DIMENSION = 16, - VT_INPUT_SPATIAL_DIMENSIONS = 18, - VT_KERNEL_INPUT_FEATURE_DIMENSION = 20, - VT_KERNEL_OUTPUT_FEATURE_DIMENSION = 22, - VT_KERNEL_SPATIAL_DIMENSIONS = 24, - VT_OUTPUT_BATCH_DIMENSION = 26, - VT_OUTPUT_FEATURE_DIMENSION = 28, - VT_OUTPUT_SPATIAL_DIMENSIONS = 30, - VT_FEATURE_GROUP_COUNT = 32, - VT_BATCH_GROUP_COUNT = 34, - VT_PRECISION_CONFIG = 36 - }; - const flatbuffers::Vector *window_strides() const { - return GetPointer *>(VT_WINDOW_STRIDES); - } - const flatbuffers::Vector *padding() const { - return GetPointer *>(VT_PADDING); - } - const flatbuffers::Vector *lhs_dilation() const { - return GetPointer *>(VT_LHS_DILATION); - } - const flatbuffers::Vector *rhs_dilation() const { - return GetPointer *>(VT_RHS_DILATION); - } - const flatbuffers::Vector *window_reversal() const { - return GetPointer *>(VT_WINDOW_REVERSAL); - } - int64_t input_batch_dimension() const { - return GetField(VT_INPUT_BATCH_DIMENSION, 0); - } - int64_t input_feature_dimension() const { - return GetField(VT_INPUT_FEATURE_DIMENSION, 0); - } - const flatbuffers::Vector *input_spatial_dimensions() const { - return GetPointer *>(VT_INPUT_SPATIAL_DIMENSIONS); - } - int64_t kernel_input_feature_dimension() const { - return GetField(VT_KERNEL_INPUT_FEATURE_DIMENSION, 0); - } - int64_t kernel_output_feature_dimension() const { - return GetField(VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 0); - } - const flatbuffers::Vector *kernel_spatial_dimensions() const { - return GetPointer *>(VT_KERNEL_SPATIAL_DIMENSIONS); - } - int64_t output_batch_dimension() const { - return GetField(VT_OUTPUT_BATCH_DIMENSION, 0); - } - int64_t output_feature_dimension() const { - return GetField(VT_OUTPUT_FEATURE_DIMENSION, 0); - } - const flatbuffers::Vector *output_spatial_dimensions() const { - return GetPointer *>(VT_OUTPUT_SPATIAL_DIMENSIONS); - } - int64_t feature_group_count() const { - return GetField(VT_FEATURE_GROUP_COUNT, 0); - } - int64_t batch_group_count() const { - return GetField(VT_BATCH_GROUP_COUNT, 0); - } - const flatbuffers::Vector *precision_config() const { - return GetPointer *>(VT_PRECISION_CONFIG); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_WINDOW_STRIDES) && - verifier.VerifyVector(window_strides()) && - VerifyOffset(verifier, VT_PADDING) && - verifier.VerifyVector(padding()) && - VerifyOffset(verifier, VT_LHS_DILATION) && - verifier.VerifyVector(lhs_dilation()) && - VerifyOffset(verifier, VT_RHS_DILATION) && - verifier.VerifyVector(rhs_dilation()) && - VerifyOffset(verifier, VT_WINDOW_REVERSAL) && - verifier.VerifyVector(window_reversal()) && - VerifyField(verifier, VT_INPUT_BATCH_DIMENSION, 8) && - VerifyField(verifier, VT_INPUT_FEATURE_DIMENSION, 8) && - VerifyOffset(verifier, VT_INPUT_SPATIAL_DIMENSIONS) && - verifier.VerifyVector(input_spatial_dimensions()) && - VerifyField(verifier, VT_KERNEL_INPUT_FEATURE_DIMENSION, 8) && - VerifyField(verifier, VT_KERNEL_OUTPUT_FEATURE_DIMENSION, 8) && - VerifyOffset(verifier, VT_KERNEL_SPATIAL_DIMENSIONS) && - verifier.VerifyVector(kernel_spatial_dimensions()) && - VerifyField(verifier, VT_OUTPUT_BATCH_DIMENSION, 8) && - VerifyField(verifier, VT_OUTPUT_FEATURE_DIMENSION, 8) && - VerifyOffset(verifier, VT_OUTPUT_SPATIAL_DIMENSIONS) && - verifier.VerifyVector(output_spatial_dimensions()) && - VerifyField(verifier, VT_FEATURE_GROUP_COUNT, 8) && - VerifyField(verifier, VT_BATCH_GROUP_COUNT, 8) && - VerifyOffset(verifier, VT_PRECISION_CONFIG) && - verifier.VerifyVector(precision_config()) && - verifier.EndTable(); - } - StablehloConvolutionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloConvolutionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloConvolutionOptionsBuilder { - typedef StablehloConvolutionOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_window_strides(flatbuffers::Offset> window_strides) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_STRIDES, window_strides); - } - void add_padding(flatbuffers::Offset> padding) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_PADDING, padding); - } - void add_lhs_dilation(flatbuffers::Offset> lhs_dilation) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_LHS_DILATION, lhs_dilation); - } - void add_rhs_dilation(flatbuffers::Offset> rhs_dilation) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_RHS_DILATION, rhs_dilation); - } - void add_window_reversal(flatbuffers::Offset> window_reversal) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_WINDOW_REVERSAL, window_reversal); - } - void add_input_batch_dimension(int64_t input_batch_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_INPUT_BATCH_DIMENSION, input_batch_dimension, 0); - } - void add_input_feature_dimension(int64_t input_feature_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_INPUT_FEATURE_DIMENSION, input_feature_dimension, 0); - } - void add_input_spatial_dimensions(flatbuffers::Offset> input_spatial_dimensions) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_INPUT_SPATIAL_DIMENSIONS, input_spatial_dimensions); - } - void add_kernel_input_feature_dimension(int64_t kernel_input_feature_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_KERNEL_INPUT_FEATURE_DIMENSION, kernel_input_feature_dimension, 0); - } - void add_kernel_output_feature_dimension(int64_t kernel_output_feature_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_KERNEL_OUTPUT_FEATURE_DIMENSION, kernel_output_feature_dimension, 0); - } - void add_kernel_spatial_dimensions(flatbuffers::Offset> kernel_spatial_dimensions) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_KERNEL_SPATIAL_DIMENSIONS, kernel_spatial_dimensions); - } - void add_output_batch_dimension(int64_t output_batch_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_OUTPUT_BATCH_DIMENSION, output_batch_dimension, 0); - } - void add_output_feature_dimension(int64_t output_feature_dimension) { - fbb_.AddElement(StablehloConvolutionOptions::VT_OUTPUT_FEATURE_DIMENSION, output_feature_dimension, 0); - } - void add_output_spatial_dimensions(flatbuffers::Offset> output_spatial_dimensions) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_OUTPUT_SPATIAL_DIMENSIONS, output_spatial_dimensions); - } - void add_feature_group_count(int64_t feature_group_count) { - fbb_.AddElement(StablehloConvolutionOptions::VT_FEATURE_GROUP_COUNT, feature_group_count, 0); - } - void add_batch_group_count(int64_t batch_group_count) { - fbb_.AddElement(StablehloConvolutionOptions::VT_BATCH_GROUP_COUNT, batch_group_count, 0); - } - void add_precision_config(flatbuffers::Offset> precision_config) { - fbb_.AddOffset(StablehloConvolutionOptions::VT_PRECISION_CONFIG, precision_config); - } - explicit StablehloConvolutionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloConvolutionOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> window_strides = 0, - flatbuffers::Offset> padding = 0, - flatbuffers::Offset> lhs_dilation = 0, - flatbuffers::Offset> rhs_dilation = 0, - flatbuffers::Offset> window_reversal = 0, - int64_t input_batch_dimension = 0, - int64_t input_feature_dimension = 0, - flatbuffers::Offset> input_spatial_dimensions = 0, - int64_t kernel_input_feature_dimension = 0, - int64_t kernel_output_feature_dimension = 0, - flatbuffers::Offset> kernel_spatial_dimensions = 0, - int64_t output_batch_dimension = 0, - int64_t output_feature_dimension = 0, - flatbuffers::Offset> output_spatial_dimensions = 0, - int64_t feature_group_count = 0, - int64_t batch_group_count = 0, - flatbuffers::Offset> precision_config = 0) { - StablehloConvolutionOptionsBuilder builder_(_fbb); - builder_.add_batch_group_count(batch_group_count); - builder_.add_feature_group_count(feature_group_count); - builder_.add_output_feature_dimension(output_feature_dimension); - builder_.add_output_batch_dimension(output_batch_dimension); - builder_.add_kernel_output_feature_dimension(kernel_output_feature_dimension); - builder_.add_kernel_input_feature_dimension(kernel_input_feature_dimension); - builder_.add_input_feature_dimension(input_feature_dimension); - builder_.add_input_batch_dimension(input_batch_dimension); - builder_.add_precision_config(precision_config); - builder_.add_output_spatial_dimensions(output_spatial_dimensions); - builder_.add_kernel_spatial_dimensions(kernel_spatial_dimensions); - builder_.add_input_spatial_dimensions(input_spatial_dimensions); - builder_.add_window_reversal(window_reversal); - builder_.add_rhs_dilation(rhs_dilation); - builder_.add_lhs_dilation(lhs_dilation); - builder_.add_padding(padding); - builder_.add_window_strides(window_strides); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloConvolutionOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *window_strides = nullptr, - const std::vector *padding = nullptr, - const std::vector *lhs_dilation = nullptr, - const std::vector *rhs_dilation = nullptr, - const std::vector *window_reversal = nullptr, - int64_t input_batch_dimension = 0, - int64_t input_feature_dimension = 0, - const std::vector *input_spatial_dimensions = nullptr, - int64_t kernel_input_feature_dimension = 0, - int64_t kernel_output_feature_dimension = 0, - const std::vector *kernel_spatial_dimensions = nullptr, - int64_t output_batch_dimension = 0, - int64_t output_feature_dimension = 0, - const std::vector *output_spatial_dimensions = nullptr, - int64_t feature_group_count = 0, - int64_t batch_group_count = 0, - const std::vector *precision_config = nullptr) { - auto window_strides__ = window_strides ? _fbb.CreateVector(*window_strides) : 0; - auto padding__ = padding ? _fbb.CreateVector(*padding) : 0; - auto lhs_dilation__ = lhs_dilation ? _fbb.CreateVector(*lhs_dilation) : 0; - auto rhs_dilation__ = rhs_dilation ? _fbb.CreateVector(*rhs_dilation) : 0; - auto window_reversal__ = window_reversal ? _fbb.CreateVector(*window_reversal) : 0; - auto input_spatial_dimensions__ = input_spatial_dimensions ? _fbb.CreateVector(*input_spatial_dimensions) : 0; - auto kernel_spatial_dimensions__ = kernel_spatial_dimensions ? _fbb.CreateVector(*kernel_spatial_dimensions) : 0; - auto output_spatial_dimensions__ = output_spatial_dimensions ? _fbb.CreateVector(*output_spatial_dimensions) : 0; - auto precision_config__ = precision_config ? _fbb.CreateVector(*precision_config) : 0; - return tflite_micro::CreateStablehloConvolutionOptions( - _fbb, - window_strides__, - padding__, - lhs_dilation__, - rhs_dilation__, - window_reversal__, - input_batch_dimension, - input_feature_dimension, - input_spatial_dimensions__, - kernel_input_feature_dimension, - kernel_output_feature_dimension, - kernel_spatial_dimensions__, - output_batch_dimension, - output_feature_dimension, - output_spatial_dimensions__, - feature_group_count, - batch_group_count, - precision_config__); -} - -flatbuffers::Offset CreateStablehloConvolutionOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloScatterOptionsT : public flatbuffers::NativeTable { - typedef StablehloScatterOptions TableType; - bool indices_are_sorted = false; - std::vector update_window_dims{}; - std::vector inserted_window_dims{}; - std::vector scatter_dims_to_operand_dims{}; - int64_t index_vector_dim = 0; - bool unique_indices = false; - int32_t update_computation_subgraph_index = 0; -}; - -struct StablehloScatterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloScatterOptionsT NativeTableType; - typedef StablehloScatterOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INDICES_ARE_SORTED = 4, - VT_UPDATE_WINDOW_DIMS = 6, - VT_INSERTED_WINDOW_DIMS = 8, - VT_SCATTER_DIMS_TO_OPERAND_DIMS = 10, - VT_INDEX_VECTOR_DIM = 12, - VT_UNIQUE_INDICES = 14, - VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX = 16 - }; - bool indices_are_sorted() const { - return GetField(VT_INDICES_ARE_SORTED, 0) != 0; - } - const flatbuffers::Vector *update_window_dims() const { - return GetPointer *>(VT_UPDATE_WINDOW_DIMS); - } - const flatbuffers::Vector *inserted_window_dims() const { - return GetPointer *>(VT_INSERTED_WINDOW_DIMS); - } - const flatbuffers::Vector *scatter_dims_to_operand_dims() const { - return GetPointer *>(VT_SCATTER_DIMS_TO_OPERAND_DIMS); - } - int64_t index_vector_dim() const { - return GetField(VT_INDEX_VECTOR_DIM, 0); - } - bool unique_indices() const { - return GetField(VT_UNIQUE_INDICES, 0) != 0; - } - int32_t update_computation_subgraph_index() const { - return GetField(VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_INDICES_ARE_SORTED, 1) && - VerifyOffset(verifier, VT_UPDATE_WINDOW_DIMS) && - verifier.VerifyVector(update_window_dims()) && - VerifyOffset(verifier, VT_INSERTED_WINDOW_DIMS) && - verifier.VerifyVector(inserted_window_dims()) && - VerifyOffset(verifier, VT_SCATTER_DIMS_TO_OPERAND_DIMS) && - verifier.VerifyVector(scatter_dims_to_operand_dims()) && - VerifyField(verifier, VT_INDEX_VECTOR_DIM, 8) && - VerifyField(verifier, VT_UNIQUE_INDICES, 1) && - VerifyField(verifier, VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - StablehloScatterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloScatterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloScatterOptionsBuilder { - typedef StablehloScatterOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_indices_are_sorted(bool indices_are_sorted) { - fbb_.AddElement(StablehloScatterOptions::VT_INDICES_ARE_SORTED, static_cast(indices_are_sorted), 0); - } - void add_update_window_dims(flatbuffers::Offset> update_window_dims) { - fbb_.AddOffset(StablehloScatterOptions::VT_UPDATE_WINDOW_DIMS, update_window_dims); - } - void add_inserted_window_dims(flatbuffers::Offset> inserted_window_dims) { - fbb_.AddOffset(StablehloScatterOptions::VT_INSERTED_WINDOW_DIMS, inserted_window_dims); - } - void add_scatter_dims_to_operand_dims(flatbuffers::Offset> scatter_dims_to_operand_dims) { - fbb_.AddOffset(StablehloScatterOptions::VT_SCATTER_DIMS_TO_OPERAND_DIMS, scatter_dims_to_operand_dims); - } - void add_index_vector_dim(int64_t index_vector_dim) { - fbb_.AddElement(StablehloScatterOptions::VT_INDEX_VECTOR_DIM, index_vector_dim, 0); - } - void add_unique_indices(bool unique_indices) { - fbb_.AddElement(StablehloScatterOptions::VT_UNIQUE_INDICES, static_cast(unique_indices), 0); - } - void add_update_computation_subgraph_index(int32_t update_computation_subgraph_index) { - fbb_.AddElement(StablehloScatterOptions::VT_UPDATE_COMPUTATION_SUBGRAPH_INDEX, update_computation_subgraph_index, 0); - } - explicit StablehloScatterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloScatterOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool indices_are_sorted = false, - flatbuffers::Offset> update_window_dims = 0, - flatbuffers::Offset> inserted_window_dims = 0, - flatbuffers::Offset> scatter_dims_to_operand_dims = 0, - int64_t index_vector_dim = 0, - bool unique_indices = false, - int32_t update_computation_subgraph_index = 0) { - StablehloScatterOptionsBuilder builder_(_fbb); - builder_.add_index_vector_dim(index_vector_dim); - builder_.add_update_computation_subgraph_index(update_computation_subgraph_index); - builder_.add_scatter_dims_to_operand_dims(scatter_dims_to_operand_dims); - builder_.add_inserted_window_dims(inserted_window_dims); - builder_.add_update_window_dims(update_window_dims); - builder_.add_unique_indices(unique_indices); - builder_.add_indices_are_sorted(indices_are_sorted); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateStablehloScatterOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - bool indices_are_sorted = false, - const std::vector *update_window_dims = nullptr, - const std::vector *inserted_window_dims = nullptr, - const std::vector *scatter_dims_to_operand_dims = nullptr, - int64_t index_vector_dim = 0, - bool unique_indices = false, - int32_t update_computation_subgraph_index = 0) { - auto update_window_dims__ = update_window_dims ? _fbb.CreateVector(*update_window_dims) : 0; - auto inserted_window_dims__ = inserted_window_dims ? _fbb.CreateVector(*inserted_window_dims) : 0; - auto scatter_dims_to_operand_dims__ = scatter_dims_to_operand_dims ? _fbb.CreateVector(*scatter_dims_to_operand_dims) : 0; - return tflite_micro::CreateStablehloScatterOptions( - _fbb, - indices_are_sorted, - update_window_dims__, - inserted_window_dims__, - scatter_dims_to_operand_dims__, - index_vector_dim, - unique_indices, - update_computation_subgraph_index); -} - -flatbuffers::Offset CreateStablehloScatterOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StablehloRngBitGeneratorOptionsT : public flatbuffers::NativeTable { - typedef StablehloRngBitGeneratorOptions TableType; - tflite_micro::RngAlgorithm algorithm = tflite_micro::RngAlgorithm_DEFAULT; -}; - -struct StablehloRngBitGeneratorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StablehloRngBitGeneratorOptionsT NativeTableType; - typedef StablehloRngBitGeneratorOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALGORITHM = 4 - }; - tflite_micro::RngAlgorithm algorithm() const { - return static_cast(GetField(VT_ALGORITHM, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALGORITHM, 1) && - verifier.EndTable(); - } - StablehloRngBitGeneratorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StablehloRngBitGeneratorOptionsBuilder { - typedef StablehloRngBitGeneratorOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_algorithm(tflite_micro::RngAlgorithm algorithm) { - fbb_.AddElement(StablehloRngBitGeneratorOptions::VT_ALGORITHM, static_cast(algorithm), 0); - } - explicit StablehloRngBitGeneratorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStablehloRngBitGeneratorOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::RngAlgorithm algorithm = tflite_micro::RngAlgorithm_DEFAULT) { - StablehloRngBitGeneratorOptionsBuilder builder_(_fbb); - builder_.add_algorithm(algorithm); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStablehloRngBitGeneratorOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Conv2DOptionsT : public flatbuffers::NativeTable { - typedef Conv2DOptions TableType; - tflite_micro::Padding padding = tflite_micro::Padding_SAME; - int32_t stride_w = 0; - int32_t stride_h = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - int32_t dilation_w_factor = 1; - int32_t dilation_h_factor = 1; - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_INT32; -}; - -struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Conv2DOptionsT NativeTableType; - typedef Conv2DOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FUSED_ACTIVATION_FUNCTION = 10, - VT_DILATION_W_FACTOR = 12, - VT_DILATION_H_FACTOR = 14, - VT_QUANTIZED_BIAS_TYPE = 16 - }; - tflite_micro::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 1); - } - int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 1); - } - tflite_micro::TensorType quantized_bias_type() const { - return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING, 1) && - VerifyField(verifier, VT_STRIDE_W, 4) && - VerifyField(verifier, VT_STRIDE_H, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && - VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && - VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && - verifier.EndTable(); - } - Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Conv2DOptionsBuilder { - typedef Conv2DOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite_micro::Padding padding) { - fbb_.AddElement(Conv2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(Conv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(Conv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { - fbb_.AddElement(Conv2DOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); - } - explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::Padding padding = tflite_micro::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, - int32_t dilation_h_factor = 1, - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { - Conv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_quantized_bias_type(quantized_bias_type); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Conv3DOptionsT : public flatbuffers::NativeTable { - typedef Conv3DOptions TableType; - tflite_micro::Padding padding = tflite_micro::Padding_SAME; - int32_t stride_d = 0; - int32_t stride_w = 0; - int32_t stride_h = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - int32_t dilation_d_factor = 1; - int32_t dilation_w_factor = 1; - int32_t dilation_h_factor = 1; -}; - -struct Conv3DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Conv3DOptionsT NativeTableType; - typedef Conv3DOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_D = 6, - VT_STRIDE_W = 8, - VT_STRIDE_H = 10, - VT_FUSED_ACTIVATION_FUNCTION = 12, - VT_DILATION_D_FACTOR = 14, - VT_DILATION_W_FACTOR = 16, - VT_DILATION_H_FACTOR = 18 - }; - tflite_micro::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_d() const { - return GetField(VT_STRIDE_D, 0); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_d_factor() const { - return GetField(VT_DILATION_D_FACTOR, 1); - } - int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 1); - } - int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 1); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING, 1) && - VerifyField(verifier, VT_STRIDE_D, 4) && - VerifyField(verifier, VT_STRIDE_W, 4) && - VerifyField(verifier, VT_STRIDE_H, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_DILATION_D_FACTOR, 4) && - VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && - VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && - verifier.EndTable(); - } - Conv3DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Conv3DOptionsBuilder { - typedef Conv3DOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite_micro::Padding padding) { - fbb_.AddElement(Conv3DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_d(int32_t stride_d) { - fbb_.AddElement(Conv3DOptions::VT_STRIDE_D, stride_d, 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(Conv3DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(Conv3DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Conv3DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_dilation_d_factor(int32_t dilation_d_factor) { - fbb_.AddElement(Conv3DOptions::VT_DILATION_D_FACTOR, dilation_d_factor, 1); - } - void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(Conv3DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(Conv3DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit Conv3DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConv3DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::Padding padding = tflite_micro::Padding_SAME, - int32_t stride_d = 0, - int32_t stride_w = 0, - int32_t stride_h = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - int32_t dilation_d_factor = 1, - int32_t dilation_w_factor = 1, - int32_t dilation_h_factor = 1) { - Conv3DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_dilation_d_factor(dilation_d_factor); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_stride_d(stride_d); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Pool2DOptionsT : public flatbuffers::NativeTable { - typedef Pool2DOptions TableType; - tflite_micro::Padding padding = tflite_micro::Padding_SAME; - int32_t stride_w = 0; - int32_t stride_h = 0; - int32_t filter_width = 0; - int32_t filter_height = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; -}; - -struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Pool2DOptionsT NativeTableType; - typedef Pool2DOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FILTER_WIDTH = 10, - VT_FILTER_HEIGHT = 12, - VT_FUSED_ACTIVATION_FUNCTION = 14 - }; - tflite_micro::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - int32_t filter_width() const { - return GetField(VT_FILTER_WIDTH, 0); - } - int32_t filter_height() const { - return GetField(VT_FILTER_HEIGHT, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING, 1) && - VerifyField(verifier, VT_STRIDE_W, 4) && - VerifyField(verifier, VT_STRIDE_H, 4) && - VerifyField(verifier, VT_FILTER_WIDTH, 4) && - VerifyField(verifier, VT_FILTER_HEIGHT, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - verifier.EndTable(); - } - Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Pool2DOptionsBuilder { - typedef Pool2DOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite_micro::Padding padding) { - fbb_.AddElement(Pool2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(Pool2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(Pool2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_filter_width(int32_t filter_width) { - fbb_.AddElement(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0); - } - void add_filter_height(int32_t filter_height) { - fbb_.AddElement(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePool2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::Padding padding = tflite_micro::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - int32_t filter_width = 0, - int32_t filter_height = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { - Pool2DOptionsBuilder builder_(_fbb); - builder_.add_filter_height(filter_height); - builder_.add_filter_width(filter_width); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable { - typedef DepthwiseConv2DOptions TableType; - tflite_micro::Padding padding = tflite_micro::Padding_SAME; - int32_t stride_w = 0; - int32_t stride_h = 0; - int32_t depth_multiplier = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - int32_t dilation_w_factor = 1; - int32_t dilation_h_factor = 1; -}; - -struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DepthwiseConv2DOptionsT NativeTableType; - typedef DepthwiseConv2DOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_DEPTH_MULTIPLIER = 10, - VT_FUSED_ACTIVATION_FUNCTION = 12, - VT_DILATION_W_FACTOR = 14, - VT_DILATION_H_FACTOR = 16 - }; - tflite_micro::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - int32_t depth_multiplier() const { - return GetField(VT_DEPTH_MULTIPLIER, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - int32_t dilation_w_factor() const { - return GetField(VT_DILATION_W_FACTOR, 1); - } - int32_t dilation_h_factor() const { - return GetField(VT_DILATION_H_FACTOR, 1); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING, 1) && - VerifyField(verifier, VT_STRIDE_W, 4) && - VerifyField(verifier, VT_STRIDE_H, 4) && - VerifyField(verifier, VT_DEPTH_MULTIPLIER, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_DILATION_W_FACTOR, 4) && - VerifyField(verifier, VT_DILATION_H_FACTOR, 4) && - verifier.EndTable(); - } - DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DepthwiseConv2DOptionsBuilder { - typedef DepthwiseConv2DOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite_micro::Padding padding) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0); - } - void add_depth_multiplier(int32_t depth_multiplier) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_dilation_w_factor(int32_t dilation_w_factor) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1); - } - void add_dilation_h_factor(int32_t dilation_h_factor) { - fbb_.AddElement(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1); - } - explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDepthwiseConv2DOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::Padding padding = tflite_micro::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - int32_t depth_multiplier = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - int32_t dilation_w_factor = 1, - int32_t dilation_h_factor = 1) { - DepthwiseConv2DOptionsBuilder builder_(_fbb); - builder_.add_dilation_h_factor(dilation_h_factor); - builder_.add_dilation_w_factor(dilation_w_factor); - builder_.add_depth_multiplier(depth_multiplier); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable { - typedef ConcatEmbeddingsOptions TableType; - int32_t num_channels = 0; - std::vector num_columns_per_channel{}; - std::vector embedding_dim_per_channel{}; -}; - -struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ConcatEmbeddingsOptionsT NativeTableType; - typedef ConcatEmbeddingsOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_CHANNELS = 4, - VT_NUM_COLUMNS_PER_CHANNEL = 6, - VT_EMBEDDING_DIM_PER_CHANNEL = 8 - }; - int32_t num_channels() const { - return GetField(VT_NUM_CHANNELS, 0); - } - const flatbuffers::Vector *num_columns_per_channel() const { - return GetPointer *>(VT_NUM_COLUMNS_PER_CHANNEL); - } - const flatbuffers::Vector *embedding_dim_per_channel() const { - return GetPointer *>(VT_EMBEDDING_DIM_PER_CHANNEL); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_CHANNELS, 4) && - VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) && - verifier.VerifyVector(num_columns_per_channel()) && - VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) && - verifier.VerifyVector(embedding_dim_per_channel()) && - verifier.EndTable(); - } - ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ConcatEmbeddingsOptionsBuilder { - typedef ConcatEmbeddingsOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_channels(int32_t num_channels) { - fbb_.AddElement(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0); - } - void add_num_columns_per_channel(flatbuffers::Offset> num_columns_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel); - } - void add_embedding_dim_per_channel(flatbuffers::Offset> embedding_dim_per_channel) { - fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel); - } - explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConcatEmbeddingsOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_channels = 0, - flatbuffers::Offset> num_columns_per_channel = 0, - flatbuffers::Offset> embedding_dim_per_channel = 0) { - ConcatEmbeddingsOptionsBuilder builder_(_fbb); - builder_.add_embedding_dim_per_channel(embedding_dim_per_channel); - builder_.add_num_columns_per_channel(num_columns_per_channel); - builder_.add_num_channels(num_channels); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateConcatEmbeddingsOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_channels = 0, - const std::vector *num_columns_per_channel = nullptr, - const std::vector *embedding_dim_per_channel = nullptr) { - auto num_columns_per_channel__ = num_columns_per_channel ? _fbb.CreateVector(*num_columns_per_channel) : 0; - auto embedding_dim_per_channel__ = embedding_dim_per_channel ? _fbb.CreateVector(*embedding_dim_per_channel) : 0; - return tflite_micro::CreateConcatEmbeddingsOptions( - _fbb, - num_channels, - num_columns_per_channel__, - embedding_dim_per_channel__); -} - -flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LSHProjectionOptionsT : public flatbuffers::NativeTable { - typedef LSHProjectionOptions TableType; - tflite_micro::LSHProjectionType type = tflite_micro::LSHProjectionType_UNKNOWN; -}; - -struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LSHProjectionOptionsT NativeTableType; - typedef LSHProjectionOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TYPE = 4 - }; - tflite_micro::LSHProjectionType type() const { - return static_cast(GetField(VT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TYPE, 1) && - verifier.EndTable(); - } - LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LSHProjectionOptionsBuilder { - typedef LSHProjectionOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_type(tflite_micro::LSHProjectionType type) { - fbb_.AddElement(LSHProjectionOptions::VT_TYPE, static_cast(type), 0); - } - explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLSHProjectionOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::LSHProjectionType type = tflite_micro::LSHProjectionType_UNKNOWN) { - LSHProjectionOptionsBuilder builder_(_fbb); - builder_.add_type(type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SVDFOptionsT : public flatbuffers::NativeTable { - typedef SVDFOptions TableType; - int32_t rank = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool asymmetric_quantize_inputs = false; -}; - -struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SVDFOptionsT NativeTableType; - typedef SVDFOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_RANK = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - int32_t rank() const { - return GetField(VT_RANK, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_RANK, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SVDFOptionsBuilder { - typedef SVDFOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_rank(int32_t rank) { - fbb_.AddElement(SVDFOptions::VT_RANK, rank, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSVDFOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t rank = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - SVDFOptionsBuilder builder_(_fbb); - builder_.add_rank(rank); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RNNOptionsT : public flatbuffers::NativeTable { - typedef RNNOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool asymmetric_quantize_inputs = false; -}; - -struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RNNOptionsT NativeTableType; - typedef RNNOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 6 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RNNOptionsBuilder { - typedef RNNOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - RNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SequenceRNNOptionsT : public flatbuffers::NativeTable { - typedef SequenceRNNOptions TableType; - bool time_major = false; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool asymmetric_quantize_inputs = false; -}; - -struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SequenceRNNOptionsT NativeTableType; - typedef SequenceRNNOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TIME_MAJOR, 1) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SequenceRNNOptionsBuilder { - typedef SequenceRNNOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) { - fbb_.AddElement(SequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool time_major = false, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool asymmetric_quantize_inputs = false) { - SequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable { - typedef BidirectionalSequenceRNNOptions TableType; - bool time_major = false; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool merge_outputs = false; - bool asymmetric_quantize_inputs = false; -}; - -struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BidirectionalSequenceRNNOptionsT NativeTableType; - typedef BidirectionalSequenceRNNOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TIME_MAJOR = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6, - VT_MERGE_OUTPUTS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10 - }; - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool merge_outputs() const { - return GetField(VT_MERGE_OUTPUTS, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TIME_MAJOR, 1) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_MERGE_OUTPUTS, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BidirectionalSequenceRNNOptionsBuilder { - typedef BidirectionalSequenceRNNOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_time_major(bool time_major) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_merge_outputs(bool merge_outputs) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool time_major = false, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool merge_outputs = false, - bool asymmetric_quantize_inputs = false) { - BidirectionalSequenceRNNOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_time_major(time_major); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FullyConnectedOptionsT : public flatbuffers::NativeTable { - typedef FullyConnectedOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - tflite_micro::FullyConnectedOptionsWeightsFormat weights_format = tflite_micro::FullyConnectedOptionsWeightsFormat_DEFAULT; - bool keep_num_dims = false; - bool asymmetric_quantize_inputs = false; - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32; -}; - -struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FullyConnectedOptionsT NativeTableType; - typedef FullyConnectedOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_WEIGHTS_FORMAT = 6, - VT_KEEP_NUM_DIMS = 8, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 10, - VT_QUANTIZED_BIAS_TYPE = 12 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - tflite_micro::FullyConnectedOptionsWeightsFormat weights_format() const { - return static_cast(GetField(VT_WEIGHTS_FORMAT, 0)); - } - bool keep_num_dims() const { - return GetField(VT_KEEP_NUM_DIMS, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - tflite_micro::TensorType quantized_bias_type() const { - return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_WEIGHTS_FORMAT, 1) && - VerifyField(verifier, VT_KEEP_NUM_DIMS, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && - verifier.EndTable(); - } - FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FullyConnectedOptionsBuilder { - typedef FullyConnectedOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_weights_format(tflite_micro::FullyConnectedOptionsWeightsFormat weights_format) { - fbb_.AddElement(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast(weights_format), 0); - } - void add_keep_num_dims(bool keep_num_dims) { - fbb_.AddElement(FullyConnectedOptions::VT_KEEP_NUM_DIMS, static_cast(keep_num_dims), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { - fbb_.AddElement(FullyConnectedOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); - } - explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFullyConnectedOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - tflite_micro::FullyConnectedOptionsWeightsFormat weights_format = tflite_micro::FullyConnectedOptionsWeightsFormat_DEFAULT, - bool keep_num_dims = false, - bool asymmetric_quantize_inputs = false, - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { - FullyConnectedOptionsBuilder builder_(_fbb); - builder_.add_quantized_bias_type(quantized_bias_type); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_keep_num_dims(keep_num_dims); - builder_.add_weights_format(weights_format); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SoftmaxOptionsT : public flatbuffers::NativeTable { - typedef SoftmaxOptions TableType; - float beta = 0.0f; -}; - -struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SoftmaxOptionsT NativeTableType; - typedef SoftmaxOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BETA = 4 - }; - float beta() const { - return GetField(VT_BETA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BETA, 4) && - verifier.EndTable(); - } - SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SoftmaxOptionsBuilder { - typedef SoftmaxOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_beta(float beta) { - fbb_.AddElement(SoftmaxOptions::VT_BETA, beta, 0.0f); - } - explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float beta = 0.0f) { - SoftmaxOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ConcatenationOptionsT : public flatbuffers::NativeTable { - typedef ConcatenationOptions TableType; - int32_t axis = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; -}; - -struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ConcatenationOptionsT NativeTableType; - typedef ConcatenationOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4, - VT_FUSED_ACTIVATION_FUNCTION = 6 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - verifier.EndTable(); - } - ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ConcatenationOptionsBuilder { - typedef ConcatenationOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(ConcatenationOptions::VT_AXIS, axis, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateConcatenationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { - ConcatenationOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AddOptionsT : public flatbuffers::NativeTable { - typedef AddOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool pot_scale_int16 = true; -}; - -struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AddOptionsT NativeTableType; - typedef AddOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_POT_SCALE_INT16 = 6 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool pot_scale_int16() const { - return GetField(VT_POT_SCALE_INT16, 1) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_POT_SCALE_INT16, 1) && - verifier.EndTable(); - } - AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AddOptionsBuilder { - typedef AddOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_pot_scale_int16(bool pot_scale_int16) { - fbb_.AddElement(AddOptions::VT_POT_SCALE_INT16, static_cast(pot_scale_int16), 1); - } - explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAddOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool pot_scale_int16 = true) { - AddOptionsBuilder builder_(_fbb); - builder_.add_pot_scale_int16(pot_scale_int16); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MulOptionsT : public flatbuffers::NativeTable { - typedef MulOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; -}; - -struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MulOptionsT NativeTableType; - typedef MulOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - verifier.EndTable(); - } - MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MulOptionsBuilder { - typedef MulOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { - MulOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct L2NormOptionsT : public flatbuffers::NativeTable { - typedef L2NormOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; -}; - -struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef L2NormOptionsT NativeTableType; - typedef L2NormOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - verifier.EndTable(); - } - L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct L2NormOptionsBuilder { - typedef L2NormOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateL2NormOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { - L2NormOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable { - typedef LocalResponseNormalizationOptions TableType; - int32_t radius = 0; - float bias = 0.0f; - float alpha = 0.0f; - float beta = 0.0f; -}; - -struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LocalResponseNormalizationOptionsT NativeTableType; - typedef LocalResponseNormalizationOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_RADIUS = 4, - VT_BIAS = 6, - VT_ALPHA = 8, - VT_BETA = 10 - }; - int32_t radius() const { - return GetField(VT_RADIUS, 0); - } - float bias() const { - return GetField(VT_BIAS, 0.0f); - } - float alpha() const { - return GetField(VT_ALPHA, 0.0f); - } - float beta() const { - return GetField(VT_BETA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_RADIUS, 4) && - VerifyField(verifier, VT_BIAS, 4) && - VerifyField(verifier, VT_ALPHA, 4) && - VerifyField(verifier, VT_BETA, 4) && - verifier.EndTable(); - } - LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LocalResponseNormalizationOptionsBuilder { - typedef LocalResponseNormalizationOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_radius(int32_t radius) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0); - } - void add_bias(float bias) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f); - } - void add_alpha(float alpha) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f); - } - void add_beta(float beta) { - fbb_.AddElement(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f); - } - explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLocalResponseNormalizationOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t radius = 0, - float bias = 0.0f, - float alpha = 0.0f, - float beta = 0.0f) { - LocalResponseNormalizationOptionsBuilder builder_(_fbb); - builder_.add_beta(beta); - builder_.add_alpha(alpha); - builder_.add_bias(bias); - builder_.add_radius(radius); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LSTMOptionsT : public flatbuffers::NativeTable { - typedef LSTMOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - float cell_clip = 0.0f; - float proj_clip = 0.0f; - tflite_micro::LSTMKernelType kernel_type = tflite_micro::LSTMKernelType_FULL; - bool asymmetric_quantize_inputs = false; -}; - -struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LSTMOptionsT NativeTableType; - typedef LSTMOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_KERNEL_TYPE = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - tflite_micro::LSTMKernelType kernel_type() const { - return static_cast(GetField(VT_KERNEL_TYPE, 0)); - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_CELL_CLIP, 4) && - VerifyField(verifier, VT_PROJ_CLIP, 4) && - VerifyField(verifier, VT_KERNEL_TYPE, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LSTMOptionsBuilder { - typedef LSTMOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_kernel_type(tflite_micro::LSTMKernelType kernel_type) { - fbb_.AddElement(LSTMOptions::VT_KERNEL_TYPE, static_cast(kernel_type), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - tflite_micro::LSTMKernelType kernel_type = tflite_micro::LSTMKernelType_FULL, - bool asymmetric_quantize_inputs = false) { - LSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_kernel_type(kernel_type); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { - typedef UnidirectionalSequenceLSTMOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - float cell_clip = 0.0f; - float proj_clip = 0.0f; - bool time_major = false; - bool asymmetric_quantize_inputs = false; - bool diagonal_recurrent_tensors = false; -}; - -struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnidirectionalSequenceLSTMOptionsT NativeTableType; - typedef UnidirectionalSequenceLSTMOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_TIME_MAJOR = 10, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 12, - VT_DIAGONAL_RECURRENT_TENSORS = 14 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - bool time_major() const { - return GetField(VT_TIME_MAJOR, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool diagonal_recurrent_tensors() const { - return GetField(VT_DIAGONAL_RECURRENT_TENSORS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_CELL_CLIP, 4) && - VerifyField(verifier, VT_PROJ_CLIP, 4) && - VerifyField(verifier, VT_TIME_MAJOR, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - VerifyField(verifier, VT_DIAGONAL_RECURRENT_TENSORS, 1) && - verifier.EndTable(); - } - UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnidirectionalSequenceLSTMOptionsBuilder { - typedef UnidirectionalSequenceLSTMOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_time_major(bool time_major) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - void add_diagonal_recurrent_tensors(bool diagonal_recurrent_tensors) { - fbb_.AddElement(UnidirectionalSequenceLSTMOptions::VT_DIAGONAL_RECURRENT_TENSORS, static_cast(diagonal_recurrent_tensors), 0); - } - explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - bool time_major = false, - bool asymmetric_quantize_inputs = false, - bool diagonal_recurrent_tensors = false) { - UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_diagonal_recurrent_tensors(diagonal_recurrent_tensors); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable { - typedef BidirectionalSequenceLSTMOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - float cell_clip = 0.0f; - float proj_clip = 0.0f; - bool merge_outputs = false; - bool time_major = true; - bool asymmetric_quantize_inputs = false; -}; - -struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BidirectionalSequenceLSTMOptionsT NativeTableType; - typedef BidirectionalSequenceLSTMOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_CELL_CLIP = 6, - VT_PROJ_CLIP = 8, - VT_MERGE_OUTPUTS = 10, - VT_TIME_MAJOR = 12, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 14 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - float cell_clip() const { - return GetField(VT_CELL_CLIP, 0.0f); - } - float proj_clip() const { - return GetField(VT_PROJ_CLIP, 0.0f); - } - bool merge_outputs() const { - return GetField(VT_MERGE_OUTPUTS, 0) != 0; - } - bool time_major() const { - return GetField(VT_TIME_MAJOR, 1) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_CELL_CLIP, 4) && - VerifyField(verifier, VT_PROJ_CLIP, 4) && - VerifyField(verifier, VT_MERGE_OUTPUTS, 1) && - VerifyField(verifier, VT_TIME_MAJOR, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BidirectionalSequenceLSTMOptionsBuilder { - typedef BidirectionalSequenceLSTMOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_cell_clip(float cell_clip) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f); - } - void add_proj_clip(float proj_clip) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f); - } - void add_merge_outputs(bool merge_outputs) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, static_cast(merge_outputs), 0); - } - void add_time_major(bool time_major) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast(time_major), 1); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - float cell_clip = 0.0f, - float proj_clip = 0.0f, - bool merge_outputs = false, - bool time_major = true, - bool asymmetric_quantize_inputs = false) { - BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb); - builder_.add_proj_clip(proj_clip); - builder_.add_cell_clip(cell_clip); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_time_major(time_major); - builder_.add_merge_outputs(merge_outputs); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ResizeBilinearOptionsT : public flatbuffers::NativeTable { - typedef ResizeBilinearOptions TableType; - bool align_corners = false; - bool half_pixel_centers = false; -}; - -struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ResizeBilinearOptionsT NativeTableType; - typedef ResizeBilinearOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALIGN_CORNERS = 8, - VT_HALF_PIXEL_CENTERS = 10 - }; - bool align_corners() const { - return GetField(VT_ALIGN_CORNERS, 0) != 0; - } - bool half_pixel_centers() const { - return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALIGN_CORNERS, 1) && - VerifyField(verifier, VT_HALF_PIXEL_CENTERS, 1) && - verifier.EndTable(); - } - ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ResizeBilinearOptionsBuilder { - typedef ResizeBilinearOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) { - fbb_.AddElement(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) { - fbb_.AddElement(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); - } - explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateResizeBilinearOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool align_corners = false, - bool half_pixel_centers = false) { - ResizeBilinearOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable { - typedef ResizeNearestNeighborOptions TableType; - bool align_corners = false; - bool half_pixel_centers = false; -}; - -struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ResizeNearestNeighborOptionsT NativeTableType; - typedef ResizeNearestNeighborOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALIGN_CORNERS = 4, - VT_HALF_PIXEL_CENTERS = 6 - }; - bool align_corners() const { - return GetField(VT_ALIGN_CORNERS, 0) != 0; - } - bool half_pixel_centers() const { - return GetField(VT_HALF_PIXEL_CENTERS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALIGN_CORNERS, 1) && - VerifyField(verifier, VT_HALF_PIXEL_CENTERS, 1) && - verifier.EndTable(); - } - ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ResizeNearestNeighborOptionsBuilder { - typedef ResizeNearestNeighborOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_align_corners(bool align_corners) { - fbb_.AddElement(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast(align_corners), 0); - } - void add_half_pixel_centers(bool half_pixel_centers) { - fbb_.AddElement(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast(half_pixel_centers), 0); - } - explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateResizeNearestNeighborOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool align_corners = false, - bool half_pixel_centers = false) { - ResizeNearestNeighborOptionsBuilder builder_(_fbb); - builder_.add_half_pixel_centers(half_pixel_centers); - builder_.add_align_corners(align_corners); - return builder_.Finish(); -} - -flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CallOptionsT : public flatbuffers::NativeTable { - typedef CallOptions TableType; - uint32_t subgraph = 0; -}; - -struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CallOptionsT NativeTableType; - typedef CallOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SUBGRAPH = 4 - }; - uint32_t subgraph() const { - return GetField(VT_SUBGRAPH, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SUBGRAPH, 4) && - verifier.EndTable(); - } - CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CallOptionsBuilder { - typedef CallOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_subgraph(uint32_t subgraph) { - fbb_.AddElement(CallOptions::VT_SUBGRAPH, subgraph, 0); - } - explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCallOptions( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t subgraph = 0) { - CallOptionsBuilder builder_(_fbb); - builder_.add_subgraph(subgraph); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PadOptionsT : public flatbuffers::NativeTable { - typedef PadOptions TableType; -}; - -struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PadOptionsT NativeTableType; - typedef PadOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PadOptionsBuilder { - typedef PadOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePadOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - PadOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PadV2OptionsT : public flatbuffers::NativeTable { - typedef PadV2Options TableType; -}; - -struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PadV2OptionsT NativeTableType; - typedef PadV2OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PadV2OptionsBuilder { - typedef PadV2Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePadV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - PadV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReshapeOptionsT : public flatbuffers::NativeTable { - typedef ReshapeOptions TableType; - std::vector new_shape{}; -}; - -struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReshapeOptionsT NativeTableType; - typedef ReshapeOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NEW_SHAPE = 4 - }; - const flatbuffers::Vector *new_shape() const { - return GetPointer *>(VT_NEW_SHAPE); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NEW_SHAPE) && - verifier.VerifyVector(new_shape()) && - verifier.EndTable(); - } - ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReshapeOptionsBuilder { - typedef ReshapeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_new_shape(flatbuffers::Offset> new_shape) { - fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape); - } - explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReshapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> new_shape = 0) { - ReshapeOptionsBuilder builder_(_fbb); - builder_.add_new_shape(new_shape); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateReshapeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *new_shape = nullptr) { - auto new_shape__ = new_shape ? _fbb.CreateVector(*new_shape) : 0; - return tflite_micro::CreateReshapeOptions( - _fbb, - new_shape__); -} - -flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable { - typedef SpaceToBatchNDOptions TableType; -}; - -struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SpaceToBatchNDOptionsT NativeTableType; - typedef SpaceToBatchNDOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SpaceToBatchNDOptionsBuilder { - typedef SpaceToBatchNDOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSpaceToBatchNDOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SpaceToBatchNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable { - typedef BatchToSpaceNDOptions TableType; -}; - -struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BatchToSpaceNDOptionsT NativeTableType; - typedef BatchToSpaceNDOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BatchToSpaceNDOptionsBuilder { - typedef BatchToSpaceNDOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBatchToSpaceNDOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - BatchToSpaceNDOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SkipGramOptionsT : public flatbuffers::NativeTable { - typedef SkipGramOptions TableType; - int32_t ngram_size = 0; - int32_t max_skip_size = 0; - bool include_all_ngrams = false; -}; - -struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SkipGramOptionsT NativeTableType; - typedef SkipGramOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NGRAM_SIZE = 4, - VT_MAX_SKIP_SIZE = 6, - VT_INCLUDE_ALL_NGRAMS = 8 - }; - int32_t ngram_size() const { - return GetField(VT_NGRAM_SIZE, 0); - } - int32_t max_skip_size() const { - return GetField(VT_MAX_SKIP_SIZE, 0); - } - bool include_all_ngrams() const { - return GetField(VT_INCLUDE_ALL_NGRAMS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NGRAM_SIZE, 4) && - VerifyField(verifier, VT_MAX_SKIP_SIZE, 4) && - VerifyField(verifier, VT_INCLUDE_ALL_NGRAMS, 1) && - verifier.EndTable(); - } - SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SkipGramOptionsBuilder { - typedef SkipGramOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_ngram_size(int32_t ngram_size) { - fbb_.AddElement(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0); - } - void add_max_skip_size(int32_t max_skip_size) { - fbb_.AddElement(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0); - } - void add_include_all_ngrams(bool include_all_ngrams) { - fbb_.AddElement(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast(include_all_ngrams), 0); - } - explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSkipGramOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t ngram_size = 0, - int32_t max_skip_size = 0, - bool include_all_ngrams = false) { - SkipGramOptionsBuilder builder_(_fbb); - builder_.add_max_skip_size(max_skip_size); - builder_.add_ngram_size(ngram_size); - builder_.add_include_all_ngrams(include_all_ngrams); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SpaceToDepthOptionsT : public flatbuffers::NativeTable { - typedef SpaceToDepthOptions TableType; - int32_t block_size = 0; -}; - -struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SpaceToDepthOptionsT NativeTableType; - typedef SpaceToDepthOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { - return GetField(VT_BLOCK_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BLOCK_SIZE, 4) && - verifier.EndTable(); - } - SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SpaceToDepthOptionsBuilder { - typedef SpaceToDepthOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) { - fbb_.AddElement(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSpaceToDepthOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t block_size = 0) { - SpaceToDepthOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DepthToSpaceOptionsT : public flatbuffers::NativeTable { - typedef DepthToSpaceOptions TableType; - int32_t block_size = 0; -}; - -struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DepthToSpaceOptionsT NativeTableType; - typedef DepthToSpaceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BLOCK_SIZE = 4 - }; - int32_t block_size() const { - return GetField(VT_BLOCK_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BLOCK_SIZE, 4) && - verifier.EndTable(); - } - DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DepthToSpaceOptionsBuilder { - typedef DepthToSpaceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_block_size(int32_t block_size) { - fbb_.AddElement(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0); - } - explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDepthToSpaceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t block_size = 0) { - DepthToSpaceOptionsBuilder builder_(_fbb); - builder_.add_block_size(block_size); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SubOptionsT : public flatbuffers::NativeTable { - typedef SubOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - bool pot_scale_int16 = true; -}; - -struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SubOptionsT NativeTableType; - typedef SubOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4, - VT_POT_SCALE_INT16 = 6 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool pot_scale_int16() const { - return GetField(VT_POT_SCALE_INT16, 1) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_POT_SCALE_INT16, 1) && - verifier.EndTable(); - } - SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SubOptionsBuilder { - typedef SubOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_pot_scale_int16(bool pot_scale_int16) { - fbb_.AddElement(SubOptions::VT_POT_SCALE_INT16, static_cast(pot_scale_int16), 1); - } - explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSubOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - bool pot_scale_int16 = true) { - SubOptionsBuilder builder_(_fbb); - builder_.add_pot_scale_int16(pot_scale_int16); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DivOptionsT : public flatbuffers::NativeTable { - typedef DivOptions TableType; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; -}; - -struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DivOptionsT NativeTableType; - typedef DivOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_FUSED_ACTIVATION_FUNCTION = 4 - }; - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - verifier.EndTable(); - } - DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DivOptionsBuilder { - typedef DivOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDivOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE) { - DivOptionsBuilder builder_(_fbb); - builder_.add_fused_activation_function(fused_activation_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TopKV2OptionsT : public flatbuffers::NativeTable { - typedef TopKV2Options TableType; -}; - -struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TopKV2OptionsT NativeTableType; - typedef TopKV2OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TopKV2OptionsBuilder { - typedef TopKV2Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTopKV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - TopKV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable { - typedef EmbeddingLookupSparseOptions TableType; - tflite_micro::CombinerType combiner = tflite_micro::CombinerType_SUM; -}; - -struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EmbeddingLookupSparseOptionsT NativeTableType; - typedef EmbeddingLookupSparseOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COMBINER = 4 - }; - tflite_micro::CombinerType combiner() const { - return static_cast(GetField(VT_COMBINER, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COMBINER, 1) && - verifier.EndTable(); - } - EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct EmbeddingLookupSparseOptionsBuilder { - typedef EmbeddingLookupSparseOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_combiner(tflite_micro::CombinerType combiner) { - fbb_.AddElement(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast(combiner), 0); - } - explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::CombinerType combiner = tflite_micro::CombinerType_SUM) { - EmbeddingLookupSparseOptionsBuilder builder_(_fbb); - builder_.add_combiner(combiner); - return builder_.Finish(); -} - -flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GatherOptionsT : public flatbuffers::NativeTable { - typedef GatherOptions TableType; - int32_t axis = 0; - int32_t batch_dims = 0; -}; - -struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GatherOptionsT NativeTableType; - typedef GatherOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4, - VT_BATCH_DIMS = 6 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - int32_t batch_dims() const { - return GetField(VT_BATCH_DIMS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS, 4) && - VerifyField(verifier, VT_BATCH_DIMS, 4) && - verifier.EndTable(); - } - GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GatherOptionsBuilder { - typedef GatherOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(GatherOptions::VT_AXIS, axis, 0); - } - void add_batch_dims(int32_t batch_dims) { - fbb_.AddElement(GatherOptions::VT_BATCH_DIMS, batch_dims, 0); - } - explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGatherOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0, - int32_t batch_dims = 0) { - GatherOptionsBuilder builder_(_fbb); - builder_.add_batch_dims(batch_dims); - builder_.add_axis(axis); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TransposeOptionsT : public flatbuffers::NativeTable { - typedef TransposeOptions TableType; -}; - -struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TransposeOptionsT NativeTableType; - typedef TransposeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TransposeOptionsBuilder { - typedef TransposeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTransposeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - TransposeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ExpOptionsT : public flatbuffers::NativeTable { - typedef ExpOptions TableType; -}; - -struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ExpOptionsT NativeTableType; - typedef ExpOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ExpOptionsBuilder { - typedef ExpOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateExpOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ExpOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CosOptionsT : public flatbuffers::NativeTable { - typedef CosOptions TableType; -}; - -struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CosOptionsT NativeTableType; - typedef CosOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CosOptionsBuilder { - typedef CosOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCosOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - CosOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReducerOptionsT : public flatbuffers::NativeTable { - typedef ReducerOptions TableType; - bool keep_dims = false; -}; - -struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReducerOptionsT NativeTableType; - typedef ReducerOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_KEEP_DIMS = 4 - }; - bool keep_dims() const { - return GetField(VT_KEEP_DIMS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_KEEP_DIMS, 1) && - verifier.EndTable(); - } - ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReducerOptionsBuilder { - typedef ReducerOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_keep_dims(bool keep_dims) { - fbb_.AddElement(ReducerOptions::VT_KEEP_DIMS, static_cast(keep_dims), 0); - } - explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReducerOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool keep_dims = false) { - ReducerOptionsBuilder builder_(_fbb); - builder_.add_keep_dims(keep_dims); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SqueezeOptionsT : public flatbuffers::NativeTable { - typedef SqueezeOptions TableType; - std::vector squeeze_dims{}; -}; - -struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SqueezeOptionsT NativeTableType; - typedef SqueezeOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SQUEEZE_DIMS = 4 - }; - const flatbuffers::Vector *squeeze_dims() const { - return GetPointer *>(VT_SQUEEZE_DIMS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_SQUEEZE_DIMS) && - verifier.VerifyVector(squeeze_dims()) && - verifier.EndTable(); - } - SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SqueezeOptionsBuilder { - typedef SqueezeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_squeeze_dims(flatbuffers::Offset> squeeze_dims) { - fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims); - } - explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSqueezeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> squeeze_dims = 0) { - SqueezeOptionsBuilder builder_(_fbb); - builder_.add_squeeze_dims(squeeze_dims); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSqueezeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *squeeze_dims = nullptr) { - auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector(*squeeze_dims) : 0; - return tflite_micro::CreateSqueezeOptions( - _fbb, - squeeze_dims__); -} - -flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SplitOptionsT : public flatbuffers::NativeTable { - typedef SplitOptions TableType; - int32_t num_splits = 0; -}; - -struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SplitOptionsT NativeTableType; - typedef SplitOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { - return GetField(VT_NUM_SPLITS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_SPLITS, 4) && - verifier.EndTable(); - } - SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SplitOptionsBuilder { - typedef SplitOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) { - fbb_.AddElement(SplitOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSplitOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) { - SplitOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SplitVOptionsT : public flatbuffers::NativeTable { - typedef SplitVOptions TableType; - int32_t num_splits = 0; -}; - -struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SplitVOptionsT NativeTableType; - typedef SplitVOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_SPLITS = 4 - }; - int32_t num_splits() const { - return GetField(VT_NUM_SPLITS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_SPLITS, 4) && - verifier.EndTable(); - } - SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SplitVOptionsBuilder { - typedef SplitVOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_splits(int32_t num_splits) { - fbb_.AddElement(SplitVOptions::VT_NUM_SPLITS, num_splits, 0); - } - explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSplitVOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_splits = 0) { - SplitVOptionsBuilder builder_(_fbb); - builder_.add_num_splits(num_splits); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct StridedSliceOptionsT : public flatbuffers::NativeTable { - typedef StridedSliceOptions TableType; - int32_t begin_mask = 0; - int32_t end_mask = 0; - int32_t ellipsis_mask = 0; - int32_t new_axis_mask = 0; - int32_t shrink_axis_mask = 0; - bool offset = false; -}; - -struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef StridedSliceOptionsT NativeTableType; - typedef StridedSliceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BEGIN_MASK = 4, - VT_END_MASK = 6, - VT_ELLIPSIS_MASK = 8, - VT_NEW_AXIS_MASK = 10, - VT_SHRINK_AXIS_MASK = 12, - VT_OFFSET = 14 - }; - int32_t begin_mask() const { - return GetField(VT_BEGIN_MASK, 0); - } - int32_t end_mask() const { - return GetField(VT_END_MASK, 0); - } - int32_t ellipsis_mask() const { - return GetField(VT_ELLIPSIS_MASK, 0); - } - int32_t new_axis_mask() const { - return GetField(VT_NEW_AXIS_MASK, 0); - } - int32_t shrink_axis_mask() const { - return GetField(VT_SHRINK_AXIS_MASK, 0); - } - bool offset() const { - return GetField(VT_OFFSET, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_BEGIN_MASK, 4) && - VerifyField(verifier, VT_END_MASK, 4) && - VerifyField(verifier, VT_ELLIPSIS_MASK, 4) && - VerifyField(verifier, VT_NEW_AXIS_MASK, 4) && - VerifyField(verifier, VT_SHRINK_AXIS_MASK, 4) && - VerifyField(verifier, VT_OFFSET, 1) && - verifier.EndTable(); - } - StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct StridedSliceOptionsBuilder { - typedef StridedSliceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_begin_mask(int32_t begin_mask) { - fbb_.AddElement(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0); - } - void add_end_mask(int32_t end_mask) { - fbb_.AddElement(StridedSliceOptions::VT_END_MASK, end_mask, 0); - } - void add_ellipsis_mask(int32_t ellipsis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0); - } - void add_new_axis_mask(int32_t new_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0); - } - void add_shrink_axis_mask(int32_t shrink_axis_mask) { - fbb_.AddElement(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0); - } - void add_offset(bool offset) { - fbb_.AddElement(StridedSliceOptions::VT_OFFSET, static_cast(offset), 0); - } - explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateStridedSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t begin_mask = 0, - int32_t end_mask = 0, - int32_t ellipsis_mask = 0, - int32_t new_axis_mask = 0, - int32_t shrink_axis_mask = 0, - bool offset = false) { - StridedSliceOptionsBuilder builder_(_fbb); - builder_.add_shrink_axis_mask(shrink_axis_mask); - builder_.add_new_axis_mask(new_axis_mask); - builder_.add_ellipsis_mask(ellipsis_mask); - builder_.add_end_mask(end_mask); - builder_.add_begin_mask(begin_mask); - builder_.add_offset(offset); - return builder_.Finish(); -} - -flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogSoftmaxOptionsT : public flatbuffers::NativeTable { - typedef LogSoftmaxOptions TableType; -}; - -struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogSoftmaxOptionsT NativeTableType; - typedef LogSoftmaxOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogSoftmaxOptionsBuilder { - typedef LogSoftmaxOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogSoftmaxOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogSoftmaxOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CastOptionsT : public flatbuffers::NativeTable { - typedef CastOptions TableType; - tflite_micro::TensorType in_data_type = tflite_micro::TensorType_FLOAT32; - tflite_micro::TensorType out_data_type = tflite_micro::TensorType_FLOAT32; -}; - -struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CastOptionsT NativeTableType; - typedef CastOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_IN_DATA_TYPE = 4, - VT_OUT_DATA_TYPE = 6 - }; - tflite_micro::TensorType in_data_type() const { - return static_cast(GetField(VT_IN_DATA_TYPE, 0)); - } - tflite_micro::TensorType out_data_type() const { - return static_cast(GetField(VT_OUT_DATA_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_IN_DATA_TYPE, 1) && - VerifyField(verifier, VT_OUT_DATA_TYPE, 1) && - verifier.EndTable(); - } - CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CastOptionsBuilder { - typedef CastOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_in_data_type(tflite_micro::TensorType in_data_type) { - fbb_.AddElement(CastOptions::VT_IN_DATA_TYPE, static_cast(in_data_type), 0); - } - void add_out_data_type(tflite_micro::TensorType out_data_type) { - fbb_.AddElement(CastOptions::VT_OUT_DATA_TYPE, static_cast(out_data_type), 0); - } - explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCastOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::TensorType in_data_type = tflite_micro::TensorType_FLOAT32, - tflite_micro::TensorType out_data_type = tflite_micro::TensorType_FLOAT32) { - CastOptionsBuilder builder_(_fbb); - builder_.add_out_data_type(out_data_type); - builder_.add_in_data_type(in_data_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DequantizeOptionsT : public flatbuffers::NativeTable { - typedef DequantizeOptions TableType; -}; - -struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DequantizeOptionsT NativeTableType; - typedef DequantizeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DequantizeOptionsBuilder { - typedef DequantizeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDequantizeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DequantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MaximumMinimumOptionsT : public flatbuffers::NativeTable { - typedef MaximumMinimumOptions TableType; -}; - -struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MaximumMinimumOptionsT NativeTableType; - typedef MaximumMinimumOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MaximumMinimumOptionsBuilder { - typedef MaximumMinimumOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMaximumMinimumOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MaximumMinimumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TileOptionsT : public flatbuffers::NativeTable { - typedef TileOptions TableType; -}; - -struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TileOptionsT NativeTableType; - typedef TileOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TileOptionsBuilder { - typedef TileOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTileOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - TileOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ArgMaxOptionsT : public flatbuffers::NativeTable { - typedef ArgMaxOptions TableType; - tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32; -}; - -struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ArgMaxOptionsT NativeTableType; - typedef ArgMaxOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPUT_TYPE = 4 - }; - tflite_micro::TensorType output_type() const { - return static_cast(GetField(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUTPUT_TYPE, 1) && - verifier.EndTable(); - } - ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ArgMaxOptionsBuilder { - typedef ArgMaxOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(tflite_micro::TensorType output_type) { - fbb_.AddElement(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); - } - explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateArgMaxOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32) { - ArgMaxOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ArgMinOptionsT : public flatbuffers::NativeTable { - typedef ArgMinOptions TableType; - tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32; -}; - -struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ArgMinOptionsT NativeTableType; - typedef ArgMinOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUTPUT_TYPE = 4 - }; - tflite_micro::TensorType output_type() const { - return static_cast(GetField(VT_OUTPUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUTPUT_TYPE, 1) && - verifier.EndTable(); - } - ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ArgMinOptionsBuilder { - typedef ArgMinOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_output_type(tflite_micro::TensorType output_type) { - fbb_.AddElement(ArgMinOptions::VT_OUTPUT_TYPE, static_cast(output_type), 0); - } - explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateArgMinOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::TensorType output_type = tflite_micro::TensorType_FLOAT32) { - ArgMinOptionsBuilder builder_(_fbb); - builder_.add_output_type(output_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GreaterOptionsT : public flatbuffers::NativeTable { - typedef GreaterOptions TableType; -}; - -struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GreaterOptionsT NativeTableType; - typedef GreaterOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GreaterOptionsBuilder { - typedef GreaterOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGreaterOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GreaterOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GreaterEqualOptionsT : public flatbuffers::NativeTable { - typedef GreaterEqualOptions TableType; -}; - -struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GreaterEqualOptionsT NativeTableType; - typedef GreaterEqualOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GreaterEqualOptionsBuilder { - typedef GreaterEqualOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGreaterEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GreaterEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LessOptionsT : public flatbuffers::NativeTable { - typedef LessOptions TableType; -}; - -struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LessOptionsT NativeTableType; - typedef LessOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LessOptionsBuilder { - typedef LessOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLessOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LessOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LessEqualOptionsT : public flatbuffers::NativeTable { - typedef LessEqualOptions TableType; -}; - -struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LessEqualOptionsT NativeTableType; - typedef LessEqualOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LessEqualOptionsBuilder { - typedef LessEqualOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLessEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LessEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NegOptionsT : public flatbuffers::NativeTable { - typedef NegOptions TableType; -}; - -struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NegOptionsT NativeTableType; - typedef NegOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NegOptionsBuilder { - typedef NegOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNegOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - NegOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SelectOptionsT : public flatbuffers::NativeTable { - typedef SelectOptions TableType; -}; - -struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SelectOptionsT NativeTableType; - typedef SelectOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SelectOptionsBuilder { - typedef SelectOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSelectOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SelectOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SliceOptionsT : public flatbuffers::NativeTable { - typedef SliceOptions TableType; -}; - -struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SliceOptionsT NativeTableType; - typedef SliceOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SliceOptionsBuilder { - typedef SliceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SliceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TransposeConvOptionsT : public flatbuffers::NativeTable { - typedef TransposeConvOptions TableType; - tflite_micro::Padding padding = tflite_micro::Padding_SAME; - int32_t stride_w = 0; - int32_t stride_h = 0; - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE; - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32; -}; - -struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TransposeConvOptionsT NativeTableType; - typedef TransposeConvOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_PADDING = 4, - VT_STRIDE_W = 6, - VT_STRIDE_H = 8, - VT_FUSED_ACTIVATION_FUNCTION = 10, - VT_QUANTIZED_BIAS_TYPE = 12 - }; - tflite_micro::Padding padding() const { - return static_cast(GetField(VT_PADDING, 0)); - } - int32_t stride_w() const { - return GetField(VT_STRIDE_W, 0); - } - int32_t stride_h() const { - return GetField(VT_STRIDE_H, 0); - } - tflite_micro::ActivationFunctionType fused_activation_function() const { - return static_cast(GetField(VT_FUSED_ACTIVATION_FUNCTION, 0)); - } - tflite_micro::TensorType quantized_bias_type() const { - return static_cast(GetField(VT_QUANTIZED_BIAS_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_PADDING, 1) && - VerifyField(verifier, VT_STRIDE_W, 4) && - VerifyField(verifier, VT_STRIDE_H, 4) && - VerifyField(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) && - VerifyField(verifier, VT_QUANTIZED_BIAS_TYPE, 1) && - verifier.EndTable(); - } - TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TransposeConvOptionsBuilder { - typedef TransposeConvOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_padding(tflite_micro::Padding padding) { - fbb_.AddElement(TransposeConvOptions::VT_PADDING, static_cast(padding), 0); - } - void add_stride_w(int32_t stride_w) { - fbb_.AddElement(TransposeConvOptions::VT_STRIDE_W, stride_w, 0); - } - void add_stride_h(int32_t stride_h) { - fbb_.AddElement(TransposeConvOptions::VT_STRIDE_H, stride_h, 0); - } - void add_fused_activation_function(tflite_micro::ActivationFunctionType fused_activation_function) { - fbb_.AddElement(TransposeConvOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast(fused_activation_function), 0); - } - void add_quantized_bias_type(tflite_micro::TensorType quantized_bias_type) { - fbb_.AddElement(TransposeConvOptions::VT_QUANTIZED_BIAS_TYPE, static_cast(quantized_bias_type), 0); - } - explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTransposeConvOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::Padding padding = tflite_micro::Padding_SAME, - int32_t stride_w = 0, - int32_t stride_h = 0, - tflite_micro::ActivationFunctionType fused_activation_function = tflite_micro::ActivationFunctionType_NONE, - tflite_micro::TensorType quantized_bias_type = tflite_micro::TensorType_FLOAT32) { - TransposeConvOptionsBuilder builder_(_fbb); - builder_.add_stride_h(stride_h); - builder_.add_stride_w(stride_w); - builder_.add_quantized_bias_type(quantized_bias_type); - builder_.add_fused_activation_function(fused_activation_function); - builder_.add_padding(padding); - return builder_.Finish(); -} - -flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ExpandDimsOptionsT : public flatbuffers::NativeTable { - typedef ExpandDimsOptions TableType; -}; - -struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ExpandDimsOptionsT NativeTableType; - typedef ExpandDimsOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ExpandDimsOptionsBuilder { - typedef ExpandDimsOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateExpandDimsOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ExpandDimsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SparseToDenseOptionsT : public flatbuffers::NativeTable { - typedef SparseToDenseOptions TableType; - bool validate_indices = false; -}; - -struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SparseToDenseOptionsT NativeTableType; - typedef SparseToDenseOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALIDATE_INDICES = 4 - }; - bool validate_indices() const { - return GetField(VT_VALIDATE_INDICES, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VALIDATE_INDICES, 1) && - verifier.EndTable(); - } - SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SparseToDenseOptionsBuilder { - typedef SparseToDenseOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_validate_indices(bool validate_indices) { - fbb_.AddElement(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast(validate_indices), 0); - } - explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSparseToDenseOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool validate_indices = false) { - SparseToDenseOptionsBuilder builder_(_fbb); - builder_.add_validate_indices(validate_indices); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct EqualOptionsT : public flatbuffers::NativeTable { - typedef EqualOptions TableType; -}; - -struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef EqualOptionsT NativeTableType; - typedef EqualOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct EqualOptionsBuilder { - typedef EqualOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - EqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NotEqualOptionsT : public flatbuffers::NativeTable { - typedef NotEqualOptions TableType; -}; - -struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NotEqualOptionsT NativeTableType; - typedef NotEqualOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NotEqualOptionsBuilder { - typedef NotEqualOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNotEqualOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - NotEqualOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ShapeOptionsT : public flatbuffers::NativeTable { - typedef ShapeOptions TableType; - tflite_micro::TensorType out_type = tflite_micro::TensorType_FLOAT32; -}; - -struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ShapeOptionsT NativeTableType; - typedef ShapeOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OUT_TYPE = 4 - }; - tflite_micro::TensorType out_type() const { - return static_cast(GetField(VT_OUT_TYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OUT_TYPE, 1) && - verifier.EndTable(); - } - ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ShapeOptionsBuilder { - typedef ShapeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_out_type(tflite_micro::TensorType out_type) { - fbb_.AddElement(ShapeOptions::VT_OUT_TYPE, static_cast(out_type), 0); - } - explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateShapeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::TensorType out_type = tflite_micro::TensorType_FLOAT32) { - ShapeOptionsBuilder builder_(_fbb); - builder_.add_out_type(out_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RankOptionsT : public flatbuffers::NativeTable { - typedef RankOptions TableType; -}; - -struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RankOptionsT NativeTableType; - typedef RankOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RankOptionsBuilder { - typedef RankOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRankOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - RankOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PowOptionsT : public flatbuffers::NativeTable { - typedef PowOptions TableType; -}; - -struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PowOptionsT NativeTableType; - typedef PowOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PowOptionsBuilder { - typedef PowOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePowOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - PowOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FakeQuantOptionsT : public flatbuffers::NativeTable { - typedef FakeQuantOptions TableType; - float min = 0.0f; - float max = 0.0f; - int32_t num_bits = 0; - bool narrow_range = false; -}; - -struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FakeQuantOptionsT NativeTableType; - typedef FakeQuantOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MIN = 4, - VT_MAX = 6, - VT_NUM_BITS = 8, - VT_NARROW_RANGE = 10 - }; - float min() const { - return GetField(VT_MIN, 0.0f); - } - float max() const { - return GetField(VT_MAX, 0.0f); - } - int32_t num_bits() const { - return GetField(VT_NUM_BITS, 0); - } - bool narrow_range() const { - return GetField(VT_NARROW_RANGE, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_MIN, 4) && - VerifyField(verifier, VT_MAX, 4) && - VerifyField(verifier, VT_NUM_BITS, 4) && - VerifyField(verifier, VT_NARROW_RANGE, 1) && - verifier.EndTable(); - } - FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FakeQuantOptionsBuilder { - typedef FakeQuantOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_min(float min) { - fbb_.AddElement(FakeQuantOptions::VT_MIN, min, 0.0f); - } - void add_max(float max) { - fbb_.AddElement(FakeQuantOptions::VT_MAX, max, 0.0f); - } - void add_num_bits(int32_t num_bits) { - fbb_.AddElement(FakeQuantOptions::VT_NUM_BITS, num_bits, 0); - } - void add_narrow_range(bool narrow_range) { - fbb_.AddElement(FakeQuantOptions::VT_NARROW_RANGE, static_cast(narrow_range), 0); - } - explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFakeQuantOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float min = 0.0f, - float max = 0.0f, - int32_t num_bits = 0, - bool narrow_range = false) { - FakeQuantOptionsBuilder builder_(_fbb); - builder_.add_num_bits(num_bits); - builder_.add_max(max); - builder_.add_min(min); - builder_.add_narrow_range(narrow_range); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct PackOptionsT : public flatbuffers::NativeTable { - typedef PackOptions TableType; - int32_t values_count = 0; - int32_t axis = 0; -}; - -struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef PackOptionsT NativeTableType; - typedef PackOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VALUES_COUNT = 4, - VT_AXIS = 6 - }; - int32_t values_count() const { - return GetField(VT_VALUES_COUNT, 0); - } - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VALUES_COUNT, 4) && - VerifyField(verifier, VT_AXIS, 4) && - verifier.EndTable(); - } - PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct PackOptionsBuilder { - typedef PackOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_values_count(int32_t values_count) { - fbb_.AddElement(PackOptions::VT_VALUES_COUNT, values_count, 0); - } - void add_axis(int32_t axis) { - fbb_.AddElement(PackOptions::VT_AXIS, axis, 0); - } - explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreatePackOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t values_count = 0, - int32_t axis = 0) { - PackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_values_count(values_count); - return builder_.Finish(); -} - -flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalOrOptionsT : public flatbuffers::NativeTable { - typedef LogicalOrOptions TableType; -}; - -struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalOrOptionsT NativeTableType; - typedef LogicalOrOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalOrOptionsBuilder { - typedef LogicalOrOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalOrOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalOrOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OneHotOptionsT : public flatbuffers::NativeTable { - typedef OneHotOptions TableType; - int32_t axis = 0; -}; - -struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OneHotOptionsT NativeTableType; - typedef OneHotOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_AXIS = 4 - }; - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_AXIS, 4) && - verifier.EndTable(); - } - OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct OneHotOptionsBuilder { - typedef OneHotOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_axis(int32_t axis) { - fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); - } - explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOneHotOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t axis = 0) { - OneHotOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - return builder_.Finish(); -} - -flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AbsOptionsT : public flatbuffers::NativeTable { - typedef AbsOptions TableType; -}; - -struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AbsOptionsT NativeTableType; - typedef AbsOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AbsOptionsBuilder { - typedef AbsOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAbsOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - AbsOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HardSwishOptionsT : public flatbuffers::NativeTable { - typedef HardSwishOptions TableType; -}; - -struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HardSwishOptionsT NativeTableType; - typedef HardSwishOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HardSwishOptionsBuilder { - typedef HardSwishOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHardSwishOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - HardSwishOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalAndOptionsT : public flatbuffers::NativeTable { - typedef LogicalAndOptions TableType; -}; - -struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalAndOptionsT NativeTableType; - typedef LogicalAndOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalAndOptionsBuilder { - typedef LogicalAndOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalAndOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalAndOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LogicalNotOptionsT : public flatbuffers::NativeTable { - typedef LogicalNotOptions TableType; -}; - -struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LogicalNotOptionsT NativeTableType; - typedef LogicalNotOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LogicalNotOptionsBuilder { - typedef LogicalNotOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLogicalNotOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - LogicalNotOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnpackOptionsT : public flatbuffers::NativeTable { - typedef UnpackOptions TableType; - int32_t num = 0; - int32_t axis = 0; -}; - -struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnpackOptionsT NativeTableType; - typedef UnpackOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM = 4, - VT_AXIS = 6 - }; - int32_t num() const { - return GetField(VT_NUM, 0); - } - int32_t axis() const { - return GetField(VT_AXIS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM, 4) && - VerifyField(verifier, VT_AXIS, 4) && - verifier.EndTable(); - } - UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnpackOptionsBuilder { - typedef UnpackOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num(int32_t num) { - fbb_.AddElement(UnpackOptions::VT_NUM, num, 0); - } - void add_axis(int32_t axis) { - fbb_.AddElement(UnpackOptions::VT_AXIS, axis, 0); - } - explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnpackOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num = 0, - int32_t axis = 0) { - UnpackOptionsBuilder builder_(_fbb); - builder_.add_axis(axis); - builder_.add_num(num); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FloorDivOptionsT : public flatbuffers::NativeTable { - typedef FloorDivOptions TableType; -}; - -struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FloorDivOptionsT NativeTableType; - typedef FloorDivOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FloorDivOptionsBuilder { - typedef FloorDivOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFloorDivOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FloorDivOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SquareOptionsT : public flatbuffers::NativeTable { - typedef SquareOptions TableType; -}; - -struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SquareOptionsT NativeTableType; - typedef SquareOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SquareOptionsBuilder { - typedef SquareOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSquareOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SquareOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ZerosLikeOptionsT : public flatbuffers::NativeTable { - typedef ZerosLikeOptions TableType; -}; - -struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ZerosLikeOptionsT NativeTableType; - typedef ZerosLikeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ZerosLikeOptionsBuilder { - typedef ZerosLikeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateZerosLikeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ZerosLikeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FillOptionsT : public flatbuffers::NativeTable { - typedef FillOptions TableType; -}; - -struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FillOptionsT NativeTableType; - typedef FillOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FillOptionsBuilder { - typedef FillOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFillOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FillOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct FloorModOptionsT : public flatbuffers::NativeTable { - typedef FloorModOptions TableType; -}; - -struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef FloorModOptionsT NativeTableType; - typedef FloorModOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct FloorModOptionsBuilder { - typedef FloorModOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateFloorModOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - FloorModOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RangeOptionsT : public flatbuffers::NativeTable { - typedef RangeOptions TableType; -}; - -struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RangeOptionsT NativeTableType; - typedef RangeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RangeOptionsBuilder { - typedef RangeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRangeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - RangeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct LeakyReluOptionsT : public flatbuffers::NativeTable { - typedef LeakyReluOptions TableType; - float alpha = 0.0f; -}; - -struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef LeakyReluOptionsT NativeTableType; - typedef LeakyReluOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ALPHA = 4 - }; - float alpha() const { - return GetField(VT_ALPHA, 0.0f); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ALPHA, 4) && - verifier.EndTable(); - } - LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct LeakyReluOptionsBuilder { - typedef LeakyReluOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_alpha(float alpha) { - fbb_.AddElement(LeakyReluOptions::VT_ALPHA, alpha, 0.0f); - } - explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateLeakyReluOptions( - flatbuffers::FlatBufferBuilder &_fbb, - float alpha = 0.0f) { - LeakyReluOptionsBuilder builder_(_fbb); - builder_.add_alpha(alpha); - return builder_.Finish(); -} - -flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable { - typedef SquaredDifferenceOptions TableType; -}; - -struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SquaredDifferenceOptionsT NativeTableType; - typedef SquaredDifferenceOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SquaredDifferenceOptionsBuilder { - typedef SquaredDifferenceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSquaredDifferenceOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SquaredDifferenceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MirrorPadOptionsT : public flatbuffers::NativeTable { - typedef MirrorPadOptions TableType; - tflite_micro::MirrorPadMode mode = tflite_micro::MirrorPadMode_REFLECT; -}; - -struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MirrorPadOptionsT NativeTableType; - typedef MirrorPadOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_MODE = 4 - }; - tflite_micro::MirrorPadMode mode() const { - return static_cast(GetField(VT_MODE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_MODE, 1) && - verifier.EndTable(); - } - MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MirrorPadOptionsBuilder { - typedef MirrorPadOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_mode(tflite_micro::MirrorPadMode mode) { - fbb_.AddElement(MirrorPadOptions::VT_MODE, static_cast(mode), 0); - } - explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMirrorPadOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::MirrorPadMode mode = tflite_micro::MirrorPadMode_REFLECT) { - MirrorPadOptionsBuilder builder_(_fbb); - builder_.add_mode(mode); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UniqueOptionsT : public flatbuffers::NativeTable { - typedef UniqueOptions TableType; - tflite_micro::TensorType idx_out_type = tflite_micro::TensorType_INT32; -}; - -struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UniqueOptionsT NativeTableType; - typedef UniqueOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_IDX_OUT_TYPE = 4 - }; - tflite_micro::TensorType idx_out_type() const { - return static_cast(GetField(VT_IDX_OUT_TYPE, 2)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_IDX_OUT_TYPE, 1) && - verifier.EndTable(); - } - UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UniqueOptionsBuilder { - typedef UniqueOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_idx_out_type(tflite_micro::TensorType idx_out_type) { - fbb_.AddElement(UniqueOptions::VT_IDX_OUT_TYPE, static_cast(idx_out_type), 2); - } - explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUniqueOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::TensorType idx_out_type = tflite_micro::TensorType_INT32) { - UniqueOptionsBuilder builder_(_fbb); - builder_.add_idx_out_type(idx_out_type); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReverseV2OptionsT : public flatbuffers::NativeTable { - typedef ReverseV2Options TableType; -}; - -struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReverseV2OptionsT NativeTableType; - typedef ReverseV2OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReverseV2OptionsBuilder { - typedef ReverseV2Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReverseV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - ReverseV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AddNOptionsT : public flatbuffers::NativeTable { - typedef AddNOptions TableType; -}; - -struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AddNOptionsT NativeTableType; - typedef AddNOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AddNOptionsBuilder { - typedef AddNOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAddNOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - AddNOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GatherNdOptionsT : public flatbuffers::NativeTable { - typedef GatherNdOptions TableType; -}; - -struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GatherNdOptionsT NativeTableType; - typedef GatherNdOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GatherNdOptionsBuilder { - typedef GatherNdOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGatherNdOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - GatherNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct WhereOptionsT : public flatbuffers::NativeTable { - typedef WhereOptions TableType; -}; - -struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef WhereOptionsT NativeTableType; - typedef WhereOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct WhereOptionsBuilder { - typedef WhereOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateWhereOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - WhereOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReverseSequenceOptionsT : public flatbuffers::NativeTable { - typedef ReverseSequenceOptions TableType; - int32_t seq_dim = 0; - int32_t batch_dim = 0; -}; - -struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReverseSequenceOptionsT NativeTableType; - typedef ReverseSequenceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SEQ_DIM = 4, - VT_BATCH_DIM = 6 - }; - int32_t seq_dim() const { - return GetField(VT_SEQ_DIM, 0); - } - int32_t batch_dim() const { - return GetField(VT_BATCH_DIM, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SEQ_DIM, 4) && - VerifyField(verifier, VT_BATCH_DIM, 4) && - verifier.EndTable(); - } - ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReverseSequenceOptionsBuilder { - typedef ReverseSequenceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_seq_dim(int32_t seq_dim) { - fbb_.AddElement(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0); - } - void add_batch_dim(int32_t batch_dim) { - fbb_.AddElement(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0); - } - explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReverseSequenceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t seq_dim = 0, - int32_t batch_dim = 0) { - ReverseSequenceOptionsBuilder builder_(_fbb); - builder_.add_batch_dim(batch_dim); - builder_.add_seq_dim(seq_dim); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MatrixDiagOptionsT : public flatbuffers::NativeTable { - typedef MatrixDiagOptions TableType; -}; - -struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MatrixDiagOptionsT NativeTableType; - typedef MatrixDiagOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MatrixDiagOptionsBuilder { - typedef MatrixDiagOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMatrixDiagOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MatrixDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct QuantizeOptionsT : public flatbuffers::NativeTable { - typedef QuantizeOptions TableType; -}; - -struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef QuantizeOptionsT NativeTableType; - typedef QuantizeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct QuantizeOptionsBuilder { - typedef QuantizeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateQuantizeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - QuantizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable { - typedef MatrixSetDiagOptions TableType; -}; - -struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MatrixSetDiagOptionsT NativeTableType; - typedef MatrixSetDiagOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MatrixSetDiagOptionsBuilder { - typedef MatrixSetDiagOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMatrixSetDiagOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - MatrixSetDiagOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct IfOptionsT : public flatbuffers::NativeTable { - typedef IfOptions TableType; - int32_t then_subgraph_index = 0; - int32_t else_subgraph_index = 0; -}; - -struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef IfOptionsT NativeTableType; - typedef IfOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_THEN_SUBGRAPH_INDEX = 4, - VT_ELSE_SUBGRAPH_INDEX = 6 - }; - int32_t then_subgraph_index() const { - return GetField(VT_THEN_SUBGRAPH_INDEX, 0); - } - int32_t else_subgraph_index() const { - return GetField(VT_ELSE_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_THEN_SUBGRAPH_INDEX, 4) && - VerifyField(verifier, VT_ELSE_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct IfOptionsBuilder { - typedef IfOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_then_subgraph_index(int32_t then_subgraph_index) { - fbb_.AddElement(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0); - } - void add_else_subgraph_index(int32_t else_subgraph_index) { - fbb_.AddElement(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0); - } - explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateIfOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t then_subgraph_index = 0, - int32_t else_subgraph_index = 0) { - IfOptionsBuilder builder_(_fbb); - builder_.add_else_subgraph_index(else_subgraph_index); - builder_.add_then_subgraph_index(then_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CallOnceOptionsT : public flatbuffers::NativeTable { - typedef CallOnceOptions TableType; - int32_t init_subgraph_index = 0; -}; - -struct CallOnceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CallOnceOptionsT NativeTableType; - typedef CallOnceOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INIT_SUBGRAPH_INDEX = 4 - }; - int32_t init_subgraph_index() const { - return GetField(VT_INIT_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_INIT_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - CallOnceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CallOnceOptionsBuilder { - typedef CallOnceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_init_subgraph_index(int32_t init_subgraph_index) { - fbb_.AddElement(CallOnceOptions::VT_INIT_SUBGRAPH_INDEX, init_subgraph_index, 0); - } - explicit CallOnceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCallOnceOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t init_subgraph_index = 0) { - CallOnceOptionsBuilder builder_(_fbb); - builder_.add_init_subgraph_index(init_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct WhileOptionsT : public flatbuffers::NativeTable { - typedef WhileOptions TableType; - int32_t cond_subgraph_index = 0; - int32_t body_subgraph_index = 0; -}; - -struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef WhileOptionsT NativeTableType; - typedef WhileOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_COND_SUBGRAPH_INDEX = 4, - VT_BODY_SUBGRAPH_INDEX = 6 - }; - int32_t cond_subgraph_index() const { - return GetField(VT_COND_SUBGRAPH_INDEX, 0); - } - int32_t body_subgraph_index() const { - return GetField(VT_BODY_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_COND_SUBGRAPH_INDEX, 4) && - VerifyField(verifier, VT_BODY_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct WhileOptionsBuilder { - typedef WhileOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_cond_subgraph_index(int32_t cond_subgraph_index) { - fbb_.AddElement(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0); - } - void add_body_subgraph_index(int32_t body_subgraph_index) { - fbb_.AddElement(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0); - } - explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateWhileOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t cond_subgraph_index = 0, - int32_t body_subgraph_index = 0) { - WhileOptionsBuilder builder_(_fbb); - builder_.add_body_subgraph_index(body_subgraph_index); - builder_.add_cond_subgraph_index(cond_subgraph_index); - return builder_.Finish(); -} - -flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable { - typedef NonMaxSuppressionV4Options TableType; -}; - -struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NonMaxSuppressionV4OptionsT NativeTableType; - typedef NonMaxSuppressionV4OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NonMaxSuppressionV4OptionsBuilder { - typedef NonMaxSuppressionV4Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNonMaxSuppressionV4Options( - flatbuffers::FlatBufferBuilder &_fbb) { - NonMaxSuppressionV4OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable { - typedef NonMaxSuppressionV5Options TableType; -}; - -struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef NonMaxSuppressionV5OptionsT NativeTableType; - typedef NonMaxSuppressionV5OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct NonMaxSuppressionV5OptionsBuilder { - typedef NonMaxSuppressionV5Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateNonMaxSuppressionV5Options( - flatbuffers::FlatBufferBuilder &_fbb) { - NonMaxSuppressionV5OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ScatterNdOptionsT : public flatbuffers::NativeTable { - typedef ScatterNdOptions TableType; -}; - -struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ScatterNdOptionsT NativeTableType; - typedef ScatterNdOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ScatterNdOptionsBuilder { - typedef ScatterNdOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateScatterNdOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ScatterNdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SelectV2OptionsT : public flatbuffers::NativeTable { - typedef SelectV2Options TableType; -}; - -struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SelectV2OptionsT NativeTableType; - typedef SelectV2OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SelectV2OptionsBuilder { - typedef SelectV2Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSelectV2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - SelectV2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DensifyOptionsT : public flatbuffers::NativeTable { - typedef DensifyOptions TableType; -}; - -struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DensifyOptionsT NativeTableType; - typedef DensifyOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DensifyOptionsBuilder { - typedef DensifyOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDensifyOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DensifyOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SegmentSumOptionsT : public flatbuffers::NativeTable { - typedef SegmentSumOptions TableType; -}; - -struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SegmentSumOptionsT NativeTableType; - typedef SegmentSumOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SegmentSumOptionsBuilder { - typedef SegmentSumOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSegmentSumOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SegmentSumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BatchMatMulOptionsT : public flatbuffers::NativeTable { - typedef BatchMatMulOptions TableType; - bool adj_x = false; - bool adj_y = false; - bool asymmetric_quantize_inputs = false; -}; - -struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BatchMatMulOptionsT NativeTableType; - typedef BatchMatMulOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_ADJ_X = 4, - VT_ADJ_Y = 6, - VT_ASYMMETRIC_QUANTIZE_INPUTS = 8 - }; - bool adj_x() const { - return GetField(VT_ADJ_X, 0) != 0; - } - bool adj_y() const { - return GetField(VT_ADJ_Y, 0) != 0; - } - bool asymmetric_quantize_inputs() const { - return GetField(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_ADJ_X, 1) && - VerifyField(verifier, VT_ADJ_Y, 1) && - VerifyField(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) && - verifier.EndTable(); - } - BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BatchMatMulOptionsBuilder { - typedef BatchMatMulOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_adj_x(bool adj_x) { - fbb_.AddElement(BatchMatMulOptions::VT_ADJ_X, static_cast(adj_x), 0); - } - void add_adj_y(bool adj_y) { - fbb_.AddElement(BatchMatMulOptions::VT_ADJ_Y, static_cast(adj_y), 0); - } - void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) { - fbb_.AddElement(BatchMatMulOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast(asymmetric_quantize_inputs), 0); - } - explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBatchMatMulOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool adj_x = false, - bool adj_y = false, - bool asymmetric_quantize_inputs = false) { - BatchMatMulOptionsBuilder builder_(_fbb); - builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs); - builder_.add_adj_y(adj_y); - builder_.add_adj_x(adj_x); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct CumsumOptionsT : public flatbuffers::NativeTable { - typedef CumsumOptions TableType; - bool exclusive = false; - bool reverse = false; -}; - -struct CumsumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef CumsumOptionsT NativeTableType; - typedef CumsumOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_EXCLUSIVE = 4, - VT_REVERSE = 6 - }; - bool exclusive() const { - return GetField(VT_EXCLUSIVE, 0) != 0; - } - bool reverse() const { - return GetField(VT_REVERSE, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_EXCLUSIVE, 1) && - VerifyField(verifier, VT_REVERSE, 1) && - verifier.EndTable(); - } - CumsumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct CumsumOptionsBuilder { - typedef CumsumOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_exclusive(bool exclusive) { - fbb_.AddElement(CumsumOptions::VT_EXCLUSIVE, static_cast(exclusive), 0); - } - void add_reverse(bool reverse) { - fbb_.AddElement(CumsumOptions::VT_REVERSE, static_cast(reverse), 0); - } - explicit CumsumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateCumsumOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool exclusive = false, - bool reverse = false) { - CumsumOptionsBuilder builder_(_fbb); - builder_.add_reverse(reverse); - builder_.add_exclusive(exclusive); - return builder_.Finish(); -} - -flatbuffers::Offset CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BroadcastToOptionsT : public flatbuffers::NativeTable { - typedef BroadcastToOptions TableType; -}; - -struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BroadcastToOptionsT NativeTableType; - typedef BroadcastToOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - BroadcastToOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BroadcastToOptionsBuilder { - typedef BroadcastToOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BroadcastToOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBroadcastToOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - BroadcastToOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct Rfft2dOptionsT : public flatbuffers::NativeTable { - typedef Rfft2dOptions TableType; -}; - -struct Rfft2dOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef Rfft2dOptionsT NativeTableType; - typedef Rfft2dOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - Rfft2dOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct Rfft2dOptionsBuilder { - typedef Rfft2dOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit Rfft2dOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRfft2dOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - Rfft2dOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HashtableOptionsT : public flatbuffers::NativeTable { - typedef HashtableOptions TableType; - int32_t table_id = 0; - tflite_micro::TensorType key_dtype = tflite_micro::TensorType_FLOAT32; - tflite_micro::TensorType value_dtype = tflite_micro::TensorType_FLOAT32; -}; - -struct HashtableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HashtableOptionsT NativeTableType; - typedef HashtableOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TABLE_ID = 4, - VT_KEY_DTYPE = 6, - VT_VALUE_DTYPE = 8 - }; - int32_t table_id() const { - return GetField(VT_TABLE_ID, 0); - } - tflite_micro::TensorType key_dtype() const { - return static_cast(GetField(VT_KEY_DTYPE, 0)); - } - tflite_micro::TensorType value_dtype() const { - return static_cast(GetField(VT_VALUE_DTYPE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_TABLE_ID, 4) && - VerifyField(verifier, VT_KEY_DTYPE, 1) && - VerifyField(verifier, VT_VALUE_DTYPE, 1) && - verifier.EndTable(); - } - HashtableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HashtableOptionsBuilder { - typedef HashtableOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_table_id(int32_t table_id) { - fbb_.AddElement(HashtableOptions::VT_TABLE_ID, table_id, 0); - } - void add_key_dtype(tflite_micro::TensorType key_dtype) { - fbb_.AddElement(HashtableOptions::VT_KEY_DTYPE, static_cast(key_dtype), 0); - } - void add_value_dtype(tflite_micro::TensorType value_dtype) { - fbb_.AddElement(HashtableOptions::VT_VALUE_DTYPE, static_cast(value_dtype), 0); - } - explicit HashtableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHashtableOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t table_id = 0, - tflite_micro::TensorType key_dtype = tflite_micro::TensorType_FLOAT32, - tflite_micro::TensorType value_dtype = tflite_micro::TensorType_FLOAT32) { - HashtableOptionsBuilder builder_(_fbb); - builder_.add_table_id(table_id); - builder_.add_value_dtype(value_dtype); - builder_.add_key_dtype(key_dtype); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HashtableFindOptionsT : public flatbuffers::NativeTable { - typedef HashtableFindOptions TableType; -}; - -struct HashtableFindOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HashtableFindOptionsT NativeTableType; - typedef HashtableFindOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - HashtableFindOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HashtableFindOptionsBuilder { - typedef HashtableFindOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HashtableFindOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHashtableFindOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - HashtableFindOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HashtableImportOptionsT : public flatbuffers::NativeTable { - typedef HashtableImportOptions TableType; -}; - -struct HashtableImportOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HashtableImportOptionsT NativeTableType; - typedef HashtableImportOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - HashtableImportOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HashtableImportOptionsBuilder { - typedef HashtableImportOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HashtableImportOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHashtableImportOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - HashtableImportOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct HashtableSizeOptionsT : public flatbuffers::NativeTable { - typedef HashtableSizeOptions TableType; -}; - -struct HashtableSizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef HashtableSizeOptionsT NativeTableType; - typedef HashtableSizeOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - HashtableSizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct HashtableSizeOptionsBuilder { - typedef HashtableSizeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit HashtableSizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateHashtableSizeOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - HashtableSizeOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct VarHandleOptionsT : public flatbuffers::NativeTable { - typedef VarHandleOptions TableType; - std::string container{}; - std::string shared_name{}; -}; - -struct VarHandleOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef VarHandleOptionsT NativeTableType; - typedef VarHandleOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_CONTAINER = 4, - VT_SHARED_NAME = 6 - }; - const flatbuffers::String *container() const { - return GetPointer(VT_CONTAINER); - } - const flatbuffers::String *shared_name() const { - return GetPointer(VT_SHARED_NAME); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_CONTAINER) && - verifier.VerifyString(container()) && - VerifyOffset(verifier, VT_SHARED_NAME) && - verifier.VerifyString(shared_name()) && - verifier.EndTable(); - } - VarHandleOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct VarHandleOptionsBuilder { - typedef VarHandleOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_container(flatbuffers::Offset container) { - fbb_.AddOffset(VarHandleOptions::VT_CONTAINER, container); - } - void add_shared_name(flatbuffers::Offset shared_name) { - fbb_.AddOffset(VarHandleOptions::VT_SHARED_NAME, shared_name); - } - explicit VarHandleOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateVarHandleOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset container = 0, - flatbuffers::Offset shared_name = 0) { - VarHandleOptionsBuilder builder_(_fbb); - builder_.add_shared_name(shared_name); - builder_.add_container(container); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateVarHandleOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *container = nullptr, - const char *shared_name = nullptr) { - auto container__ = container ? _fbb.CreateString(container) : 0; - auto shared_name__ = shared_name ? _fbb.CreateString(shared_name) : 0; - return tflite_micro::CreateVarHandleOptions( - _fbb, - container__, - shared_name__); -} - -flatbuffers::Offset CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReadVariableOptionsT : public flatbuffers::NativeTable { - typedef ReadVariableOptions TableType; -}; - -struct ReadVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReadVariableOptionsT NativeTableType; - typedef ReadVariableOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ReadVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReadVariableOptionsBuilder { - typedef ReadVariableOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ReadVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReadVariableOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - ReadVariableOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct AssignVariableOptionsT : public flatbuffers::NativeTable { - typedef AssignVariableOptions TableType; -}; - -struct AssignVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef AssignVariableOptionsT NativeTableType; - typedef AssignVariableOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - AssignVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct AssignVariableOptionsBuilder { - typedef AssignVariableOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit AssignVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateAssignVariableOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - AssignVariableOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RandomOptionsT : public flatbuffers::NativeTable { - typedef RandomOptions TableType; - int64_t seed = 0; - int64_t seed2 = 0; -}; - -struct RandomOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RandomOptionsT NativeTableType; - typedef RandomOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_SEED = 4, - VT_SEED2 = 6 - }; - int64_t seed() const { - return GetField(VT_SEED, 0); - } - int64_t seed2() const { - return GetField(VT_SEED2, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_SEED, 8) && - VerifyField(verifier, VT_SEED2, 8) && - verifier.EndTable(); - } - RandomOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RandomOptionsBuilder { - typedef RandomOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_seed(int64_t seed) { - fbb_.AddElement(RandomOptions::VT_SEED, seed, 0); - } - void add_seed2(int64_t seed2) { - fbb_.AddElement(RandomOptions::VT_SEED2, seed2, 0); - } - explicit RandomOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRandomOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int64_t seed = 0, - int64_t seed2 = 0) { - RandomOptionsBuilder builder_(_fbb); - builder_.add_seed2(seed2); - builder_.add_seed(seed); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BucketizeOptionsT : public flatbuffers::NativeTable { - typedef BucketizeOptions TableType; - std::vector boundaries{}; -}; - -struct BucketizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BucketizeOptionsT NativeTableType; - typedef BucketizeOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_BOUNDARIES = 4 - }; - const flatbuffers::Vector *boundaries() const { - return GetPointer *>(VT_BOUNDARIES); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_BOUNDARIES) && - verifier.VerifyVector(boundaries()) && - verifier.EndTable(); - } - BucketizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BucketizeOptionsBuilder { - typedef BucketizeOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_boundaries(flatbuffers::Offset> boundaries) { - fbb_.AddOffset(BucketizeOptions::VT_BOUNDARIES, boundaries); - } - explicit BucketizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBucketizeOptions( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> boundaries = 0) { - BucketizeOptionsBuilder builder_(_fbb); - builder_.add_boundaries(boundaries); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateBucketizeOptionsDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *boundaries = nullptr) { - auto boundaries__ = boundaries ? _fbb.CreateVector(*boundaries) : 0; - return tflite_micro::CreateBucketizeOptions( - _fbb, - boundaries__); -} - -flatbuffers::Offset CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct GeluOptionsT : public flatbuffers::NativeTable { - typedef GeluOptions TableType; - bool approximate = false; -}; - -struct GeluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef GeluOptionsT NativeTableType; - typedef GeluOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_APPROXIMATE = 4 - }; - bool approximate() const { - return GetField(VT_APPROXIMATE, 0) != 0; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_APPROXIMATE, 1) && - verifier.EndTable(); - } - GeluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct GeluOptionsBuilder { - typedef GeluOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_approximate(bool approximate) { - fbb_.AddElement(GeluOptions::VT_APPROXIMATE, static_cast(approximate), 0); - } - explicit GeluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateGeluOptions( - flatbuffers::FlatBufferBuilder &_fbb, - bool approximate = false) { - GeluOptionsBuilder builder_(_fbb); - builder_.add_approximate(approximate); - return builder_.Finish(); -} - -flatbuffers::Offset CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DynamicUpdateSliceOptionsT : public flatbuffers::NativeTable { - typedef DynamicUpdateSliceOptions TableType; -}; - -struct DynamicUpdateSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DynamicUpdateSliceOptionsT NativeTableType; - typedef DynamicUpdateSliceOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DynamicUpdateSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DynamicUpdateSliceOptionsBuilder { - typedef DynamicUpdateSliceOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DynamicUpdateSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDynamicUpdateSliceOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DynamicUpdateSliceOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnsortedSegmentProdOptionsT : public flatbuffers::NativeTable { - typedef UnsortedSegmentProdOptions TableType; -}; - -struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnsortedSegmentProdOptionsT NativeTableType; - typedef UnsortedSegmentProdOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - UnsortedSegmentProdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnsortedSegmentProdOptionsBuilder { - typedef UnsortedSegmentProdOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit UnsortedSegmentProdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnsortedSegmentProdOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - UnsortedSegmentProdOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnsortedSegmentMaxOptionsT : public flatbuffers::NativeTable { - typedef UnsortedSegmentMaxOptions TableType; -}; - -struct UnsortedSegmentMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnsortedSegmentMaxOptionsT NativeTableType; - typedef UnsortedSegmentMaxOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - UnsortedSegmentMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnsortedSegmentMaxOptionsBuilder { - typedef UnsortedSegmentMaxOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit UnsortedSegmentMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnsortedSegmentMaxOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - UnsortedSegmentMaxOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnsortedSegmentSumOptionsT : public flatbuffers::NativeTable { - typedef UnsortedSegmentSumOptions TableType; -}; - -struct UnsortedSegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnsortedSegmentSumOptionsT NativeTableType; - typedef UnsortedSegmentSumOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - UnsortedSegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnsortedSegmentSumOptionsBuilder { - typedef UnsortedSegmentSumOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit UnsortedSegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnsortedSegmentSumOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - UnsortedSegmentSumOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ATan2OptionsT : public flatbuffers::NativeTable { - typedef ATan2Options TableType; -}; - -struct ATan2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ATan2OptionsT NativeTableType; - typedef ATan2OptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - ATan2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ATan2OptionsBuilder { - typedef ATan2Options Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit ATan2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateATan2Options( - flatbuffers::FlatBufferBuilder &_fbb) { - ATan2OptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct UnsortedSegmentMinOptionsT : public flatbuffers::NativeTable { - typedef UnsortedSegmentMinOptions TableType; -}; - -struct UnsortedSegmentMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnsortedSegmentMinOptionsT NativeTableType; - typedef UnsortedSegmentMinOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - UnsortedSegmentMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnsortedSegmentMinOptionsBuilder { - typedef UnsortedSegmentMinOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit UnsortedSegmentMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnsortedSegmentMinOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - UnsortedSegmentMinOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SignOptionsT : public flatbuffers::NativeTable { - typedef SignOptions TableType; -}; - -struct SignOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SignOptionsT NativeTableType; - typedef SignOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - SignOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SignOptionsBuilder { - typedef SignOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit SignOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSignOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - SignOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BitcastOptionsT : public flatbuffers::NativeTable { - typedef BitcastOptions TableType; -}; - -struct BitcastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BitcastOptionsT NativeTableType; - typedef BitcastOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - BitcastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BitcastOptionsBuilder { - typedef BitcastOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BitcastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBitcastOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - BitcastOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BitwiseXorOptionsT : public flatbuffers::NativeTable { - typedef BitwiseXorOptions TableType; -}; - -struct BitwiseXorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BitwiseXorOptionsT NativeTableType; - typedef BitwiseXorOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - BitwiseXorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BitwiseXorOptionsBuilder { - typedef BitwiseXorOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit BitwiseXorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBitwiseXorOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - BitwiseXorOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct RightShiftOptionsT : public flatbuffers::NativeTable { - typedef RightShiftOptions TableType; -}; - -struct RightShiftOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef RightShiftOptionsT NativeTableType; - typedef RightShiftOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - RightShiftOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct RightShiftOptionsBuilder { - typedef RightShiftOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit RightShiftOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateRightShiftOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - RightShiftOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct DilateOptionsT : public flatbuffers::NativeTable { - typedef DilateOptions TableType; -}; - -struct DilateOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef DilateOptionsT NativeTableType; - typedef DilateOptionsBuilder Builder; - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - verifier.EndTable(); - } - DilateOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(DilateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct DilateOptionsBuilder { - typedef DilateOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - explicit DilateOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateDilateOptions( - flatbuffers::FlatBufferBuilder &_fbb) { - DilateOptionsBuilder builder_(_fbb); - return builder_.Finish(); -} - -flatbuffers::Offset CreateDilateOptions(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ReduceWindowOptionsT : public flatbuffers::NativeTable { - typedef ReduceWindowOptions TableType; - tflite_micro::ReduceWindowFunction reduce_function = tflite_micro::ReduceWindowFunction_UNSUPPORTED; -}; - -struct ReduceWindowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ReduceWindowOptionsT NativeTableType; - typedef ReduceWindowOptionsBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_REDUCE_FUNCTION = 4 - }; - tflite_micro::ReduceWindowFunction reduce_function() const { - return static_cast(GetField(VT_REDUCE_FUNCTION, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_REDUCE_FUNCTION, 4) && - verifier.EndTable(); - } - ReduceWindowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ReduceWindowOptionsBuilder { - typedef ReduceWindowOptions Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_reduce_function(tflite_micro::ReduceWindowFunction reduce_function) { - fbb_.AddElement(ReduceWindowOptions::VT_REDUCE_FUNCTION, static_cast(reduce_function), 0); - } - explicit ReduceWindowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateReduceWindowOptions( - flatbuffers::FlatBufferBuilder &_fbb, - tflite_micro::ReduceWindowFunction reduce_function = tflite_micro::ReduceWindowFunction_UNSUPPORTED) { - ReduceWindowOptionsBuilder builder_(_fbb); - builder_.add_reduce_function(reduce_function); - return builder_.Finish(); -} - -flatbuffers::Offset CreateReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OperatorCodeT : public flatbuffers::NativeTable { - typedef OperatorCode TableType; - int8_t deprecated_builtin_code = 0; - std::string custom_code{}; - int32_t version = 1; - tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD; -}; - -struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OperatorCodeT NativeTableType; - typedef OperatorCodeBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DEPRECATED_BUILTIN_CODE = 4, - VT_CUSTOM_CODE = 6, - VT_VERSION = 8, - VT_BUILTIN_CODE = 10 - }; - int8_t deprecated_builtin_code() const { - return GetField(VT_DEPRECATED_BUILTIN_CODE, 0); - } - const flatbuffers::String *custom_code() const { - return GetPointer(VT_CUSTOM_CODE); - } - int32_t version() const { - return GetField(VT_VERSION, 1); - } - tflite_micro::BuiltinOperator builtin_code() const { - return static_cast(GetField(VT_BUILTIN_CODE, 0)); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_DEPRECATED_BUILTIN_CODE, 1) && - VerifyOffset(verifier, VT_CUSTOM_CODE) && - verifier.VerifyString(custom_code()) && - VerifyField(verifier, VT_VERSION, 4) && - VerifyField(verifier, VT_BUILTIN_CODE, 4) && - verifier.EndTable(); - } - OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct OperatorCodeBuilder { - typedef OperatorCode Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_deprecated_builtin_code(int8_t deprecated_builtin_code) { - fbb_.AddElement(OperatorCode::VT_DEPRECATED_BUILTIN_CODE, deprecated_builtin_code, 0); - } - void add_custom_code(flatbuffers::Offset custom_code) { - fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code); - } - void add_version(int32_t version) { - fbb_.AddElement(OperatorCode::VT_VERSION, version, 1); - } - void add_builtin_code(tflite_micro::BuiltinOperator builtin_code) { - fbb_.AddElement(OperatorCode::VT_BUILTIN_CODE, static_cast(builtin_code), 0); - } - explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOperatorCode( - flatbuffers::FlatBufferBuilder &_fbb, - int8_t deprecated_builtin_code = 0, - flatbuffers::Offset custom_code = 0, - int32_t version = 1, - tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD) { - OperatorCodeBuilder builder_(_fbb); - builder_.add_builtin_code(builtin_code); - builder_.add_version(version); - builder_.add_custom_code(custom_code); - builder_.add_deprecated_builtin_code(deprecated_builtin_code); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateOperatorCodeDirect( - flatbuffers::FlatBufferBuilder &_fbb, - int8_t deprecated_builtin_code = 0, - const char *custom_code = nullptr, - int32_t version = 1, - tflite_micro::BuiltinOperator builtin_code = tflite_micro::BuiltinOperator_ADD) { - auto custom_code__ = custom_code ? _fbb.CreateString(custom_code) : 0; - return tflite_micro::CreateOperatorCode( - _fbb, - deprecated_builtin_code, - custom_code__, - version, - builtin_code); -} - -flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct OperatorT : public flatbuffers::NativeTable { - typedef Operator TableType; - uint32_t opcode_index = 0; - std::vector inputs{}; - std::vector outputs{}; - tflite_micro::BuiltinOptionsUnion builtin_options{}; - std::vector custom_options{}; - tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS; - std::vector mutating_variable_inputs{}; - std::vector intermediates{}; - uint64_t large_custom_options_offset = 0; - uint64_t large_custom_options_size = 0; - tflite_micro::BuiltinOptions2Union builtin_options_2{}; -}; - -struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef OperatorT NativeTableType; - typedef OperatorBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_OPCODE_INDEX = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_BUILTIN_OPTIONS_TYPE = 10, - VT_BUILTIN_OPTIONS = 12, - VT_CUSTOM_OPTIONS = 14, - VT_CUSTOM_OPTIONS_FORMAT = 16, - VT_MUTATING_VARIABLE_INPUTS = 18, - VT_INTERMEDIATES = 20, - VT_LARGE_CUSTOM_OPTIONS_OFFSET = 22, - VT_LARGE_CUSTOM_OPTIONS_SIZE = 24, - VT_BUILTIN_OPTIONS_2_TYPE = 26, - VT_BUILTIN_OPTIONS_2 = 28 - }; - uint32_t opcode_index() const { - return GetField(VT_OPCODE_INDEX, 0); - } - const flatbuffers::Vector *inputs() const { - return GetPointer *>(VT_INPUTS); - } - const flatbuffers::Vector *outputs() const { - return GetPointer *>(VT_OUTPUTS); - } - tflite_micro::BuiltinOptions builtin_options_type() const { - return static_cast(GetField(VT_BUILTIN_OPTIONS_TYPE, 0)); - } - const void *builtin_options() const { - return GetPointer(VT_BUILTIN_OPTIONS); - } - template const T *builtin_options_as() const; - const tflite_micro::Conv2DOptions *builtin_options_as_Conv2DOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_Conv2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DepthwiseConv2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ConcatEmbeddingsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LSHProjectionOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::Pool2DOptions *builtin_options_as_Pool2DOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_Pool2DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SVDFOptions *builtin_options_as_SVDFOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SVDFOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::RNNOptions *builtin_options_as_RNNOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_RNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_FullyConnectedOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SoftmaxOptions *builtin_options_as_SoftmaxOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SoftmaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ConcatenationOptions *builtin_options_as_ConcatenationOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ConcatenationOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::AddOptions *builtin_options_as_AddOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_AddOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::L2NormOptions *builtin_options_as_L2NormOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_L2NormOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LocalResponseNormalizationOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LSTMOptions *builtin_options_as_LSTMOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ResizeBilinearOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::CallOptions *builtin_options_as_CallOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_CallOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ReshapeOptions *builtin_options_as_ReshapeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ReshapeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SkipGramOptions *builtin_options_as_SkipGramOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SkipGramOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SpaceToDepthOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::MulOptions *builtin_options_as_MulOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_MulOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::PadOptions *builtin_options_as_PadOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_PadOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::GatherOptions *builtin_options_as_GatherOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_GatherOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BatchToSpaceNDOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SpaceToBatchNDOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::TransposeOptions *builtin_options_as_TransposeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_TransposeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ReducerOptions *builtin_options_as_ReducerOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ReducerOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SubOptions *builtin_options_as_SubOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SubOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DivOptions *builtin_options_as_DivOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DivOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SqueezeOptions *builtin_options_as_SqueezeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SqueezeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SequenceRNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::StridedSliceOptions *builtin_options_as_StridedSliceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_StridedSliceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ExpOptions *builtin_options_as_ExpOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ExpOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::TopKV2Options *builtin_options_as_TopKV2Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_TopKV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SplitOptions *builtin_options_as_SplitOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SplitOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LogSoftmaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::CastOptions *builtin_options_as_CastOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_CastOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DequantizeOptions *builtin_options_as_DequantizeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DequantizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_MaximumMinimumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ArgMaxOptions *builtin_options_as_ArgMaxOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ArgMaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LessOptions *builtin_options_as_LessOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LessOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::NegOptions *builtin_options_as_NegOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_NegOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::PadV2Options *builtin_options_as_PadV2Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_PadV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::GreaterOptions *builtin_options_as_GreaterOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_GreaterOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_GreaterEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LessEqualOptions *builtin_options_as_LessEqualOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LessEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SelectOptions *builtin_options_as_SelectOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SelectOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SliceOptions *builtin_options_as_SliceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SliceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::TransposeConvOptions *builtin_options_as_TransposeConvOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_TransposeConvOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SparseToDenseOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::TileOptions *builtin_options_as_TileOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_TileOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ExpandDimsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::EqualOptions *builtin_options_as_EqualOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_EqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::NotEqualOptions *builtin_options_as_NotEqualOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_NotEqualOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ShapeOptions *builtin_options_as_ShapeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ShapeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::PowOptions *builtin_options_as_PowOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_PowOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ArgMinOptions *builtin_options_as_ArgMinOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ArgMinOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::FakeQuantOptions *builtin_options_as_FakeQuantOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_FakeQuantOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::PackOptions *builtin_options_as_PackOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::OneHotOptions *builtin_options_as_OneHotOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LogicalAndOptions *builtin_options_as_LogicalAndOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalAndOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LogicalNotOptions *builtin_options_as_LogicalNotOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LogicalNotOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnpackOptions *builtin_options_as_UnpackOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnpackOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::FloorDivOptions *builtin_options_as_FloorDivOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_FloorDivOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SquareOptions *builtin_options_as_SquareOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SquareOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ZerosLikeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::FillOptions *builtin_options_as_FillOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_FillOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BidirectionalSequenceLSTMOptions *builtin_options_as_BidirectionalSequenceLSTMOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BidirectionalSequenceRNNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnidirectionalSequenceLSTMOptions *builtin_options_as_UnidirectionalSequenceLSTMOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnidirectionalSequenceLSTMOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::FloorModOptions *builtin_options_as_FloorModOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_FloorModOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::RangeOptions *builtin_options_as_RangeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_RangeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ResizeNearestNeighborOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::LeakyReluOptions *builtin_options_as_LeakyReluOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_LeakyReluOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SquaredDifferenceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::MirrorPadOptions *builtin_options_as_MirrorPadOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_MirrorPadOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::AbsOptions *builtin_options_as_AbsOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_AbsOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SplitVOptions *builtin_options_as_SplitVOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SplitVOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UniqueOptions *builtin_options_as_UniqueOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UniqueOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ReverseV2Options *builtin_options_as_ReverseV2Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ReverseV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::AddNOptions *builtin_options_as_AddNOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_AddNOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::GatherNdOptions *builtin_options_as_GatherNdOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_GatherNdOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::CosOptions *builtin_options_as_CosOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_CosOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::WhereOptions *builtin_options_as_WhereOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_WhereOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::RankOptions *builtin_options_as_RankOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_RankOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ReverseSequenceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_MatrixDiagOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::QuantizeOptions *builtin_options_as_QuantizeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_QuantizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_MatrixSetDiagOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::HardSwishOptions *builtin_options_as_HardSwishOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_HardSwishOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::IfOptions *builtin_options_as_IfOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_IfOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::WhileOptions *builtin_options_as_WhileOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_WhileOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DepthToSpaceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_NonMaxSuppressionV4Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_NonMaxSuppressionV5Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ScatterNdOptions *builtin_options_as_ScatterNdOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ScatterNdOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SelectV2Options *builtin_options_as_SelectV2Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SelectV2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DensifyOptions *builtin_options_as_DensifyOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DensifyOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SegmentSumOptions *builtin_options_as_SegmentSumOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SegmentSumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BatchMatMulOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::CumsumOptions *builtin_options_as_CumsumOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_CumsumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::CallOnceOptions *builtin_options_as_CallOnceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_CallOnceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BroadcastToOptions *builtin_options_as_BroadcastToOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BroadcastToOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::Rfft2dOptions *builtin_options_as_Rfft2dOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_Rfft2dOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::Conv3DOptions *builtin_options_as_Conv3DOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_Conv3DOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::HashtableOptions *builtin_options_as_HashtableOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::HashtableFindOptions *builtin_options_as_HashtableFindOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableFindOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::HashtableImportOptions *builtin_options_as_HashtableImportOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableImportOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::HashtableSizeOptions *builtin_options_as_HashtableSizeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_HashtableSizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::VarHandleOptions *builtin_options_as_VarHandleOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_VarHandleOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ReadVariableOptions *builtin_options_as_ReadVariableOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ReadVariableOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::AssignVariableOptions *builtin_options_as_AssignVariableOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_AssignVariableOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::RandomOptions *builtin_options_as_RandomOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_RandomOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BucketizeOptions *builtin_options_as_BucketizeOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BucketizeOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::GeluOptions *builtin_options_as_GeluOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_GeluOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::DynamicUpdateSliceOptions *builtin_options_as_DynamicUpdateSliceOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_DynamicUpdateSliceOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnsortedSegmentProdOptions *builtin_options_as_UnsortedSegmentProdOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentProdOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnsortedSegmentMaxOptions *builtin_options_as_UnsortedSegmentMaxOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentMaxOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnsortedSegmentMinOptions *builtin_options_as_UnsortedSegmentMinOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentMinOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::UnsortedSegmentSumOptions *builtin_options_as_UnsortedSegmentSumOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_UnsortedSegmentSumOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::ATan2Options *builtin_options_as_ATan2Options() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_ATan2Options ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::SignOptions *builtin_options_as_SignOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_SignOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BitcastOptions *builtin_options_as_BitcastOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BitcastOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::BitwiseXorOptions *builtin_options_as_BitwiseXorOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_BitwiseXorOptions ? static_cast(builtin_options()) : nullptr; - } - const tflite_micro::RightShiftOptions *builtin_options_as_RightShiftOptions() const { - return builtin_options_type() == tflite_micro::BuiltinOptions_RightShiftOptions ? static_cast(builtin_options()) : nullptr; - } - const flatbuffers::Vector *custom_options() const { - return GetPointer *>(VT_CUSTOM_OPTIONS); - } - tflite_micro::CustomOptionsFormat custom_options_format() const { - return static_cast(GetField(VT_CUSTOM_OPTIONS_FORMAT, 0)); - } - const flatbuffers::Vector *mutating_variable_inputs() const { - return GetPointer *>(VT_MUTATING_VARIABLE_INPUTS); - } - const flatbuffers::Vector *intermediates() const { - return GetPointer *>(VT_INTERMEDIATES); - } - uint64_t large_custom_options_offset() const { - return GetField(VT_LARGE_CUSTOM_OPTIONS_OFFSET, 0); - } - uint64_t large_custom_options_size() const { - return GetField(VT_LARGE_CUSTOM_OPTIONS_SIZE, 0); - } - tflite_micro::BuiltinOptions2 builtin_options_2_type() const { - return static_cast(GetField(VT_BUILTIN_OPTIONS_2_TYPE, 0)); - } - const void *builtin_options_2() const { - return GetPointer(VT_BUILTIN_OPTIONS_2); - } - template const T *builtin_options_2_as() const; - const tflite_micro::StablehloConcatenateOptions *builtin_options_2_as_StablehloConcatenateOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloConcatenateOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloBroadcastInDimOptions *builtin_options_2_as_StablehloBroadcastInDimOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloBroadcastInDimOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloSliceOptions *builtin_options_2_as_StablehloSliceOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloSliceOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloConvolutionOptions *builtin_options_2_as_StablehloConvolutionOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloConvolutionOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloCustomCallOptions *builtin_options_2_as_StablehloCustomCallOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloCustomCallOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloReduceOptions *builtin_options_2_as_StablehloReduceOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloReduceOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloScatterOptions *builtin_options_2_as_StablehloScatterOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloScatterOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloCompareOptions *builtin_options_2_as_StablehloCompareOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloCompareOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloDynamicSliceOptions *builtin_options_2_as_StablehloDynamicSliceOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloDynamicSliceOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloPadOptions *builtin_options_2_as_StablehloPadOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloPadOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloIotaOptions *builtin_options_2_as_StablehloIotaOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloIotaOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloDotGeneralOptions *builtin_options_2_as_StablehloDotGeneralOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloDotGeneralOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloReduceWindowOptions *builtin_options_2_as_StablehloReduceWindowOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloReduceWindowOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloSortOptions *builtin_options_2_as_StablehloSortOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloSortOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloWhileOptions *builtin_options_2_as_StablehloWhileOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloWhileOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloGatherOptions *builtin_options_2_as_StablehloGatherOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloGatherOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloTransposeOptions *builtin_options_2_as_StablehloTransposeOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloTransposeOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::DilateOptions *builtin_options_2_as_DilateOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_DilateOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::StablehloRngBitGeneratorOptions *builtin_options_2_as_StablehloRngBitGeneratorOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_StablehloRngBitGeneratorOptions ? static_cast(builtin_options_2()) : nullptr; - } - const tflite_micro::ReduceWindowOptions *builtin_options_2_as_ReduceWindowOptions() const { - return builtin_options_2_type() == tflite_micro::BuiltinOptions2_ReduceWindowOptions ? static_cast(builtin_options_2()) : nullptr; - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_OPCODE_INDEX, 4) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - VerifyField(verifier, VT_BUILTIN_OPTIONS_TYPE, 1) && - VerifyOffset(verifier, VT_BUILTIN_OPTIONS) && - VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) && - VerifyOffset(verifier, VT_CUSTOM_OPTIONS) && - verifier.VerifyVector(custom_options()) && - VerifyField(verifier, VT_CUSTOM_OPTIONS_FORMAT, 1) && - VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) && - verifier.VerifyVector(mutating_variable_inputs()) && - VerifyOffset(verifier, VT_INTERMEDIATES) && - verifier.VerifyVector(intermediates()) && - VerifyField(verifier, VT_LARGE_CUSTOM_OPTIONS_OFFSET, 8) && - VerifyField(verifier, VT_LARGE_CUSTOM_OPTIONS_SIZE, 8) && - VerifyField(verifier, VT_BUILTIN_OPTIONS_2_TYPE, 1) && - VerifyOffset(verifier, VT_BUILTIN_OPTIONS_2) && - VerifyBuiltinOptions2(verifier, builtin_options_2(), builtin_options_2_type()) && - verifier.EndTable(); - } - OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -template<> inline const tflite_micro::Conv2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_Conv2DOptions(); -} - -template<> inline const tflite_micro::DepthwiseConv2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_DepthwiseConv2DOptions(); -} - -template<> inline const tflite_micro::ConcatEmbeddingsOptions *Operator::builtin_options_as() const { - return builtin_options_as_ConcatEmbeddingsOptions(); -} - -template<> inline const tflite_micro::LSHProjectionOptions *Operator::builtin_options_as() const { - return builtin_options_as_LSHProjectionOptions(); -} - -template<> inline const tflite_micro::Pool2DOptions *Operator::builtin_options_as() const { - return builtin_options_as_Pool2DOptions(); -} - -template<> inline const tflite_micro::SVDFOptions *Operator::builtin_options_as() const { - return builtin_options_as_SVDFOptions(); -} - -template<> inline const tflite_micro::RNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_RNNOptions(); -} - -template<> inline const tflite_micro::FullyConnectedOptions *Operator::builtin_options_as() const { - return builtin_options_as_FullyConnectedOptions(); -} - -template<> inline const tflite_micro::SoftmaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_SoftmaxOptions(); -} - -template<> inline const tflite_micro::ConcatenationOptions *Operator::builtin_options_as() const { - return builtin_options_as_ConcatenationOptions(); -} - -template<> inline const tflite_micro::AddOptions *Operator::builtin_options_as() const { - return builtin_options_as_AddOptions(); -} - -template<> inline const tflite_micro::L2NormOptions *Operator::builtin_options_as() const { - return builtin_options_as_L2NormOptions(); -} - -template<> inline const tflite_micro::LocalResponseNormalizationOptions *Operator::builtin_options_as() const { - return builtin_options_as_LocalResponseNormalizationOptions(); -} - -template<> inline const tflite_micro::LSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_LSTMOptions(); -} - -template<> inline const tflite_micro::ResizeBilinearOptions *Operator::builtin_options_as() const { - return builtin_options_as_ResizeBilinearOptions(); -} - -template<> inline const tflite_micro::CallOptions *Operator::builtin_options_as() const { - return builtin_options_as_CallOptions(); -} - -template<> inline const tflite_micro::ReshapeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReshapeOptions(); -} - -template<> inline const tflite_micro::SkipGramOptions *Operator::builtin_options_as() const { - return builtin_options_as_SkipGramOptions(); -} - -template<> inline const tflite_micro::SpaceToDepthOptions *Operator::builtin_options_as() const { - return builtin_options_as_SpaceToDepthOptions(); -} - -template<> inline const tflite_micro::EmbeddingLookupSparseOptions *Operator::builtin_options_as() const { - return builtin_options_as_EmbeddingLookupSparseOptions(); -} - -template<> inline const tflite_micro::MulOptions *Operator::builtin_options_as() const { - return builtin_options_as_MulOptions(); -} - -template<> inline const tflite_micro::PadOptions *Operator::builtin_options_as() const { - return builtin_options_as_PadOptions(); -} - -template<> inline const tflite_micro::GatherOptions *Operator::builtin_options_as() const { - return builtin_options_as_GatherOptions(); -} - -template<> inline const tflite_micro::BatchToSpaceNDOptions *Operator::builtin_options_as() const { - return builtin_options_as_BatchToSpaceNDOptions(); -} - -template<> inline const tflite_micro::SpaceToBatchNDOptions *Operator::builtin_options_as() const { - return builtin_options_as_SpaceToBatchNDOptions(); -} - -template<> inline const tflite_micro::TransposeOptions *Operator::builtin_options_as() const { - return builtin_options_as_TransposeOptions(); -} - -template<> inline const tflite_micro::ReducerOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReducerOptions(); -} - -template<> inline const tflite_micro::SubOptions *Operator::builtin_options_as() const { - return builtin_options_as_SubOptions(); -} - -template<> inline const tflite_micro::DivOptions *Operator::builtin_options_as() const { - return builtin_options_as_DivOptions(); -} - -template<> inline const tflite_micro::SqueezeOptions *Operator::builtin_options_as() const { - return builtin_options_as_SqueezeOptions(); -} - -template<> inline const tflite_micro::SequenceRNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_SequenceRNNOptions(); -} - -template<> inline const tflite_micro::StridedSliceOptions *Operator::builtin_options_as() const { - return builtin_options_as_StridedSliceOptions(); -} - -template<> inline const tflite_micro::ExpOptions *Operator::builtin_options_as() const { - return builtin_options_as_ExpOptions(); -} - -template<> inline const tflite_micro::TopKV2Options *Operator::builtin_options_as() const { - return builtin_options_as_TopKV2Options(); -} - -template<> inline const tflite_micro::SplitOptions *Operator::builtin_options_as() const { - return builtin_options_as_SplitOptions(); -} - -template<> inline const tflite_micro::LogSoftmaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogSoftmaxOptions(); -} - -template<> inline const tflite_micro::CastOptions *Operator::builtin_options_as() const { - return builtin_options_as_CastOptions(); -} - -template<> inline const tflite_micro::DequantizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_DequantizeOptions(); -} - -template<> inline const tflite_micro::MaximumMinimumOptions *Operator::builtin_options_as() const { - return builtin_options_as_MaximumMinimumOptions(); -} - -template<> inline const tflite_micro::ArgMaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_ArgMaxOptions(); -} - -template<> inline const tflite_micro::LessOptions *Operator::builtin_options_as() const { - return builtin_options_as_LessOptions(); -} - -template<> inline const tflite_micro::NegOptions *Operator::builtin_options_as() const { - return builtin_options_as_NegOptions(); -} - -template<> inline const tflite_micro::PadV2Options *Operator::builtin_options_as() const { - return builtin_options_as_PadV2Options(); -} - -template<> inline const tflite_micro::GreaterOptions *Operator::builtin_options_as() const { - return builtin_options_as_GreaterOptions(); -} - -template<> inline const tflite_micro::GreaterEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_GreaterEqualOptions(); -} - -template<> inline const tflite_micro::LessEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_LessEqualOptions(); -} - -template<> inline const tflite_micro::SelectOptions *Operator::builtin_options_as() const { - return builtin_options_as_SelectOptions(); -} - -template<> inline const tflite_micro::SliceOptions *Operator::builtin_options_as() const { - return builtin_options_as_SliceOptions(); -} - -template<> inline const tflite_micro::TransposeConvOptions *Operator::builtin_options_as() const { - return builtin_options_as_TransposeConvOptions(); -} - -template<> inline const tflite_micro::SparseToDenseOptions *Operator::builtin_options_as() const { - return builtin_options_as_SparseToDenseOptions(); -} - -template<> inline const tflite_micro::TileOptions *Operator::builtin_options_as() const { - return builtin_options_as_TileOptions(); -} - -template<> inline const tflite_micro::ExpandDimsOptions *Operator::builtin_options_as() const { - return builtin_options_as_ExpandDimsOptions(); -} - -template<> inline const tflite_micro::EqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_EqualOptions(); -} - -template<> inline const tflite_micro::NotEqualOptions *Operator::builtin_options_as() const { - return builtin_options_as_NotEqualOptions(); -} - -template<> inline const tflite_micro::ShapeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ShapeOptions(); -} - -template<> inline const tflite_micro::PowOptions *Operator::builtin_options_as() const { - return builtin_options_as_PowOptions(); -} - -template<> inline const tflite_micro::ArgMinOptions *Operator::builtin_options_as() const { - return builtin_options_as_ArgMinOptions(); -} - -template<> inline const tflite_micro::FakeQuantOptions *Operator::builtin_options_as() const { - return builtin_options_as_FakeQuantOptions(); -} - -template<> inline const tflite_micro::PackOptions *Operator::builtin_options_as() const { - return builtin_options_as_PackOptions(); -} - -template<> inline const tflite_micro::LogicalOrOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalOrOptions(); -} - -template<> inline const tflite_micro::OneHotOptions *Operator::builtin_options_as() const { - return builtin_options_as_OneHotOptions(); -} - -template<> inline const tflite_micro::LogicalAndOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalAndOptions(); -} - -template<> inline const tflite_micro::LogicalNotOptions *Operator::builtin_options_as() const { - return builtin_options_as_LogicalNotOptions(); -} - -template<> inline const tflite_micro::UnpackOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnpackOptions(); -} - -template<> inline const tflite_micro::FloorDivOptions *Operator::builtin_options_as() const { - return builtin_options_as_FloorDivOptions(); -} - -template<> inline const tflite_micro::SquareOptions *Operator::builtin_options_as() const { - return builtin_options_as_SquareOptions(); -} - -template<> inline const tflite_micro::ZerosLikeOptions *Operator::builtin_options_as() const { - return builtin_options_as_ZerosLikeOptions(); -} - -template<> inline const tflite_micro::FillOptions *Operator::builtin_options_as() const { - return builtin_options_as_FillOptions(); -} - -template<> inline const tflite_micro::BidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_BidirectionalSequenceLSTMOptions(); -} - -template<> inline const tflite_micro::BidirectionalSequenceRNNOptions *Operator::builtin_options_as() const { - return builtin_options_as_BidirectionalSequenceRNNOptions(); -} - -template<> inline const tflite_micro::UnidirectionalSequenceLSTMOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnidirectionalSequenceLSTMOptions(); -} - -template<> inline const tflite_micro::FloorModOptions *Operator::builtin_options_as() const { - return builtin_options_as_FloorModOptions(); -} - -template<> inline const tflite_micro::RangeOptions *Operator::builtin_options_as() const { - return builtin_options_as_RangeOptions(); -} - -template<> inline const tflite_micro::ResizeNearestNeighborOptions *Operator::builtin_options_as() const { - return builtin_options_as_ResizeNearestNeighborOptions(); -} - -template<> inline const tflite_micro::LeakyReluOptions *Operator::builtin_options_as() const { - return builtin_options_as_LeakyReluOptions(); -} - -template<> inline const tflite_micro::SquaredDifferenceOptions *Operator::builtin_options_as() const { - return builtin_options_as_SquaredDifferenceOptions(); -} - -template<> inline const tflite_micro::MirrorPadOptions *Operator::builtin_options_as() const { - return builtin_options_as_MirrorPadOptions(); -} - -template<> inline const tflite_micro::AbsOptions *Operator::builtin_options_as() const { - return builtin_options_as_AbsOptions(); -} - -template<> inline const tflite_micro::SplitVOptions *Operator::builtin_options_as() const { - return builtin_options_as_SplitVOptions(); -} - -template<> inline const tflite_micro::UniqueOptions *Operator::builtin_options_as() const { - return builtin_options_as_UniqueOptions(); -} - -template<> inline const tflite_micro::ReverseV2Options *Operator::builtin_options_as() const { - return builtin_options_as_ReverseV2Options(); -} - -template<> inline const tflite_micro::AddNOptions *Operator::builtin_options_as() const { - return builtin_options_as_AddNOptions(); -} - -template<> inline const tflite_micro::GatherNdOptions *Operator::builtin_options_as() const { - return builtin_options_as_GatherNdOptions(); -} - -template<> inline const tflite_micro::CosOptions *Operator::builtin_options_as() const { - return builtin_options_as_CosOptions(); -} - -template<> inline const tflite_micro::WhereOptions *Operator::builtin_options_as() const { - return builtin_options_as_WhereOptions(); -} - -template<> inline const tflite_micro::RankOptions *Operator::builtin_options_as() const { - return builtin_options_as_RankOptions(); -} - -template<> inline const tflite_micro::ReverseSequenceOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReverseSequenceOptions(); -} - -template<> inline const tflite_micro::MatrixDiagOptions *Operator::builtin_options_as() const { - return builtin_options_as_MatrixDiagOptions(); -} - -template<> inline const tflite_micro::QuantizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_QuantizeOptions(); -} - -template<> inline const tflite_micro::MatrixSetDiagOptions *Operator::builtin_options_as() const { - return builtin_options_as_MatrixSetDiagOptions(); -} - -template<> inline const tflite_micro::HardSwishOptions *Operator::builtin_options_as() const { - return builtin_options_as_HardSwishOptions(); -} - -template<> inline const tflite_micro::IfOptions *Operator::builtin_options_as() const { - return builtin_options_as_IfOptions(); -} - -template<> inline const tflite_micro::WhileOptions *Operator::builtin_options_as() const { - return builtin_options_as_WhileOptions(); -} - -template<> inline const tflite_micro::DepthToSpaceOptions *Operator::builtin_options_as() const { - return builtin_options_as_DepthToSpaceOptions(); -} - -template<> inline const tflite_micro::NonMaxSuppressionV4Options *Operator::builtin_options_as() const { - return builtin_options_as_NonMaxSuppressionV4Options(); -} - -template<> inline const tflite_micro::NonMaxSuppressionV5Options *Operator::builtin_options_as() const { - return builtin_options_as_NonMaxSuppressionV5Options(); -} - -template<> inline const tflite_micro::ScatterNdOptions *Operator::builtin_options_as() const { - return builtin_options_as_ScatterNdOptions(); -} - -template<> inline const tflite_micro::SelectV2Options *Operator::builtin_options_as() const { - return builtin_options_as_SelectV2Options(); -} - -template<> inline const tflite_micro::DensifyOptions *Operator::builtin_options_as() const { - return builtin_options_as_DensifyOptions(); -} - -template<> inline const tflite_micro::SegmentSumOptions *Operator::builtin_options_as() const { - return builtin_options_as_SegmentSumOptions(); -} - -template<> inline const tflite_micro::BatchMatMulOptions *Operator::builtin_options_as() const { - return builtin_options_as_BatchMatMulOptions(); -} - -template<> inline const tflite_micro::CumsumOptions *Operator::builtin_options_as() const { - return builtin_options_as_CumsumOptions(); -} - -template<> inline const tflite_micro::CallOnceOptions *Operator::builtin_options_as() const { - return builtin_options_as_CallOnceOptions(); -} - -template<> inline const tflite_micro::BroadcastToOptions *Operator::builtin_options_as() const { - return builtin_options_as_BroadcastToOptions(); -} - -template<> inline const tflite_micro::Rfft2dOptions *Operator::builtin_options_as() const { - return builtin_options_as_Rfft2dOptions(); -} - -template<> inline const tflite_micro::Conv3DOptions *Operator::builtin_options_as() const { - return builtin_options_as_Conv3DOptions(); -} - -template<> inline const tflite_micro::HashtableOptions *Operator::builtin_options_as() const { - return builtin_options_as_HashtableOptions(); -} - -template<> inline const tflite_micro::HashtableFindOptions *Operator::builtin_options_as() const { - return builtin_options_as_HashtableFindOptions(); -} - -template<> inline const tflite_micro::HashtableImportOptions *Operator::builtin_options_as() const { - return builtin_options_as_HashtableImportOptions(); -} - -template<> inline const tflite_micro::HashtableSizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_HashtableSizeOptions(); -} - -template<> inline const tflite_micro::VarHandleOptions *Operator::builtin_options_as() const { - return builtin_options_as_VarHandleOptions(); -} - -template<> inline const tflite_micro::ReadVariableOptions *Operator::builtin_options_as() const { - return builtin_options_as_ReadVariableOptions(); -} - -template<> inline const tflite_micro::AssignVariableOptions *Operator::builtin_options_as() const { - return builtin_options_as_AssignVariableOptions(); -} - -template<> inline const tflite_micro::RandomOptions *Operator::builtin_options_as() const { - return builtin_options_as_RandomOptions(); -} - -template<> inline const tflite_micro::BucketizeOptions *Operator::builtin_options_as() const { - return builtin_options_as_BucketizeOptions(); -} - -template<> inline const tflite_micro::GeluOptions *Operator::builtin_options_as() const { - return builtin_options_as_GeluOptions(); -} - -template<> inline const tflite_micro::DynamicUpdateSliceOptions *Operator::builtin_options_as() const { - return builtin_options_as_DynamicUpdateSliceOptions(); -} - -template<> inline const tflite_micro::UnsortedSegmentProdOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnsortedSegmentProdOptions(); -} - -template<> inline const tflite_micro::UnsortedSegmentMaxOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnsortedSegmentMaxOptions(); -} - -template<> inline const tflite_micro::UnsortedSegmentMinOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnsortedSegmentMinOptions(); -} - -template<> inline const tflite_micro::UnsortedSegmentSumOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnsortedSegmentSumOptions(); -} - -template<> inline const tflite_micro::ATan2Options *Operator::builtin_options_as() const { - return builtin_options_as_ATan2Options(); -} - -template<> inline const tflite_micro::SignOptions *Operator::builtin_options_as() const { - return builtin_options_as_SignOptions(); -} - -template<> inline const tflite_micro::BitcastOptions *Operator::builtin_options_as() const { - return builtin_options_as_BitcastOptions(); -} - -template<> inline const tflite_micro::BitwiseXorOptions *Operator::builtin_options_as() const { - return builtin_options_as_BitwiseXorOptions(); -} - -template<> inline const tflite_micro::RightShiftOptions *Operator::builtin_options_as() const { - return builtin_options_as_RightShiftOptions(); -} - -template<> inline const tflite_micro::StablehloConcatenateOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloConcatenateOptions(); -} - -template<> inline const tflite_micro::StablehloBroadcastInDimOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloBroadcastInDimOptions(); -} - -template<> inline const tflite_micro::StablehloSliceOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloSliceOptions(); -} - -template<> inline const tflite_micro::StablehloConvolutionOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloConvolutionOptions(); -} - -template<> inline const tflite_micro::StablehloCustomCallOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloCustomCallOptions(); -} - -template<> inline const tflite_micro::StablehloReduceOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloReduceOptions(); -} - -template<> inline const tflite_micro::StablehloScatterOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloScatterOptions(); -} - -template<> inline const tflite_micro::StablehloCompareOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloCompareOptions(); -} - -template<> inline const tflite_micro::StablehloDynamicSliceOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloDynamicSliceOptions(); -} - -template<> inline const tflite_micro::StablehloPadOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloPadOptions(); -} - -template<> inline const tflite_micro::StablehloIotaOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloIotaOptions(); -} - -template<> inline const tflite_micro::StablehloDotGeneralOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloDotGeneralOptions(); -} - -template<> inline const tflite_micro::StablehloReduceWindowOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloReduceWindowOptions(); -} - -template<> inline const tflite_micro::StablehloSortOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloSortOptions(); -} - -template<> inline const tflite_micro::StablehloWhileOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloWhileOptions(); -} - -template<> inline const tflite_micro::StablehloGatherOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloGatherOptions(); -} - -template<> inline const tflite_micro::StablehloTransposeOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloTransposeOptions(); -} - -template<> inline const tflite_micro::DilateOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_DilateOptions(); -} - -template<> inline const tflite_micro::StablehloRngBitGeneratorOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_StablehloRngBitGeneratorOptions(); -} - -template<> inline const tflite_micro::ReduceWindowOptions *Operator::builtin_options_2_as() const { - return builtin_options_2_as_ReduceWindowOptions(); -} - -struct OperatorBuilder { - typedef Operator Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_opcode_index(uint32_t opcode_index) { - fbb_.AddElement(Operator::VT_OPCODE_INDEX, opcode_index, 0); - } - void add_inputs(flatbuffers::Offset> inputs) { - fbb_.AddOffset(Operator::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset> outputs) { - fbb_.AddOffset(Operator::VT_OUTPUTS, outputs); - } - void add_builtin_options_type(tflite_micro::BuiltinOptions builtin_options_type) { - fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast(builtin_options_type), 0); - } - void add_builtin_options(flatbuffers::Offset builtin_options) { - fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options); - } - void add_custom_options(flatbuffers::Offset> custom_options) { - fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options); - } - void add_custom_options_format(tflite_micro::CustomOptionsFormat custom_options_format) { - fbb_.AddElement(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast(custom_options_format), 0); - } - void add_mutating_variable_inputs(flatbuffers::Offset> mutating_variable_inputs) { - fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs); - } - void add_intermediates(flatbuffers::Offset> intermediates) { - fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates); - } - void add_large_custom_options_offset(uint64_t large_custom_options_offset) { - fbb_.AddElement(Operator::VT_LARGE_CUSTOM_OPTIONS_OFFSET, large_custom_options_offset, 0); - } - void add_large_custom_options_size(uint64_t large_custom_options_size) { - fbb_.AddElement(Operator::VT_LARGE_CUSTOM_OPTIONS_SIZE, large_custom_options_size, 0); - } - void add_builtin_options_2_type(tflite_micro::BuiltinOptions2 builtin_options_2_type) { - fbb_.AddElement(Operator::VT_BUILTIN_OPTIONS_2_TYPE, static_cast(builtin_options_2_type), 0); - } - void add_builtin_options_2(flatbuffers::Offset builtin_options_2) { - fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS_2, builtin_options_2); - } - explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateOperator( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t opcode_index = 0, - flatbuffers::Offset> inputs = 0, - flatbuffers::Offset> outputs = 0, - tflite_micro::BuiltinOptions builtin_options_type = tflite_micro::BuiltinOptions_NONE, - flatbuffers::Offset builtin_options = 0, - flatbuffers::Offset> custom_options = 0, - tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS, - flatbuffers::Offset> mutating_variable_inputs = 0, - flatbuffers::Offset> intermediates = 0, - uint64_t large_custom_options_offset = 0, - uint64_t large_custom_options_size = 0, - tflite_micro::BuiltinOptions2 builtin_options_2_type = tflite_micro::BuiltinOptions2_NONE, - flatbuffers::Offset builtin_options_2 = 0) { - OperatorBuilder builder_(_fbb); - builder_.add_large_custom_options_size(large_custom_options_size); - builder_.add_large_custom_options_offset(large_custom_options_offset); - builder_.add_builtin_options_2(builtin_options_2); - builder_.add_intermediates(intermediates); - builder_.add_mutating_variable_inputs(mutating_variable_inputs); - builder_.add_custom_options(custom_options); - builder_.add_builtin_options(builtin_options); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_opcode_index(opcode_index); - builder_.add_builtin_options_2_type(builtin_options_2_type); - builder_.add_custom_options_format(custom_options_format); - builder_.add_builtin_options_type(builtin_options_type); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateOperatorDirect( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t opcode_index = 0, - const std::vector *inputs = nullptr, - const std::vector *outputs = nullptr, - tflite_micro::BuiltinOptions builtin_options_type = tflite_micro::BuiltinOptions_NONE, - flatbuffers::Offset builtin_options = 0, - const std::vector *custom_options = nullptr, - tflite_micro::CustomOptionsFormat custom_options_format = tflite_micro::CustomOptionsFormat_FLEXBUFFERS, - const std::vector *mutating_variable_inputs = nullptr, - const std::vector *intermediates = nullptr, - uint64_t large_custom_options_offset = 0, - uint64_t large_custom_options_size = 0, - tflite_micro::BuiltinOptions2 builtin_options_2_type = tflite_micro::BuiltinOptions2_NONE, - flatbuffers::Offset builtin_options_2 = 0) { - auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; - auto custom_options__ = custom_options ? _fbb.CreateVector(*custom_options) : 0; - auto mutating_variable_inputs__ = mutating_variable_inputs ? _fbb.CreateVector(*mutating_variable_inputs) : 0; - auto intermediates__ = intermediates ? _fbb.CreateVector(*intermediates) : 0; - return tflite_micro::CreateOperator( - _fbb, - opcode_index, - inputs__, - outputs__, - builtin_options_type, - builtin_options, - custom_options__, - custom_options_format, - mutating_variable_inputs__, - intermediates__, - large_custom_options_offset, - large_custom_options_size, - builtin_options_2_type, - builtin_options_2); -} - -flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SubGraphT : public flatbuffers::NativeTable { - typedef SubGraph TableType; - std::vector> tensors{}; - std::vector inputs{}; - std::vector outputs{}; - std::vector> operators{}; - std::string name{}; - SubGraphT() = default; - SubGraphT(const SubGraphT &o); - SubGraphT(SubGraphT&&) FLATBUFFERS_NOEXCEPT = default; - SubGraphT &operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT; -}; - -struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SubGraphT NativeTableType; - typedef SubGraphBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_TENSORS = 4, - VT_INPUTS = 6, - VT_OUTPUTS = 8, - VT_OPERATORS = 10, - VT_NAME = 12 - }; - const flatbuffers::Vector> *tensors() const { - return GetPointer> *>(VT_TENSORS); - } - const flatbuffers::Vector *inputs() const { - return GetPointer *>(VT_INPUTS); - } - const flatbuffers::Vector *outputs() const { - return GetPointer *>(VT_OUTPUTS); - } - const flatbuffers::Vector> *operators() const { - return GetPointer> *>(VT_OPERATORS); - } - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_TENSORS) && - verifier.VerifyVector(tensors()) && - verifier.VerifyVectorOfTables(tensors()) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - VerifyOffset(verifier, VT_OPERATORS) && - verifier.VerifyVector(operators()) && - verifier.VerifyVectorOfTables(operators()) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - verifier.EndTable(); - } - SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SubGraphBuilder { - typedef SubGraph Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_tensors(flatbuffers::Offset>> tensors) { - fbb_.AddOffset(SubGraph::VT_TENSORS, tensors); - } - void add_inputs(flatbuffers::Offset> inputs) { - fbb_.AddOffset(SubGraph::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset> outputs) { - fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs); - } - void add_operators(flatbuffers::Offset>> operators) { - fbb_.AddOffset(SubGraph::VT_OPERATORS, operators); - } - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(SubGraph::VT_NAME, name); - } - explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSubGraph( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset>> tensors = 0, - flatbuffers::Offset> inputs = 0, - flatbuffers::Offset> outputs = 0, - flatbuffers::Offset>> operators = 0, - flatbuffers::Offset name = 0) { - SubGraphBuilder builder_(_fbb); - builder_.add_name(name); - builder_.add_operators(operators); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - builder_.add_tensors(tensors); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSubGraphDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector> *tensors = nullptr, - const std::vector *inputs = nullptr, - const std::vector *outputs = nullptr, - const std::vector> *operators = nullptr, - const char *name = nullptr) { - auto tensors__ = tensors ? _fbb.CreateVector>(*tensors) : 0; - auto inputs__ = inputs ? _fbb.CreateVector(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector(*outputs) : 0; - auto operators__ = operators ? _fbb.CreateVector>(*operators) : 0; - auto name__ = name ? _fbb.CreateString(name) : 0; - return tflite_micro::CreateSubGraph( - _fbb, - tensors__, - inputs__, - outputs__, - operators__, - name__); -} - -flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct BufferT : public flatbuffers::NativeTable { - typedef Buffer TableType; - std::vector data{}; - uint64_t offset = 0; - uint64_t size = 0; -}; - -struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef BufferT NativeTableType; - typedef BufferBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_DATA = 4, - VT_OFFSET = 6, - VT_SIZE = 8 - }; - const flatbuffers::Vector *data() const { - return GetPointer *>(VT_DATA); - } - uint64_t offset() const { - return GetField(VT_OFFSET, 0); - } - uint64_t size() const { - return GetField(VT_SIZE, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_DATA) && - verifier.VerifyVector(data()) && - VerifyField(verifier, VT_OFFSET, 8) && - VerifyField(verifier, VT_SIZE, 8) && - verifier.EndTable(); - } - BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct BufferBuilder { - typedef Buffer Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_data(flatbuffers::Offset> data) { - fbb_.AddOffset(Buffer::VT_DATA, data); - } - void add_offset(uint64_t offset) { - fbb_.AddElement(Buffer::VT_OFFSET, offset, 0); - } - void add_size(uint64_t size) { - fbb_.AddElement(Buffer::VT_SIZE, size, 0); - } - explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateBuffer( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset> data = 0, - uint64_t offset = 0, - uint64_t size = 0) { - BufferBuilder builder_(_fbb); - builder_.add_size(size); - builder_.add_offset(offset); - builder_.add_data(data); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateBufferDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector *data = nullptr, - uint64_t offset = 0, - uint64_t size = 0) { - if (data) { _fbb.ForceVectorAlignment(data->size(), sizeof(uint8_t), 16); } - auto data__ = data ? _fbb.CreateVector(*data) : 0; - return tflite_micro::CreateBuffer( - _fbb, - data__, - offset, - size); -} - -flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct MetadataT : public flatbuffers::NativeTable { - typedef Metadata TableType; - std::string name{}; - uint32_t buffer = 0; -}; - -struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef MetadataT NativeTableType; - typedef MetadataBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_BUFFER = 6 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - uint32_t buffer() const { - return GetField(VT_BUFFER, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyField(verifier, VT_BUFFER, 4) && - verifier.EndTable(); - } - MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct MetadataBuilder { - typedef Metadata Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(Metadata::VT_NAME, name); - } - void add_buffer(uint32_t buffer) { - fbb_.AddElement(Metadata::VT_BUFFER, buffer, 0); - } - explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateMetadata( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - uint32_t buffer = 0) { - MetadataBuilder builder_(_fbb); - builder_.add_buffer(buffer); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateMetadataDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - uint32_t buffer = 0) { - auto name__ = name ? _fbb.CreateString(name) : 0; - return tflite_micro::CreateMetadata( - _fbb, - name__, - buffer); -} - -flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct TensorMapT : public flatbuffers::NativeTable { - typedef TensorMap TableType; - std::string name{}; - uint32_t tensor_index = 0; -}; - -struct TensorMap FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef TensorMapT NativeTableType; - typedef TensorMapBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NAME = 4, - VT_TENSOR_INDEX = 6 - }; - const flatbuffers::String *name() const { - return GetPointer(VT_NAME); - } - uint32_t tensor_index() const { - return GetField(VT_TENSOR_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_NAME) && - verifier.VerifyString(name()) && - VerifyField(verifier, VT_TENSOR_INDEX, 4) && - verifier.EndTable(); - } - TensorMapT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct TensorMapBuilder { - typedef TensorMap Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_name(flatbuffers::Offset name) { - fbb_.AddOffset(TensorMap::VT_NAME, name); - } - void add_tensor_index(uint32_t tensor_index) { - fbb_.AddElement(TensorMap::VT_TENSOR_INDEX, tensor_index, 0); - } - explicit TensorMapBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateTensorMap( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset name = 0, - uint32_t tensor_index = 0) { - TensorMapBuilder builder_(_fbb); - builder_.add_tensor_index(tensor_index); - builder_.add_name(name); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateTensorMapDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const char *name = nullptr, - uint32_t tensor_index = 0) { - auto name__ = name ? _fbb.CreateString(name) : 0; - return tflite_micro::CreateTensorMap( - _fbb, - name__, - tensor_index); -} - -flatbuffers::Offset CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct SignatureDefT : public flatbuffers::NativeTable { - typedef SignatureDef TableType; - std::vector> inputs{}; - std::vector> outputs{}; - std::string signature_key{}; - uint32_t subgraph_index = 0; - SignatureDefT() = default; - SignatureDefT(const SignatureDefT &o); - SignatureDefT(SignatureDefT&&) FLATBUFFERS_NOEXCEPT = default; - SignatureDefT &operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT; -}; - -struct SignatureDef FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef SignatureDefT NativeTableType; - typedef SignatureDefBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_INPUTS = 4, - VT_OUTPUTS = 6, - VT_SIGNATURE_KEY = 8, - VT_SUBGRAPH_INDEX = 12 - }; - const flatbuffers::Vector> *inputs() const { - return GetPointer> *>(VT_INPUTS); - } - const flatbuffers::Vector> *outputs() const { - return GetPointer> *>(VT_OUTPUTS); - } - const flatbuffers::String *signature_key() const { - return GetPointer(VT_SIGNATURE_KEY); - } - uint32_t subgraph_index() const { - return GetField(VT_SUBGRAPH_INDEX, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyOffset(verifier, VT_INPUTS) && - verifier.VerifyVector(inputs()) && - verifier.VerifyVectorOfTables(inputs()) && - VerifyOffset(verifier, VT_OUTPUTS) && - verifier.VerifyVector(outputs()) && - verifier.VerifyVectorOfTables(outputs()) && - VerifyOffset(verifier, VT_SIGNATURE_KEY) && - verifier.VerifyString(signature_key()) && - VerifyField(verifier, VT_SUBGRAPH_INDEX, 4) && - verifier.EndTable(); - } - SignatureDefT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct SignatureDefBuilder { - typedef SignatureDef Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_inputs(flatbuffers::Offset>> inputs) { - fbb_.AddOffset(SignatureDef::VT_INPUTS, inputs); - } - void add_outputs(flatbuffers::Offset>> outputs) { - fbb_.AddOffset(SignatureDef::VT_OUTPUTS, outputs); - } - void add_signature_key(flatbuffers::Offset signature_key) { - fbb_.AddOffset(SignatureDef::VT_SIGNATURE_KEY, signature_key); - } - void add_subgraph_index(uint32_t subgraph_index) { - fbb_.AddElement(SignatureDef::VT_SUBGRAPH_INDEX, subgraph_index, 0); - } - explicit SignatureDefBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateSignatureDef( - flatbuffers::FlatBufferBuilder &_fbb, - flatbuffers::Offset>> inputs = 0, - flatbuffers::Offset>> outputs = 0, - flatbuffers::Offset signature_key = 0, - uint32_t subgraph_index = 0) { - SignatureDefBuilder builder_(_fbb); - builder_.add_subgraph_index(subgraph_index); - builder_.add_signature_key(signature_key); - builder_.add_outputs(outputs); - builder_.add_inputs(inputs); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateSignatureDefDirect( - flatbuffers::FlatBufferBuilder &_fbb, - const std::vector> *inputs = nullptr, - const std::vector> *outputs = nullptr, - const char *signature_key = nullptr, - uint32_t subgraph_index = 0) { - auto inputs__ = inputs ? _fbb.CreateVector>(*inputs) : 0; - auto outputs__ = outputs ? _fbb.CreateVector>(*outputs) : 0; - auto signature_key__ = signature_key ? _fbb.CreateString(signature_key) : 0; - return tflite_micro::CreateSignatureDef( - _fbb, - inputs__, - outputs__, - signature_key__, - subgraph_index); -} - -flatbuffers::Offset CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -struct ModelT : public flatbuffers::NativeTable { - typedef Model TableType; - uint32_t version = 0; - std::vector> operator_codes{}; - std::vector> subgraphs{}; - std::string description{}; - std::vector> buffers{}; - std::vector metadata_buffer{}; - std::vector> metadata{}; - std::vector> signature_defs{}; - ModelT() = default; - ModelT(const ModelT &o); - ModelT(ModelT&&) FLATBUFFERS_NOEXCEPT = default; - ModelT &operator=(ModelT o) FLATBUFFERS_NOEXCEPT; -}; - -struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef ModelT NativeTableType; - typedef ModelBuilder Builder; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_VERSION = 4, - VT_OPERATOR_CODES = 6, - VT_SUBGRAPHS = 8, - VT_DESCRIPTION = 10, - VT_BUFFERS = 12, - VT_METADATA_BUFFER = 14, - VT_METADATA = 16, - VT_SIGNATURE_DEFS = 18 - }; - uint32_t version() const { - return GetField(VT_VERSION, 0); - } - const flatbuffers::Vector> *operator_codes() const { - return GetPointer> *>(VT_OPERATOR_CODES); - } - const flatbuffers::Vector> *subgraphs() const { - return GetPointer> *>(VT_SUBGRAPHS); - } - const flatbuffers::String *description() const { - return GetPointer(VT_DESCRIPTION); - } - const flatbuffers::Vector> *buffers() const { - return GetPointer> *>(VT_BUFFERS); - } - const flatbuffers::Vector *metadata_buffer() const { - return GetPointer *>(VT_METADATA_BUFFER); - } - const flatbuffers::Vector> *metadata() const { - return GetPointer> *>(VT_METADATA); - } - const flatbuffers::Vector> *signature_defs() const { - return GetPointer> *>(VT_SIGNATURE_DEFS); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_VERSION, 4) && - VerifyOffset(verifier, VT_OPERATOR_CODES) && - verifier.VerifyVector(operator_codes()) && - verifier.VerifyVectorOfTables(operator_codes()) && - VerifyOffset(verifier, VT_SUBGRAPHS) && - verifier.VerifyVector(subgraphs()) && - verifier.VerifyVectorOfTables(subgraphs()) && - VerifyOffset(verifier, VT_DESCRIPTION) && - verifier.VerifyString(description()) && - VerifyOffset(verifier, VT_BUFFERS) && - verifier.VerifyVector(buffers()) && - verifier.VerifyVectorOfTables(buffers()) && - VerifyOffset(verifier, VT_METADATA_BUFFER) && - verifier.VerifyVector(metadata_buffer()) && - VerifyOffset(verifier, VT_METADATA) && - verifier.VerifyVector(metadata()) && - verifier.VerifyVectorOfTables(metadata()) && - VerifyOffset(verifier, VT_SIGNATURE_DEFS) && - verifier.VerifyVector(signature_defs()) && - verifier.VerifyVectorOfTables(signature_defs()) && - verifier.EndTable(); - } - ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct ModelBuilder { - typedef Model Table; - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_version(uint32_t version) { - fbb_.AddElement(Model::VT_VERSION, version, 0); - } - void add_operator_codes(flatbuffers::Offset>> operator_codes) { - fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes); - } - void add_subgraphs(flatbuffers::Offset>> subgraphs) { - fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs); - } - void add_description(flatbuffers::Offset description) { - fbb_.AddOffset(Model::VT_DESCRIPTION, description); - } - void add_buffers(flatbuffers::Offset>> buffers) { - fbb_.AddOffset(Model::VT_BUFFERS, buffers); - } - void add_metadata_buffer(flatbuffers::Offset> metadata_buffer) { - fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer); - } - void add_metadata(flatbuffers::Offset>> metadata) { - fbb_.AddOffset(Model::VT_METADATA, metadata); - } - void add_signature_defs(flatbuffers::Offset>> signature_defs) { - fbb_.AddOffset(Model::VT_SIGNATURE_DEFS, signature_defs); - } - explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateModel( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t version = 0, - flatbuffers::Offset>> operator_codes = 0, - flatbuffers::Offset>> subgraphs = 0, - flatbuffers::Offset description = 0, - flatbuffers::Offset>> buffers = 0, - flatbuffers::Offset> metadata_buffer = 0, - flatbuffers::Offset>> metadata = 0, - flatbuffers::Offset>> signature_defs = 0) { - ModelBuilder builder_(_fbb); - builder_.add_signature_defs(signature_defs); - builder_.add_metadata(metadata); - builder_.add_metadata_buffer(metadata_buffer); - builder_.add_buffers(buffers); - builder_.add_description(description); - builder_.add_subgraphs(subgraphs); - builder_.add_operator_codes(operator_codes); - builder_.add_version(version); - return builder_.Finish(); -} - -inline flatbuffers::Offset CreateModelDirect( - flatbuffers::FlatBufferBuilder &_fbb, - uint32_t version = 0, - const std::vector> *operator_codes = nullptr, - const std::vector> *subgraphs = nullptr, - const char *description = nullptr, - const std::vector> *buffers = nullptr, - const std::vector *metadata_buffer = nullptr, - const std::vector> *metadata = nullptr, - const std::vector> *signature_defs = nullptr) { - auto operator_codes__ = operator_codes ? _fbb.CreateVector>(*operator_codes) : 0; - auto subgraphs__ = subgraphs ? _fbb.CreateVector>(*subgraphs) : 0; - auto description__ = description ? _fbb.CreateString(description) : 0; - auto buffers__ = buffers ? _fbb.CreateVector>(*buffers) : 0; - auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector(*metadata_buffer) : 0; - auto metadata__ = metadata ? _fbb.CreateVector>(*metadata) : 0; - auto signature_defs__ = signature_defs ? _fbb.CreateVector>(*signature_defs) : 0; - return tflite_micro::CreateModel( - _fbb, - version, - operator_codes__, - subgraphs__, - description__, - buffers__, - metadata_buffer__, - metadata__, - signature_defs__); -} - -flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - -inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CustomQuantizationT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom.begin()); } } -} - -inline flatbuffers::Offset CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCustomQuantization(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16); - auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0; - return tflite_micro::CreateCustomQuantization( - _fbb, - _custom); -} - -inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new QuantizationParametersT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } } - { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } } - { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } } - { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } } - { auto _e = details_type(); _o->details.type = _e; } - { auto _e = details(); if (_e) _o->details.value = tflite_micro::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); } - { auto _e = quantized_dimension(); _o->quantized_dimension = _e; } -} - -inline flatbuffers::Offset QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateQuantizationParameters(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0; - auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0; - auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0; - auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0; - auto _details_type = _o->details.type; - auto _details = _o->details.Pack(_fbb); - auto _quantized_dimension = _o->quantized_dimension; - return tflite_micro::CreateQuantizationParameters( - _fbb, - _min, - _max, - _scale, - _zero_point, - _details_type, - _details, - _quantized_dimension); -} - -inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Int32VectorT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateInt32Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite_micro::CreateInt32Vector( - _fbb, - _values); -} - -inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Uint16VectorT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUint16Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4); - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite_micro::CreateUint16Vector( - _fbb, - _values); -} - -inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Uint8VectorT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values(); if (_e) { _o->values.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->values.begin()); } } -} - -inline flatbuffers::Offset Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUint8Vector(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4); - auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0; - return tflite_micro::CreateUint8Vector( - _fbb, - _values); -} - -inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DimensionMetadataT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = format(); _o->format = _e; } - { auto _e = dense_size(); _o->dense_size = _e; } - { auto _e = array_segments_type(); _o->array_segments.type = _e; } - { auto _e = array_segments(); if (_e) _o->array_segments.value = tflite_micro::SparseIndexVectorUnion::UnPack(_e, array_segments_type(), _resolver); } - { auto _e = array_indices_type(); _o->array_indices.type = _e; } - { auto _e = array_indices(); if (_e) _o->array_indices.value = tflite_micro::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); } -} - -inline flatbuffers::Offset DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDimensionMetadata(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _format = _o->format; - auto _dense_size = _o->dense_size; - auto _array_segments_type = _o->array_segments.type; - auto _array_segments = _o->array_segments.Pack(_fbb); - auto _array_indices_type = _o->array_indices.type; - auto _array_indices = _o->array_indices.Pack(_fbb); - return tflite_micro::CreateDimensionMetadata( - _fbb, - _format, - _dense_size, - _array_segments_type, - _array_segments, - _array_indices_type, - _array_indices); -} - -inline SparsityParametersT::SparsityParametersT(const SparsityParametersT &o) - : traversal_order(o.traversal_order), - block_map(o.block_map) { - dim_metadata.reserve(o.dim_metadata.size()); - for (const auto &dim_metadata_ : o.dim_metadata) { dim_metadata.emplace_back((dim_metadata_) ? new tflite_micro::DimensionMetadataT(*dim_metadata_) : nullptr); } -} - -inline SparsityParametersT &SparsityParametersT::operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT { - std::swap(traversal_order, o.traversal_order); - std::swap(block_map, o.block_map); - std::swap(dim_metadata, o.dim_metadata); - return *this; -} - -inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SparsityParametersT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } } - { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } } - { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->dim_metadata[_i]) { _e->Get(_i)->UnPackTo(_o->dim_metadata[_i].get(), _resolver); } else { _o->dim_metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } -} - -inline flatbuffers::Offset SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSparsityParameters(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0; - auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0; - auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0; - return tflite_micro::CreateSparsityParameters( - _fbb, - _traversal_order, - _block_map, - _dim_metadata); -} - -inline VariantSubTypeT *VariantSubType::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new VariantSubTypeT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void VariantSubType::UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } - { auto _e = type(); _o->type = _e; } - { auto _e = has_rank(); _o->has_rank = _e; } -} - -inline flatbuffers::Offset VariantSubType::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateVariantSubType(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VariantSubTypeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; - auto _type = _o->type; - auto _has_rank = _o->has_rank; - return tflite_micro::CreateVariantSubType( - _fbb, - _shape, - _type, - _has_rank); -} - -inline TensorT::TensorT(const TensorT &o) - : shape(o.shape), - type(o.type), - buffer(o.buffer), - name(o.name), - quantization((o.quantization) ? new tflite_micro::QuantizationParametersT(*o.quantization) : nullptr), - is_variable(o.is_variable), - sparsity((o.sparsity) ? new tflite_micro::SparsityParametersT(*o.sparsity) : nullptr), - shape_signature(o.shape_signature), - has_rank(o.has_rank) { - variant_tensors.reserve(o.variant_tensors.size()); - for (const auto &variant_tensors_ : o.variant_tensors) { variant_tensors.emplace_back((variant_tensors_) ? new tflite_micro::VariantSubTypeT(*variant_tensors_) : nullptr); } -} - -inline TensorT &TensorT::operator=(TensorT o) FLATBUFFERS_NOEXCEPT { - std::swap(shape, o.shape); - std::swap(type, o.type); - std::swap(buffer, o.buffer); - std::swap(name, o.name); - std::swap(quantization, o.quantization); - std::swap(is_variable, o.is_variable); - std::swap(sparsity, o.sparsity); - std::swap(shape_signature, o.shape_signature); - std::swap(has_rank, o.has_rank); - std::swap(variant_tensors, o.variant_tensors); - return *this; -} - -inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TensorT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } } - { auto _e = type(); _o->type = _e; } - { auto _e = buffer(); _o->buffer = _e; } - { auto _e = name(); if (_e) _o->name = _e->str(); } - { auto _e = quantization(); if (_e) { if(_o->quantization) { _e->UnPackTo(_o->quantization.get(), _resolver); } else { _o->quantization = std::unique_ptr(_e->UnPack(_resolver)); } } } - { auto _e = is_variable(); _o->is_variable = _e; } - { auto _e = sparsity(); if (_e) { if(_o->sparsity) { _e->UnPackTo(_o->sparsity.get(), _resolver); } else { _o->sparsity = std::unique_ptr(_e->UnPack(_resolver)); } } } - { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } } - { auto _e = has_rank(); _o->has_rank = _e; } - { auto _e = variant_tensors(); if (_e) { _o->variant_tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->variant_tensors[_i]) { _e->Get(_i)->UnPackTo(_o->variant_tensors[_i].get(), _resolver); } else { _o->variant_tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } -} - -inline flatbuffers::Offset Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTensor(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0; - auto _type = _o->type; - auto _buffer = _o->buffer; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0; - auto _is_variable = _o->is_variable; - auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0; - auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0; - auto _has_rank = _o->has_rank; - auto _variant_tensors = _o->variant_tensors.size() ? _fbb.CreateVector> (_o->variant_tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateVariantSubType(*__va->__fbb, __va->__o->variant_tensors[i].get(), __va->__rehasher); }, &_va ) : 0; - return tflite_micro::CreateTensor( - _fbb, - _shape, - _type, - _buffer, - _name, - _quantization, - _is_variable, - _sparsity, - _shape_signature, - _has_rank, - _variant_tensors); -} - -inline StablehloGatherOptionsT *StablehloGatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloGatherOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloGatherOptions::UnPackTo(StablehloGatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = offset_dims(); if (_e) { _o->offset_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->offset_dims[_i] = _e->Get(_i); } } } - { auto _e = collapsed_slice_dims(); if (_e) { _o->collapsed_slice_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->collapsed_slice_dims[_i] = _e->Get(_i); } } } - { auto _e = start_index_map(); if (_e) { _o->start_index_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_index_map[_i] = _e->Get(_i); } } } - { auto _e = index_vector_dim(); _o->index_vector_dim = _e; } - { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } } - { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; } -} - -inline flatbuffers::Offset StablehloGatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloGatherOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloGatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloGatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _offset_dims = _o->offset_dims.size() ? _fbb.CreateVector(_o->offset_dims) : 0; - auto _collapsed_slice_dims = _o->collapsed_slice_dims.size() ? _fbb.CreateVector(_o->collapsed_slice_dims) : 0; - auto _start_index_map = _o->start_index_map.size() ? _fbb.CreateVector(_o->start_index_map) : 0; - auto _index_vector_dim = _o->index_vector_dim; - auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0; - auto _indices_are_sorted = _o->indices_are_sorted; - return tflite_micro::CreateStablehloGatherOptions( - _fbb, - _offset_dims, - _collapsed_slice_dims, - _start_index_map, - _index_vector_dim, - _slice_sizes, - _indices_are_sorted); -} - -inline StablehloTransposeOptionsT *StablehloTransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloTransposeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloTransposeOptions::UnPackTo(StablehloTransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = permutation(); if (_e) { _o->permutation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->permutation[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset StablehloTransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloTransposeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloTransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloTransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _permutation = _o->permutation.size() ? _fbb.CreateVector(_o->permutation) : 0; - return tflite_micro::CreateStablehloTransposeOptions( - _fbb, - _permutation); -} - -inline StablehloDotGeneralOptionsT *StablehloDotGeneralOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloDotGeneralOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloDotGeneralOptions::UnPackTo(StablehloDotGeneralOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = lhs_batching_dimensions(); if (_e) { _o->lhs_batching_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_batching_dimensions[_i] = _e->Get(_i); } } } - { auto _e = rhs_batching_dimensions(); if (_e) { _o->rhs_batching_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_batching_dimensions[_i] = _e->Get(_i); } } } - { auto _e = lhs_contracting_dimensions(); if (_e) { _o->lhs_contracting_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_contracting_dimensions[_i] = _e->Get(_i); } } } - { auto _e = rhs_contracting_dimensions(); if (_e) { _o->rhs_contracting_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_contracting_dimensions[_i] = _e->Get(_i); } } } - { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast(_e->Get(_i)); } } } -} - -inline flatbuffers::Offset StablehloDotGeneralOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloDotGeneralOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloDotGeneralOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDotGeneralOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloDotGeneralOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _lhs_batching_dimensions = _o->lhs_batching_dimensions.size() ? _fbb.CreateVector(_o->lhs_batching_dimensions) : 0; - auto _rhs_batching_dimensions = _o->rhs_batching_dimensions.size() ? _fbb.CreateVector(_o->rhs_batching_dimensions) : 0; - auto _lhs_contracting_dimensions = _o->lhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->lhs_contracting_dimensions) : 0; - auto _rhs_contracting_dimensions = _o->rhs_contracting_dimensions.size() ? _fbb.CreateVector(_o->rhs_contracting_dimensions) : 0; - auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast(flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0; - return tflite_micro::CreateStablehloDotGeneralOptions( - _fbb, - _lhs_batching_dimensions, - _rhs_batching_dimensions, - _lhs_contracting_dimensions, - _rhs_contracting_dimensions, - _precision_config); -} - -inline StablehloReduceWindowOptionsT *StablehloReduceWindowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloReduceWindowOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloReduceWindowOptions::UnPackTo(StablehloReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = window_dimensions(); if (_e) { _o->window_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dimensions[_i] = _e->Get(_i); } } } - { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } } - { auto _e = base_dilations(); if (_e) { _o->base_dilations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->base_dilations[_i] = _e->Get(_i); } } } - { auto _e = window_dilations(); if (_e) { _o->window_dilations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_dilations[_i] = _e->Get(_i); } } } - { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } } - { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } -} - -inline flatbuffers::Offset StablehloReduceWindowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloReduceWindowOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceWindowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _window_dimensions = _o->window_dimensions.size() ? _fbb.CreateVector(_o->window_dimensions) : 0; - auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0; - auto _base_dilations = _o->base_dilations.size() ? _fbb.CreateVector(_o->base_dilations) : 0; - auto _window_dilations = _o->window_dilations.size() ? _fbb.CreateVector(_o->window_dilations) : 0; - auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0; - auto _body_subgraph_index = _o->body_subgraph_index; - return tflite_micro::CreateStablehloReduceWindowOptions( - _fbb, - _window_dimensions, - _window_strides, - _base_dilations, - _window_dilations, - _padding, - _body_subgraph_index); -} - -inline StablehloWhileOptionsT *StablehloWhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloWhileOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloWhileOptions::UnPackTo(StablehloWhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } - { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } -} - -inline flatbuffers::Offset StablehloWhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloWhileOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloWhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloWhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _cond_subgraph_index = _o->cond_subgraph_index; - auto _body_subgraph_index = _o->body_subgraph_index; - return tflite_micro::CreateStablehloWhileOptions( - _fbb, - _cond_subgraph_index, - _body_subgraph_index); -} - -inline StablehloSortOptionsT *StablehloSortOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloSortOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloSortOptions::UnPackTo(StablehloSortOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = dimension(); _o->dimension = _e; } - { auto _e = is_stable(); _o->is_stable = _e; } - { auto _e = comparator_subgraph_index(); _o->comparator_subgraph_index = _e; } -} - -inline flatbuffers::Offset StablehloSortOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloSortOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloSortOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSortOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloSortOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _dimension = _o->dimension; - auto _is_stable = _o->is_stable; - auto _comparator_subgraph_index = _o->comparator_subgraph_index; - return tflite_micro::CreateStablehloSortOptions( - _fbb, - _dimension, - _is_stable, - _comparator_subgraph_index); -} - -inline StablehloConcatenateOptionsT *StablehloConcatenateOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloConcatenateOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloConcatenateOptions::UnPackTo(StablehloConcatenateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = dimension(); _o->dimension = _e; } -} - -inline flatbuffers::Offset StablehloConcatenateOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloConcatenateOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloConcatenateOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConcatenateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloConcatenateOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _dimension = _o->dimension; - return tflite_micro::CreateStablehloConcatenateOptions( - _fbb, - _dimension); -} - -inline StablehloBroadcastInDimOptionsT *StablehloBroadcastInDimOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloBroadcastInDimOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloBroadcastInDimOptions::UnPackTo(StablehloBroadcastInDimOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = broadcast_dimensions(); if (_e) { _o->broadcast_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->broadcast_dimensions[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset StablehloBroadcastInDimOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloBroadcastInDimOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloBroadcastInDimOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloBroadcastInDimOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloBroadcastInDimOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _broadcast_dimensions = _o->broadcast_dimensions.size() ? _fbb.CreateVector(_o->broadcast_dimensions) : 0; - return tflite_micro::CreateStablehloBroadcastInDimOptions( - _fbb, - _broadcast_dimensions); -} - -inline StablehloCompareOptionsT *StablehloCompareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloCompareOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloCompareOptions::UnPackTo(StablehloCompareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = comparison_direction(); _o->comparison_direction = _e; } - { auto _e = compare_type(); _o->compare_type = _e; } -} - -inline flatbuffers::Offset StablehloCompareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloCompareOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloCompareOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCompareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloCompareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _comparison_direction = _o->comparison_direction; - auto _compare_type = _o->compare_type; - return tflite_micro::CreateStablehloCompareOptions( - _fbb, - _comparison_direction, - _compare_type); -} - -inline StablehloDynamicSliceOptionsT *StablehloDynamicSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloDynamicSliceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloDynamicSliceOptions::UnPackTo(StablehloDynamicSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = slice_sizes(); if (_e) { _o->slice_sizes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->slice_sizes[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset StablehloDynamicSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloDynamicSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloDynamicSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloDynamicSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloDynamicSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _slice_sizes = _o->slice_sizes.size() ? _fbb.CreateVector(_o->slice_sizes) : 0; - return tflite_micro::CreateStablehloDynamicSliceOptions( - _fbb, - _slice_sizes); -} - -inline StablehloPadOptionsT *StablehloPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloPadOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloPadOptions::UnPackTo(StablehloPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = edge_padding_low(); if (_e) { _o->edge_padding_low.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_low[_i] = _e->Get(_i); } } } - { auto _e = edge_padding_high(); if (_e) { _o->edge_padding_high.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->edge_padding_high[_i] = _e->Get(_i); } } } - { auto _e = interior_padding(); if (_e) { _o->interior_padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->interior_padding[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset StablehloPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloPadOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _edge_padding_low = _o->edge_padding_low.size() ? _fbb.CreateVector(_o->edge_padding_low) : 0; - auto _edge_padding_high = _o->edge_padding_high.size() ? _fbb.CreateVector(_o->edge_padding_high) : 0; - auto _interior_padding = _o->interior_padding.size() ? _fbb.CreateVector(_o->interior_padding) : 0; - return tflite_micro::CreateStablehloPadOptions( - _fbb, - _edge_padding_low, - _edge_padding_high, - _interior_padding); -} - -inline StablehloIotaOptionsT *StablehloIotaOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloIotaOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloIotaOptions::UnPackTo(StablehloIotaOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = iota_dimension(); _o->iota_dimension = _e; } -} - -inline flatbuffers::Offset StablehloIotaOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloIotaOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloIotaOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloIotaOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloIotaOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _iota_dimension = _o->iota_dimension; - return tflite_micro::CreateStablehloIotaOptions( - _fbb, - _iota_dimension); -} - -inline StablehloCustomCallOptionsT *StablehloCustomCallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloCustomCallOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloCustomCallOptions::UnPackTo(StablehloCustomCallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = call_target_name(); if (_e) _o->call_target_name = _e->str(); } - { auto _e = has_side_effect(); _o->has_side_effect = _e; } - { auto _e = backend_config(); if (_e) _o->backend_config = _e->str(); } - { auto _e = api_version(); _o->api_version = _e; } - { auto _e = called_computations(); if (_e) { _o->called_computations.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->called_computations[_i] = _e->Get(_i); } } } - { auto _e = custom_attributes(); if (_e) { _o->custom_attributes.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_attributes.begin()); } } -} - -inline flatbuffers::Offset StablehloCustomCallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloCustomCallOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloCustomCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloCustomCallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloCustomCallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _call_target_name = _o->call_target_name.empty() ? 0 : _fbb.CreateString(_o->call_target_name); - auto _has_side_effect = _o->has_side_effect; - auto _backend_config = _o->backend_config.empty() ? 0 : _fbb.CreateString(_o->backend_config); - auto _api_version = _o->api_version; - auto _called_computations = _o->called_computations.size() ? _fbb.CreateVector(_o->called_computations) : 0; - auto _custom_attributes = _o->custom_attributes.size() ? _fbb.CreateVector(_o->custom_attributes) : 0; - return tflite_micro::CreateStablehloCustomCallOptions( - _fbb, - _call_target_name, - _has_side_effect, - _backend_config, - _api_version, - _called_computations, - _custom_attributes); -} - -inline StablehloReduceOptionsT *StablehloReduceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloReduceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloReduceOptions::UnPackTo(StablehloReduceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = dimensions(); if (_e) { _o->dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->dimensions[_i] = _e->Get(_i); } } } - { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } -} - -inline flatbuffers::Offset StablehloReduceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloReduceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloReduceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloReduceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloReduceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _dimensions = _o->dimensions.size() ? _fbb.CreateVector(_o->dimensions) : 0; - auto _body_subgraph_index = _o->body_subgraph_index; - return tflite_micro::CreateStablehloReduceOptions( - _fbb, - _dimensions, - _body_subgraph_index); -} - -inline StablehloSliceOptionsT *StablehloSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloSliceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloSliceOptions::UnPackTo(StablehloSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = start_indices(); if (_e) { _o->start_indices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->start_indices[_i] = _e->Get(_i); } } } - { auto _e = limit_indices(); if (_e) { _o->limit_indices.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->limit_indices[_i] = _e->Get(_i); } } } - { auto _e = strides(); if (_e) { _o->strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->strides[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset StablehloSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _start_indices = _o->start_indices.size() ? _fbb.CreateVector(_o->start_indices) : 0; - auto _limit_indices = _o->limit_indices.size() ? _fbb.CreateVector(_o->limit_indices) : 0; - auto _strides = _o->strides.size() ? _fbb.CreateVector(_o->strides) : 0; - return tflite_micro::CreateStablehloSliceOptions( - _fbb, - _start_indices, - _limit_indices, - _strides); -} - -inline StablehloConvolutionOptionsT *StablehloConvolutionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloConvolutionOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloConvolutionOptions::UnPackTo(StablehloConvolutionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = window_strides(); if (_e) { _o->window_strides.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_strides[_i] = _e->Get(_i); } } } - { auto _e = padding(); if (_e) { _o->padding.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->padding[_i] = _e->Get(_i); } } } - { auto _e = lhs_dilation(); if (_e) { _o->lhs_dilation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->lhs_dilation[_i] = _e->Get(_i); } } } - { auto _e = rhs_dilation(); if (_e) { _o->rhs_dilation.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->rhs_dilation[_i] = _e->Get(_i); } } } - { auto _e = window_reversal(); if (_e) { _o->window_reversal.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->window_reversal[_i] = _e->Get(_i) != 0; } } } - { auto _e = input_batch_dimension(); _o->input_batch_dimension = _e; } - { auto _e = input_feature_dimension(); _o->input_feature_dimension = _e; } - { auto _e = input_spatial_dimensions(); if (_e) { _o->input_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->input_spatial_dimensions[_i] = _e->Get(_i); } } } - { auto _e = kernel_input_feature_dimension(); _o->kernel_input_feature_dimension = _e; } - { auto _e = kernel_output_feature_dimension(); _o->kernel_output_feature_dimension = _e; } - { auto _e = kernel_spatial_dimensions(); if (_e) { _o->kernel_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->kernel_spatial_dimensions[_i] = _e->Get(_i); } } } - { auto _e = output_batch_dimension(); _o->output_batch_dimension = _e; } - { auto _e = output_feature_dimension(); _o->output_feature_dimension = _e; } - { auto _e = output_spatial_dimensions(); if (_e) { _o->output_spatial_dimensions.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->output_spatial_dimensions[_i] = _e->Get(_i); } } } - { auto _e = feature_group_count(); _o->feature_group_count = _e; } - { auto _e = batch_group_count(); _o->batch_group_count = _e; } - { auto _e = precision_config(); if (_e) { _o->precision_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->precision_config[_i] = static_cast(_e->Get(_i)); } } } -} - -inline flatbuffers::Offset StablehloConvolutionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloConvolutionOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloConvolutionOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloConvolutionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloConvolutionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _window_strides = _o->window_strides.size() ? _fbb.CreateVector(_o->window_strides) : 0; - auto _padding = _o->padding.size() ? _fbb.CreateVector(_o->padding) : 0; - auto _lhs_dilation = _o->lhs_dilation.size() ? _fbb.CreateVector(_o->lhs_dilation) : 0; - auto _rhs_dilation = _o->rhs_dilation.size() ? _fbb.CreateVector(_o->rhs_dilation) : 0; - auto _window_reversal = _o->window_reversal.size() ? _fbb.CreateVector(_o->window_reversal) : 0; - auto _input_batch_dimension = _o->input_batch_dimension; - auto _input_feature_dimension = _o->input_feature_dimension; - auto _input_spatial_dimensions = _o->input_spatial_dimensions.size() ? _fbb.CreateVector(_o->input_spatial_dimensions) : 0; - auto _kernel_input_feature_dimension = _o->kernel_input_feature_dimension; - auto _kernel_output_feature_dimension = _o->kernel_output_feature_dimension; - auto _kernel_spatial_dimensions = _o->kernel_spatial_dimensions.size() ? _fbb.CreateVector(_o->kernel_spatial_dimensions) : 0; - auto _output_batch_dimension = _o->output_batch_dimension; - auto _output_feature_dimension = _o->output_feature_dimension; - auto _output_spatial_dimensions = _o->output_spatial_dimensions.size() ? _fbb.CreateVector(_o->output_spatial_dimensions) : 0; - auto _feature_group_count = _o->feature_group_count; - auto _batch_group_count = _o->batch_group_count; - auto _precision_config = _o->precision_config.size() ? _fbb.CreateVectorScalarCast(flatbuffers::data(_o->precision_config), _o->precision_config.size()) : 0; - return tflite_micro::CreateStablehloConvolutionOptions( - _fbb, - _window_strides, - _padding, - _lhs_dilation, - _rhs_dilation, - _window_reversal, - _input_batch_dimension, - _input_feature_dimension, - _input_spatial_dimensions, - _kernel_input_feature_dimension, - _kernel_output_feature_dimension, - _kernel_spatial_dimensions, - _output_batch_dimension, - _output_feature_dimension, - _output_spatial_dimensions, - _feature_group_count, - _batch_group_count, - _precision_config); -} - -inline StablehloScatterOptionsT *StablehloScatterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloScatterOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloScatterOptions::UnPackTo(StablehloScatterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = indices_are_sorted(); _o->indices_are_sorted = _e; } - { auto _e = update_window_dims(); if (_e) { _o->update_window_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->update_window_dims[_i] = _e->Get(_i); } } } - { auto _e = inserted_window_dims(); if (_e) { _o->inserted_window_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inserted_window_dims[_i] = _e->Get(_i); } } } - { auto _e = scatter_dims_to_operand_dims(); if (_e) { _o->scatter_dims_to_operand_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scatter_dims_to_operand_dims[_i] = _e->Get(_i); } } } - { auto _e = index_vector_dim(); _o->index_vector_dim = _e; } - { auto _e = unique_indices(); _o->unique_indices = _e; } - { auto _e = update_computation_subgraph_index(); _o->update_computation_subgraph_index = _e; } -} - -inline flatbuffers::Offset StablehloScatterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloScatterOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloScatterOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloScatterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloScatterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _indices_are_sorted = _o->indices_are_sorted; - auto _update_window_dims = _o->update_window_dims.size() ? _fbb.CreateVector(_o->update_window_dims) : 0; - auto _inserted_window_dims = _o->inserted_window_dims.size() ? _fbb.CreateVector(_o->inserted_window_dims) : 0; - auto _scatter_dims_to_operand_dims = _o->scatter_dims_to_operand_dims.size() ? _fbb.CreateVector(_o->scatter_dims_to_operand_dims) : 0; - auto _index_vector_dim = _o->index_vector_dim; - auto _unique_indices = _o->unique_indices; - auto _update_computation_subgraph_index = _o->update_computation_subgraph_index; - return tflite_micro::CreateStablehloScatterOptions( - _fbb, - _indices_are_sorted, - _update_window_dims, - _inserted_window_dims, - _scatter_dims_to_operand_dims, - _index_vector_dim, - _unique_indices, - _update_computation_subgraph_index); -} - -inline StablehloRngBitGeneratorOptionsT *StablehloRngBitGeneratorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StablehloRngBitGeneratorOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StablehloRngBitGeneratorOptions::UnPackTo(StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = algorithm(); _o->algorithm = _e; } -} - -inline flatbuffers::Offset StablehloRngBitGeneratorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStablehloRngBitGeneratorOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStablehloRngBitGeneratorOptions(flatbuffers::FlatBufferBuilder &_fbb, const StablehloRngBitGeneratorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StablehloRngBitGeneratorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _algorithm = _o->algorithm; - return tflite_micro::CreateStablehloRngBitGeneratorOptions( - _fbb, - _algorithm); -} - -inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Conv2DOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } - { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } - { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } -} - -inline flatbuffers::Offset Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConv2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _fused_activation_function = _o->fused_activation_function; - auto _dilation_w_factor = _o->dilation_w_factor; - auto _dilation_h_factor = _o->dilation_h_factor; - auto _quantized_bias_type = _o->quantized_bias_type; - return tflite_micro::CreateConv2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _fused_activation_function, - _dilation_w_factor, - _dilation_h_factor, - _quantized_bias_type); -} - -inline Conv3DOptionsT *Conv3DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Conv3DOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Conv3DOptions::UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_d(); _o->stride_d = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = dilation_d_factor(); _o->dilation_d_factor = _e; } - { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } - { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } -} - -inline flatbuffers::Offset Conv3DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConv3DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv3DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_d = _o->stride_d; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _fused_activation_function = _o->fused_activation_function; - auto _dilation_d_factor = _o->dilation_d_factor; - auto _dilation_w_factor = _o->dilation_w_factor; - auto _dilation_h_factor = _o->dilation_h_factor; - return tflite_micro::CreateConv3DOptions( - _fbb, - _padding, - _stride_d, - _stride_w, - _stride_h, - _fused_activation_function, - _dilation_d_factor, - _dilation_w_factor, - _dilation_h_factor); -} - -inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Pool2DOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = filter_width(); _o->filter_width = _e; } - { auto _e = filter_height(); _o->filter_height = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePool2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _filter_width = _o->filter_width; - auto _filter_height = _o->filter_height; - auto _fused_activation_function = _o->fused_activation_function; - return tflite_micro::CreatePool2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _filter_width, - _filter_height, - _fused_activation_function); -} - -inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DepthwiseConv2DOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = depth_multiplier(); _o->depth_multiplier = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; } - { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; } -} - -inline flatbuffers::Offset DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _depth_multiplier = _o->depth_multiplier; - auto _fused_activation_function = _o->fused_activation_function; - auto _dilation_w_factor = _o->dilation_w_factor; - auto _dilation_h_factor = _o->dilation_h_factor; - return tflite_micro::CreateDepthwiseConv2DOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _depth_multiplier, - _fused_activation_function, - _dilation_w_factor, - _dilation_h_factor); -} - -inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ConcatEmbeddingsOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_channels(); _o->num_channels = _e; } - { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } } - { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_channels = _o->num_channels; - auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0; - auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0; - return tflite_micro::CreateConcatEmbeddingsOptions( - _fbb, - _num_channels, - _num_columns_per_channel, - _embedding_dim_per_channel); -} - -inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LSHProjectionOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = type(); _o->type = _e; } -} - -inline flatbuffers::Offset LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLSHProjectionOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _type = _o->type; - return tflite_micro::CreateLSHProjectionOptions( - _fbb, - _type); -} - -inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SVDFOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = rank(); _o->rank = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSVDFOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _rank = _o->rank; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateSVDFOptions( - _fbb, - _rank, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new RNNOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateRNNOptions( - _fbb, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SequenceRNNOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSequenceRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _time_major = _o->time_major; - auto _fused_activation_function = _o->fused_activation_function; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateSequenceRNNOptions( - _fbb, - _time_major, - _fused_activation_function, - _asymmetric_quantize_inputs); -} - -inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BidirectionalSequenceRNNOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = merge_outputs(); _o->merge_outputs = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _time_major = _o->time_major; - auto _fused_activation_function = _o->fused_activation_function; - auto _merge_outputs = _o->merge_outputs; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateBidirectionalSequenceRNNOptions( - _fbb, - _time_major, - _fused_activation_function, - _merge_outputs, - _asymmetric_quantize_inputs); -} - -inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new FullyConnectedOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = weights_format(); _o->weights_format = _e; } - { auto _e = keep_num_dims(); _o->keep_num_dims = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } - { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } -} - -inline flatbuffers::Offset FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFullyConnectedOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _weights_format = _o->weights_format; - auto _keep_num_dims = _o->keep_num_dims; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - auto _quantized_bias_type = _o->quantized_bias_type; - return tflite_micro::CreateFullyConnectedOptions( - _fbb, - _fused_activation_function, - _weights_format, - _keep_num_dims, - _asymmetric_quantize_inputs, - _quantized_bias_type); -} - -inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SoftmaxOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = beta(); _o->beta = _e; } -} - -inline flatbuffers::Offset SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSoftmaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _beta = _o->beta; - return tflite_micro::CreateSoftmaxOptions( - _fbb, - _beta); -} - -inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ConcatenationOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateConcatenationOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - auto _fused_activation_function = _o->fused_activation_function; - return tflite_micro::CreateConcatenationOptions( - _fbb, - _axis, - _fused_activation_function); -} - -inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new AddOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; } -} - -inline flatbuffers::Offset AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAddOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _pot_scale_int16 = _o->pot_scale_int16; - return tflite_micro::CreateAddOptions( - _fbb, - _fused_activation_function, - _pot_scale_int16); -} - -inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MulOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMulOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite_micro::CreateMulOptions( - _fbb, - _fused_activation_function); -} - -inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new L2NormOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateL2NormOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite_micro::CreateL2NormOptions( - _fbb, - _fused_activation_function); -} - -inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LocalResponseNormalizationOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = radius(); _o->radius = _e; } - { auto _e = bias(); _o->bias = _e; } - { auto _e = alpha(); _o->alpha = _e; } - { auto _e = beta(); _o->beta = _e; } -} - -inline flatbuffers::Offset LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _radius = _o->radius; - auto _bias = _o->bias; - auto _alpha = _o->alpha; - auto _beta = _o->beta; - return tflite_micro::CreateLocalResponseNormalizationOptions( - _fbb, - _radius, - _bias, - _alpha, - _beta); -} - -inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LSTMOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = kernel_type(); _o->kernel_type = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _kernel_type = _o->kernel_type; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _kernel_type, - _asymmetric_quantize_inputs); -} - -inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnidirectionalSequenceLSTMOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } - { auto _e = diagonal_recurrent_tensors(); _o->diagonal_recurrent_tensors = _e; } -} - -inline flatbuffers::Offset UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _time_major = _o->time_major; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - auto _diagonal_recurrent_tensors = _o->diagonal_recurrent_tensors; - return tflite_micro::CreateUnidirectionalSequenceLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _time_major, - _asymmetric_quantize_inputs, - _diagonal_recurrent_tensors); -} - -inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BidirectionalSequenceLSTMOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = cell_clip(); _o->cell_clip = _e; } - { auto _e = proj_clip(); _o->proj_clip = _e; } - { auto _e = merge_outputs(); _o->merge_outputs = _e; } - { auto _e = time_major(); _o->time_major = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _cell_clip = _o->cell_clip; - auto _proj_clip = _o->proj_clip; - auto _merge_outputs = _o->merge_outputs; - auto _time_major = _o->time_major; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateBidirectionalSequenceLSTMOptions( - _fbb, - _fused_activation_function, - _cell_clip, - _proj_clip, - _merge_outputs, - _time_major, - _asymmetric_quantize_inputs); -} - -inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ResizeBilinearOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = align_corners(); _o->align_corners = _e; } - { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } -} - -inline flatbuffers::Offset ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateResizeBilinearOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _align_corners = _o->align_corners; - auto _half_pixel_centers = _o->half_pixel_centers; - return tflite_micro::CreateResizeBilinearOptions( - _fbb, - _align_corners, - _half_pixel_centers); -} - -inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ResizeNearestNeighborOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = align_corners(); _o->align_corners = _e; } - { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; } -} - -inline flatbuffers::Offset ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _align_corners = _o->align_corners; - auto _half_pixel_centers = _o->half_pixel_centers; - return tflite_micro::CreateResizeNearestNeighborOptions( - _fbb, - _align_corners, - _half_pixel_centers); -} - -inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CallOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = subgraph(); _o->subgraph = _e; } -} - -inline flatbuffers::Offset CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCallOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _subgraph = _o->subgraph; - return tflite_micro::CreateCallOptions( - _fbb, - _subgraph); -} - -inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new PadOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePadOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreatePadOptions( - _fbb); -} - -inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new PadV2OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePadV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreatePadV2Options( - _fbb); -} - -inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReshapeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReshapeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0; - return tflite_micro::CreateReshapeOptions( - _fbb, - _new_shape); -} - -inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SpaceToBatchNDOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSpaceToBatchNDOptions( - _fbb); -} - -inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BatchToSpaceNDOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateBatchToSpaceNDOptions( - _fbb); -} - -inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SkipGramOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = ngram_size(); _o->ngram_size = _e; } - { auto _e = max_skip_size(); _o->max_skip_size = _e; } - { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; } -} - -inline flatbuffers::Offset SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSkipGramOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _ngram_size = _o->ngram_size; - auto _max_skip_size = _o->max_skip_size; - auto _include_all_ngrams = _o->include_all_ngrams; - return tflite_micro::CreateSkipGramOptions( - _fbb, - _ngram_size, - _max_skip_size, - _include_all_ngrams); -} - -inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SpaceToDepthOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = block_size(); _o->block_size = _e; } -} - -inline flatbuffers::Offset SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSpaceToDepthOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _block_size = _o->block_size; - return tflite_micro::CreateSpaceToDepthOptions( - _fbb, - _block_size); -} - -inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DepthToSpaceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = block_size(); _o->block_size = _e; } -} - -inline flatbuffers::Offset DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDepthToSpaceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _block_size = _o->block_size; - return tflite_micro::CreateDepthToSpaceOptions( - _fbb, - _block_size); -} - -inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SubOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; } -} - -inline flatbuffers::Offset SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSubOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - auto _pot_scale_int16 = _o->pot_scale_int16; - return tflite_micro::CreateSubOptions( - _fbb, - _fused_activation_function, - _pot_scale_int16); -} - -inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DivOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } -} - -inline flatbuffers::Offset DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDivOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _fused_activation_function = _o->fused_activation_function; - return tflite_micro::CreateDivOptions( - _fbb, - _fused_activation_function); -} - -inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TopKV2OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTopKV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateTopKV2Options( - _fbb); -} - -inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new EmbeddingLookupSparseOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = combiner(); _o->combiner = _e; } -} - -inline flatbuffers::Offset EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _combiner = _o->combiner; - return tflite_micro::CreateEmbeddingLookupSparseOptions( - _fbb, - _combiner); -} - -inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new GatherOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } - { auto _e = batch_dims(); _o->batch_dims = _e; } -} - -inline flatbuffers::Offset GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGatherOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - auto _batch_dims = _o->batch_dims; - return tflite_micro::CreateGatherOptions( - _fbb, - _axis, - _batch_dims); -} - -inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TransposeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTransposeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateTransposeOptions( - _fbb); -} - -inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ExpOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateExpOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateExpOptions( - _fbb); -} - -inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CosOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCosOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateCosOptions( - _fbb); -} - -inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReducerOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = keep_dims(); _o->keep_dims = _e; } -} - -inline flatbuffers::Offset ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReducerOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _keep_dims = _o->keep_dims; - return tflite_micro::CreateReducerOptions( - _fbb, - _keep_dims); -} - -inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SqueezeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSqueezeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0; - return tflite_micro::CreateSqueezeOptions( - _fbb, - _squeeze_dims); -} - -inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SplitOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_splits(); _o->num_splits = _e; } -} - -inline flatbuffers::Offset SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSplitOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_splits = _o->num_splits; - return tflite_micro::CreateSplitOptions( - _fbb, - _num_splits); -} - -inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SplitVOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_splits(); _o->num_splits = _e; } -} - -inline flatbuffers::Offset SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSplitVOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_splits = _o->num_splits; - return tflite_micro::CreateSplitVOptions( - _fbb, - _num_splits); -} - -inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new StridedSliceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = begin_mask(); _o->begin_mask = _e; } - { auto _e = end_mask(); _o->end_mask = _e; } - { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; } - { auto _e = new_axis_mask(); _o->new_axis_mask = _e; } - { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; } - { auto _e = offset(); _o->offset = _e; } -} - -inline flatbuffers::Offset StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateStridedSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _begin_mask = _o->begin_mask; - auto _end_mask = _o->end_mask; - auto _ellipsis_mask = _o->ellipsis_mask; - auto _new_axis_mask = _o->new_axis_mask; - auto _shrink_axis_mask = _o->shrink_axis_mask; - auto _offset = _o->offset; - return tflite_micro::CreateStridedSliceOptions( - _fbb, - _begin_mask, - _end_mask, - _ellipsis_mask, - _new_axis_mask, - _shrink_axis_mask, - _offset); -} - -inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LogSoftmaxOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogSoftmaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLogSoftmaxOptions( - _fbb); -} - -inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CastOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = in_data_type(); _o->in_data_type = _e; } - { auto _e = out_data_type(); _o->out_data_type = _e; } -} - -inline flatbuffers::Offset CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCastOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _in_data_type = _o->in_data_type; - auto _out_data_type = _o->out_data_type; - return tflite_micro::CreateCastOptions( - _fbb, - _in_data_type, - _out_data_type); -} - -inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DequantizeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDequantizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateDequantizeOptions( - _fbb); -} - -inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MaximumMinimumOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMaximumMinimumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateMaximumMinimumOptions( - _fbb); -} - -inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TileOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTileOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateTileOptions( - _fbb); -} - -inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ArgMaxOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = output_type(); _o->output_type = _e; } -} - -inline flatbuffers::Offset ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateArgMaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _output_type = _o->output_type; - return tflite_micro::CreateArgMaxOptions( - _fbb, - _output_type); -} - -inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ArgMinOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = output_type(); _o->output_type = _e; } -} - -inline flatbuffers::Offset ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateArgMinOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _output_type = _o->output_type; - return tflite_micro::CreateArgMinOptions( - _fbb, - _output_type); -} - -inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new GreaterOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGreaterOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateGreaterOptions( - _fbb); -} - -inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new GreaterEqualOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGreaterEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateGreaterEqualOptions( - _fbb); -} - -inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LessOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLessOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLessOptions( - _fbb); -} - -inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LessEqualOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLessEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLessEqualOptions( - _fbb); -} - -inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new NegOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNegOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateNegOptions( - _fbb); -} - -inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SelectOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSelectOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSelectOptions( - _fbb); -} - -inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SliceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSliceOptions( - _fbb); -} - -inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TransposeConvOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = padding(); _o->padding = _e; } - { auto _e = stride_w(); _o->stride_w = _e; } - { auto _e = stride_h(); _o->stride_h = _e; } - { auto _e = fused_activation_function(); _o->fused_activation_function = _e; } - { auto _e = quantized_bias_type(); _o->quantized_bias_type = _e; } -} - -inline flatbuffers::Offset TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTransposeConvOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _padding = _o->padding; - auto _stride_w = _o->stride_w; - auto _stride_h = _o->stride_h; - auto _fused_activation_function = _o->fused_activation_function; - auto _quantized_bias_type = _o->quantized_bias_type; - return tflite_micro::CreateTransposeConvOptions( - _fbb, - _padding, - _stride_w, - _stride_h, - _fused_activation_function, - _quantized_bias_type); -} - -inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ExpandDimsOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateExpandDimsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateExpandDimsOptions( - _fbb); -} - -inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SparseToDenseOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = validate_indices(); _o->validate_indices = _e; } -} - -inline flatbuffers::Offset SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSparseToDenseOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _validate_indices = _o->validate_indices; - return tflite_micro::CreateSparseToDenseOptions( - _fbb, - _validate_indices); -} - -inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new EqualOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateEqualOptions( - _fbb); -} - -inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new NotEqualOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNotEqualOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateNotEqualOptions( - _fbb); -} - -inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ShapeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = out_type(); _o->out_type = _e; } -} - -inline flatbuffers::Offset ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateShapeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _out_type = _o->out_type; - return tflite_micro::CreateShapeOptions( - _fbb, - _out_type); -} - -inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new RankOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRankOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateRankOptions( - _fbb); -} - -inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new PowOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePowOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreatePowOptions( - _fbb); -} - -inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new FakeQuantOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = min(); _o->min = _e; } - { auto _e = max(); _o->max = _e; } - { auto _e = num_bits(); _o->num_bits = _e; } - { auto _e = narrow_range(); _o->narrow_range = _e; } -} - -inline flatbuffers::Offset FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFakeQuantOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _min = _o->min; - auto _max = _o->max; - auto _num_bits = _o->num_bits; - auto _narrow_range = _o->narrow_range; - return tflite_micro::CreateFakeQuantOptions( - _fbb, - _min, - _max, - _num_bits, - _narrow_range); -} - -inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new PackOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = values_count(); _o->values_count = _e; } - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreatePackOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _values_count = _o->values_count; - auto _axis = _o->axis; - return tflite_micro::CreatePackOptions( - _fbb, - _values_count, - _axis); -} - -inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LogicalOrOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalOrOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLogicalOrOptions( - _fbb); -} - -inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new OneHotOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOneHotOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _axis = _o->axis; - return tflite_micro::CreateOneHotOptions( - _fbb, - _axis); -} - -inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new AbsOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAbsOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateAbsOptions( - _fbb); -} - -inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new HardSwishOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHardSwishOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateHardSwishOptions( - _fbb); -} - -inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LogicalAndOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalAndOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLogicalAndOptions( - _fbb); -} - -inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LogicalNotOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLogicalNotOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateLogicalNotOptions( - _fbb); -} - -inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnpackOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num(); _o->num = _e; } - { auto _e = axis(); _o->axis = _e; } -} - -inline flatbuffers::Offset UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnpackOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num = _o->num; - auto _axis = _o->axis; - return tflite_micro::CreateUnpackOptions( - _fbb, - _num, - _axis); -} - -inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new FloorDivOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFloorDivOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateFloorDivOptions( - _fbb); -} - -inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SquareOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSquareOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSquareOptions( - _fbb); -} - -inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ZerosLikeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateZerosLikeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateZerosLikeOptions( - _fbb); -} - -inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new FillOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFillOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateFillOptions( - _fbb); -} - -inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new FloorModOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateFloorModOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateFloorModOptions( - _fbb); -} - -inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new RangeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRangeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateRangeOptions( - _fbb); -} - -inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new LeakyReluOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = alpha(); _o->alpha = _e; } -} - -inline flatbuffers::Offset LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateLeakyReluOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _alpha = _o->alpha; - return tflite_micro::CreateLeakyReluOptions( - _fbb, - _alpha); -} - -inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SquaredDifferenceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSquaredDifferenceOptions( - _fbb); -} - -inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MirrorPadOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = mode(); _o->mode = _e; } -} - -inline flatbuffers::Offset MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMirrorPadOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _mode = _o->mode; - return tflite_micro::CreateMirrorPadOptions( - _fbb, - _mode); -} - -inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UniqueOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = idx_out_type(); _o->idx_out_type = _e; } -} - -inline flatbuffers::Offset UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUniqueOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _idx_out_type = _o->idx_out_type; - return tflite_micro::CreateUniqueOptions( - _fbb, - _idx_out_type); -} - -inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReverseV2OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReverseV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateReverseV2Options( - _fbb); -} - -inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new AddNOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAddNOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateAddNOptions( - _fbb); -} - -inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new GatherNdOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGatherNdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateGatherNdOptions( - _fbb); -} - -inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new WhereOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateWhereOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateWhereOptions( - _fbb); -} - -inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReverseSequenceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = seq_dim(); _o->seq_dim = _e; } - { auto _e = batch_dim(); _o->batch_dim = _e; } -} - -inline flatbuffers::Offset ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReverseSequenceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _seq_dim = _o->seq_dim; - auto _batch_dim = _o->batch_dim; - return tflite_micro::CreateReverseSequenceOptions( - _fbb, - _seq_dim, - _batch_dim); -} - -inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MatrixDiagOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMatrixDiagOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateMatrixDiagOptions( - _fbb); -} - -inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new QuantizeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateQuantizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateQuantizeOptions( - _fbb); -} - -inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MatrixSetDiagOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateMatrixSetDiagOptions( - _fbb); -} - -inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new IfOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; } - { auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; } -} - -inline flatbuffers::Offset IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateIfOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _then_subgraph_index = _o->then_subgraph_index; - auto _else_subgraph_index = _o->else_subgraph_index; - return tflite_micro::CreateIfOptions( - _fbb, - _then_subgraph_index, - _else_subgraph_index); -} - -inline CallOnceOptionsT *CallOnceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CallOnceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CallOnceOptions::UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = init_subgraph_index(); _o->init_subgraph_index = _e; } -} - -inline flatbuffers::Offset CallOnceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCallOnceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOnceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _init_subgraph_index = _o->init_subgraph_index; - return tflite_micro::CreateCallOnceOptions( - _fbb, - _init_subgraph_index); -} - -inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new WhileOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; } - { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; } -} - -inline flatbuffers::Offset WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateWhileOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _cond_subgraph_index = _o->cond_subgraph_index; - auto _body_subgraph_index = _o->body_subgraph_index; - return tflite_micro::CreateWhileOptions( - _fbb, - _cond_subgraph_index, - _body_subgraph_index); -} - -inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new NonMaxSuppressionV4OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateNonMaxSuppressionV4Options( - _fbb); -} - -inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new NonMaxSuppressionV5OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateNonMaxSuppressionV5Options( - _fbb); -} - -inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ScatterNdOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateScatterNdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateScatterNdOptions( - _fbb); -} - -inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SelectV2OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSelectV2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSelectV2Options( - _fbb); -} - -inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DensifyOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDensifyOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateDensifyOptions( - _fbb); -} - -inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SegmentSumOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSegmentSumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSegmentSumOptions( - _fbb); -} - -inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BatchMatMulOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = adj_x(); _o->adj_x = _e; } - { auto _e = adj_y(); _o->adj_y = _e; } - { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; } -} - -inline flatbuffers::Offset BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBatchMatMulOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _adj_x = _o->adj_x; - auto _adj_y = _o->adj_y; - auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs; - return tflite_micro::CreateBatchMatMulOptions( - _fbb, - _adj_x, - _adj_y, - _asymmetric_quantize_inputs); -} - -inline CumsumOptionsT *CumsumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new CumsumOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void CumsumOptions::UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = exclusive(); _o->exclusive = _e; } - { auto _e = reverse(); _o->reverse = _e; } -} - -inline flatbuffers::Offset CumsumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateCumsumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CumsumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _exclusive = _o->exclusive; - auto _reverse = _o->reverse; - return tflite_micro::CreateCumsumOptions( - _fbb, - _exclusive, - _reverse); -} - -inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BroadcastToOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset BroadcastToOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBroadcastToOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateBroadcastToOptions( - _fbb); -} - -inline Rfft2dOptionsT *Rfft2dOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new Rfft2dOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Rfft2dOptions::UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset Rfft2dOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRfft2dOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Rfft2dOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateRfft2dOptions( - _fbb); -} - -inline HashtableOptionsT *HashtableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new HashtableOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void HashtableOptions::UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = table_id(); _o->table_id = _e; } - { auto _e = key_dtype(); _o->key_dtype = _e; } - { auto _e = value_dtype(); _o->value_dtype = _e; } -} - -inline flatbuffers::Offset HashtableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHashtableOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _table_id = _o->table_id; - auto _key_dtype = _o->key_dtype; - auto _value_dtype = _o->value_dtype; - return tflite_micro::CreateHashtableOptions( - _fbb, - _table_id, - _key_dtype, - _value_dtype); -} - -inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new HashtableFindOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void HashtableFindOptions::UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset HashtableFindOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHashtableFindOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableFindOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateHashtableFindOptions( - _fbb); -} - -inline HashtableImportOptionsT *HashtableImportOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new HashtableImportOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void HashtableImportOptions::UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset HashtableImportOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHashtableImportOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableImportOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateHashtableImportOptions( - _fbb); -} - -inline HashtableSizeOptionsT *HashtableSizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new HashtableSizeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void HashtableSizeOptions::UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset HashtableSizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateHashtableSizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableSizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateHashtableSizeOptions( - _fbb); -} - -inline VarHandleOptionsT *VarHandleOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new VarHandleOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void VarHandleOptions::UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = container(); if (_e) _o->container = _e->str(); } - { auto _e = shared_name(); if (_e) _o->shared_name = _e->str(); } -} - -inline flatbuffers::Offset VarHandleOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateVarHandleOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VarHandleOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _container = _o->container.empty() ? 0 : _fbb.CreateString(_o->container); - auto _shared_name = _o->shared_name.empty() ? 0 : _fbb.CreateString(_o->shared_name); - return tflite_micro::CreateVarHandleOptions( - _fbb, - _container, - _shared_name); -} - -inline ReadVariableOptionsT *ReadVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReadVariableOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReadVariableOptions::UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ReadVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReadVariableOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReadVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateReadVariableOptions( - _fbb); -} - -inline AssignVariableOptionsT *AssignVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new AssignVariableOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void AssignVariableOptions::UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset AssignVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateAssignVariableOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AssignVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateAssignVariableOptions( - _fbb); -} - -inline RandomOptionsT *RandomOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new RandomOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void RandomOptions::UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = seed(); _o->seed = _e; } - { auto _e = seed2(); _o->seed2 = _e; } -} - -inline flatbuffers::Offset RandomOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRandomOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RandomOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _seed = _o->seed; - auto _seed2 = _o->seed2; - return tflite_micro::CreateRandomOptions( - _fbb, - _seed, - _seed2); -} - -inline BucketizeOptionsT *BucketizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BucketizeOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BucketizeOptions::UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = boundaries(); if (_e) { _o->boundaries.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->boundaries[_i] = _e->Get(_i); } } } -} - -inline flatbuffers::Offset BucketizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBucketizeOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BucketizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _boundaries = _o->boundaries.size() ? _fbb.CreateVector(_o->boundaries) : 0; - return tflite_micro::CreateBucketizeOptions( - _fbb, - _boundaries); -} - -inline GeluOptionsT *GeluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new GeluOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void GeluOptions::UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = approximate(); _o->approximate = _e; } -} - -inline flatbuffers::Offset GeluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateGeluOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GeluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _approximate = _o->approximate; - return tflite_micro::CreateGeluOptions( - _fbb, - _approximate); -} - -inline DynamicUpdateSliceOptionsT *DynamicUpdateSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DynamicUpdateSliceOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DynamicUpdateSliceOptions::UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DynamicUpdateSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDynamicUpdateSliceOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DynamicUpdateSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateDynamicUpdateSliceOptions( - _fbb); -} - -inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnsortedSegmentProdOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset UnsortedSegmentProdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnsortedSegmentProdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateUnsortedSegmentProdOptions( - _fbb); -} - -inline UnsortedSegmentMaxOptionsT *UnsortedSegmentMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnsortedSegmentMaxOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnsortedSegmentMaxOptions::UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset UnsortedSegmentMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnsortedSegmentMaxOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateUnsortedSegmentMaxOptions( - _fbb); -} - -inline UnsortedSegmentSumOptionsT *UnsortedSegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnsortedSegmentSumOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnsortedSegmentSumOptions::UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset UnsortedSegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnsortedSegmentSumOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateUnsortedSegmentSumOptions( - _fbb); -} - -inline ATan2OptionsT *ATan2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ATan2OptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ATan2Options::UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset ATan2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateATan2Options(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ATan2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateATan2Options( - _fbb); -} - -inline UnsortedSegmentMinOptionsT *UnsortedSegmentMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new UnsortedSegmentMinOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void UnsortedSegmentMinOptions::UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset UnsortedSegmentMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnsortedSegmentMinOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateUnsortedSegmentMinOptions( - _fbb); -} - -inline SignOptionsT *SignOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SignOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SignOptions::UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset SignOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSignOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateSignOptions( - _fbb); -} - -inline BitcastOptionsT *BitcastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BitcastOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BitcastOptions::UnPackTo(BitcastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset BitcastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBitcastOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBitcastOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitcastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitcastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateBitcastOptions( - _fbb); -} - -inline BitwiseXorOptionsT *BitwiseXorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BitwiseXorOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void BitwiseXorOptions::UnPackTo(BitwiseXorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset BitwiseXorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBitwiseXorOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBitwiseXorOptions(flatbuffers::FlatBufferBuilder &_fbb, const BitwiseXorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BitwiseXorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateBitwiseXorOptions( - _fbb); -} - -inline RightShiftOptionsT *RightShiftOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new RightShiftOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void RightShiftOptions::UnPackTo(RightShiftOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset RightShiftOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateRightShiftOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateRightShiftOptions(flatbuffers::FlatBufferBuilder &_fbb, const RightShiftOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RightShiftOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateRightShiftOptions( - _fbb); -} - -inline DilateOptionsT *DilateOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new DilateOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void DilateOptions::UnPackTo(DilateOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; -} - -inline flatbuffers::Offset DilateOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateDilateOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateDilateOptions(flatbuffers::FlatBufferBuilder &_fbb, const DilateOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DilateOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - return tflite_micro::CreateDilateOptions( - _fbb); -} - -inline ReduceWindowOptionsT *ReduceWindowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ReduceWindowOptionsT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void ReduceWindowOptions::UnPackTo(ReduceWindowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = reduce_function(); _o->reduce_function = _e; } -} - -inline flatbuffers::Offset ReduceWindowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateReduceWindowOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateReduceWindowOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReduceWindowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReduceWindowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _reduce_function = _o->reduce_function; - return tflite_micro::CreateReduceWindowOptions( - _fbb, - _reduce_function); -} - -inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new OperatorCodeT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = deprecated_builtin_code(); _o->deprecated_builtin_code = _e; } - { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); } - { auto _e = version(); _o->version = _e; } - { auto _e = builtin_code(); _o->builtin_code = _e; } -} - -inline flatbuffers::Offset OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOperatorCode(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _deprecated_builtin_code = _o->deprecated_builtin_code; - auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code); - auto _version = _o->version; - auto _builtin_code = _o->builtin_code; - return tflite_micro::CreateOperatorCode( - _fbb, - _deprecated_builtin_code, - _custom_code, - _version, - _builtin_code); -} - -inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new OperatorT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = opcode_index(); _o->opcode_index = _e; } - { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } - { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } - { auto _e = builtin_options_type(); _o->builtin_options.type = _e; } - { auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite_micro::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); } - { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_options.begin()); } } - { auto _e = custom_options_format(); _o->custom_options_format = _e; } - { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } } - { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } } - { auto _e = large_custom_options_offset(); _o->large_custom_options_offset = _e; } - { auto _e = large_custom_options_size(); _o->large_custom_options_size = _e; } - { auto _e = builtin_options_2_type(); _o->builtin_options_2.type = _e; } - { auto _e = builtin_options_2(); if (_e) _o->builtin_options_2.value = tflite_micro::BuiltinOptions2Union::UnPack(_e, builtin_options_2_type(), _resolver); } -} - -inline flatbuffers::Offset Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateOperator(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _opcode_index = _o->opcode_index; - auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; - auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; - auto _builtin_options_type = _o->builtin_options.type; - auto _builtin_options = _o->builtin_options.Pack(_fbb); - auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0; - auto _custom_options_format = _o->custom_options_format; - auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0; - auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0; - auto _large_custom_options_offset = _o->large_custom_options_offset; - auto _large_custom_options_size = _o->large_custom_options_size; - auto _builtin_options_2_type = _o->builtin_options_2.type; - auto _builtin_options_2 = _o->builtin_options_2.Pack(_fbb); - return tflite_micro::CreateOperator( - _fbb, - _opcode_index, - _inputs, - _outputs, - _builtin_options_type, - _builtin_options, - _custom_options, - _custom_options_format, - _mutating_variable_inputs, - _intermediates, - _large_custom_options_offset, - _large_custom_options_size, - _builtin_options_2_type, - _builtin_options_2); -} - -inline SubGraphT::SubGraphT(const SubGraphT &o) - : inputs(o.inputs), - outputs(o.outputs), - name(o.name) { - tensors.reserve(o.tensors.size()); - for (const auto &tensors_ : o.tensors) { tensors.emplace_back((tensors_) ? new tflite_micro::TensorT(*tensors_) : nullptr); } - operators.reserve(o.operators.size()); - for (const auto &operators_ : o.operators) { operators.emplace_back((operators_) ? new tflite_micro::OperatorT(*operators_) : nullptr); } -} - -inline SubGraphT &SubGraphT::operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT { - std::swap(tensors, o.tensors); - std::swap(inputs, o.inputs); - std::swap(outputs, o.outputs); - std::swap(operators, o.operators); - std::swap(name, o.name); - return *this; -} - -inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SubGraphT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->tensors[_i]) { _e->Get(_i)->UnPackTo(_o->tensors[_i].get(), _resolver); } else { _o->tensors[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } } - { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } } - { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operators[_i]) { _e->Get(_i)->UnPackTo(_o->operators[_i].get(), _resolver); } else { _o->operators[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = name(); if (_e) _o->name = _e->str(); } -} - -inline flatbuffers::Offset SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSubGraph(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _tensors = _o->tensors.size() ? _fbb.CreateVector> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0; - auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0; - auto _operators = _o->operators.size() ? _fbb.CreateVector> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - return tflite_micro::CreateSubGraph( - _fbb, - _tensors, - _inputs, - _outputs, - _operators, - _name); -} - -inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new BufferT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } } - { auto _e = offset(); _o->offset = _e; } - { auto _e = size(); _o->size = _e; } -} - -inline flatbuffers::Offset Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateBuffer(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - _fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16); - auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0; - auto _offset = _o->offset; - auto _size = _o->size; - return tflite_micro::CreateBuffer( - _fbb, - _data, - _offset, - _size); -} - -inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new MetadataT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = name(); if (_e) _o->name = _e->str(); } - { auto _e = buffer(); _o->buffer = _e; } -} - -inline flatbuffers::Offset Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateMetadata(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _buffer = _o->buffer; - return tflite_micro::CreateMetadata( - _fbb, - _name, - _buffer); -} - -inline TensorMapT *TensorMap::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new TensorMapT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void TensorMap::UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = name(); if (_e) _o->name = _e->str(); } - { auto _e = tensor_index(); _o->tensor_index = _e; } -} - -inline flatbuffers::Offset TensorMap::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateTensorMap(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorMapT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); - auto _tensor_index = _o->tensor_index; - return tflite_micro::CreateTensorMap( - _fbb, - _name, - _tensor_index); -} - -inline SignatureDefT::SignatureDefT(const SignatureDefT &o) - : signature_key(o.signature_key), - subgraph_index(o.subgraph_index) { - inputs.reserve(o.inputs.size()); - for (const auto &inputs_ : o.inputs) { inputs.emplace_back((inputs_) ? new tflite_micro::TensorMapT(*inputs_) : nullptr); } - outputs.reserve(o.outputs.size()); - for (const auto &outputs_ : o.outputs) { outputs.emplace_back((outputs_) ? new tflite_micro::TensorMapT(*outputs_) : nullptr); } -} - -inline SignatureDefT &SignatureDefT::operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT { - std::swap(inputs, o.inputs); - std::swap(outputs, o.outputs); - std::swap(signature_key, o.signature_key); - std::swap(subgraph_index, o.subgraph_index); - return *this; -} - -inline SignatureDefT *SignatureDef::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new SignatureDefT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void SignatureDef::UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->inputs[_i]) { _e->Get(_i)->UnPackTo(_o->inputs[_i].get(), _resolver); } else { _o->inputs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = signature_key(); if (_e) _o->signature_key = _e->str(); } - { auto _e = subgraph_index(); _o->subgraph_index = _e; } -} - -inline flatbuffers::Offset SignatureDef::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateSignatureDef(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignatureDefT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _inputs = _o->inputs.size() ? _fbb.CreateVector> (_o->inputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->inputs[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _outputs = _o->outputs.size() ? _fbb.CreateVector> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _signature_key = _o->signature_key.empty() ? 0 : _fbb.CreateString(_o->signature_key); - auto _subgraph_index = _o->subgraph_index; - return tflite_micro::CreateSignatureDef( - _fbb, - _inputs, - _outputs, - _signature_key, - _subgraph_index); -} - -inline ModelT::ModelT(const ModelT &o) - : version(o.version), - description(o.description), - metadata_buffer(o.metadata_buffer) { - operator_codes.reserve(o.operator_codes.size()); - for (const auto &operator_codes_ : o.operator_codes) { operator_codes.emplace_back((operator_codes_) ? new tflite_micro::OperatorCodeT(*operator_codes_) : nullptr); } - subgraphs.reserve(o.subgraphs.size()); - for (const auto &subgraphs_ : o.subgraphs) { subgraphs.emplace_back((subgraphs_) ? new tflite_micro::SubGraphT(*subgraphs_) : nullptr); } - buffers.reserve(o.buffers.size()); - for (const auto &buffers_ : o.buffers) { buffers.emplace_back((buffers_) ? new tflite_micro::BufferT(*buffers_) : nullptr); } - metadata.reserve(o.metadata.size()); - for (const auto &metadata_ : o.metadata) { metadata.emplace_back((metadata_) ? new tflite_micro::MetadataT(*metadata_) : nullptr); } - signature_defs.reserve(o.signature_defs.size()); - for (const auto &signature_defs_ : o.signature_defs) { signature_defs.emplace_back((signature_defs_) ? new tflite_micro::SignatureDefT(*signature_defs_) : nullptr); } -} - -inline ModelT &ModelT::operator=(ModelT o) FLATBUFFERS_NOEXCEPT { - std::swap(version, o.version); - std::swap(operator_codes, o.operator_codes); - std::swap(subgraphs, o.subgraphs); - std::swap(description, o.description); - std::swap(buffers, o.buffers); - std::swap(metadata_buffer, o.metadata_buffer); - std::swap(metadata, o.metadata); - std::swap(signature_defs, o.signature_defs); - return *this; -} - -inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = std::unique_ptr(new ModelT()); - UnPackTo(_o.get(), _resolver); - return _o.release(); -} - -inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = version(); _o->version = _e; } - { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operator_codes[_i]) { _e->Get(_i)->UnPackTo(_o->operator_codes[_i].get(), _resolver); } else { _o->operator_codes[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraphs[_i]) { _e->Get(_i)->UnPackTo(_o->subgraphs[_i].get(), _resolver); } else { _o->subgraphs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = description(); if (_e) _o->description = _e->str(); } - { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->buffers[_i]) { _e->Get(_i)->UnPackTo(_o->buffers[_i].get(), _resolver); } else { _o->buffers[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } } - { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->metadata[_i]) { _e->Get(_i)->UnPackTo(_o->metadata[_i].get(), _resolver); } else { _o->metadata[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } - { auto _e = signature_defs(); if (_e) { _o->signature_defs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->signature_defs[_i]) { _e->Get(_i)->UnPackTo(_o->signature_defs[_i].get(), _resolver); } else { _o->signature_defs[_i] = std::unique_ptr(_e->Get(_i)->UnPack(_resolver)); }; } } } -} - -inline flatbuffers::Offset Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateModel(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _version = _o->version; - auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description); - auto _buffers = _o->buffers.size() ? _fbb.CreateVector> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0; - auto _metadata = _o->metadata.size() ? _fbb.CreateVector> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0; - auto _signature_defs = _o->signature_defs.size() ? _fbb.CreateVector> (_o->signature_defs.size(), [](size_t i, _VectorArgs *__va) { return CreateSignatureDef(*__va->__fbb, __va->__o->signature_defs[i].get(), __va->__rehasher); }, &_va ) : 0; - return tflite_micro::CreateModel( - _fbb, - _version, - _operator_codes, - _subgraphs, - _description, - _buffers, - _metadata_buffer, - _metadata, - _signature_defs); -} - -inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) { - switch (type) { - case QuantizationDetails_NONE: { - return true; - } - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyQuantizationDetails( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) { - (void)resolver; - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - (void)_rehasher; - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(value); - return CreateCustomQuantization(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline QuantizationDetailsUnion::QuantizationDetailsUnion(const QuantizationDetailsUnion &u) : type(u.type), value(nullptr) { - switch (type) { - case QuantizationDetails_CustomQuantization: { - value = new tflite_micro::CustomQuantizationT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void QuantizationDetailsUnion::Reset() { - switch (type) { - case QuantizationDetails_CustomQuantization: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = QuantizationDetails_NONE; -} - -inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) { - switch (type) { - case SparseIndexVector_NONE: { - return true; - } - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifySparseIndexVector( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) { - (void)resolver; - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - (void)_rehasher; - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(value); - return CreateInt32Vector(_fbb, ptr, _rehasher).Union(); - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(value); - return CreateUint16Vector(_fbb, ptr, _rehasher).Union(); - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(value); - return CreateUint8Vector(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline SparseIndexVectorUnion::SparseIndexVectorUnion(const SparseIndexVectorUnion &u) : type(u.type), value(nullptr) { - switch (type) { - case SparseIndexVector_Int32Vector: { - value = new tflite_micro::Int32VectorT(*reinterpret_cast(u.value)); - break; - } - case SparseIndexVector_Uint16Vector: { - value = new tflite_micro::Uint16VectorT(*reinterpret_cast(u.value)); - break; - } - case SparseIndexVector_Uint8Vector: { - value = new tflite_micro::Uint8VectorT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void SparseIndexVectorUnion::Reset() { - switch (type) { - case SparseIndexVector_Int32Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case SparseIndexVector_Uint16Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case SparseIndexVector_Uint8Vector: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = SparseIndexVector_NONE; -} - -inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) { - switch (type) { - case BuiltinOptions_NONE: { - return true; - } - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CumsumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_CallOnceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BroadcastToOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Rfft2dOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_Conv3DOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HashtableOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HashtableFindOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HashtableImportOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_HashtableSizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_VarHandleOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ReadVariableOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_AssignVariableOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RandomOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BucketizeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_GeluOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_DynamicUpdateSliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnsortedSegmentMaxOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnsortedSegmentMinOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_UnsortedSegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_ATan2Options: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_SignOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BitcastOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_BitwiseXorOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions_RightShiftOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyBuiltinOptions( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) { - (void)resolver; - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CumsumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_CallOnceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BroadcastToOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_Rfft2dOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_Conv3DOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HashtableOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HashtableFindOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HashtableImportOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_HashtableSizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_VarHandleOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ReadVariableOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_AssignVariableOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RandomOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BucketizeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_GeluOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_DynamicUpdateSliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnsortedSegmentMaxOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnsortedSegmentMinOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_UnsortedSegmentSumOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_ATan2Options: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_SignOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BitcastOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_BitwiseXorOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions_RightShiftOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - (void)_rehasher; - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(value); - return CreateConv2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(value); - return CreateDepthwiseConv2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(value); - return CreateConcatEmbeddingsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(value); - return CreateLSHProjectionOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(value); - return CreatePool2DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(value); - return CreateSVDFOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(value); - return CreateFullyConnectedOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateSoftmaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(value); - return CreateConcatenationOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(value); - return CreateAddOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(value); - return CreateL2NormOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(value); - return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(value); - return CreateResizeBilinearOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(value); - return CreateCallOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(value); - return CreateReshapeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(value); - return CreateSkipGramOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(value); - return CreateSpaceToDepthOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(value); - return CreateEmbeddingLookupSparseOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(value); - return CreateMulOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(value); - return CreatePadOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(value); - return CreateGatherOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(value); - return CreateBatchToSpaceNDOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(value); - return CreateSpaceToBatchNDOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(value); - return CreateTransposeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(value); - return CreateReducerOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(value); - return CreateSubOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(value); - return CreateDivOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(value); - return CreateSqueezeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(value); - return CreateExpOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(value); - return CreateTopKV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(value); - return CreateSplitOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(value); - return CreateCastOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(value); - return CreateMaximumMinimumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(value); - return CreateLessOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(value); - return CreateNegOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(value); - return CreatePadV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(value); - return CreateGreaterOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(value); - return CreateSelectOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(value); - return CreateTransposeConvOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(value); - return CreateSparseToDenseOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(value); - return CreateTileOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(value); - return CreateExpandDimsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(value); - return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(value); - return CreateShapeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(value); - return CreatePowOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(value); - return CreateArgMinOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(value); - return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(value); - return CreatePackOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(value); - return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalAndOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(value); - return CreateLogicalNotOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnpackOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(value); - return CreateFloorDivOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(value); - return CreateSquareOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(value); - return CreateZerosLikeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(value); - return CreateFillOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateBidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - return CreateBidirectionalSequenceRNNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(value); - return CreateFloorModOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(value); - return CreateRangeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(value); - return CreateResizeNearestNeighborOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(value); - return CreateLeakyReluOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(value); - return CreateSquaredDifferenceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(value); - return CreateMirrorPadOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(value); - return CreateAbsOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(value); - return CreateSplitVOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(value); - return CreateUniqueOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(value); - return CreateReverseV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(value); - return CreateAddNOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(value); - return CreateGatherNdOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(value); - return CreateCosOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(value); - return CreateWhereOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(value); - return CreateRankOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(value); - return CreateReverseSequenceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(value); - return CreateMatrixDiagOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateQuantizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(value); - return CreateMatrixSetDiagOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(value); - return CreateHardSwishOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(value); - return CreateIfOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(value); - return CreateWhileOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(value); - return CreateDepthToSpaceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(value); - return CreateNonMaxSuppressionV4Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(value); - return CreateNonMaxSuppressionV5Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(value); - return CreateScatterNdOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(value); - return CreateSelectV2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(value); - return CreateDensifyOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(value); - return CreateSegmentSumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(value); - return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CumsumOptions: { - auto ptr = reinterpret_cast(value); - return CreateCumsumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_CallOnceOptions: { - auto ptr = reinterpret_cast(value); - return CreateCallOnceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BroadcastToOptions: { - auto ptr = reinterpret_cast(value); - return CreateBroadcastToOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_Rfft2dOptions: { - auto ptr = reinterpret_cast(value); - return CreateRfft2dOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_Conv3DOptions: { - auto ptr = reinterpret_cast(value); - return CreateConv3DOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HashtableOptions: { - auto ptr = reinterpret_cast(value); - return CreateHashtableOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HashtableFindOptions: { - auto ptr = reinterpret_cast(value); - return CreateHashtableFindOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HashtableImportOptions: { - auto ptr = reinterpret_cast(value); - return CreateHashtableImportOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_HashtableSizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateHashtableSizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_VarHandleOptions: { - auto ptr = reinterpret_cast(value); - return CreateVarHandleOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ReadVariableOptions: { - auto ptr = reinterpret_cast(value); - return CreateReadVariableOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_AssignVariableOptions: { - auto ptr = reinterpret_cast(value); - return CreateAssignVariableOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RandomOptions: { - auto ptr = reinterpret_cast(value); - return CreateRandomOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BucketizeOptions: { - auto ptr = reinterpret_cast(value); - return CreateBucketizeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_GeluOptions: { - auto ptr = reinterpret_cast(value); - return CreateGeluOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_DynamicUpdateSliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateDynamicUpdateSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnsortedSegmentProdOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnsortedSegmentMaxOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnsortedSegmentMaxOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnsortedSegmentMinOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnsortedSegmentMinOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_UnsortedSegmentSumOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnsortedSegmentSumOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_ATan2Options: { - auto ptr = reinterpret_cast(value); - return CreateATan2Options(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_SignOptions: { - auto ptr = reinterpret_cast(value); - return CreateSignOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BitcastOptions: { - auto ptr = reinterpret_cast(value); - return CreateBitcastOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_BitwiseXorOptions: { - auto ptr = reinterpret_cast(value); - return CreateBitwiseXorOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions_RightShiftOptions: { - auto ptr = reinterpret_cast(value); - return CreateRightShiftOptions(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) : type(u.type), value(nullptr) { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - value = new tflite_micro::Conv2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DepthwiseConv2DOptions: { - value = new tflite_micro::DepthwiseConv2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - value = new tflite_micro::ConcatEmbeddingsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LSHProjectionOptions: { - value = new tflite_micro::LSHProjectionOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_Pool2DOptions: { - value = new tflite_micro::Pool2DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SVDFOptions: { - value = new tflite_micro::SVDFOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RNNOptions: { - value = new tflite_micro::RNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FullyConnectedOptions: { - value = new tflite_micro::FullyConnectedOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SoftmaxOptions: { - value = new tflite_micro::SoftmaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ConcatenationOptions: { - value = new tflite_micro::ConcatenationOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AddOptions: { - value = new tflite_micro::AddOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_L2NormOptions: { - value = new tflite_micro::L2NormOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - value = new tflite_micro::LocalResponseNormalizationOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LSTMOptions: { - value = new tflite_micro::LSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ResizeBilinearOptions: { - value = new tflite_micro::ResizeBilinearOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CallOptions: { - value = new tflite_micro::CallOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReshapeOptions: { - value = new tflite_micro::ReshapeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SkipGramOptions: { - value = new tflite_micro::SkipGramOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SpaceToDepthOptions: { - value = new tflite_micro::SpaceToDepthOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - value = new tflite_micro::EmbeddingLookupSparseOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MulOptions: { - value = new tflite_micro::MulOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PadOptions: { - value = new tflite_micro::PadOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GatherOptions: { - value = new tflite_micro::GatherOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BatchToSpaceNDOptions: { - value = new tflite_micro::BatchToSpaceNDOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SpaceToBatchNDOptions: { - value = new tflite_micro::SpaceToBatchNDOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TransposeOptions: { - value = new tflite_micro::TransposeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReducerOptions: { - value = new tflite_micro::ReducerOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SubOptions: { - value = new tflite_micro::SubOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DivOptions: { - value = new tflite_micro::DivOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SqueezeOptions: { - value = new tflite_micro::SqueezeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SequenceRNNOptions: { - value = new tflite_micro::SequenceRNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_StridedSliceOptions: { - value = new tflite_micro::StridedSliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ExpOptions: { - value = new tflite_micro::ExpOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TopKV2Options: { - value = new tflite_micro::TopKV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SplitOptions: { - value = new tflite_micro::SplitOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogSoftmaxOptions: { - value = new tflite_micro::LogSoftmaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CastOptions: { - value = new tflite_micro::CastOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DequantizeOptions: { - value = new tflite_micro::DequantizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MaximumMinimumOptions: { - value = new tflite_micro::MaximumMinimumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ArgMaxOptions: { - value = new tflite_micro::ArgMaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LessOptions: { - value = new tflite_micro::LessOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NegOptions: { - value = new tflite_micro::NegOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PadV2Options: { - value = new tflite_micro::PadV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GreaterOptions: { - value = new tflite_micro::GreaterOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GreaterEqualOptions: { - value = new tflite_micro::GreaterEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LessEqualOptions: { - value = new tflite_micro::LessEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SelectOptions: { - value = new tflite_micro::SelectOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SliceOptions: { - value = new tflite_micro::SliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TransposeConvOptions: { - value = new tflite_micro::TransposeConvOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SparseToDenseOptions: { - value = new tflite_micro::SparseToDenseOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_TileOptions: { - value = new tflite_micro::TileOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ExpandDimsOptions: { - value = new tflite_micro::ExpandDimsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_EqualOptions: { - value = new tflite_micro::EqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NotEqualOptions: { - value = new tflite_micro::NotEqualOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ShapeOptions: { - value = new tflite_micro::ShapeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PowOptions: { - value = new tflite_micro::PowOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ArgMinOptions: { - value = new tflite_micro::ArgMinOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FakeQuantOptions: { - value = new tflite_micro::FakeQuantOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_PackOptions: { - value = new tflite_micro::PackOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalOrOptions: { - value = new tflite_micro::LogicalOrOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_OneHotOptions: { - value = new tflite_micro::OneHotOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalAndOptions: { - value = new tflite_micro::LogicalAndOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LogicalNotOptions: { - value = new tflite_micro::LogicalNotOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnpackOptions: { - value = new tflite_micro::UnpackOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FloorDivOptions: { - value = new tflite_micro::FloorDivOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SquareOptions: { - value = new tflite_micro::SquareOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ZerosLikeOptions: { - value = new tflite_micro::ZerosLikeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FillOptions: { - value = new tflite_micro::FillOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - value = new tflite_micro::BidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - value = new tflite_micro::BidirectionalSequenceRNNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - value = new tflite_micro::UnidirectionalSequenceLSTMOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_FloorModOptions: { - value = new tflite_micro::FloorModOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RangeOptions: { - value = new tflite_micro::RangeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - value = new tflite_micro::ResizeNearestNeighborOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_LeakyReluOptions: { - value = new tflite_micro::LeakyReluOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SquaredDifferenceOptions: { - value = new tflite_micro::SquaredDifferenceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MirrorPadOptions: { - value = new tflite_micro::MirrorPadOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AbsOptions: { - value = new tflite_micro::AbsOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SplitVOptions: { - value = new tflite_micro::SplitVOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UniqueOptions: { - value = new tflite_micro::UniqueOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReverseV2Options: { - value = new tflite_micro::ReverseV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AddNOptions: { - value = new tflite_micro::AddNOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GatherNdOptions: { - value = new tflite_micro::GatherNdOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CosOptions: { - value = new tflite_micro::CosOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_WhereOptions: { - value = new tflite_micro::WhereOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RankOptions: { - value = new tflite_micro::RankOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReverseSequenceOptions: { - value = new tflite_micro::ReverseSequenceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MatrixDiagOptions: { - value = new tflite_micro::MatrixDiagOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_QuantizeOptions: { - value = new tflite_micro::QuantizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_MatrixSetDiagOptions: { - value = new tflite_micro::MatrixSetDiagOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HardSwishOptions: { - value = new tflite_micro::HardSwishOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_IfOptions: { - value = new tflite_micro::IfOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_WhileOptions: { - value = new tflite_micro::WhileOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DepthToSpaceOptions: { - value = new tflite_micro::DepthToSpaceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - value = new tflite_micro::NonMaxSuppressionV4OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - value = new tflite_micro::NonMaxSuppressionV5OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ScatterNdOptions: { - value = new tflite_micro::ScatterNdOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SelectV2Options: { - value = new tflite_micro::SelectV2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DensifyOptions: { - value = new tflite_micro::DensifyOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SegmentSumOptions: { - value = new tflite_micro::SegmentSumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BatchMatMulOptions: { - value = new tflite_micro::BatchMatMulOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CumsumOptions: { - value = new tflite_micro::CumsumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_CallOnceOptions: { - value = new tflite_micro::CallOnceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BroadcastToOptions: { - value = new tflite_micro::BroadcastToOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_Rfft2dOptions: { - value = new tflite_micro::Rfft2dOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_Conv3DOptions: { - value = new tflite_micro::Conv3DOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HashtableOptions: { - value = new tflite_micro::HashtableOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HashtableFindOptions: { - value = new tflite_micro::HashtableFindOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HashtableImportOptions: { - value = new tflite_micro::HashtableImportOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_HashtableSizeOptions: { - value = new tflite_micro::HashtableSizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_VarHandleOptions: { - value = new tflite_micro::VarHandleOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ReadVariableOptions: { - value = new tflite_micro::ReadVariableOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_AssignVariableOptions: { - value = new tflite_micro::AssignVariableOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RandomOptions: { - value = new tflite_micro::RandomOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BucketizeOptions: { - value = new tflite_micro::BucketizeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_GeluOptions: { - value = new tflite_micro::GeluOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_DynamicUpdateSliceOptions: { - value = new tflite_micro::DynamicUpdateSliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnsortedSegmentProdOptions: { - value = new tflite_micro::UnsortedSegmentProdOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnsortedSegmentMaxOptions: { - value = new tflite_micro::UnsortedSegmentMaxOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnsortedSegmentMinOptions: { - value = new tflite_micro::UnsortedSegmentMinOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_UnsortedSegmentSumOptions: { - value = new tflite_micro::UnsortedSegmentSumOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_ATan2Options: { - value = new tflite_micro::ATan2OptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_SignOptions: { - value = new tflite_micro::SignOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BitcastOptions: { - value = new tflite_micro::BitcastOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_BitwiseXorOptions: { - value = new tflite_micro::BitwiseXorOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions_RightShiftOptions: { - value = new tflite_micro::RightShiftOptionsT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void BuiltinOptionsUnion::Reset() { - switch (type) { - case BuiltinOptions_Conv2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DepthwiseConv2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ConcatEmbeddingsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LSHProjectionOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_Pool2DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SVDFOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FullyConnectedOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SoftmaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ConcatenationOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AddOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_L2NormOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LocalResponseNormalizationOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ResizeBilinearOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CallOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReshapeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SkipGramOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SpaceToDepthOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_EmbeddingLookupSparseOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MulOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PadOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GatherOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BatchToSpaceNDOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SpaceToBatchNDOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TransposeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReducerOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SubOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DivOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SqueezeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_StridedSliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ExpOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TopKV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SplitOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogSoftmaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CastOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DequantizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MaximumMinimumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ArgMaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LessOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NegOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PadV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GreaterOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GreaterEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LessEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SelectOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TransposeConvOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SparseToDenseOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_TileOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ExpandDimsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_EqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NotEqualOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ShapeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PowOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ArgMinOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FakeQuantOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_PackOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalOrOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_OneHotOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalAndOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LogicalNotOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnpackOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FloorDivOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SquareOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ZerosLikeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FillOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BidirectionalSequenceRNNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnidirectionalSequenceLSTMOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_FloorModOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RangeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ResizeNearestNeighborOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_LeakyReluOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SquaredDifferenceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MirrorPadOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AbsOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SplitVOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UniqueOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReverseV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AddNOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GatherNdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CosOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_WhereOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RankOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReverseSequenceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MatrixDiagOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_QuantizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_MatrixSetDiagOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HardSwishOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_IfOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_WhileOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DepthToSpaceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NonMaxSuppressionV4Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_NonMaxSuppressionV5Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ScatterNdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SelectV2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DensifyOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SegmentSumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BatchMatMulOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CumsumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_CallOnceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BroadcastToOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_Rfft2dOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_Conv3DOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HashtableOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HashtableFindOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HashtableImportOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_HashtableSizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_VarHandleOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ReadVariableOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_AssignVariableOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RandomOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BucketizeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_GeluOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_DynamicUpdateSliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnsortedSegmentMaxOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnsortedSegmentMinOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_UnsortedSegmentSumOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_ATan2Options: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_SignOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BitcastOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_BitwiseXorOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions_RightShiftOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = BuiltinOptions_NONE; -} - -inline bool VerifyBuiltinOptions2(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions2 type) { - switch (type) { - case BuiltinOptions2_NONE: { - return true; - } - case BuiltinOptions2_StablehloConcatenateOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloBroadcastInDimOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloSliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloConvolutionOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloCustomCallOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloReduceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloScatterOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloCompareOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloDynamicSliceOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloPadOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloIotaOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloDotGeneralOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloReduceWindowOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloSortOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloWhileOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloGatherOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloTransposeOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_DilateOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_StablehloRngBitGeneratorOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - case BuiltinOptions2_ReduceWindowOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } - default: return true; - } -} - -inline bool VerifyBuiltinOptions2Vector(flatbuffers::Verifier &verifier, const flatbuffers::Vector> *values, const flatbuffers::Vector *types) { - if (!values || !types) return !values && !types; - if (values->size() != types->size()) return false; - for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) { - if (!VerifyBuiltinOptions2( - verifier, values->Get(i), types->GetEnum(i))) { - return false; - } - } - return true; -} - -inline void *BuiltinOptions2Union::UnPack(const void *obj, BuiltinOptions2 type, const flatbuffers::resolver_function_t *resolver) { - (void)resolver; - switch (type) { - case BuiltinOptions2_StablehloConcatenateOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloBroadcastInDimOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloSliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloConvolutionOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloCustomCallOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloReduceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloScatterOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloCompareOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloDynamicSliceOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloPadOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloIotaOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloDotGeneralOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloReduceWindowOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloSortOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloWhileOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloGatherOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloTransposeOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_DilateOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_StablehloRngBitGeneratorOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - case BuiltinOptions2_ReduceWindowOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } - default: return nullptr; - } -} - -inline flatbuffers::Offset BuiltinOptions2Union::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const { - (void)_rehasher; - switch (type) { - case BuiltinOptions2_StablehloConcatenateOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloConcatenateOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloBroadcastInDimOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloBroadcastInDimOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloSliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloConvolutionOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloConvolutionOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloCustomCallOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloCustomCallOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloReduceOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloReduceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloScatterOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloScatterOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloCompareOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloCompareOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloDynamicSliceOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloDynamicSliceOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloPadOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloPadOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloIotaOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloIotaOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloDotGeneralOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloDotGeneralOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloReduceWindowOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloReduceWindowOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloSortOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloSortOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloWhileOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloWhileOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloGatherOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloGatherOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloTransposeOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloTransposeOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_DilateOptions: { - auto ptr = reinterpret_cast(value); - return CreateDilateOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_StablehloRngBitGeneratorOptions: { - auto ptr = reinterpret_cast(value); - return CreateStablehloRngBitGeneratorOptions(_fbb, ptr, _rehasher).Union(); - } - case BuiltinOptions2_ReduceWindowOptions: { - auto ptr = reinterpret_cast(value); - return CreateReduceWindowOptions(_fbb, ptr, _rehasher).Union(); - } - default: return 0; - } -} - -inline BuiltinOptions2Union::BuiltinOptions2Union(const BuiltinOptions2Union &u) : type(u.type), value(nullptr) { - switch (type) { - case BuiltinOptions2_StablehloConcatenateOptions: { - value = new tflite_micro::StablehloConcatenateOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloBroadcastInDimOptions: { - value = new tflite_micro::StablehloBroadcastInDimOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloSliceOptions: { - value = new tflite_micro::StablehloSliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloConvolutionOptions: { - value = new tflite_micro::StablehloConvolutionOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloCustomCallOptions: { - value = new tflite_micro::StablehloCustomCallOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloReduceOptions: { - value = new tflite_micro::StablehloReduceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloScatterOptions: { - value = new tflite_micro::StablehloScatterOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloCompareOptions: { - value = new tflite_micro::StablehloCompareOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloDynamicSliceOptions: { - value = new tflite_micro::StablehloDynamicSliceOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloPadOptions: { - value = new tflite_micro::StablehloPadOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloIotaOptions: { - value = new tflite_micro::StablehloIotaOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloDotGeneralOptions: { - value = new tflite_micro::StablehloDotGeneralOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloReduceWindowOptions: { - value = new tflite_micro::StablehloReduceWindowOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloSortOptions: { - value = new tflite_micro::StablehloSortOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloWhileOptions: { - value = new tflite_micro::StablehloWhileOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloGatherOptions: { - value = new tflite_micro::StablehloGatherOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloTransposeOptions: { - value = new tflite_micro::StablehloTransposeOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_DilateOptions: { - value = new tflite_micro::DilateOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_StablehloRngBitGeneratorOptions: { - value = new tflite_micro::StablehloRngBitGeneratorOptionsT(*reinterpret_cast(u.value)); - break; - } - case BuiltinOptions2_ReduceWindowOptions: { - value = new tflite_micro::ReduceWindowOptionsT(*reinterpret_cast(u.value)); - break; - } - default: - break; - } -} - -inline void BuiltinOptions2Union::Reset() { - switch (type) { - case BuiltinOptions2_StablehloConcatenateOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloBroadcastInDimOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloSliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloConvolutionOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloCustomCallOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloReduceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloScatterOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloCompareOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloDynamicSliceOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloPadOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloIotaOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloDotGeneralOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloReduceWindowOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloSortOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloWhileOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloGatherOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloTransposeOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_DilateOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_StablehloRngBitGeneratorOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - case BuiltinOptions2_ReduceWindowOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } - default: break; - } - value = nullptr; - type = BuiltinOptions2_NONE; -} - -inline const tflite_micro::Model *GetModel(const void *buf) { - return flatbuffers::GetRoot(buf); -} - -inline const tflite_micro::Model *GetSizePrefixedModel(const void *buf) { - return flatbuffers::GetSizePrefixedRoot(buf); -} - -inline const char *ModelIdentifier() { - return "TFL3"; -} - -inline bool ModelBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, ModelIdentifier()); -} - -inline bool SizePrefixedModelBufferHasIdentifier(const void *buf) { - return flatbuffers::BufferHasIdentifier( - buf, ModelIdentifier(), true); -} - -inline bool VerifyModelBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifyBuffer(ModelIdentifier()); -} - -inline bool VerifySizePrefixedModelBuffer( - flatbuffers::Verifier &verifier) { - return verifier.VerifySizePrefixedBuffer(ModelIdentifier()); -} - -inline const char *ModelExtension() { - return "tflite"; -} - -inline void FinishModelBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.Finish(root, ModelIdentifier()); -} - -inline void FinishSizePrefixedModelBuffer( - flatbuffers::FlatBufferBuilder &fbb, - flatbuffers::Offset root) { - fbb.FinishSizePrefixed(root, ModelIdentifier()); -} - -inline std::unique_ptr UnPackModel( - const void *buf, - const flatbuffers::resolver_function_t *res = nullptr) { - return std::unique_ptr(GetModel(buf)->UnPack(res)); -} - -inline std::unique_ptr UnPackSizePrefixedModel( - const void *buf, - const flatbuffers::resolver_function_t *res = nullptr) { - return std::unique_ptr(GetSizePrefixedModel(buf)->UnPack(res)); -} - -} // namespace tflite_micro - -#endif // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_ diff --git a/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h b/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h deleted file mode 100644 index 36b02e0b..00000000 --- a/new_ai_tools/include/tensorflow/lite/schema/schema_utils.h +++ /dev/null @@ -1,33 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ -#define TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ - -#include "flatbuffers/flatbuffers.h" -#include "tensorflow/lite/schema/schema_generated.h" - -namespace tflite_micro { - -// The following methods are introduced to resolve op builtin code shortage -// problem. The new builtin operator will be assigned to the extended builtin -// code field in the flatbuffer schema. Those methods helps to hide builtin code -// details. -BuiltinOperator GetBuiltinCode(const OperatorCode *op_code); - -BuiltinOperator GetBuiltinCode(const OperatorCodeT *op_code); - -} // namespace tflite_micro - -#endif // TENSORFLOW_LITE_SCHEMA_SCHEMA_UTILS_H_ diff --git a/new_ai_tools/include/tile_ram_server.h b/new_ai_tools/include/tile_ram_server.h deleted file mode 100644 index d104ebc1..00000000 --- a/new_ai_tools/include/tile_ram_server.h +++ /dev/null @@ -1,38 +0,0 @@ -#ifndef _tile_ram_server_h_ -#define _tile_ram_server_h_ - -#include "flash_server.h" - -/** - * Function that runs a flash-server. A flash server is a thread that serves one - * or more clients. There is one flash server per flash-device, and the server - * can serve clients on one or more tiles. - * - * The flash server takes the following commands: - * - Read a whole model from the flash. - * - Read some parameters from the flash - * - (future extension) Read code for an operator from flash - * - * This function does, at present, never return. It could be made to return if - * all clients close their connection - * - * \param c_tile_ram_clients Array of channels; one per client. - * Each client is served in turn - * NOTE MUST BE 1 FOR NOW - * \param headers Space to store a header for each client - * The header for the client describes the local - * "filesystem" for that client - * \param n_tile_ram_clients Number of clients. The - * arrays in the first and second parameters should have this many entries - * \param data Tile ram data - * \param n_tile_ram_flash Number of bytes in array - */ -#ifdef __XC__ -void tile_ram_server(chanend c_tile_ram_clients[], flash_t headers[], - int n_tile_ram_clients, const int8_t data[]); -#else -void tile_ram_server(chanend_t *c_tile_ram_clients, flash_t *headers, - int n_tile_ram_clients, const int8_t *data); -#endif - -#endif diff --git a/new_ai_tools/libxtflitemicro.cmake b/new_ai_tools/libxtflitemicro.cmake index 11419d6b..768b9809 100644 --- a/new_ai_tools/libxtflitemicro.cmake +++ b/new_ai_tools/libxtflitemicro.cmake @@ -6,7 +6,7 @@ set(XMOS_AITOOLSLIB_DEFINITIONS ) set(XMOS_AITOOLSLIB_LIBRARIES "${CMAKE_CURRENT_LIST_DIR}/libxtflitemicro_vx4a.a") -set(XMOS_AITOOLSLIB_INCLUDES "${CMAKE_CURRENT_LIST_DIR}/include") +set(XMOS_AITOOLSLIB_INCLUDES "${XMOS_AITOOLSLIB_PATH}/include") # list(APPEND APP_COMPILER_FLAGS diff --git 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zGKszRL*hd6QTdSkhNzOS2d9Z?;$!l2>STQ1`^vOO?Zmtn!iQ&#IrpiiYi2w;dG%>m zpS;JqVBYnO!I?8>v;;2(+hm(OSb0YLLH@)h~3AW)x_SBWQt7u+R(E;ov!!T03D z;v3M#=>CS;<33SurwyxUC@yGOJDl(SHAkS2fzML|Lb4= z&%b`>`~Uvar=S1r@3*xa>MoM#*v8ol&%0>Z)gS-Njknyk_Kv$Y-gEDLpZkaVKmYkJ zeBle}|NQa5f8UWY$9A)=i`9k6^RRb5q(5_Q@HTJcY;KI!hIEF19btYr=?wkK%=}Wu z9PZG+nK1YrZDZ`P1Z6zRpZyz-9v}Wa&S -#endif - -// #define TFLMC_XCORE_PROFILE -// #define TFLMC_CONV2D_PROFILE -// #define TFLMC_PRINT_TENSORS -// #define TFLMC_PRINT_INPUT_TENSORS - -#if defined __GNUC__ -#define ALIGN(X) __attribute__((aligned(X))) -#elif defined _MSC_VER -#define ALIGN(X) __declspec(align(X)) -#elif defined __TASKING__ -#define ALIGN(X) __align(X) -#endif - -// Check target arch -#ifdef __XS3A__ -static_assert(1 == 0, "Model has not been compiled for XS3A!"); -#elif __VX4A__ || __VX4B__ -static_assert(1 == 1, "Model has not been compiled for VX4A!"); -#endif - -// Check lib_nn and lib_tflite_micro versions -// NOTE: xformer version is saved for debugging purposes -// If lib_nn and lib_tflite_micro versions are as expected, -// then the xformer version doesn't matter as the model should execute -// If major version is zero, then minor versions must match -// Otherwise, major versions must match and binary minor version -// must be less or equal to runtime minor version -// Check if runtime lib_tflite_micro version matches with compiled version -static_assert((0 == 0 && lib_tflite_micro::major_version == 0 && 8 == lib_tflite_micro::minor_version) || - (0 == lib_tflite_micro::major_version) || - (8 < lib_tflite_micro::minor_version), - "Model has been compiled with lib_tflite_micro version incompatible with runtime lib_tflite_micro version!"); - -// Check if runtime lib_nn version matches with compiled version -static_assert((0 == 0 && lib_nn::major_version == 0 && 5 == lib_nn::minor_version) || - (0 == lib_nn::major_version) || - (5 < lib_nn::minor_version), - "Model has been compiled with lib_nn version incompatible with runtime lib_nn version!"); - -namespace tflite_micro { -namespace ops { -namespace micro { -namespace xcore { -extern TFLMRegistration *Register_XC_conv2d_v2(void); -extern TFLMRegistration *Register_XC_slice(void); -extern TFLMRegistration *Register_XC_no_op(void); -extern TFLMRegistration *Register_XC_lookup(void); -} // namespace xcore -} // namespace micro -} // namespace ops -} // namespace tflite_micro - - - -constexpr int kTensorArenaSize = 840; -#ifndef SHARED_TENSOR_ARENA -namespace { -uint8_t tensor_arena[kTensorArenaSize] ALIGN(8); -} -#else -extern uint8_t tensor_arena[]; -#endif - -namespace { -template struct TfArray { - int sz; T elem[SZ]; -}; -enum used_operators_e { - OP_XC_conv2d_v2, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, OP_LAST -}; - -#if defined(TFLMC_XCORE_PROFILE) || defined(TFLMC_PRINT_TENSORS) || defined(TFLMC_PRINT_INPUT_TENSORS) || defined(TFLMC_CONV2D_PROFILE) -const char *op_strs[] = { -"OP_XC_conv2d_v2", "OP_XC_slice", "OP_XC_no_op", "OP_XC_lookup", }; - -#endif -#if defined(TFLMC_XCORE_PROFILE) || defined(TFLMC_PRINT_TENSORS) || defined(TFLMC_PRINT_INPUT_TENSORS) -unsigned char checksum(char *data, unsigned int length) -{ - static char sum; - static char * end; - sum = 0; - end = data + length; - - do - { - sum -= *data++; - } while (data != end); - return sum; -} - -#endif - -#ifdef TFLMC_XCORE_PROFILE -int op_times[OP_LAST]; -int op_counts[OP_LAST]; -int64_t op_times_summed; -int time_t0, time_t1; -#endif - -TfLiteContext ctx{}; - -TFLMRegistration registrations[OP_LAST]; - -struct { -const TfArray<4, int> tensor_dimension0 = { 4, { 1,1,4,24 } }; -const TfArray<1, float> quant0_scale = { 1, { 0.11438909918069839, } }; -const TfArray<1, int> quant0_zero = { 1, { 127 } }; -const TfLiteAffineQuantization quant0 = { (TfLiteFloatArray*)&quant0_scale, (TfLiteIntArray*)&quant0_zero, 0 }; -const ALIGN(8) uint8_t tensor_data1[256] = { - 129, 129, 130, 130, 130, 131, 131, 132, 133, 134, - 135, 136, 137, 139, 141, 143, 146, 149, 153, 157, - 162, 167, 173, 180, 187, 195, 204, 214, 224, 234, - 245, 0, 11, 22, 32, 42, 52, 61, 69, 76, - 83, 89, 94, 99, 103, 107, 110, 113, 115, 117, - 119, 120, 121, 122, 123, 124, 125, 125, 126, 126, - 126, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, - 127, 127, 127, 127, 127, 127, 127, 127, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, - 128, 129, 129, 129, 129, 129, -}; -const TfArray<1, int> tensor_dimension1 = { 1, { 256 } }; -const ALIGN(8) int16_t tensor_data2[128] = { - 9995, 10980, 1112, 1018, 8471, 6201, 13185, 7779, 960, 12163, - 8559, 5352, 7723, 1478, 7106, 7075, -293, -617, -849, -941, - -544, -788, -577, -565, -990, -661, -553, -867, -443, -978, - -1006, -512, 11348, 5426, 1411, 10800, 4680, 6043, 11071, 973, - 7618, 1143, 23811, 905, 7064, 10330, 5678, 12963, -599, -497, - -1030, -800, 67, -689, -624, -882, -751, -1080, -975, -989, - -683, -390, -469, -519, 9093, 1436, 12302, 2487, 10722, 15381, - 9388, 9812, 8092, 12649, 11091, 1237, 12354, 8280, 14331, 9768, - -1066, -1096, -564, -543, -1825, -850, -569, -587, -847, -489, - -790, -1018, -585, -637, -1897, -620, 11690, 11114, 9194, 9223, - 15636, 4424, 10925, 1003, 7456, 982, 9832, 10600, 1254, 1301, - 11318, 998, -1132, -554, -535, -572, -1098, -261, -677, -942, - -856, -1031, -653, -593, -907, -1051, -613, -874, -}; -const TfArray<1, int> tensor_dimension2 = { 1, { 128 } }; -const ALIGN(8) int8_t tensor_data3[1664] = { - -11, 19, -19, 12, -6, -2, -22, -6, 66, 14, - 23, 23, -22, -127, -98, -6, 46, 53, 82, 96, - -65, -90, -26, 59, -34, 75, -127, 78, -62, 27, - 64, 80, 26, 49, 41, 19, 13, -3, -16, 33, - -33, -2, -93, -11, 58, -1, -25, 11, 55, 74, - 30, 94, -37, -3, 31, -6, -44, 22, 35, 32, - -19, 53, -67, 83, -41, -5, 127, 70, 91, 17, - 66, -13, 6, -4, 2, 22, 0, 11, 6, -20, - -13, 9, -12, -9, 11, -44, -42, 35, -127, -109, - 68, 70, 7, 85, -14, 34, 73, -83, -100, -127, - -48, 62, 69, 36, 40, -10, 57, -71, -47, 104, - 91, 13, 68, 11, 74, 86, -12, -76, -7, -68, - -17, 30, 14, 4, 14, 58, 10, 21, -38, -127, - -122, -8, 67, 41, 24, 42, 6, -22, 0, -37, - 32, -37, 29, 13, -6, 11, 0, -6, 11, 16, - -1, -2, 9, 15, 22, 5, 28, -3, -127, 4, - 38, -33, 25, -17, 0, 28, -21, 9, -50, 126, - 95, 104, 105, 89, -33, -51, 127, -63, 87, -16, - 98, -33, -15, 6, 92, 82, 91, 6, 127, -76, - 124, 3, -2, 0, 13, 8, -26, -22, 6, 61, - 40, -43, -62, 17, 127, -113, -27, 31, -3, 73, - -68, -60, 9, -57, -24, 119, 46, -105, 127, -82, - -3, 20, 9, 8, -3, -11, 3, -1, 3, -15, - -9, 6, 16, -6, -3, -16, 11, -6, 6, 3, - 41, -63, 52, -78, 49, 63, 47, 27, -20, 12, - 9, -6, 45, 30, 16, -65, 63, -4, -118, 37, - -53, -127, 51, 43, -11, 14, -22, 6, 2, 11, - -11, -14, 13, 15, 0, -4, 41, -30, 18, 73, - -62, -79, 53, 76, -28, -127, -22, 77, 37, 112, - 110, 24, 112, 82, 7, 124, -29, 97, 127, -55, - -11, -30, -88, 125, -73, -103, 25, 107, 88, 3, - -17, 93, 41, 85, -6, 74, 90, -70, 18, -17, - 124, -84, -4, 68, 80, -39, -70, 127, 60, 81, - -100, 63, 45, 52, -79, 84, -4, 14, -7, -17, - 21, 47, -9, -12, -49, 6, 61, 35, -53, 66, - -15, -127, 11, 33, -3, -4, 32, -31, 16, -7, - -1, -8, -8, -13, -15, -8, -6, -16, 3, 4, - 16, 16, -7, 78, -6, -127, 5, 24, 32, -24, - -30, -15, 20, 8, -6, 8, 1, 0, -1, 5, - -2, -7, -7, -2, 40, 55, -45, -127, 36, 59, - -24, 17, 1, -23, 14, -11, 19, -4, 5, -14, - -10, -14, 7, -2, -2, -26, 6, -27, -14, 10, - 32, -54, -28, -7, -12, 81, 17, 119, 82, 51, - -127, -120, 8, -17, 7, -42, -21, -19, -27, -49, - -127, 103, 56, 20, -3, 17, 13, 65, -44, 34, - 4, -50, 15, 11, 22, -6, 6, 4, -66, 27, - 19, -13, 27, -40, -41, -11, 8, -8, -42, -87, - -86, -21, 52, 37, 79, 71, 9, 47, -52, 127, - -2, 24, 104, 105, -34, -61, 71, -91, 44, 119, - 127, 52, 59, 75, 127, 108, 35, 117, -31, 2, - 58, -1, 112, -33, 127, -62, -12, 14, -17, 10, - -5, -5, -8, 5, -2, 7, -10, 3, -10, 9, - 4, -14, 13, -2, -7, 20, -11, -1, 99, 112, - 64, 87, -48, -25, 67, 127, 113, -27, -17, 123, - -26, -43, 120, 69, 124, 49, -45, 60, 46, 19, - 63, -89, 3, 7, 39, 70, 120, 43, -112, -127, - -79, -34, 38, 33, 19, -30, 60, 12, -11, 19, - -24, 31, -19, -59, 64, -16, 11, -4, 112, 87, - -87, 64, 68, 7, 127, 67, 11, -53, 116, -60, - -83, 120, -107, 2, 63, 15, 51, 74, 110, 71, - 1, -3, -9, 7, -18, -62, 89, 44, -36, -95, - 127, -5, -83, 81, -52, 19, 4, -26, 33, 11, - -12, -4, -17, 19, -18, 34, 44, 45, 14, -29, - 45, 19, -41, -21, 9, 13, -7, 20, -16, -3, - -4, 71, -3, -13, -32, -4, -127, 5, -6, -14, - -127, -60, -8, -27, -91, -62, -49, -20, 10, 26, - -18, -39, -6, 68, 29, -1, 18, 41, 60, 24, - -21, 14, -9, 27, -5, -8, 15, 36, 7, 24, - 27, 51, 56, -24, -127, -67, 26, -5, -6, 31, - -11, 8, 6, -32, 50, -28, -30, 20, 116, 85, - -43, -63, 40, 113, 98, 10, 40, 29, 2, -31, - 84, -36, -22, 127, 44, -22, 101, -31, 16, 111, - -1, 12, -33, -2, -5, -46, -59, -14, -8, -81, - -127, -103, 90, -76, 30, 72, 99, 34, 99, -2, - 7, 46, 62, 7, -4, 0, 1, 8, -3, 4, - -2, 2, 6, -3, -7, 12, 15, -1, -10, 35, - 3, -64, -53, -80, -2, 127, 11, 10, 7, -4, - 25, -27, -84, 91, 55, -82, -37, 127, -15, -104, - 116, -86, 45, 2, -4, -2, -12, -7, -19, 11, - 11, 7, -31, 24, 100, 127, -26, 25, 68, -52, - 27, -29, 31, -18, 0, -2, -19, 12, -26, -40, - -3, 15, -4, -13, 1, 21, -49, 112, -127, 72, - -14, 79, 25, -1, -52, -64, -41, 53, 52, 19, - -24, 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127, -25, 51, -36, -16, - -62, 12, -38, -71, -101, 53, 56, -91, 21, 4, - 51, 60, 127, 72, 28, -24, -58, 38, -23, -4, - -19, 29, 80, -127, 42, -50, 69, -18, 4, -6, - -38, -1, 27, 28, -47, 23, 57, -70, 12, 13, - 9, -7, -19, 12, -11, 76, -29, 29, -67, 127, - 121, 76, -7, 112, 26, -3, 66, -73, 21, -5, - -82, 18, 13, 50, 121, 76, 52, 120, 9, 4, - -17, 10, 127, 48, -6, 38, 64, 37, -38, -42, - -54, -30, 2, -49, 2, -7, 18, -33, -7, -14, - 45, -15, 125, 0, 29, -7, 127, 49, 102, -90, - 127, -36, -37, -15, -26, 25, 19, 18, -52, 111, - -23, 109, 111, 5, 61, 15, 6, -8, 0, -8, - -15, 37, -38, -68, 127, -41, -48, 108, -92, 81, - -53, 37, -19, 14, -9, 5, -2, -3, 12, -6, - 97, 113, 127, 73, 56, -17, 102, -31, 57, 34, - 30, -37, -31, 84, 78, 37, -16, -44, 51, 35, - -70, 30, 111, -18, 127, 57, -45, -25, 54, 18, - -12, 100, -8, 54, -2, 59, 21, 64, 2, -22, - -56, -19, 46, 70, 84, -1, 53, 30, 6, 3, - -23, -27, 44, 1, -50, 17, 55, -83, 125, -127, - 97, -53, 41, 9, -33, 12, -23, 14, -14, 17, - 19, -10, 4, -5, 15, -11, 28, 46, 34, -21, - -72, -93, 19, 96, 14, -20, 53, -4, -127, 25, - 29, 8, 11, -13, 11, -10, -29, 74, 62, -109, - 60, 106, -102, 74, 101, -45, 24, 127, 118, -6, - 110, -60, 76, 78, 58, 64, 108, 39, 112, 48, - 23, -34, -28, 56, 127, -7, -90, -39, 35, 74, - 39, -19, 14, 4, 3, 19, 5, -33, 32, -91, - 28, 32, -48, -41, 115, 76, -29, 55, 24, -121, - -25, -10, 114, 37, -62, 13, 77, 85, 28, -27, - 72, -5, 26, 113, 97, 116, 127, -13, 6, 1, - -7, -5, 16, -17, -36, 87, -67, 83, -95, 78, - -9, -60, 127, -77, 25, -26, 8, -5, 9, -8, - 17, -10, -13, 17, -35, -23, 2, 1, 29, -20, - 1, 9, -2, 17, -12, -127, 17, 76, -34, -96, - -17, -14, 56, -73, -5, 30, 36, -35, -65, 127, - 29, -84, 79, 28, -44, 25, -5, -13, 22, -17, - -9, 8, 6, 2, -9, 7, -2, 13, -24, -1, - 40, -83, 13, 83, -127, 56, -21, 1, -10, 26, - 1, -6, 26, 29, -53, -17, 70, -59, -2, 24, - 30, 4, -59, 18, -2, 2, 6, 5, -5, 2, - 3, 7, 7, -15, -8, 0, 34, -35, 48, -16, - -51, 34, -9, -45, 20, 66, 56, -127, -5, 6, - 14, 3, 5, 13, -5, 27, 30, 8, -40, -127, - -40, 68, 22, 23, 7, -25, 31, -11, -2, -23, - 16, -4, -104, 17, 127, 7, -6, -5, 3, -7, - -17, 28, 16, 6, -10, 24, 6, 1, -14, 20, - -12, 9, -20, 14, 4, 18, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, -}; -const TfArray<1, int> tensor_dimension3 = { 1, { 1664 } }; -const ALIGN(8) int16_t tensor_data4[64] = { - 4161, 1435, 1477, 1690, 10134, 2279, 7528, 1827, 9639, 2416, - 3265, 14307, 5774, 2408, 8157, 11297, -490, 306, -122, -206, - -1207, -263, -670, -54, -2053, -373, -1004, -2354, -520, -1281, - -1288, -863, 3311, 2286, 8294, 18246, 19006, 7112, 7327, 2791, - 3303, 1343, 3734, 5021, 2339, 3670, 4287, 9436, -393, -213, - -284, -1844, -12825, -1535, -1024, 106, -499, 383, -1763, -786, - -998, -600, -448, -1571, -}; -const TfArray<1, int> tensor_dimension4 = { 1, { 64 } }; -const ALIGN(8) int8_t tensor_data5[2048] = { - 4, 5, -1, 2, -127, 8, 1, 7, 2, 9, - 1, 9, -49, 0, 3, -11, -7, 10, -1, 4, - 2, 5, 0, -2, 0, 4, 4, 1, -1, -1, - -3, -15, 0, -23, 4, 0, -32, 37, -4, -17, - -4, -8, 0, 6, -3, -3, 2, 8, -11, -2, - 0, -19, 4, -5, -3, 1, 0, 4, -6, 4, - -21, 9, 6, 3, -77, 0, 6, 2, -50, -26, - 3, -48, -15, -46, 7, -17, 16, -11, 18, -30, - -63, 10, 3, 2, 1, -127, -51, 10, -3, -15, - -12, -14, -8, -17, -28, -31, -13, -6, -8, 1, - -3, 12, 12, 0, -4, -3, -7, 50, -6, 0, - 15, -47, -127, 2, -3, -4, 12, 22, 12, -6, - 7, 5, 16, -4, 5, 14, -23, -26, 3, 2, - 0, 2, 1, -8, -127, 1, -3, 5, -1, 0, - 1, 1, 9, 3, 1, 1, 1, 0, -1, 0, - 1, 1, -3, 0, -100, -3, -10, -7, 0, 0, - 21, 31, -3, 4, -49, 4, -4, -7, 7, 28, - -104, -34, -3, -10, 24, 14, -19, -127, 9, -28, - -70, -1, -41, 0, -14, 6, -10, -4, 13, -46, - -13, 41, 7, -28, 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-23, 15, 3, 51, -62, -2, - -28, -12, 11, 12, 34, -92, -8, 38, -3, 27, - 11, -59, -45, 21, -12, -10, 36, -9, -22, -36, - 5, -6, -21, 14, -8, 18, -11, -13, -6, -1, - 42, 5, -7, -17, 39, -13, 13, -5, -1, -10, - 2, -2, -5, -39, -15, 8, -23, 0, 5, 6, - 97, -36, 11, -8, -52, 11, -127, -5, 2, 5, - -2, 15, -11, 5, -67, 3, -6, -1, -8, -9, - -3, -2, 3, -5, 6, -7, -8, 10, -7, 2, - -28, -5, -10, -5, 0, -2, -7, -2, 59, -3, - -52, -28, 52, -56, -26, -6, -7, 23, -20, 25, - 45, -36, 4, -16, -5, -12, -5, 103, 15, 5, - -18, -21, -4, 18, -48, -25, 13, 1, 10, -21, - -15, -6, -5, -9, -127, -12, -17, 4, 4, -15, - -5, 2, -13, 1, 2, 4, -1, 1, 19, 0, - 0, -5, -2, 1, 10, -2, 0, 11, -4, 1, - -7, 2, -116, 7, 16, -9, -31, -25, -12, -35, - 7, 2, -10, 14, -11, 37, -10, 8, 9, -69, - -65, 70, 36, -25, 16, -20, 24, -7, 8, 33, - -24, -16, 26, -6, 5, 1, -4, -3, 5, -5, - -1, -7, 19, 4, 0, -2, -6, -1, -6, -1, - -24, 9, -3, 5, -3, 18, -22, -3, -9, 0, - 2, -7, 3, -4, -16, -1, -10, 12, 35, 27, - -90, -47, 11, 1, -28, -1, -17, -9, -1, 74, - 9, 12, -9, 90, -33, 28, -31, 11, -28, -11, - 20, 19, -32, -68, 19, -14, -85, 23, -13, -10, - 17, -23, 42, -74, 42, -5, 36, 6, 10, -4, - 56, 16, -19, 6, 4, 23, 45, -25, -17, -32, - -20, 12, -24, 1, 29, -49, -12, 8, -18, 23, - 74, 19, 20, -9, 127, -5, 22, 3, 90, 10, - 16, -22, 3, 25, 35, 16, 7, 10, -7, 20, - 36, -37, 20, -16, 19, 30, 23, 27, -17, 13, - 10, 19, 1, 0, -3, -15, -127, -24, -19, -6, - 17, 3, 9, 0, 26, 4, -5, -18, -12, -120, - 10, -4, -24, 23, 5, -5, 12, -11, 1, -15, - -5, 8, -3, -7, 12, -10, -3, -5, -2, 10, - -11, -11, -4, 0, -42, 14, -24, 0, 12, -6, - -127, -3, -1, -10, -4, 3, -23, -5, -19, 4, - 0, 5, -1, -6, 6, -15, 3, -2, -2, -10, - 27, -7, 15, -5, 3, 33, -5, 0, -2, 5, - 13, -2, 1, -3, 10, 56, -9, -9, 1, 10, - 52, -6, -6, 4, -19, -15, 8, -30, -15, 27, - 9, -7, 5, -1, 7, 23, 3, -28, -3, 14, - -5, -2, 10, -26, 16, -127, 3, 6, -8, 8, - 0, -12, -19, -2, -6, -8, -26, -38, 4, 13, - 81, -21, -8, 21, -6, 58, -8, -16, 0, -42, - -12, -49, -65, 15, 42, 15, 2, -83, 13, 9, - -21, 15, 19, -17, 54, -2, 8, -11, -124, -5, - -33, 1, -3, -15, 6, 8, -8, -28, -1, 1, - 1, -13, 2, 14, 1, 2, -127, -1, 0, 14, - 1, -15, 3, -2, 2, -8, 2, 5, -6, 1, - 11, 18, 7, 1, -35, -52, -4, -4, -69, -37, - -12, -29, 13, -58, 6, -25, -30, 0, -1, 28, - 59, -26, 1, -1, -20, 78, -17, 10, 5, -1, - -24, 3, -127, 9, 28, -34, -31, 50, -5, 13, - -34, 44, 9, 37, 22, 26, 14, 33, 8, 23, - 6, -44, -42, -44, 27, 4, 39, 9, 127, -30, - 10, 27, 9, 35, -45, -1, 37, -1, 13, 18, - -7, 6, 62, 32, 12, -1, -4, -109, 15, -11, - -127, -12, 45, 13, 34, -2, 6, -16, 1, 6, - 1, 5, -12, -12, -2, 2, -86, -20, 9, -10, - -15, 2, -1, 15, -4, -16, -46, -7, -3, 2, - 8, -35, -5, 5, -9, 3, -56, -52, -9, -3, - -17, -31, 60, 1, -2, -13, 34, -8, -54, 34, - -23, 9, 12, -7, 1, 0, -5, 1, 0, -2, - 7, 6, -2, -4, 6, 2, -4, -3, -16, -2, - 3, -1, -1, 4, -37, 6, -2, -5, 20, -4, - -1, 8, -18, -4, -10, -24, 2, 5, -7, 2, - -17, -15, 5, 14, -14, -2, -2, 0, 15, -15, - 5, 4, -1, 4, -1, 15, -73, -2, -9, 0, - 0, -3, -2, 2, -15, -19, 12, -84, 2, 0, - -3, -65, -6, -5, 0, -10, -14, -80, -8, 1, - -84, -9, -17, -54, 2, -64, 8, -14, -28, 2, - -32, -1, 3, -1, -28, -13, -6, -8, -2, -5, - -1, -2, 2, 2, -3, -1, 0, 8, 2, 2, - 1, 0, 1, 0, 3, 1, -2, 0, 0, 1, - -3, 1, 1, 1, -127, 1, -1, 1, 3, 1, - 20, 13, 0, -5, 7, -19, -7, 6, 4, 3, - 0, -16, -5, 3, -3, 2, 7, 2, -2, -5, - 2, 2, -3, 4, -3, -5, -3, -4, -7, 2, - 1, 14, 14, 127, -12, -5, 52, -9, 7, -14, - -15, -56, 17, -30, -25, -3, 17, 2, 13, -42, - -9, -17, -16, 10, 4, 15, -25, 4, -13, 14, - 21, 0, -7, -35, 5, 16, 11, 13, -34, -57, - -1, -24, 4, -67, 2, -1, 54, 8, 18, 7, - -86, -2, 5, 11, -1, -4, -7, 14, 9, -10, - -1, -2, -28, -4, 14, 11, 6, 3, -22, 5, - 36, -37, 8, -1, 5, -6, -21, -3, 38, -10, - 5, -13, -4, -9, -2, 2, 2, 10, -6, 3, - 12, 0, -15, -18, 2, 0, -10, -4, -34, 8, - 0, -14, -127, -13, -1, -2, 8, -10, -20, 4, - 5, -1, -2, 2, -6, -10, -9, 7, -25, -8, - 11, 10, 11, -10, -4, -17, -7, 2, -7, 5, - -97, -11, 16, -12, 9, 33, 22, -3, 25, 2, - -98, 10, 40, -16, 20, -8, -9, -29, 28, 44, - 1, 12, 15, -11, -63, -9, 9, -2, -7, 3, - 13, -2, 8, -13, -42, -5, 45, -127, -55, 10, - -2, -3, -10, -15, -17, -4, -11, 2, -13, 23, - -13, -86, -43, -9, -25, -15, -16, 20, -36, -42, - 11, -25, -76, 9, -90, 1, 0, 16, 27, 7, - 2, -4, -36, -2, 5, 0, -36, -1, -78, 6, - 0, 3, -2, 3, -9, 3, 4, 7, -4, 8, - -8, 13, 5, 8, 8, 1, -10, -1, -30, 5, - 4, -79, 6, 0, -71, -13, -9, 11, -82, -4, - -27, -4, 6, -8, -48, -62, 11, -26, -8, -2, - -32, -12, -22, -17, 12, -7, -2, -5, 33, -1, - 7, -39, 41, -66, -3, -16, 18, -19, -15, 14, - 19, 14, 25, 112, 2, -12, 13, -1, -36, 86, - 106, -16, 64, -8, 15, 35, -11, 42, 117, -15, - -45, 4, -2, -7, 111, -9, 15, -12, 0, 7, - -64, -4, 6, -8, -68, -21, -12, 15, -87, 19, - 20, -7, 18, 14, 1, 13, 15, 11, 9, -1, - -10, 10, -24, -6, -4, -36, 127, 59, -17, -1, - -38, -10, -9, 15, -42, 3, 23, 58, 25, 12, - -6, -12, 26, -27, 95, -16, 7, 5, -19, 113, - -8, -4, 109, 3, 3, -4, 9, 6, -121, -127, - 13, 1, 11, -13, 4, -2, 25, -10, 16, -16, - 3, 0, 1, 6, -4, 22, -15, -4, -2, -5, - 0, 16, -3, 1, -8, -3, -5, 1, -15, 1, - 16, 15, -3, 3, 17, -15, -5, 0, -54, -22, - 19, -127, 2, -8, 9, -30, -4, 4, -11, 1, - -6, 7, -26, -16, 3, 0, -4, 3, -127, 2, - -11, -1, -62, -94, -5, -2, -71, -1, -60, 1, - 2, -15, -89, -17, -2, -7, 4, -8, -58, 15, - -44, 2, -29, -1, -31, -31, -2, 2, -24, -1, - -4, 0, 15, -4, -2, -6, -7, -25, 3, -2, - -1, 1, 0, -2, 0, -1, -6, -1, -6, -24, - -9, -1, 1, 0, 0, 2, 1, 3, 3, -2, - -1, -2, 0, -4, 4, 0, -127, 20, 15, 2, - -2, 13, -5, 6, 2, -2, -1, -3, 1, 5, - 8, 3, -14, 2, 20, 0, -2, -2, 5, 19, - 0, 0, 4, -2, 18, -5, -37, -54, 36, -40, - -41, -15, 7, 18, 81, 14, 0, 16, -7, -9, - -15, 18, 7, 55, -45, -1, -25, 12, 9, -18, - 9, -10, -18, -3, -5, 3, 3, -7, -4, -27, - 21, 15, 9, -2, 50, 11, -9, -2, -22, 10, - -12, 13, 3, 7, 92, -127, -39, -17, 4, -9, - -12, 3, 7, 12, -8, 3, -6, 15, -}; -const TfArray<1, int> tensor_dimension5 = { 1, { 2048 } }; -const ALIGN(8) int16_t tensor_data6[64] = { - 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, - 31923, 31923, 31923, 31923, 31923, 31923, -8862, -13159, -1739, -23447, - -3753, -11321, -7357, -13811, -5229, -4252, -11796, -16623, -12464, -5743, - -12876, -17339, 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, - 31923, 31923, 31923, 31923, 31923, 31923, 31923, 31923, -9607, -5212, - -12677, -2967, -15437, -4139, -11895, -7783, -16902, -4614, -12643, -17487, - -4246, -6202, -10536, -3372, -}; -const ALIGN(8) int8_t tensor_data7[4096] = { - -1, -88, 1, -3, 2, -2, 3, 0, 7, -9, - 1, -1, 3, 2, 1, -8, -1, -11, 6, -2, - -1, 3, -5, 2, 0, -3, 3, 3, 5, -3, - -5, 3, 3, 0, 6, 3, 1, 4, -4, 9, - 2, 8, 6, 3, -2, -5, 5, 2, 7, 6, - -1, 4, -8, -9, -7, -3, 5, -13, -3, 10, - 8, 1, 6, -4, -6, -2, -2, -5, -1, -5, - 12, -2, 5, -1, -10, -3, 6, 9, -3, -3, - -9, 2, -7, -4, -2, 7, 5, -2, -2, 16, - -13, -1, -10, 2, -5, 11, -5, -1, -5, -3, - -7, -6, 2, -6, 2, -4, -4, -2, 5, 3, - -6, -7, -7, -2, -1, -3, 4, -2, -7, 1, - -2, 10, 4, -3, -3, -4, -2, 3, -3, -9, - 0, -12, -4, -2, 5, -13, 4, -1, -4, -11, - 2, 2, -4, -2, -3, -6, 4, -1, -2, 6, - -3, 2, 1, 1, -7, 1, -1, -1, -50, 6, - 2, 4, -1, 1, 3, 3, -5, 4, -10, 4, - 5, 1, -3, -9, 0, 3, 5, 5, -4, 2, - -127, -2, -6, -3, 2, -11, -4, -2, 2, 4, - 5, -4, -3, 0, 0, -3, -1, -3, 4, -3, - 2, -10, -4, -5, 3, 0, -3, -4, -3, -1, - 5, 0, -2, 2, 3, 3, -4, 9, 4, -2, - -1, -2, -2, 3, -6, -10, 0, -6, 5, -5, - 7, 0, 2, -3, -5, -3, -1, 6, -2, -5, - -3, -9, -17, -5, -1, 9, 2, -1, 2, -6, - 6, 4, 3, -6, -2, 11, 3, 9, -6, 2, - 1, 2, -3, 0, -24, 0, 2, 5, 1, -6, - 5, 0, 2, -1, -4, 2, -95, 3, -5, -5, - 4, -7, -2, -11, 2, 4, 1, -3, 2, -2, - 3, 2, 1, 3, -5, 6, 1, 3, 4, 4, - -5, -2, 7, 5, 3, 2, -6, 4, -5, -1, - -8, -5, 4, -10, -3, 4, 2, 5, 3, -3, - 0, -17, 0, -1, 3, -2, -2, 0, 3, -3, - -1, 1, 2, 1, -1, -2, -2, -1, 1, -3, - 0, 5, 2, 1, -4, 0, -10, 0, -4, -2, - 1, -15, -3, -2, 1, -4, 1, -1, 7, 0, - 1, -3, -4, -3, 3, 1, -4, -1, -4, -12, - 6, -4, -3, 4, 4, 2, -1, 10, 6, -3, - -3, -2, -2, 6, -1, -2, -33, -6, -4, -4, - 7, -8, 0, -2, 0, -4, 3, -1, 3, 0, - -7, 1, -5, -2, 0, 1, 5, 0, 3, -5, - 0, -5, -19, 1, -3, -5, 3, -2, 2, 2, - 3, 4, -6, 3, -1, 4, 3, 4, -3, -7, - 5, 3, 3, 2, -4, 4, -3, -1, -11, -4, - 3, -10, -5, 4, 3, 2, 2, -1, -6, -3, - -4, -7, -4, 0, -2, -6, 6, -3, -4, 2, - 6, 5, -9, -5, -6, 0, -14, -3, -1, 5, - 3, -2, -4, 8, -12, 0, -4, -4, -2, 0, - -4, -1, 2, -5, -2, -7, 7, -2, -8, -5, - -6, -6, 3, 4, -4, -5, -6, 0, 7, -1, - -25, 3, 8, 4, -4, 12, 5, -4, -5, -1, - -3, 7, -2, -26, -1, -2, -1, -2, 3, 0, - 2, -10, 6, -3, 1, 1, -1, -5, -2, -16, - 7, -3, 1, -1, -3, 2, 3, 7, 4, -4, - 1, 1, -2, 5, -1, -13, 1, -4, -3, -2, - -1, -9, 0, -4, -1, -1, -3, 5, 2, -4, - -4, -8, -5, -2, -2, 1, -24, -2, 3, -16, - 1, 2, -5, 2, -7, 7, -4, -2, -1, -4, - 0, -4, 3, 1, 2, 0, -9, -8, 6, 7, - -1, -1, -8, 1, -4, -2, -1, 6, 1, -2, - -2, 12, -7, -1, -1, -1, -3, 6, -2, 1, - -4, -4, -3, -6, 4, -3, 2, -3, -6, 1, - 3, 3, -2, -5, 0, -1, 1, 0, 5, 1, - -9, 1, -5, -1, 3, -1, -3, -4, -1, 0, - 0, -1, -2, -7, -4, -2, 7, -10, 1, -1, - 0, -6, 1, 0, -3, -4, -1, -3, 4, -1, - -1, 6, 1, 1, 3, 3, -1, -1, -7, -2, - -26, 6, 2, -6, 1, 1, 2, 0, -6, 0, - -2, 1, 1, 3, -3, 4, 0, 3, -1, -3, - -6, 2, 0, 1, -6, -3, 5, -8, -4, 1, - 4, 3, -2, 0, -2, 0, -1, -3, -3, -4, - 1, -2, 3, -2, -3, -5, 2, 3, -1, -5, - -1, -3, 6, 0, -1, 2, 4, 2, -4, 7, - 4, 0, -3, -4, 0, 4, -3, -2, -3, -7, - -1, -4, 6, 1, -1, -1, -3, -2, -1, 5, - -4, -4, -3, -8, 1, -4, 3, 7, 8, 0, - -2, 17, 4, -1, 2, -2, -1, 9, 3, 0, - 2, 4, 1, 5, -5, 4, 4, 4, 4, 3, - -1, -4, 0, 4, 2, 5, -4, 4, -3, -3, - -6, -2, 2, -9, -3, 7, 3, 3, 2, -5, - 2, 0, 0, 2, 2, 0, -4, 1, -5, 1, - 2, 2, -5, -1, 3, 1, 3, 1, -5, 2, - -6, 0, -7, -1, 3, -6, -2, -2, 2, 2, - 2, -1, -3, -7, 0, -1, -2, -1, -4, -1, - -1, -3, 2, -1, 1, 0, 1, -3, 0, -2, - -1, -3, 3, 4, -3, 1, 2, 5, -8, -3, - -1, 1, 0, -2, -2, 0, 1, -2, -1, 0, - 3, 0, 2, 0, -4, -6, 2, 3, -1, -3, - -5, -9, 6, 0, -1, 1, 3, 0, -2, 8, - 4, 1, -4, -3, -1, 5, -1, -1, -28, -6, - -2, -3, 5, -5, 0, 0, 0, -2, 2, 0, - 4, -2, -6, 1, -1, -2, 2, 1, 3, 2, - 0, -3, 0, 1, -11, -1, 0, -3, 2, 0, - 1, 3, 3, 1, -4, 3, -2, 2, 1, 5, - -4, -2, 3, 2, 3, 1, -3, 1, -4, -1, - -6, -1, 3, -6, -2, 0, 3, 2, 2, -2, - -4, 0, -3, -2, -4, -1, 5, -6, 3, -1, - -7, -3, 4, 6, -1, -1, -2, 0, -11, 0, - -1, 3, 6, -3, -4, 14, -5, -1, -7, 3, - -2, 7, -1, -1, 1, -4, -2, -6, 3, -4, - -3, -2, -3, -3, 3, 3, -4, -4, -3, -1, - 9, -1, -17, 0, 6, 2, -6, 6, 5, -4, - -4, 0, -2, 6, -2, -21, -1, -3, -5, -3, - 3, 1, 1, -1, 8, -4, 4, 1, 0, -1, - -3, -8, 3, -3, 1, 4, 2, 2, 5, 5, - 4, -3, -1, 2, -1, 1, 2, 2, 1, 4, - 2, 4, -2, 4, 3, 4, 6, 2, -1, -8, - 7, 2, 5, 3, -2, 3, -5, 1, -7, -2, - 4, -6, -2, 4, 5, 4, 1, -5, -2, -1, - -1, -4, 1, -7, 11, 0, -1, 0, -5, -4, - 5, 7, 1, -3, -8, 4, -3, 0, -2, 8, - 3, -2, -4, 12, -6, -2, -4, -1, -1, 4, - -2, 2, -3, -1, -6, -5, 5, -5, 1, -3, - -6, -2, 4, 0, -4, -4, -4, -3, -1, -1, - 2, 0, 4, 0, -3, 9, 2, 1, -4, 0, - -2, 5, -1, -1, 1, -3, 0, 0, 7, -3, - 0, -4, -3, -4, 2, 0, -6, -1, -3, -7, - 5, 1, -1, 3, 0, 1, -2, 17, -6, -4, - -3, -4, -24, 7, 2, -3, 4, 1, 2, 2, - -7, 0, 2, 4, 1, 4, -1, -9, 3, 3, - 3, 1, -5, 2, -5, -1, -6, -3, 3, -8, - -3, 5, 5, 1, 2, -3, -1, 0, 0, -1, - -2, -5, 6, -4, 1, -4, -3, -1, 4, 4, - -3, -3, -1, -3, 4, -2, -2, 1, 1, 1, - -4, 4, 5, -2, 0, -1, -1, 6, -3, -3, - 0, -3, 0, -1, -5, -2, -2, 0, -3, -5, - -1, 4, -4, -2, -4, -3, 0, -1, 1, 5, - 3, 1, 0, -4, 6, 2, -1, 0, 0, 7, - -1, -16, 4, -3, 1, -3, -2, -3, 1, -4, - -5, 3, -2, 5, 0, -3, -3, -5, -8, 0, - -1, 1, -9, -5, 3, -14, -2, 3, -4, 2, - -6, 5, 2, 2, 0, 1, 1, 2, -5, 3, - -20, 4, 3, 5, -2, -17, 0, 2, 4, 5, - -1, 1, -77, -2, -7, -3, 2, -5, -2, -1, - 5, 3, 2, -3, -2, -7, -1, -1, -3, -4, - -4, -1, -1, -3, 2, 0, 0, 1, -2, -1, - 0, -1, -4, -1, 0, 4, -3, 2, -2, 8, - -6, -3, -1, 0, 1, -7, -1, 0, 0, -3, - -3, -1, 7, -1, 3, -2, -4, -2, 2, 3, - -1, -1, -7, -9, 6, -4, -1, 3, 2, 1, - -4, 7, 5, -1, 0, -1, 0, 4, -1, 1, - -20, -5, -3, 0, 2, -6, 2, 0, -3, -3, - 1, 0, 3, -2, -5, 2, -7, -1, 1, -1, - -10, 1, 1, -16, 0, -1, -13, 2, -2, 0, - 2, 0, -1, 2, 3, 2, -4, 4, -9, 1, - 1, 3, 0, 1, 1, 1, 2, 5, -3, 2, - -3, -1, -3, -1, 3, -7, 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0, -2, 0, -6, 4, -2, 0, 2, - 1, 1, -3, 0, 0, -5, -5, -3, -8, 4, - 1, 3, -4, -2, 1, -1, -5, -2, 2, 0, - 2, 4, -1, 4, 2, -2, 0, -5, -1, 1, - -6, 3, -5, -2, 1, -2, 0, -5, 1, 3, - -2, 1, -3, 1, -1, -3, -1, -2, 4, -1, - 1, -1, -4, -3, 2, 1, -2, -1, -3, -2, - 6, -2, 0, 3, 1, 1, -4, 8, 3, -1, - -2, 0, -3, 4, 2, 0, 3, 3, 1, 4, - -3, 4, 0, 3, 3, -2, -2, -4, 3, 5, - 2, 8, -2, 1, -16, -4, -5, -1, 2, -10, - -2, 1, 7, 1, 7, -2, -4, 0, 0, 0, - -2, -1, 4, -3, 3, -4, -4, -4, 3, 4, - -3, 0, -2, -3, 5, -2, 2, 0, 2, 2, - -2, 11, 2, 1, 1, -1, -2, 5, 0, 0, - -19, 2, 0, -1, 3, -2, 1, 1, -1, 1, - -4, 0, 0, -1, -5, -5, 1, -3, 1, 0, - 1, 3, 2, -1, 2, -2, -5, -1, -2, -6, - -1, 2, -1, -1, -5, -3, 3, -2, 1, -3, - -3, -2, 2, 5, -4, -4, -3, -1, 6, -3, - 1, 2, 2, 2, -3, 4, 4, -1, -3, -1, - -2, 7, 2, -2, 2, 1, 4, 3, -5, 6, - 1, 2, 2, 1, -3, -1, 3, 3, 3, 1, - -1, 4, -4, -1, -9, -3, 3, -7, -2, 1, - -1, 3, -1, -1, 3, -4, 1, 1, 3, 3, - -5, 2, 3, 1, 4, 7, -2, -4, 5, -1, - 5, 3, -2, 2, -2, -2, -6, -3, 3, -10, - -4, 3, 4, 0, 2, -4, 2, 9, -11, -2, - 2, 1, 0, -4, -11, -1, 2, 5, -3, 3, - -1, 1, 1, -4, -2, 0, -58, 3, -9, -2, - 1, -2, -4, -21, -4, 5, -1, 2, 2, -14, - 6, 0, 4, 2, -6, 1, 6, 3, -2, 3, - -3, 2, 6, 1, 1, 0, -7, 3, -6, -4, - -7, -8, 7, -11, -3, 10, 9, 2, -1, 0, - 3, -5, 4, 0, 4, 4, -5, 1, 5, 2, - 5, 2, -3, -6, 3, 2, 2, 1, -3, 3, - -1, 0, -6, -6, 1, -9, -5, 9, 3, 3, - 1, -2, -3, 1, -13, -1, 1, -3, 5, -7, - -10, -2, -2, -1, 4, 3, -4, -5, -5, 0, - 1, -5, -18, -7, 5, 2, -3, 2, 5, -4, - -5, 0, -1, -5, 0, -4, -2, 3, -1, -2, - -15, 2, -1, 2, 1, -1, -1, 1, 4, -3, - -5, -1, -3, -1, 2, 4, -3, 2, 0, -1, - -13, -5, -1, 2, 1, -3, 2, 1, 0, 0, - 3, 1, -3, 5, -6, 2, 4, 3, -3, -10, - 1, 3, 4, 4, -4, 2, -17, -2, -4, -3, - 1, -7, -2, -2, 4, 3, 2, -5, -5, -6, - 1, -3, 0, -3, 6, -31, 4, -14, -1, -8, - 2, 3, -2, -3, -2, -19, 6, -4, -3, 8, - -1, 1, 3, 12, -4, 2, -6, -1, -42, 8, - -3, -2, -1, 1, 1, -2, 8, -5, 2, -5, - -1, -3, -2, 3, 0, -3, -1, -7, 5, -3, - 0, 6, -1, 3, -3, 9, 2, -4, -9, -4, - -13, 6, 2, 7, -5, 2, -1, 2, -4, 0, - -54, 2, 0, 5, -3, -8, 2, 2, 3, 5, - 0, 1, -96, 2, -3, -3, 1, -3, -2, -12, - 1, 4, 5, -1, -3, 2, 1, -3, -5, -2, - 5, -6, 4, 0, -5, -5, 1, 3, -1, -3, - -3, -4, 5, -3, -1, 3, 2, 4, -5, 10, - 5, -1, -4, -3, -5, 5, 1, -22, 9, -1, - 4, -2, -5, 3, 3, 3, -4, 3, -3, 2, - -2, -1, 3, 0, -3, 5, -6, -4, -1, -3, - 2, -27, -2, 13, 6, -1, -1, -15, -4, 1, - 1, -1, -2, -3, 3, -4, 1, -3, -7, -6, - 3, 5, -2, -1, -6, -5, 5, -3, 1, 3, - 0, 2, -3, 11, 4, 0, -3, -1, -1, 5, - 0, 0, -21, -1, 1, -1, 1, -1, -2, -1, - -2, -4, -6, 0, 2, 2, -1, -6, -2, -3, - -1, 2, 4, 2, 2, 1, 4, -6, -3, -1, - 0, -11, -1, -1, 1, -4, -4, -3, 5, -6, - 0, -3, -5, -7, 4, 4, -3, -4, -4, -2, - 4, -2, -1, 0, 3, 3, -5, 12, 3, -1, - 0, -1, -3, 7, 4, 2, -1, 2, 2, 2, - -6, 3, -11, 5, 4, 6, -3, -7, 2, 3, - 4, 4, -5, 2, -106, -1, -6, -3, 5, -9, - -4, -4, 2, 2, 4, -5, -}; -const TfArray<1, int> tensor_dimension7 = { 1, { 4096 } }; -const ALIGN(8) int16_t tensor_data8[20] = { - 22083, 0, 0, 0, -2980, 1983, 1983, 1983, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -}; -const TfArray<1, int> tensor_dimension8 = { 1, { 20 } }; -const ALIGN(8) int8_t tensor_data9[512] = { - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, -69, -31, 73, -73, - -72, -67, 64, 78, -47, -88, 52, -82, -42, -106, - 21, -99, 57, -46, -68, -43, 95, -64, 76, -92, - -87, 55, -127, -28, 93, 76, 88, 36, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, -}; -const TfArray<1, int> tensor_dimension9 = { 1, { 512 } }; -const TfArray<4, int> tensor_dimension11 = { 4, { 1,1,4,64 } }; -const TfArray<1, float> quant11_scale = { 1, { 0.24111956357955933, } }; -const TfArray<1, int> quant11_zero = { 1, { -128 } }; -const TfLiteAffineQuantization quant11 = { (TfLiteFloatArray*)&quant11_scale, (TfLiteIntArray*)&quant11_zero, 0 }; -const TfArray<4, int> tensor_dimension12 = { 4, { 1,1,4,32 } }; -const TfArray<1, float> quant12_scale = { 1, { 0.33315452933311462, } }; -const TfArray<1, int> quant12_zero = { 1, { -128 } }; -const TfLiteAffineQuantization quant12 = { (TfLiteFloatArray*)&quant12_scale, (TfLiteIntArray*)&quant12_zero, 0 }; -const TfArray<4, int> tensor_dimension13 = { 4, { 1,1,1,32 } }; -const TfArray<1, float> quant13_scale = { 1, { 0.79527455568313599, } }; -const TfArray<1, int> quant13_zero = { 1, { -128 } }; -const TfLiteAffineQuantization quant13 = { (TfLiteFloatArray*)&quant13_scale, (TfLiteIntArray*)&quant13_zero, 0 }; -const TfArray<4, int> tensor_dimension15 = { 4, { 1,1,1,4 } }; -const TfArray<1, float> quant15_scale = { 1, { 0.17188803851604462, } }; -const TfArray<1, int> quant15_zero = { 1, { 31 } }; -const TfLiteAffineQuantization quant15 = { (TfLiteFloatArray*)&quant15_scale, (TfLiteIntArray*)&quant15_zero, 0 }; -const TfArray<4, int> tensor_dimension16 = { 4, { 1,1,1,1 } }; -const TfArray<2, int> tensor_dimension17 = { 2, { 1,1 } }; -const TfArray<1, float> quant18_scale = { 1, { 0.00390625, } }; -const TfArray<1, int> quant18_zero = { 1, { -128 } }; -const TfLiteAffineQuantization quant18 = { (TfLiteFloatArray*)&quant18_scale, (TfLiteIntArray*)&quant18_zero, 0 }; -uint8_t ALIGN(4) opdata0[152] = { 109, 112, 0, 40, 96, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 232, 255, 255, 255, 248, 255, 255, 255, 72, 0, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0, 97, 0, 8, 64, 0, 0, 0, 24, 0, 0, 0, 0, 111, 0, 8, 64, 0, 0, 0, 3, 0, 250, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 80, 6, 127, 71, 60, 12, 9, 7, 1, 7, 87, 1, 133, 78, 23, 64, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs0 = { 5, { 0,3,2,-1,10 } }; -const TfArray<1, int> outputs0 = { 1, { 11 } }; -uint8_t ALIGN(4) opdata1[136] = { 109, 112, 0, 8, 0, 1, 0, 0, 64, 0, 0, 0, 0, 97, 0, 24, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 192, 0, 0, 0, 0, 111, 0, 8, 32, 0, 0, 0, 3, 0, 248, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 96, 6, 111, 71, 60, 12, 9, 7, 1, 7, 103, 0, 117, 78, 23, 0, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs1 = { 5, { 11,5,4,-1,-1 } }; -const TfArray<1, int> outputs1 = { 1, { 12 } }; -uint8_t ALIGN(4) opdata2[136] = { 109, 112, 0, 8, 128, 0, 0, 0, 128, 0, 0, 0, 0, 97, 0, 24, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 111, 0, 8, 32, 0, 0, 0, 1, 0, 252, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 96, 6, 111, 71, 60, 12, 9, 7, 1, 7, 103, 0, 117, 78, 23, 0, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs2 = { 5, { 12,7,6,-1,-1 } }; -const TfArray<1, int> outputs2 = { 1, { 13 } }; -uint8_t ALIGN(4) opdata3[152] = { 109, 112, 0, 40, 32, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 224, 255, 255, 255, 0, 0, 0, 0, 0, 0, 0, 0, 255, 255, 255, 255, 0, 0, 0, 0, 0, 97, 0, 8, 4, 0, 0, 0, 32, 0, 0, 0, 0, 111, 0, 8, 4, 0, 0, 0, 3, 0, 254, 255, 0, 112, 0, 42, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 48, 48, 0, 1, 44, 20, 115, 0, 107, 0, 116, 0, 7, 80, 6, 127, 71, 60, 12, 9, 7, 1, 7, 87, 1, 133, 78, 23, 64, 0, 20, 4, 20, 20, 40, 4, 4, 14, 36, 1, }; /* custom_initial_data */ -const TfArray<5, int> inputs3 = { 5, { 13,9,8,-1,14 } }; -const TfArray<1, int> outputs3 = { 1, { 15 } }; -uint8_t ALIGN(4) opdata4[32] = { 115, 0, 111, 0, 108, 0, 110, 0, 118, 0, 5, 7, 6, 11, 14, 7, 5, 1, 5, 1, 1, 4, 0, 0, 4, 4, 4, 4, 104, 10, 36, 1, }; /* custom_initial_data */ -const TfArray<1, int> inputs4 = { 1, { 15 } }; -const TfArray<1, int> outputs4 = { 1, { 16 } }; -uint8_t ALIGN(4) opdata5[0] = { }; /* custom_initial_data */ -const TfArray<1, int> inputs5 = { 1, { 16 } }; -const TfArray<1, int> outputs5 = { 1, { 17 } }; -const TfArray<2, int> inputs6 = { 2, { 17,1 } }; -const TfArray<1, int> outputs6 = { 1, { 18 } }; -} g0; - -TfLiteTensor tflTensors[] = -{{ {(int32_t*)(tensor_arena + 256)},(TfLiteIntArray*)&g0.tensor_dimension0, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant0)) }, {g0.quant0.scale->data[0], g0.quant0.zero_point->data[0] },}, -{ {(int32_t*)g0.tensor_data1},(TfLiteIntArray*)&g0.tensor_dimension1, kTfLiteUInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data2},(TfLiteIntArray*)&g0.tensor_dimension2, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data3},(TfLiteIntArray*)&g0.tensor_dimension3, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data4},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data5},(TfLiteIntArray*)&g0.tensor_dimension5, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data6},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data7},(TfLiteIntArray*)&g0.tensor_dimension7, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data8},(TfLiteIntArray*)&g0.tensor_dimension8, kTfLiteInt16, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)g0.tensor_data9},(TfLiteIntArray*)&g0.tensor_dimension9, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)(tensor_arena + 352)},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension11, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant11)) }, {g0.quant11.scale->data[0], g0.quant11.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 256)},(TfLiteIntArray*)&g0.tensor_dimension12, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant12)) }, {g0.quant12.scale->data[0], g0.quant12.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 64)},(TfLiteIntArray*)&g0.tensor_dimension13, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant13)) }, {g0.quant13.scale->data[0], g0.quant13.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension4, kTfLiteInt8, {kTfLiteNoQuantization, nullptr }, {0,0},}, -{ {(int32_t*)(tensor_arena + 96)},(TfLiteIntArray*)&g0.tensor_dimension15, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant15)) }, {g0.quant15.scale->data[0], g0.quant15.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 8)},(TfLiteIntArray*)&g0.tensor_dimension16, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant15)) }, {g0.quant15.scale->data[0], g0.quant15.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 8)},(TfLiteIntArray*)&g0.tensor_dimension17, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant15)) }, {g0.quant15.scale->data[0], g0.quant15.zero_point->data[0] },}, -{ {(int32_t*)(tensor_arena + 0)},(TfLiteIntArray*)&g0.tensor_dimension17, kTfLiteInt8, {kTfLiteAffineQuantization, const_cast(static_cast(&g0.quant18)) }, {g0.quant18.scale->data[0], g0.quant18.zero_point->data[0] },}, -}; - -TfLiteNode tflNodes[] = -{{ (TfLiteIntArray*)&g0.inputs0, (TfLiteIntArray*)&g0.outputs0, (TfLiteIntArray*)&g0.inputs0, const_cast(static_cast(&g0.opdata0)), 152, }, -{ (TfLiteIntArray*)&g0.inputs1, (TfLiteIntArray*)&g0.outputs1, (TfLiteIntArray*)&g0.inputs1, const_cast(static_cast(&g0.opdata1)), 136, }, -{ (TfLiteIntArray*)&g0.inputs2, (TfLiteIntArray*)&g0.outputs2, (TfLiteIntArray*)&g0.inputs2, const_cast(static_cast(&g0.opdata2)), 136, }, -{ (TfLiteIntArray*)&g0.inputs3, (TfLiteIntArray*)&g0.outputs3, (TfLiteIntArray*)&g0.inputs3, const_cast(static_cast(&g0.opdata3)), 152, }, -{ (TfLiteIntArray*)&g0.inputs4, (TfLiteIntArray*)&g0.outputs4, (TfLiteIntArray*)&g0.inputs4, const_cast(static_cast(&g0.opdata4)), 32, }, -{ (TfLiteIntArray*)&g0.inputs5, (TfLiteIntArray*)&g0.outputs5, (TfLiteIntArray*)&g0.inputs5, const_cast(static_cast(&g0.opdata5)), 0, }, -{ (TfLiteIntArray*)&g0.inputs6, (TfLiteIntArray*)&g0.outputs6, (TfLiteIntArray*)&g0.inputs6, const_cast(static_cast(&g0.opdata5)), 0, }, -}; - -used_operators_e used_ops[] = -{OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_conv2d_v2, OP_XC_slice, OP_XC_no_op, OP_XC_lookup, }; - - -// Indices into tflTensors and tflNodes for subgraphs -size_t tflTensors_subgraph_index[] = {0, 19, }; -size_t tflNodes_subgraph_index[] = {0, 7, }; - -// Variable tensors -size_t varTensors_index[] = {}; - -// Input/output tensors -static const int inTensorIndices[] = { - 0, -}; - -static const int outTensorIndices[] = { - 18, -}; - -static const int externalInOutTensorIndices[] = { - -}; -static const int externalInOutTensorOffsets[] = { - -}; - -// Indices into inTensors and outTensors for subgraphs -size_t inTensors_subgraph_index[] = {0, 1, }; -size_t outTensors_subgraph_index[] = {0, 1, }; - -// Indices for output tensors that are modified by certain TFLite ops and -// have to be reset in model_init() if the tensor arena gets trashed -static const int tfliteModifiedOutputTensorIndices[] = {}; -TfLiteIntArray* tfliteModifiedOutputTensorOriginalDims[] = {}; - -// Scratch buffer variables -int scratch_buffer_idx; -const int scratch_buffer_offsets[0] = { }; -tflite_micro::MicroContext mc; -tflite_micro::MicroGraph micro_graph; -size_t currentSubgraphIndex = 0; - -// Xcore context and thread variables -xc_context_config_t xc_config; -// When using USE_DDR_FIX for enabling LPDDR support, only one thread can be used -#ifdef USE_DDR_FIX -static_assert((1 == 1), - "Only one thread can be used when using USE_DDR_FIX! Please recompile with one thread!"); -#endif - -// Persistent buffer ptr -// Initialized to the tail end of the tensor arena -uint8_t *persistentBufferPtr; -// Functions to be used as function pointers for TfLiteContext and MicroContext -static void* AllocatePersistentBuffer(struct TfLiteContext* ctx, - size_t bytes) { - // Align to double word - bytes = ((bytes + 7) / 8) * 8; - persistentBufferPtr -= bytes; - return persistentBufferPtr; -} - -static TfLiteEvalTensor *GetEvalTensor(const struct TfLiteContext *context, - int tensor_idx) { - return (TfLiteEvalTensor*)&tflTensors[tflTensors_subgraph_index[currentSubgraphIndex] + tensor_idx]; -} - -static TfLiteStatus RequestScratchBufferInArena(struct TfLiteContext *context, size_t bytes, - int *buffer_idx) { - *buffer_idx = scratch_buffer_idx++; - return kTfLiteOk; -}; - -static void *GetScratchBuffer(struct TfLiteContext *context, - int buffer_idx) { - return tensor_arena + scratch_buffer_offsets[buffer_idx]; -} - -static bool IsConstantTensor(struct TfLiteContext *context, - TfLiteTensor* tensor) { - bool constant = true; - if(tensor->data.data > &tensor_arena[0] && tensor->data.data < &tensor_arena[kTensorArenaSize - 1]){ - constant = false; - } - return constant; -} - -static bool IsVariableTensor(struct TfLiteContext *context, - TfLiteTensor* tensor) { - bool found = false; - for (int i = 0; i < 0; i++) { - if(tensor == &tflTensors[varTensors_index[i]]){ - found = true; - } - } - return found; -} - -static size_t TensorBytes(TfLiteTensor *tensor) { - int element_count = 1; - for (int i = 0; i < tensor->dims->size; ++i) { - element_count *= tensor->dims->data[i]; - } - - size_t bytes_per_element; - tflite_micro::TfLiteTypeSizeOf(tensor->type, &bytes_per_element); - return element_count * bytes_per_element; -} - -static TfLiteTensor* mc_AllocateTempInputTensor(const TfLiteNode* node, int index) { - if (node->inputs->data[index] < 0) { - return nullptr; - } - return &ctx.tensors[tflTensors_subgraph_index[currentSubgraphIndex] + node->inputs->data[index]]; -} - -static TfLiteTensor* mc_AllocateTempOutputTensor(const TfLiteNode* node, int index) { - if (node->outputs->data[index] < 0) { - return nullptr; - } - return &ctx.tensors[tflTensors_subgraph_index[currentSubgraphIndex] + node->outputs->data[index]]; -} - -static void mc_DeallocateTempTfLiteTensor(TfLiteTensor* tensor) { -} - -static void* mc_external_context() { - return &xc_config; -} - -static tflite_micro::MicroGraph& mc_graph() { - return micro_graph; -} - -static int mg_NumSubgraphs(){ - return sizeof(tflTensors_subgraph_index)/sizeof(size_t) - 1; -} - -static size_t mg_NumSubgraphInputs(int subgraph_idx){ - return inTensors_subgraph_index[subgraph_idx+1] - inTensors_subgraph_index[subgraph_idx]; -} - -static size_t mg_NumSubgraphOutputs(int subgraph_idx){ - return outTensors_subgraph_index[subgraph_idx+1] - outTensors_subgraph_index[subgraph_idx]; -} - -static TfLiteEvalTensor* mg_GetSubgraphInput(int subgraph_idx, int i){ - return (TfLiteEvalTensor*)&tflTensors[tflTensors_subgraph_index[subgraph_idx] + inTensorIndices[inTensors_subgraph_index[subgraph_idx] + i]]; -} - -static TfLiteEvalTensor* mg_GetSubgraphOutput(int subgraph_idx, int i){ - return (TfLiteEvalTensor*)&tflTensors[tflTensors_subgraph_index[subgraph_idx] + outTensorIndices[outTensors_subgraph_index[subgraph_idx] + i]]; -} - -static TfLiteStatus mg_InvokeSubgraph(int g){ - int prevSubgraphIndex = currentSubgraphIndex; - currentSubgraphIndex = g; -#ifdef TFLMC_PRINT_TENSORS -printf("[\n"); -#endif - - for (size_t i = tflNodes_subgraph_index[g]; i < tflNodes_subgraph_index[g+1]; ++i) { - -#ifdef TFLMC_PRINT_INPUT_TENSORS - // print every input tensor - printf("node in %d\n", i); - for (int j = 0; j < tflNodes[i].inputs->size; j++) { - // -1 such as in case of no bias tensor for conv - if (tflNodes[i].inputs->data[j] != -1) { - printf("tensor %d, input %d, %d bytes, checksum %d\n", tflNodes[i].inputs->data[j], j, TensorBytes(&tflTensors[tflNodes[i].inputs->data[j]]), checksum(tflTensors[tflNodes[i].inputs->data[j]].data.raw, TensorBytes(&tflTensors[tflNodes[i].inputs->data[j]]))); - for (int k = 0; k < TensorBytes(&tflTensors[tflTensors_subgraph_index[g] + tflNodes[i].inputs->data[j]]); k++) { - printf("%d,", (int8_t)tflTensors[tflTensors_subgraph_index[g] + tflNodes[i].inputs->data[j]].data.raw[k]); - } - printf("\n"); - } - } - printf("\n"); -#endif - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t0 = get_reference_time(); -#endif -#endif - - TfLiteStatus status = registrations[used_ops[i]].invoke(&ctx, &tflNodes[i]); - - if (status != kTfLiteOk) { -#ifdef TFLMC_XCORE_PROFILE - printf("\nERROR: Node %d (%s) invocation failed with status %d\n", i, op_strs[used_ops[i]], status); - printf("Model invocation aborted\n\n"); -#endif - currentSubgraphIndex = prevSubgraphIndex; - return status; - } - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t1 = get_reference_time(); -#endif - op_times[used_ops[i]] += time_t1 - time_t0; - op_counts[used_ops[i]] += 1; - printf("node %-5d %-32s %-12d\n", i, op_strs[used_ops[i]], time_t1 - time_t0); -#endif - -#ifdef TFLMC_PRINT_TENSORS - // print every output tensor - printf("{\"node\" : \"%d\", \"op\" : \"%s\", \"data\" : [\n", i, op_strs[used_ops[i]]); - for (int j = 0; j < tflNodes[i].outputs->size; j++) { - printf("{\"tensor\" : %d, \"output\" : %d, \"offset\" : %d, \"bytes\" : %d, \"checksum\" : %d,\n", tflNodes[i].outputs->data[j], j, tflTensors[tflNodes[i].outputs->data[j]].data.uint8 - tensor_arena, TensorBytes(&tflTensors[tflNodes[i].outputs->data[j]]), checksum(tflTensors[tflNodes[i].outputs->data[j]].data.raw, TensorBytes(&tflTensors[tflNodes[i].outputs->data[j]]))); - printf("\"val\" : ["); - for (int k = 0; k < TensorBytes(&tflTensors[tflTensors_subgraph_index[g] + tflNodes[i].outputs->data[j]]); k++) { - printf("%d", (int8_t)tflTensors[tflTensors_subgraph_index[g] + tflNodes[i].outputs->data[j]].data.raw[k]); - if (k < TensorBytes(&tflTensors[tflTensors_subgraph_index[g] + tflNodes[i].outputs->data[j]]) - 1) { - printf(","); - } - } - if (j < tflNodes[i].outputs->size - 1) { - printf("]},\n"); - } else { - printf("]}]\n"); - } - } - - if (i < ((tflNodes_subgraph_index[g+1] - tflNodes_subgraph_index[g]) - 1)) { - printf("},\n"); - } else { - printf("}\n"); - } -#endif - } -#ifdef TFLMC_PRINT_TENSORS -printf("]\n"); -#endif - - currentSubgraphIndex = prevSubgraphIndex; - return kTfLiteOk; -} - - -void InitExternalTensors(void *paging_ptr){ - int len = sizeof(externalInOutTensorIndices) / sizeof(int); - for (int n = 0; n < len; ++n) { - auto t = &tflTensors[externalInOutTensorIndices[n]]; - t->data.data = (int32_t *)(((int8_t *)paging_ptr) + externalInOutTensorOffsets[n]); - } -} - -void ResetModifiedTFLiteOutputTensorDims(){ - int len = sizeof(tfliteModifiedOutputTensorIndices) / sizeof(int); - for (int n = 0; n < len; ++n) { - auto t = &tflTensors[tfliteModifiedOutputTensorIndices[n]]; - t->dims = tfliteModifiedOutputTensorOriginalDims[n]; - } -} - -} // namespace - -TfLiteTensor* model_input(int index) { - return &ctx.tensors[inTensorIndices[index]]; -} - -TfLiteTensor* model_output(int index) { - return &ctx.tensors[outTensorIndices[index]]; -} - -size_t model_input_size(int index) { - return TensorBytes(model_input(index)); -} - -size_t model_output_size(int index) { - return TensorBytes(model_output(index)); -} - - -#pragma stackfunction 1000 -TfLiteStatus model_init_with_paging(void *weights_data_ptr, void *paging_ptr) { - -// Set target arch based on the compiled model - SetNNTargetArch(nn_target_arch_t::TARGET_ARCH_VX4A); - - // Clear and initialize - scratch_buffer_idx = 0; - persistentBufferPtr = tensor_arena + kTensorArenaSize; - - // Set weights data ptr in xcore context config - xc_config.weights_data_ptr = weights_data_ptr; - // Set paging ptr in xcore context config - xc_config.paging_ptr = paging_ptr; - // Set thread count specified in the compiler - xc_config.model_thread_count = 1; - - // Initialize externally allocated input/output tensors with paging_ptr - InitExternalTensors(paging_ptr); - - // Reset output tensor dims that are modified by certain TFLite ops and - // have to be reset if the tensor arena gets trashed - ResetModifiedTFLiteOutputTensorDims(); - - // Setup microcontext functions - mc.AllocateTempInputTensor = &mc_AllocateTempInputTensor; - mc.AllocateTempOutputTensor = &mc_AllocateTempOutputTensor; - mc.DeallocateTempTfLiteTensor = &mc_DeallocateTempTfLiteTensor; - mc.external_context = &mc_external_context; - mc.graph = &mc_graph; - - micro_graph.NumSubgraphs = &mg_NumSubgraphs; - micro_graph.NumSubgraphInputs = &mg_NumSubgraphInputs; - micro_graph.NumSubgraphOutputs = &mg_NumSubgraphOutputs; - micro_graph.GetSubgraphInput = &mg_GetSubgraphInput; - micro_graph.GetSubgraphOutput = &mg_GetSubgraphOutput; - micro_graph.InvokeSubgraph = &mg_InvokeSubgraph; - - // Setup tflitecontext functions - ctx.AllocatePersistentBuffer = &AllocatePersistentBuffer; - ctx.GetEvalTensor = &GetEvalTensor; - ctx.RequestScratchBufferInArena = &RequestScratchBufferInArena; - ctx.GetScratchBuffer = &GetScratchBuffer; - ctx.IsConstantTensor = &IsConstantTensor; - ctx.IsVariableTensor = &IsVariableTensor; - - // Set microcontext as the context ptr - ctx.impl_ = (void*)&mc; - ctx.tensors = tflTensors; - ctx.tensors_size = 19; - registrations[OP_XC_conv2d_v2] = *(tflite_micro::ops::micro::xcore::Register_XC_conv2d_v2()); - registrations[OP_XC_slice] = *(tflite_micro::ops::micro::xcore::Register_XC_slice()); - registrations[OP_XC_no_op] = *(tflite_micro::ops::micro::xcore::Register_XC_no_op()); - registrations[OP_XC_lookup] = *(tflite_micro::ops::micro::xcore::Register_XC_lookup()); - - - // Allocate persistent buffers for variable tensors - for (int i = 0; i < 0; i++) { - tflTensors[varTensors_index[i]].data.data = AllocatePersistentBuffer(&ctx, TensorBytes(&tflTensors[varTensors_index[i]])); - } - -#ifdef TFLMC_XCORE_PROFILE - printf("Profiling init()...\n"); - memset(op_times, 0, sizeof(op_times)); - op_times_summed = 0; -#endif - - for(size_t g = 0; g < 1; ++g) { - currentSubgraphIndex = g; - for(size_t i = tflNodes_subgraph_index[g]; i < tflNodes_subgraph_index[g+1]; ++i) { - if (registrations[used_ops[i]].init) { - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t0 = get_reference_time(); -#endif -#endif - - tflNodes[i].user_data = registrations[used_ops[i]].init(&ctx, (const char*)tflNodes[i].builtin_data, tflNodes[i].custom_initial_data_size); - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t1 = get_reference_time(); -#endif - op_times[used_ops[i]] += time_t1 - time_t0; - printf("node %-5d %-32s %-12d\n", i, op_strs[used_ops[i]], time_t1 - time_t0); -#endif - - } - } - } - currentSubgraphIndex = 0; - -#ifdef TFLMC_XCORE_PROFILE - printf("\n\nCumulative times for init()...\n"); - for (int i = 0; i < OP_LAST; i++) { - op_times_summed += op_times[i]; - printf("%-32s %-12d %.2fms\n", op_strs[i], op_times[i], op_times[i]/100000.0); - } - printf("\nTotal time for init() - %-10lld %.2fms\n", op_times_summed, op_times_summed/100000.0); - printf("\n\n\nProfiling prepare()...\n"); - memset(op_times, 0, sizeof(op_times)); - op_times_summed = 0; -#endif - - for(size_t g = 0; g < 1; ++g) { - currentSubgraphIndex = g; - for(size_t i = tflNodes_subgraph_index[g]; i < tflNodes_subgraph_index[g+1]; ++i) { - if (registrations[used_ops[i]].prepare) { - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t0 = get_reference_time(); -#endif -#endif - - TfLiteStatus status = registrations[used_ops[i]].prepare(&ctx, &tflNodes[i]); - -#ifdef TFLMC_XCORE_PROFILE -#if defined(__xcore__) || defined(__riscv_xxcore) - time_t1 = get_reference_time(); -#endif - op_times[used_ops[i]] += time_t1 - time_t0; - printf("node %-5d %-32s %-12d\n", i, op_strs[used_ops[i]], time_t1 - time_t0); -#endif - - if (status != kTfLiteOk) { - return status; - } - } - } - } - currentSubgraphIndex = 0; - -#ifdef TFLMC_XCORE_PROFILE - printf("\nCumulative times for prepare()...\n"); - for (int i = 0; i < OP_LAST; i++) { - op_times_summed += op_times[i]; - printf("%-32s %-12d %.2fms\n", op_strs[i], op_times[i], op_times[i]/100000.0); - } - printf("\nTotal time for prepare() - %-10lld %.2fms\n", op_times_summed, op_times_summed/100000.0); -#endif - - return kTfLiteOk; -} - -#pragma stackfunction 1000 -TfLiteStatus model_init(void *weights_data_ptr) { - return model_init_with_paging(weights_data_ptr, nullptr); -} - -#if defined(__VX4A__) || defined(__VX4B__) -#define STACKFUNCTION(FN, BYTES) \ - asm(".globl " # FN ); \ - asm(".resource_list_empty " # FN ", \"callees\""); \ - asm(".resource_list_empty " # FN ", \"tail_callees\""); \ - asm(".resource_list_empty " # FN ", \"parallel_callees\""); \ - asm(".resource_const " # FN ", \"stack_frame_bytes\", " # BYTES); - -#define STACKFUNCTION_STATIC(FN, BYTES) \ - asm(".resource_list_empty " # FN ", \"callees\""); \ - asm(".resource_list_empty " # FN ", \"tail_callees\""); \ - asm(".resource_list_empty " # FN ", \"parallel_callees\""); \ - asm(".resource_const " # FN ", \"stack_frame_bytes\", " # BYTES); - -STACKFUNCTION(_Z22model_init_with_pagingPvS_, 1000); -STACKFUNCTION(fast_read_loop, 1000); -// STACKFUNCTION(_Z12model_invokev, 1000); -STACKFUNCTION(__call_exitprocs_impl, 1000); -STACKFUNCTION_STATIC(_ZN12_GLOBAL__N_117mg_InvokeSubgraphEi, 1000); -// STACKFUNCTION(_Z10model_initPv); -#endif - -TfLiteStatus mg_status; -#pragma stackfunction 1000 -extern "C" void invoke_subgraph_c_trampoline(){ - mg_status = mg_InvokeSubgraph(0); -} - -extern "C" void par_invoke_1(thread_info_t *thread_info); - -#pragma stackfunction 1000 -TfLiteStatus model_invoke() { - -#ifdef TFLMC_XCORE_PROFILE - printf("\n\n\nProfiling invoke()...\n"); - memset(op_times, 0, sizeof(op_times)); - memset(op_counts, 0, sizeof(op_counts)); - op_times_summed = 0; -#endif - - par_invoke_1(&xc_config.thread_info); - if (mg_status != kTfLiteOk) { - return mg_status; - } - - #ifdef TFLMC_CONV2D_PROFILE - struct convopdata{ - const char * name; - size_t thread_count; - int evalStartTime; - int threadsStartTime; - int threadsDoneTime; - }; - int conv_times1 = 0, conv_times2 = 0; - printf("\nConv()...\n"); - for (size_t g = 0; g < 1; ++g) { - for (size_t i = tflNodes_subgraph_index[g]; i < tflNodes_subgraph_index[g+1]; ++i) { - if (used_ops[i] == OP_XC_conv2d_v2) { - auto *op_data = reinterpret_cast(tflNodes[i].user_data); - conv_times1 += op_data->threadsStartTime - op_data->evalStartTime; - conv_times2 += op_data->threadsDoneTime - op_data->threadsStartTime; - printf("node %-5d %-25s %-25s %-6d %-6d %-12d\n", i, op_strs[used_ops[i]], op_data->name, op_data->thread_count, op_data->threadsStartTime - op_data->evalStartTime, op_data->threadsDoneTime - op_data->threadsStartTime); - } - } - } - printf("Summed - %-10d %-10d\n", conv_times1, conv_times2); -#endif - -#ifdef TFLMC_XCORE_PROFILE - printf("\nCumulative times for invoke()...\n"); - for (int i = 0; i < OP_LAST; i++) { - op_times_summed += op_times[i]; - printf("%-5d %-32s %-12d %.2fms\n", op_counts[i], op_strs[i], op_times[i], op_times[i]/100000.0); - } - printf("\nTotal time for invoke() - %-10lld %.2fms\n", op_times_summed, op_times_summed/100000.0); -#endif - - return kTfLiteOk; -} - -TfLiteStatus model_reset() { - // Reset variable tensors - for (int i = 0; i < 0; i++) { - memset(tflTensors[varTensors_index[i]].data.data, tflTensors[varTensors_index[i]].params.zero_point, TensorBytes(&tflTensors[varTensors_index[i]])); - } - return kTfLiteOk; -} - -#if defined(__xcore__) && defined(USB_TILE) -#include "ioserver.h" -#include -extern "C" { -extern int read_sswitch_reg(unsigned tile, unsigned reg, unsigned *data); -extern int write_sswitch_reg(unsigned tile, unsigned reg, unsigned data); -} - -#pragma stackfunction 1000 -void model_ioserver(chanend_t c) { - unsigned tensor_num = 0; - extern unsigned tile[]; - while(1) { - int cmd = ioserver_command_receive(c, &tensor_num); - switch(cmd) { - case IOSERVER_TENSOR_RECV_INPUT: { - ioserver_tensor_recv_input( - c, (unsigned int *) model_input(tensor_num)->data.u32, - (model_input_size(tensor_num) + 3) / sizeof(int)); - break; - } - case IOSERVER_TENSOR_SEND_OUTPUT: { - ioserver_tensor_send_output( - c, (unsigned int*) model_output(tensor_num)->data.u32, - (model_output_size(tensor_num) + 3) / sizeof(int)); - break; - } - case IOSERVER_INVOKE: { - model_invoke(); - ioserver_command_acknowledge(c, IOSERVER_ACK); - break; - } - case IOSERVER_RESET: { - model_reset(); - ioserver_command_acknowledge(c, IOSERVER_ACK); - break; - } - case IOSERVER_EXIT: { - ioserver_command_acknowledge(c, IOSERVER_ACK); - unsigned pll_ctrl; - hwtimer_t timer = hwtimer_alloc(); - hwtimer_delay(timer, 100000); - hwtimer_free(timer); - read_sswitch_reg(USB_TILE, XS1_SSWITCH_PLL_CTL_NUM, &pll_ctrl); - write_sswitch_reg(USB_TILE, XS1_SSWITCH_PLL_CTL_NUM, pll_ctrl); - return; - } - default: { - ioserver_command_acknowledge(c, IOSERVER_NACK); - break; - } - } - } -} -#else - -void model_ioserver(void *io_channel) {} - -#endif // __xcore__ - diff --git a/new_ai_tools/vnr_model/trained_model_xcore.tflite.h b/new_ai_tools/vnr_model/trained_model_xcore.tflite.h deleted file mode 100644 index 12150bbd..00000000 --- a/new_ai_tools/vnr_model/trained_model_xcore.tflite.h +++ /dev/null @@ -1,82 +0,0 @@ -// This file is generated. Do not edit. -// Generated on: 06.03.2026 13:52:45 - -#ifndef model_GEN_H -#define model_GEN_H - -#include "tensorflow/lite/c/common.h" - -#ifdef SHARED_TENSOR_ARENA - #ifndef LARGEST_TENSOR_ARENA_SIZE - #define LARGEST_TENSOR_ARENA_SIZE 840 - #elif LARGEST_TENSOR_ARENA_SIZE < 840 - #define LARGEST_TENSOR_ARENA_SIZE 840 - #endif -#endif - -// Sets up the model with init and prepare steps. -TfLiteStatus model_init(void *weights_data_ptr); -// Returns the input tensor with the given index. -TfLiteTensor *model_input(int index); -// Returns the output tensor with the given index. -TfLiteTensor *model_output(int index); -// Runs inference for the model. -TfLiteStatus model_invoke(); -// Resets variable tensors in the model. -// This should be called after invoking a model with stateful ops such as LSTM. -TfLiteStatus model_reset(); - -// Returns the number of input tensors. -inline size_t model_inputs() { - return 1; -} -// Returns the number of output tensors. -inline size_t model_outputs() { - return 1; -} - -inline void *model_input_ptr(int index) { - return model_input(index)->data.data; -} -size_t model_input_size(int index); -inline int model_input_dims_len(int index) { - return model_input(index)->dims->data[0]; -} -inline int *model_input_dims(int index) { - return &model_input(index)->dims->data[1]; -} - -inline void *model_output_ptr(int index) { - return model_output(index)->data.data; -} -size_t model_output_size(int index); -inline int model_output_dims_len(int index) { - return model_output(index)->dims->data[0]; -} -inline int *model_output_dims(int index) { - return &model_output(index)->dims->data[1]; -} -// Only returns valid value if input is quantized -inline int32_t model_input_zeropoint(int index) { - return model_input(index)->params.zero_point; -} -// Only returns valid value if input is quantized -inline float model_input_scale(int index) { - return model_input(index)->params.scale; -} -// Only returns valid value if output is quantized -inline int32_t model_output_zeropoint(int index) { - return model_output(index)->params.zero_point; -} -// Only returns valid value if output is quantized -inline float model_output_scale(int index) { - return model_output(index)->params.scale; -} - -// Sets up the model part of ioserver to communicate -// with this model from host. -// Requires that ioserver() has been setup and running. -// This is an infinite loop and does not exit. -TfLiteStatus model_ioserver(unsigned io_channel); - -#endif From 2227f8da1f9d15aa5bbcb2236c5cdf7c2e47ff30 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 12 Mar 2026 13:20:58 +0000 Subject: [PATCH 10/17] enabling whole non-native library build, adding and fixing test_aec_schedule --- Jenkinsfile | 9 +++ lib_voice/lib_build_info.cmake | 59 ++++++++----------- lib_voice/src/aec/aec_process_frame.c | 4 +- .../lib_aec/test_aec_schedule/CMakeLists.txt | 7 ++- tests/lib_aec/test_aec_schedule/conftest.py | 16 +++++ tests/lib_aec/test_aec_schedule/src/main.c | 16 +++++ tests/lib_aec/test_aec_schedule/src/main.xc | 41 ------------- .../test_aec_schedule/test_aec_schedule.py | 6 +- 8 files changed, 78 insertions(+), 80 deletions(-) create mode 100644 tests/lib_aec/test_aec_schedule/conftest.py delete mode 100644 tests/lib_aec/test_aec_schedule/src/main.xc diff --git a/Jenkinsfile b/Jenkinsfile index 554e815f..b7654306 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -167,6 +167,9 @@ pipeline { dir("lib_aec/aec_unit_tests") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416 -DTEST_SPEEDUP_FACTOR=8") } + dir("lib_aec/test_aec_schedule") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } dir("lib_ns/ns_unit_tests") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } @@ -372,6 +375,12 @@ pipeline { sh "pytest --arch vx4b --junitxml=pytest_result.xml" junit "pytest_result.xml" } + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + dir("lib_aec/test_aec_schedule") { + sh "pytest --arch vx4b --junitxml=pytest_result.xml" + junit "pytest_result.xml" + } + } dir("lib_ns/ns_unit_tests"){ sh "pytest --arch vx4b --junitxml=pytest_result.xml" junit "pytest_result.xml" diff --git a/lib_voice/lib_build_info.cmake b/lib_voice/lib_build_info.cmake index 4deb8212..2f894836 100644 --- a/lib_voice/lib_build_info.cmake +++ b/lib_voice/lib_build_info.cmake @@ -19,24 +19,17 @@ set(lib_ASM_SRCS "") include(${CMAKE_CURRENT_LIST_DIR}/vnr_model.cmake) file(RELATIVE_PATH MODEL_OUT_DIR_REL ${CMAKE_CURRENT_LIST_DIR} ${MODEL_OUT_DIR}) -file(GLOB_RECURSE LIB_C_SRCS RELATIVE ${CMAKE_CURRENT_LIST_DIR} - "${CMAKE_CURRENT_LIST_DIR}/src/aec/*.c" - "${CMAKE_CURRENT_LIST_DIR}/src/agc/*.c" - "${CMAKE_CURRENT_LIST_DIR}/src/ns/*.c" - "${CMAKE_CURRENT_LIST_DIR}/src/vnr/*.c" - ) - set(LIB_INCLUDES api - # api/adec + api/adec api/aec src/aec api/agc - # api/ic - # src/ic + api/ic + src/ic api/ns src/ns - # api/stage1 + api/stage1 api/vnr src/vnr ${MODEL_OUT_DIR_REL} @@ -49,32 +42,32 @@ list(APPEND LIB_CXX_SRCS ${VNR_MODEL_SOURCES} ${VNR_CXX_SOURCES}) XMOS_REGISTER_MODULE() -# include(${CMAKE_CURRENT_LIST_DIR}/aec_schedule.cmake) +include(${CMAKE_CURRENT_LIST_DIR}/aec_schedule.cmake) -# # Discover schedules -# aec_collect_schedules(single_config_sched multi_config_name multi_config_sched) +# Discover schedules +aec_collect_schedules(single_config_sched multi_config_name multi_config_sched) -# list(LENGTH single_config_sched singleconfig_list_len) -# list(LENGTH multi_config_sched multi_config_list_len) +list(LENGTH single_config_sched singleconfig_list_len) +list(LENGTH multi_config_sched multi_config_list_len) foreach(target ${APP_BUILD_TARGETS}) -# # Apply AEC schedule if needed -# if(singleconfig_list_len EQUAL 1) # App only does set(AEC_SCHEDULE_CONFIG ). Attach this schedule to all targets -# list(GET single_config_sched 0 sched) -# message(VERBOSE "Target ${target} using AEC_SCHEDULE_CONFIG schedule ${sched}") -# generate_schedule(${target} "${sched}") -# elseif(multi_config_list_len GREATER 0) -# math(EXPR _last "${multi_config_list_len} - 1") -# foreach(i RANGE 0 ${_last}) -# list(GET multi_config_name ${i} config) -# if(target MATCHES "${config}$") # target ends with -# list(GET multi_config_sched ${i} sched) # Get the corresponding schedule for this config -# message(VERBOSE "aec schedule config ${config}, matches target ${target} at index ${i}. schedule = ${sched}") -# generate_schedule(${target} "${sched}") # Generate schedule and add to target sources and includes -# break() -# endif() -# endforeach() -# endif() + # Apply AEC schedule if needed + if(singleconfig_list_len EQUAL 1) # App only does set(AEC_SCHEDULE_CONFIG ). Attach this schedule to all targets + list(GET single_config_sched 0 sched) + message(VERBOSE "Target ${target} using AEC_SCHEDULE_CONFIG schedule ${sched}") + generate_schedule(${target} "${sched}") + elseif(multi_config_list_len GREATER 0) + math(EXPR _last "${multi_config_list_len} - 1") + foreach(i RANGE 0 ${_last}) + list(GET multi_config_name ${i} config) + if(target MATCHES "${config}$") # target ends with + list(GET multi_config_sched ${i} sched) # Get the corresponding schedule for this config + message(VERBOSE "aec schedule config ${config}, matches target ${target} at index ${i}. schedule = ${sched}") + generate_schedule(${target} "${sched}") # Generate schedule and add to target sources and includes + break() + endif() + endforeach() + endif() # Link aitools with the targets target_link_libraries(${target} PRIVATE tflite_micro) diff --git a/lib_voice/src/aec/aec_process_frame.c b/lib_voice/src/aec/aec_process_frame.c index 83ac3cff..6feb687d 100644 --- a/lib_voice/src/aec/aec_process_frame.c +++ b/lib_voice/src/aec/aec_process_frame.c @@ -10,7 +10,7 @@ enum e_fft {Y_FFT, X_FFT, ERROR_FFT}; #define REF_ACTIVE_THRESHOLD_dB (-60) // Reference input level above which it is considered active #define REF_ACTIVE_THRESHOLD f64_to_float_s32(pow(10, REF_ACTIVE_THRESHOLD_dB/20.0)) -#ifdef __XS3A__ +#if defined(__XS3A__) || defined(__VX4B__) #include DECLARE_JOB(calc_time_domain_ema_energy_task, (const aec_par_tasks_and_channels_t*, aec_filter_state_t *, int32_t*, int, int, enum e_td_ema)); DECLARE_JOB(fft_task, (const aec_par_tasks_and_channels_t*, aec_filter_state_t*, aec_filter_state_t*, int, int, enum e_fft)); @@ -273,7 +273,7 @@ void filter_adapt_task(const aec_par_tasks_t *s, aec_filter_state_t *main_state, } } -#ifdef __XS3A__ +#if defined(__XS3A__) || defined(__VX4B__) #define PAR_THREADS_PJOBS(FUNC, ARR, NUM_THREADS, ...) \ do { \ diff --git a/tests/lib_aec/test_aec_schedule/CMakeLists.txt b/tests/lib_aec/test_aec_schedule/CMakeLists.txt index e991daf5..8378b17b 100644 --- a/tests/lib_aec/test_aec_schedule/CMakeLists.txt +++ b/tests/lib_aec/test_aec_schedule/CMakeLists.txt @@ -2,7 +2,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(test_aec_schedule) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../../..) set(CONFIG_XSCOPE_PATH ${XMOS_SANDBOX_DIR}/lib_voice/tests/shared/file_utils) diff --git a/tests/lib_aec/test_aec_schedule/conftest.py b/tests/lib_aec/test_aec_schedule/conftest.py new file mode 100644 index 00000000..d997de0f --- /dev/null +++ b/tests/lib_aec/test_aec_schedule/conftest.py @@ -0,0 +1,16 @@ + +def pytest_addoption(parser): + parser.addoption( + "--arch", + nargs = "+", + default = ["xs3a"], + help = "One or more architectures to run on (e.g. --arch xs3a sim)", + choices = ["xs3a", "vx4b"], + ) + +def pytest_generate_tests(metafunc): + if "target" in metafunc.fixturenames: + selected_arches = metafunc.config.getoption("arch") + if isinstance(selected_arches, str): + selected_arches = [selected_arches] + metafunc.parametrize("target", selected_arches) diff --git a/tests/lib_aec/test_aec_schedule/src/main.c b/tests/lib_aec/test_aec_schedule/src/main.c index c4beebfe..96ab9f04 100644 --- a/tests/lib_aec/test_aec_schedule/src/main.c +++ b/tests/lib_aec/test_aec_schedule/src/main.c @@ -3,6 +3,10 @@ #include "voice.h" #include +#include +#if TEST_WAV_XSCOPE +#include "xscope_io_device.h" +#endif extern aec_task_distribution_t tdist; void test_aec(int32_t (*input)[AEC_FRAME_ADVANCE], @@ -49,3 +53,15 @@ void wrapper_task(const char *input_file_name, const char *output_file_name) file_close(&output_file); shutdown_session(); } + +int main() { +#if TEST_WAV_XSCOPE + chanend_t xscope_chan = chanend_alloc(); + xscope_io_init(xscope_chan); +#endif + wrapper_task("input.bin", "output.bin"); +#if TEST_WAV_XSCOPE + chanend_free(xscope_chan); +#endif + return 0; +} diff --git a/tests/lib_aec/test_aec_schedule/src/main.xc b/tests/lib_aec/test_aec_schedule/src/main.xc deleted file mode 100644 index a712dc1a..00000000 --- a/tests/lib_aec/test_aec_schedule/src/main.xc +++ /dev/null @@ -1,41 +0,0 @@ -// Copyright 2026 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include -#include -#include -#include -#ifdef __XC__ -#define chanend_t chanend -#else -#include -#endif - -extern "C" { -#include "xmath/xmath.h" -void wrapper_task(const char *input_file_name, const char *output_file_name); -#if TEST_WAV_XSCOPE - - #include "xscope_io_device.h" -#endif -} - -#define IN_WAV_FILE_NAME "input.bin" -#define OUT_WAV_FILE_NAME "output.bin" -int main (void) -{ - chan xscope_chan; - par - { -#if TEST_WAV_XSCOPE - xscope_host_data(xscope_chan); -#endif - on tile[0]: { -#if TEST_WAV_XSCOPE - xscope_io_init(xscope_chan); -#endif - wrapper_task(IN_WAV_FILE_NAME, OUT_WAV_FILE_NAME); - _Exit(0); - } - } - return 0; -} diff --git a/tests/lib_aec/test_aec_schedule/test_aec_schedule.py b/tests/lib_aec/test_aec_schedule/test_aec_schedule.py index 69f60856..ff2fa316 100644 --- a/tests/lib_aec/test_aec_schedule/test_aec_schedule.py +++ b/tests/lib_aec/test_aec_schedule/test_aec_schedule.py @@ -7,7 +7,7 @@ from run_dut import run_dut import numpy as np -def test_aec_schedule(): +def test_aec_schedule(target): one_thread_xe = Path(__file__).parent / "bin" / "aec_std_arch_1thread" / "test_aec_schedule_aec_std_arch_1thread.xe" two_thread_xe = Path(__file__).parent / "bin" / "aec_std_arch_2threads" / "test_aec_schedule_aec_std_arch_2threads.xe" @@ -24,8 +24,8 @@ def test_aec_schedule(): assert input_data.shape[0] == 4 input_data = pvc.interleave_channel_frames(input_data, 240) - out1, _ = run_dut(input_data, one_thread_xe) - out2, _ = run_dut(input_data, two_thread_xe) + out1, _ = run_dut(input_data, one_thread_xe, target) + out2, _ = run_dut(input_data, two_thread_xe, target) assert isinstance(out1, np.ndarray) and isinstance(out2, np.ndarray) assert out1.shape == out2.shape, "Output shapes differ" assert np.array_equal(out1, out2), "Outputs differ between 1-thread and 2-thread schedules" From 3dea1fb46a3d23e6e1eb2e2209bf2d6622b4e482 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 12 Mar 2026 13:33:51 +0000 Subject: [PATCH 11/17] fixing test_calc_vnr_pred build --- tests/lib_ic/test_calc_vnr_pred/CMakeLists.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/lib_ic/test_calc_vnr_pred/CMakeLists.txt b/tests/lib_ic/test_calc_vnr_pred/CMakeLists.txt index e8ee3211..50b336b5 100644 --- a/tests/lib_ic/test_calc_vnr_pred/CMakeLists.txt +++ b/tests/lib_ic/test_calc_vnr_pred/CMakeLists.txt @@ -18,11 +18,6 @@ file(GLOB APP_C_SRCS ${XMOS_SANDBOX_DIR}/lib_voice/tests/lib_vnr/vnr_unit_tests/src/main.c ${CMAKE_CURRENT_LIST_DIR}/src/*.c) -file(RELATIVE_PATH APP_XC_SRCS - ${CMAKE_CURRENT_LIST_DIR} - ${XMOS_SANDBOX_DIR}/lib_voice/tests/lib_vnr/vnr_unit_tests/src/main.xc - ) - set(APP_INCLUDES ${CMAKE_CURRENT_LIST_DIR}/src ${CMAKE_CURRENT_LIST_DIR}/../../shared/file_utils/src) From 325d980c5affff66eafb13f04dbe0c7ec2bff2dc Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Thu, 12 Mar 2026 16:32:55 +0000 Subject: [PATCH 12/17] rewriting the pipelines example to run on single tile, building it on jenkins --- Jenkinsfile | 13 +---- examples/CMakeLists.txt | 6 +++ examples/app_pipeline/CMakeLists.txt | 7 ++- examples/app_pipeline/src/app.c | 58 +++++++++++++++------- examples/app_pipeline/src/main.xc | 32 ------------ examples/app_pipeline/src/pipeline.c | 12 ++--- examples/app_pipeline/src/pipeline_state.h | 4 +- 7 files changed, 61 insertions(+), 71 deletions(-) delete mode 100644 examples/app_pipeline/src/main.xc diff --git a/Jenkinsfile b/Jenkinsfile index b7654306..48a4a453 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -69,20 +69,9 @@ pipeline { checkoutScmShallow() createVenv(reqFile: "requirements.txt") } - // dir("${REPO}/examples") { - // withVenv { - // xcoreBuild(archiveBins: false) - // } - // } dir("${REPO}/examples") { withVenv { - // only an aec example builds on vx4b so far - dir("app_aec") { - xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") - } - dir("app_vnr") { - xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") - } + xcoreBuild(archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } } } diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index d60b0b75..42c64515 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -2,6 +2,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(lib_voice_examples) +if(DEFINED XCORE_TARGET) + set(XCORE_TARGET ${XCORE_TARGET}) +else() + set(XCORE_TARGET XK-EVK-XU316) +endif() + add_subdirectory(app_aec) add_subdirectory(app_vnr) diff --git a/examples/app_pipeline/CMakeLists.txt b/examples/app_pipeline/CMakeLists.txt index 2e0da47c..ffbcc014 100644 --- a/examples/app_pipeline/CMakeLists.txt +++ b/examples/app_pipeline/CMakeLists.txt @@ -2,7 +2,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(app_pipeline) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(APP_COMPILER_FLAGS_std_arch -report) set(APP_COMPILER_FLAGS_alt_arch -report -DALT_ARCH_MODE=1) diff --git a/examples/app_pipeline/src/app.c b/examples/app_pipeline/src/app.c index cd7a7f20..488850d5 100644 --- a/examples/app_pipeline/src/app.c +++ b/examples/app_pipeline/src/app.c @@ -6,17 +6,21 @@ #include #include #include +#include -extern void pipeline_tile0_init(pipeline_state_tile0_t *state); -extern void pipeline_tile1_init(pipeline_state_tile1_t *state); +DECLARE_JOB(pipeline_wrapper_thread0, (chanend_t)); +DECLARE_JOB(pipeline_wrapper_thread1, (chanend_t)); -extern void pipeline_process_frame_tile0(pipeline_state_tile0_t *state, +extern void pipeline_thread0_init(pipeline_state_thread0_t *state); +extern void pipeline_thread1_init(pipeline_state_thread1_t *state); + +extern void pipeline_process_frame_thread0(pipeline_state_thread0_t *state, int32_t (*input_y_data)[AP_FRAME_ADVANCE], int32_t (*input_x_data)[AP_FRAME_ADVANCE], int32_t (*output_data)[AP_FRAME_ADVANCE], pipeline_metadata_t *md_output); -extern void pipeline_process_frame_tile1(pipeline_state_tile1_t *state, pipeline_metadata_t *md_input, +extern void pipeline_process_frame_thread1(pipeline_state_thread1_t *state, pipeline_metadata_t *md_input, int32_t (*input_data)[AP_FRAME_ADVANCE], int32_t output_data[AP_FRAME_ADVANCE]); @@ -39,43 +43,61 @@ static inline void consumer(int32_t frame_y[AP_FRAME_ADVANCE]) { printf("frame done\n"); } -void pipeline_wrapper_tile0(chanend_t c_pcm_out) +void pipeline_wrapper_thread0(chanend_t c_pcm_out) { int32_t DWORD_ALIGNED frame_y[AP_MAX_Y_CHANNELS][AP_FRAME_ADVANCE]; int32_t DWORD_ALIGNED frame_x[AP_MAX_X_CHANNELS][AP_FRAME_ADVANCE]; // Initialise pipeline - pipeline_state_tile0_t DWORD_ALIGNED pipeline_tile0_state; - pipeline_tile0_init(&pipeline_tile0_state); + pipeline_state_thread0_t DWORD_ALIGNED pipeline_thread0_state; + pipeline_thread0_init(&pipeline_thread0_state); for(unsigned b = 0; b < 5; b++){ producer(frame_y, frame_x); pipeline_metadata_t md; - int32_t DWORD_ALIGNED tile0_output[AP_MAX_Y_CHANNELS][AP_FRAME_ADVANCE]; - pipeline_process_frame_tile0(&pipeline_tile0_state, frame_y, frame_x, tile0_output, &md); + int32_t DWORD_ALIGNED thread0_output[AP_MAX_Y_CHANNELS][AP_FRAME_ADVANCE]; + pipeline_process_frame_thread0(&pipeline_thread0_state, frame_y, frame_x, thread0_output, &md); - // Send data to process to the other tile and receive processed output back - //Transfer to other tile + // Send data to process to the other thread and receive processed output back + //Transfer to other thread + chan_out_byte(c_pcm_out, 1); chan_out_buf_byte(c_pcm_out, (uint8_t*)&md, sizeof(pipeline_metadata_t)); - chan_out_buf_word(c_pcm_out, (uint32_t*)&tile0_output[0][0], (AP_MAX_Y_CHANNELS * AP_FRAME_ADVANCE)); + chan_out_buf_word(c_pcm_out, (uint32_t*)&thread0_output[0][0], (AP_MAX_Y_CHANNELS * AP_FRAME_ADVANCE)); } + chan_out_byte(c_pcm_out, 0); } -void pipeline_wrapper_tile1(chanend_t c_pcm_in) +void pipeline_wrapper_thread1(chanend_t c_pcm_in) { - pipeline_state_tile1_t DWORD_ALIGNED pipeline_tile1_state; + pipeline_state_thread1_t DWORD_ALIGNED pipeline_thread1_state; pipeline_metadata_t md; - int32_t DWORD_ALIGNED tile0_output[AP_MAX_Y_CHANNELS][AP_FRAME_ADVANCE]; + int32_t DWORD_ALIGNED thread0_output[AP_MAX_Y_CHANNELS][AP_FRAME_ADVANCE]; int32_t DWORD_ALIGNED pipeline_output[AP_FRAME_ADVANCE]; - pipeline_tile1_init(&pipeline_tile1_state); + pipeline_thread1_init(&pipeline_thread1_state); while(1) { + uint8_t status = chan_in_byte(c_pcm_in); + if (!status) break; chan_in_buf_byte(c_pcm_in, (uint8_t*)&md, sizeof(pipeline_metadata_t)); - chan_in_buf_word(c_pcm_in, (uint32_t*)&tile0_output[0][0], (AP_MAX_Y_CHANNELS * AP_FRAME_ADVANCE)); + chan_in_buf_word(c_pcm_in, (uint32_t*)&thread0_output[0][0], (AP_MAX_Y_CHANNELS * AP_FRAME_ADVANCE)); - pipeline_process_frame_tile1(&pipeline_tile1_state, &md, tile0_output, pipeline_output); + pipeline_process_frame_thread1(&pipeline_thread1_state, &md, thread0_output, pipeline_output); consumer(pipeline_output); } } + +// producer -> stage1 -> (thread0_to_thread1) -> stage2 -> stage3 -> stage4 -> consumer +// producer and stage1 run on thread0 +// stage2, stage3, stage4 and consumer run on thread1 + +int main() { + channel_t ch = chan_alloc(); + PAR_JOBS( + PJOB(pipeline_wrapper_thread0, (ch.end_a)), + PJOB(pipeline_wrapper_thread1, (ch.end_b)) + ); + chan_free(ch); + return 0; +} diff --git a/examples/app_pipeline/src/main.xc b/examples/app_pipeline/src/main.xc deleted file mode 100644 index a2ea9435..00000000 --- a/examples/app_pipeline/src/main.xc +++ /dev/null @@ -1,32 +0,0 @@ -// Copyright 2017-2026 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include -#include -#include -#include - -// producer -> stage1 -> (tile0_to_tile1) -> stage2 -> stage3 -> stage4 -> consumer -// producer and stage1 run on tile0 -// stage2, stage3, stage4 and consumer run on tile1 - -extern "C" { - extern void pipeline_wrapper_tile0(chanend c_pcm_out); - extern void pipeline_wrapper_tile1(chanend c_pcm_in); -} - -int main(){ - chan c_tile0_to_tile1; - - par { - on tile[0]: - { - pipeline_wrapper_tile0(c_tile0_to_tile1); - _Exit(0); - } - on tile[1]: - { - pipeline_wrapper_tile1(c_tile0_to_tile1); - } - } - return 0; -} diff --git a/examples/app_pipeline/src/pipeline.c b/examples/app_pipeline/src/pipeline.c index 3a42ff05..890ad4cf 100644 --- a/examples/app_pipeline/src/pipeline.c +++ b/examples/app_pipeline/src/pipeline.c @@ -5,8 +5,8 @@ #include "pipeline_state.h" -void pipeline_tile0_init(pipeline_state_tile0_t *state) { - memset(state, 0, sizeof(pipeline_state_tile0_t)); +void pipeline_thread0_init(pipeline_state_thread0_t *state) { + memset(state, 0, sizeof(pipeline_state_thread0_t)); // Initialise AEC, DE, ADEC stages aec_conf_t aec_de_mode_conf, aec_non_de_mode_conf; @@ -44,8 +44,8 @@ void pipeline_tile0_init(pipeline_state_tile0_t *state) { stage1_init(&state->stage_1_state, &aec_de_mode_conf, &aec_non_de_mode_conf, &adec_conf); } -void pipeline_tile1_init(pipeline_state_tile1_t *state) { - memset(state, 0, sizeof(pipeline_state_tile1_t)); +void pipeline_thread1_init(pipeline_state_thread1_t *state) { + memset(state, 0, sizeof(pipeline_state_thread1_t)); // Initialise IC, VNR ic_init(&state->ic_state); @@ -58,7 +58,7 @@ void pipeline_tile1_init(pipeline_state_tile1_t *state) { agc_init(&state->agc_state, &agc_conf_asr); } -void pipeline_process_frame_tile0(pipeline_state_tile0_t *state, +void pipeline_process_frame_thread0(pipeline_state_thread0_t *state, int32_t (*input_y_data)[AP_FRAME_ADVANCE], int32_t (*input_x_data)[AP_FRAME_ADVANCE], int32_t (*output_data)[AP_FRAME_ADVANCE], @@ -79,7 +79,7 @@ void pipeline_process_frame_tile0(pipeline_state_tile0_t *state, memcpy(md_output, &md, sizeof(pipeline_metadata_t)); } -void pipeline_process_frame_tile1(pipeline_state_tile1_t *state, pipeline_metadata_t *md_input, +void pipeline_process_frame_thread1(pipeline_state_thread1_t *state, pipeline_metadata_t *md_input, int32_t (*input_data)[AP_FRAME_ADVANCE], int32_t output_data[AP_FRAME_ADVANCE]) { diff --git a/examples/app_pipeline/src/pipeline_state.h b/examples/app_pipeline/src/pipeline_state.h index 3ddb0d97..685ddb27 100644 --- a/examples/app_pipeline/src/pipeline_state.h +++ b/examples/app_pipeline/src/pipeline_state.h @@ -16,7 +16,7 @@ typedef struct { typedef struct { // Stage1 - AEC, DE, ADEC stage1_t DWORD_ALIGNED stage_1_state; -} pipeline_state_tile0_t; +} pipeline_state_thread0_t; typedef struct { // IC, VNR @@ -26,6 +26,6 @@ typedef struct { ns_state_t DWORD_ALIGNED ns_state; // AGC agc_state_t agc_state; -} pipeline_state_tile1_t; +} pipeline_state_thread1_t; #endif From 6e15d887ab141256994f0d08f638d9c32f40c565 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Fri, 13 Mar 2026 13:40:30 +0000 Subject: [PATCH 13/17] rewriting pipelines test to run on one tile, enabling some xs3a tests on jenkins --- Jenkinsfile | 325 +++++++++++++++++----------------- tests/pipeline/src/main.xc | 40 ----- tests/pipeline/src/test_bin.c | 17 ++ 3 files changed, 180 insertions(+), 202 deletions(-) delete mode 100644 tests/pipeline/src/main.xc diff --git a/Jenkinsfile b/Jenkinsfile index 48a4a453..d4848738 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -210,15 +210,15 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - // script { - // if (env.FULL_TEST == "1") { - // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partA") - // } - // else { - // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partA") - // } - // } - // stash name: 'xcommon_cmake_build_xcore_partA', includes: '**/bin/**/*.xe' + script { + if (env.FULL_TEST == "1") { + xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partA") + } + else { + xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partA") + } + } + stash name: 'xcommon_cmake_build_xcore_partA', includes: '**/bin/**/*.xe' } } } @@ -287,15 +287,15 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - // script { - // if (env.FULL_TEST == "1") { - // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") - // } - // else { - // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") - // } - // } - // stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' + script { + if (env.FULL_TEST == "1") { + xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") + } + else { + xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") + } + } + stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' } } } @@ -426,26 +426,27 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { - sh "cmake -B build_xcommon_cmake" // to fetch lib_xcore_math - - // Build x86 versions locally as we had problems with moving bins and libs over from previous build due to brew - dir("custom_cmake_build") { - sh "cmake --version" - sh 'cmake -B build' - sh 'make -C build -j$(nproc)' - } - // We do this again on the NUCs for verification later, but this just checks we have no build error - dir("lib_ic/py_c_frame_compare") { - sh "python build_ic_frame_proc.py" - } - // We do this again on the NUCs for verification later, but this just checks we have no build error - dir("lib_vnr/test_vnr_cffi") { - sh "python build_vnr_cffi.py" - } - dir("stage_b") { - sh "python build_c_code.py" - } - // unstash 'xcommon_cmake_build_xcore' + // sh "cmake -B build_xcommon_cmake" // to fetch lib_xcore_math + + // // Build x86 versions locally as we had problems with moving bins and libs over from previous build due to brew + // dir("custom_cmake_build") { + // sh "cmake --version" + // sh 'cmake -B build' + // sh 'make -C build -j$(nproc)' + // } + // // We do this again on the NUCs for verification later, but this just checks we have no build error + // dir("lib_ic/py_c_frame_compare") { + // sh "python build_ic_frame_proc.py" + // } + // // We do this again on the NUCs for verification later, but this just checks we have no build error + // dir("lib_vnr/test_vnr_cffi") { + // sh "python build_vnr_cffi.py" + // } + // dir("stage_b") { + // sh "python build_c_code.py" + // } + unstash 'xcommon_cmake_build_xcore_partA' + unstash 'xcommon_cmake_build_xcore_partB' // unstash 'xcommon_cmake_build_native' } } @@ -471,18 +472,18 @@ pipeline { dir("${REPO}/tests") { withTools(params.TOOLS_VERSION) { withVenv { - dir("profile_memory") { - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - archiveArtifacts artifacts: "lib_voice_memory.json", fingerprint: true, onlyIfSuccessful: true - } - withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("profile_mips") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - archiveArtifacts artifacts: "lib_voice_mips.json", fingerprint: true, onlyIfSuccessful: true - } - } + // dir("profile_memory") { + // sh "pytest -n 1 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // archiveArtifacts artifacts: "lib_voice_memory.json", fingerprint: true, onlyIfSuccessful: true + // } + // withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + // dir("profile_mips") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // archiveArtifacts artifacts: "lib_voice_mips.json", fingerprint: true, onlyIfSuccessful: true + // } + // } } } } @@ -501,15 +502,15 @@ pipeline { sh "pytest -n 2 --junitxml=pytest_result.xml" junit "pytest_result.xml" } - dir("test_vnr_cffi") { - sh "python build_vnr_cffi.py" - sh "pytest -n 4 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_vnr_profile") { - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } + // dir("test_vnr_cffi") { + // sh "python build_vnr_cffi.py" + // sh "pytest -n 4 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_vnr_profile") { + // sh "pytest -s --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } } } } @@ -525,14 +526,14 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("test_ns_profile"){ - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("compare_c_py"){ - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } + // dir("test_ns_profile"){ + // sh "pytest -n 1 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("compare_c_py"){ + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } dir("ns_unit_tests"){ sh "pytest -n 1 --junitxml=pytest_result.xml" junit "pytest_result.xml" @@ -552,49 +553,49 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("ic_unit_tests"){ - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("py_c_frame_compare"){ - sh "python build_ic_frame_proc.py" - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_ic_profile"){ - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_ic_spec"){ - // This test compares the model and C implementation over a range of scenarious for: - // convergence_time, db_suppression, maximum noise added to input (to test for stability) - // and expected group delay. It will fail if these are not met. - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - sh "python print_stats.py > ic_spec_summary.txt" - // This script generates a number of polar plots of attenuation vs null point angle vs freq - // It currently only uses the python model to do this. It takes about 40 mins for all plots - // and generates a series of IC_performance_xxxHz.svg files which could be archived - //sh "python plot_ic.py" - } - dir("characterise_c_py"){ - // This test compares the suppression performance across angles between model and C implementation - // and fails if they differ significantly. It requires that the C implementation run with fixed mu - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - // This script sweeps the y_delay value to find what the optimum suppression is across RT60 and angle. - // It's more of a model develpment tool than testing the implementation so not run. It take a few minutes. - //sh "python sweep_ic_delay.py" - } - dir("test_calc_vnr_pred"){ - // This is a unit test for ic_calc_vnr_pred function. - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_bad_state"){ - sh "pytest -s --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } + // dir("ic_unit_tests"){ + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("py_c_frame_compare"){ + // sh "python build_ic_frame_proc.py" + // sh "pytest -s --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_ic_profile"){ + // sh "pytest -s --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_ic_spec"){ + // // This test compares the model and C implementation over a range of scenarious for: + // // convergence_time, db_suppression, maximum noise added to input (to test for stability) + // // and expected group delay. It will fail if these are not met. + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // sh "python print_stats.py > ic_spec_summary.txt" + // // This script generates a number of polar plots of attenuation vs null point angle vs freq + // // It currently only uses the python model to do this. It takes about 40 mins for all plots + // // and generates a series of IC_performance_xxxHz.svg files which could be archived + // //sh "python plot_ic.py" + // } + // dir("characterise_c_py"){ + // // This test compares the suppression performance across angles between model and C implementation + // // and fails if they differ significantly. It requires that the C implementation run with fixed mu + // sh "pytest -s --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // // This script sweeps the y_delay value to find what the optimum suppression is across RT60 and angle. + // // It's more of a model develpment tool than testing the implementation so not run. It take a few minutes. + // //sh "python sweep_ic_delay.py" + // } + // dir("test_calc_vnr_pred"){ + // // This is a unit test for ic_calc_vnr_pred function. + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_bad_state"){ + // sh "pytest -s --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } } } } @@ -610,8 +611,8 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - sh "pytest -n 1 --junitxml=pytest_result.xml" - junit "pytest_result.xml" + // sh "pytest -n 1 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" } } } @@ -627,31 +628,31 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("de_unit_tests") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_delay_estimator") { - sh 'mkdir -p ./input_wavs/' - sh 'mkdir -p ./output_files/' - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - sh "python print_stats.py" - } - dir("test_adec_startup") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_adec") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } - dir("test_adec_profile") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - // Testing bit exactness of the AEC scheduling - sh "diff output_1_2_2_10_5.wav output_2_2_2_10_5.wav" - } + // dir("de_unit_tests") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_delay_estimator") { + // sh 'mkdir -p ./input_wavs/' + // sh 'mkdir -p ./output_files/' + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // sh "python print_stats.py" + // } + // dir("test_adec_startup") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_adec") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } + // dir("test_adec_profile") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // // Testing bit exactness of the AEC scheduling + // sh "diff output_1_2_2_10_5.wav output_2_2_2_10_5.wav" + // } } } } @@ -667,29 +668,29 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { - dir("test_aec_enhancements") { - sh "pytest -n 2 --junitxml=pytest_result.xml" - junit "pytest_result.xml" - } + // dir("test_aec_enhancements") { + // sh "pytest -n 2 --junitxml=pytest_result.xml" + // junit "pytest_result.xml" + // } dir("aec_unit_tests") { sh "pytest -n 2 --junitxml=pytest_result.xml" junit "pytest_result.xml" } - dir("test_aec_spec") { - script { - if (env.FULL_TEST == "0") { - sh 'mv excluded_tests_quick.txt excluded_tests.txt' - } - } - sh "python generate_audio.py" - sh "pytest -n 2 --junitxml=results_process.xml test_process_audio.py" - catchError { - sh "pytest --junitxml=results_check.xml test_check_output.py" - } - sh "python parse_results.py" - sh "pytest --junitxml=results_final.xml test_evaluate_results.py" - junit "results_final.xml" - } + // dir("test_aec_spec") { + // script { + // if (env.FULL_TEST == "0") { + // sh 'mv excluded_tests_quick.txt excluded_tests.txt' + // } + // } + // sh "python generate_audio.py" + // sh "pytest -n 2 --junitxml=results_process.xml test_process_audio.py" + // catchError { + // sh "pytest --junitxml=results_check.xml test_check_output.py" + // } + // sh "python parse_results.py" + // sh "pytest --junitxml=results_final.xml test_evaluate_results.py" + // junit "results_final.xml" + // } } } } @@ -754,15 +755,15 @@ pipeline { post { always { // AEC aretfacts - archiveArtifacts artifacts: "${REPO}/tests/lib_adec/test_adec_profile/**/adec_prof*.log", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_adec/test_adec_profile/**/adec_prof*.log", fingerprint: true // IC artefacts - archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_profile/ic_prof.log", fingerprint: true - archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_spec/ic_spec_summary.txt", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_profile/ic_prof.log", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_ic/test_ic_spec/ic_spec_summary.txt", fingerprint: true // NS artefacts - archiveArtifacts artifacts: "${REPO}/tests/lib_ns/test_ns_profile/ns_prof.log", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_ns/test_ns_profile/ns_prof.log", fingerprint: true // VNR artifacts - archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/*.png", fingerprint: true - archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/vnr_prof.log", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/*.png", fingerprint: true + // archiveArtifacts artifacts: "${REPO}/tests/lib_vnr/test_vnr_profile/vnr_prof.log", fingerprint: true // Pipelines tests archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.csv", fingerprint: true archiveArtifacts artifacts: "${REPO}/tests/pipeline/**/results_*.png", fingerprint: true, allowEmptyArchive: true diff --git a/tests/pipeline/src/main.xc b/tests/pipeline/src/main.xc deleted file mode 100644 index c61b4650..00000000 --- a/tests/pipeline/src/main.xc +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright 2017-2026 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include -#include -#include -#include -#include - -#include "xscope_io_device.h" - -//**** Multi tile pipeline structure ***// -// file_read -> stage1 -> (tile0_to_tile1) -> stage2 -> stage3 -> stage4 -> (tile1_to_tile0) -> file_write -// file_read, stage1 and file_write run on tile0 -// stage2, stage3 and stage4 run on tile1 - -extern "C" { - extern void main_tile0(chanend c_t0_t1, chanend c_t1_t0, const char *input_file_name, const char* output_file_name); - extern void main_tile1(chanend c_t0_t1, chanend c_t1_t0); -} - -int main(){ - chan xscope_chan; - chan c_tile0_to_tile1; - chan c_tile1_to_tile0; - - par { - xscope_host_data(xscope_chan); - on tile[0]: - { - xscope_io_init(xscope_chan); - main_tile0(c_tile0_to_tile1, c_tile1_to_tile0, "input.bin", "output.bin"); - _Exit(0); - } - on tile[1]: - { - main_tile1(c_tile0_to_tile1, c_tile1_to_tile0); - } - } - return 0; -} diff --git a/tests/pipeline/src/test_bin.c b/tests/pipeline/src/test_bin.c index 6b2f0642..de3a6297 100644 --- a/tests/pipeline/src/test_bin.c +++ b/tests/pipeline/src/test_bin.c @@ -8,6 +8,7 @@ #include #include #include "xmath/xmath.h" +#include "xscope_io_device.h" #include "fileio.h" #include "pipeline_config.h" @@ -17,6 +18,9 @@ DECLARE_JOB(tx, (chanend_t, chanend_t, const char*)); DECLARE_JOB(pipeline_tile0, (chanend_t, chanend_t)); DECLARE_JOB(rx, (chanend_t, chanend_t, const char*)); +DECLARE_JOB(main_tile0, (chanend_t, chanend_t, const char *, const char *)); +DECLARE_JOB(main_tile1, (chanend_t, chanend_t)); + extern void pipeline_tile1(chanend_t c_pcm_in_b, chanend_t c_pcm_out_a); /// tx @@ -79,3 +83,16 @@ void main_tile1(chanend_t c_t0_t1, chanend_t c_t1_t0) pipeline_tile1(c_t0_t1, c_t1_t0); } +int main() { + chanend_t xscope_chan = chanend_alloc(); + channel_t c_th0_to_th1 = chan_alloc(); + channel_t c_th1_to_th0 = chan_alloc(); + xscope_io_init(xscope_chan); + PAR_JOBS( + PJOB(main_tile0, (c_th0_to_th1.end_a, c_th1_to_th0.end_a, "input.bin", "output.bin")), + PJOB(main_tile1, (c_th0_to_th1.end_b, c_th1_to_th0.end_b)) + ); + chanend_free(xscope_chan); + chan_free(c_th0_to_th1); + chan_free(c_th1_to_th0); +} From 86c3a02da4acf613d0fec631f25abd4e6684fb18 Mon Sep 17 00:00:00 2001 From: uvvpavel Date: Fri, 13 Mar 2026 15:49:32 +0000 Subject: [PATCH 14/17] trying to run pipelines tests on vx4b --- Jenkinsfile | 34 +++++--- tests/pipeline/CMakeLists.txt | 7 +- tests/pipeline/conftest.py | 13 ++++ tests/pipeline/pipeline_test_utils.py | 18 +---- tests/pipeline/src/test_bin.c | 108 ++++++++++++++++++++++++-- tests/pipeline/test_pipeline.py | 4 +- tests/shared/python/test_wav.py | 3 +- 7 files changed, 151 insertions(+), 36 deletions(-) diff --git a/Jenkinsfile b/Jenkinsfile index d4848738..f5fe12bb 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -168,6 +168,9 @@ pipeline { dir("lib_vnr/vnr_unit_tests") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } + dir("pipeline") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } stash name: 'vx4b_build_xcore', includes: '**/bin/**/*.xe' } } @@ -287,15 +290,15 @@ pipeline { withTools(params.TOOLS_VERSION) { withVenv { dir("tests") { - script { - if (env.FULL_TEST == "1") { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") - } - else { - xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") - } - } - stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' + // script { + // if (env.FULL_TEST == "1") { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_BUILD_PART=partB") + // } + // else { + // xcoreBuild(buildDir: "build_xcommon_cmake", archiveBins: false, cmakeOpts: "-DTEST_SPEEDUP_FACTOR=4 -DTEST_BUILD_PART=partB") + // } + // } + // stash name: 'xcommon_cmake_build_xcore_partB', includes: '**/bin/**/*.xe' } } } @@ -383,6 +386,17 @@ pipeline { // sh "pytest --arch vx4b --junitxml=pytest_result.xml" // junit "pytest_result.xml" } + dir("pipeline") { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + withEnv(["PIPELINE_FULL_RUN=${PIPELINE_FULL_RUN}", "SENSORY_PATH=${env.WORKSPACE}/sensory_sdk/", "AMAZON_WWE_PATH=${env.WORKSPACE}/amazon_wwe/"]) { + echo "PIPELINE_FULL_RUN set as " + env.PIPELINE_FULL_RUN + + sh "pytest -n 2 --junitxml=pytest_result.xml -vv --arch vx4b" + junit "pytest_result.xml" + sh "python compare_keywords.py results_Avona_aec_ic_ns_agc_prev_arch_xcore.csv results_Avona_aec_ic_ns_agc_prev_arch_python.csv --pass-threshold=1" + } + } + } } } } @@ -446,7 +460,7 @@ pipeline { // sh "python build_c_code.py" // } unstash 'xcommon_cmake_build_xcore_partA' - unstash 'xcommon_cmake_build_xcore_partB' + // unstash 'xcommon_cmake_build_xcore_partB' // unstash 'xcommon_cmake_build_native' } } diff --git a/tests/pipeline/CMakeLists.txt b/tests/pipeline/CMakeLists.txt index 19572cec..a8084f5e 100644 --- a/tests/pipeline/CMakeLists.txt +++ b/tests/pipeline/CMakeLists.txt @@ -3,7 +3,12 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(test_pipeline) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() + set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../../) set(CONFIG_XSCOPE_PATH ${XMOS_SANDBOX_DIR}/lib_voice/tests/shared/file_utils) file(GLOB CONFIG_XSCOPE_REL_PATH diff --git a/tests/pipeline/conftest.py b/tests/pipeline/conftest.py index 2eb66953..decce17a 100644 --- a/tests/pipeline/conftest.py +++ b/tests/pipeline/conftest.py @@ -140,7 +140,20 @@ def pytest_sessionfinish(session): tlf.write("Input,Sensory_rpi-31000,Sensory_v6_1mb,Amazon_WR_250k.en-US\n") tlf.writelines(target_log) +def pytest_addoption(parser): + parser.addoption( + "--arch", + nargs = "+", + default = ["xs3a"], + help = "One or more architectures to run on (e.g. --arch xs3a sim)", + choices = ["xs3a", "vx4b"], + ) def pytest_generate_tests(metafunc): ids = [item[0].name + ", " + item[1] + ", " + item[2] for item in all_tests_list] metafunc.parametrize("test", all_tests_list, ids=ids) + if "target_arch" in metafunc.fixturenames: + selected_arches = metafunc.config.getoption("arch") + if isinstance(selected_arches, str): + selected_arches = [selected_arches] + metafunc.parametrize("target_arch", selected_arches) diff --git a/tests/pipeline/pipeline_test_utils.py b/tests/pipeline/pipeline_test_utils.py index 7d6c22d1..769969b3 100644 --- a/tests/pipeline/pipeline_test_utils.py +++ b/tests/pipeline/pipeline_test_utils.py @@ -14,20 +14,10 @@ sys.path.append(str(Path(__file__).parent / "py_pipeline")) import wav_pipeline -def process_xcore(xe_file, input_file, output_file): +def process_xcore(xe_file, input_file, output_file, target_arch="xs3a"): frame_advance = 240 AP_MAX_Y_CHANNELS = 2 - stdout = test_wav(xe_file, input_file, output_file, frame_advance, AP_MAX_Y_CHANNELS, frame_advance, timeout=xtag_aquire_timeout_s) - ''' - with tempfile.TemporaryDirectory(dir=".") as tmp_folder: - tmp_folder = Path(tmp_folder) - shutil.copyfile(input_file, tmp_folder / "input.wav") - - #Make sure we can wait for 2 processing occurances to finish - stdout = run_with_xscope_fileio(xe_file, tmp_folder, xtag_aquire_timeout_s) - - shutil.copyfile(tmp_folder / "output.wav", output_file) - ''' + stdout = test_wav(xe_file, input_file, output_file, frame_advance, AP_MAX_Y_CHANNELS, frame_advance, target=target_arch, timeout=xtag_aquire_timeout_s) return stdout def process_python(input_file, output_file, arch): @@ -52,13 +42,13 @@ def process_python(input_file, output_file, arch): stdo = "" return stdo -def process_file(input_file, arch, target="xcore"): +def process_file(input_file, arch, target="xcore", target_arch="xs3a"): wav_name = input_file.name output_file = Path(__file__).parent / f"{pipeline_output_base_dir}_{arch}_{target}" / wav_name if target == "xcore": pipeline_bin = pipeline_bins[arch][target] - stdout = process_xcore(pipeline_bin, input_file, output_file) + stdout = process_xcore(pipeline_bin, input_file, output_file, target_arch) elif target == "python": stdout = process_python(input_file, output_file, arch) else: diff --git a/tests/pipeline/src/test_bin.c b/tests/pipeline/src/test_bin.c index de3a6297..c7927d2d 100644 --- a/tests/pipeline/src/test_bin.c +++ b/tests/pipeline/src/test_bin.c @@ -7,6 +7,7 @@ #include #include #include +#include #include "xmath/xmath.h" #include "xscope_io_device.h" #include "fileio.h" @@ -83,16 +84,107 @@ void main_tile1(chanend_t c_t0_t1, chanend_t c_t1_t0) pipeline_tile1(c_t0_t1, c_t1_t0); } +extern void pipeline_stage_1(chanend_t c_frame_in, chanend_t c_frame_out); +extern void pipeline_stage_2(chanend_t c_frame_in, chanend_t c_frame_out); +extern void pipeline_stage_3(chanend_t c_frame_in, chanend_t c_frame_out); +extern void pipeline_stage_4(chanend_t c_frame_in, chanend_t c_frame_out); + +void st1(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + pipeline_stage_1(a, b); +} + +void st2(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + pipeline_stage_2(a, b); +} + +void st3(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + pipeline_stage_3(a, b); +} + +void st4(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + pipeline_stage_4(a, b); +} + +void tx_w(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + tx(a, b, "input.bin"); +} + +void rx_w(void *d){ + chanend_t a = ((chanend_t *)d)[0]; + chanend_t b = ((chanend_t *)d)[1]; + rx(a, b, "output.bin"); +} + +#define STACK_SIZE_FOR(F) \ + ({ \ + register unsigned r; \ + asm volatile ( \ + ".globl " #F ".stack_bytes\n\t" \ + ".resource_get " #F ".stack_bytes, \"stack_bytes\", " #F "\n\t" \ + "lui %[r], %%hi(" #F ".stack_bytes)\n\t" \ + "addi %[r], %[r], %%lo(" #F ".stack_bytes)" \ + : [r]"=r"(r)); \ + r; }) + int main() { + (void)STACK_SIZE_FOR(tx_w); + (void)STACK_SIZE_FOR(st1); + (void)STACK_SIZE_FOR(st2); + (void)STACK_SIZE_FOR(st3); + (void)STACK_SIZE_FOR(st4); + // (void)STACK_SIZE_FOR(rx_w); + chanend_t xscope_chan = chanend_alloc(); - channel_t c_th0_to_th1 = chan_alloc(); - channel_t c_th1_to_th0 = chan_alloc(); + channel_t tx_to_st1 = chan_alloc(); + channel_t tx_to_rx = chan_alloc(); + channel_t st1_to_st2 = chan_alloc(); + channel_t st2_to_st3 = chan_alloc(); + channel_t st3_to_st4 = chan_alloc(); + channel_t st4_to_rx = chan_alloc(); xscope_io_init(xscope_chan); - PAR_JOBS( - PJOB(main_tile0, (c_th0_to_th1.end_a, c_th1_to_th0.end_a, "input.bin", "output.bin")), - PJOB(main_tile1, (c_th0_to_th1.end_b, c_th1_to_th0.end_b)) - ); + + __attribute__((aligned(16))) char tx_stack[5000]; + __attribute__((aligned(16))) char st1_stack[24000]; + __attribute__((aligned(16))) char st2_stack[65000]; + __attribute__((aligned(16))) char st3_stack[28000]; + __attribute__((aligned(16))) char st4_stack[5000]; + // __attribute__((aligned(16))) char rx_stack[150000]; + + chanend_t tx_data[2] = {tx_to_st1.end_a, tx_to_rx.end_a}; + chanend_t st1_data[2] = {tx_to_st1.end_b, st1_to_st2.end_a}; + chanend_t st2_data[2] = {st1_to_st2.end_b, st2_to_st3.end_a}; + chanend_t st3_data[2] = {st2_to_st3.end_b, st3_to_st4.end_a}; + chanend_t st4_data[2] = {st3_to_st4.end_b, st4_to_rx.end_a}; + chanend_t rx_data[2] = {st4_to_rx.end_b, tx_to_rx.end_b}; + + threadgroup_t thg = thread_group_alloc(); + thread_group_add(thg, tx_w, &tx_data[0], stack_base(&tx_stack[0], sizeof(tx_stack)/4)); + thread_group_add(thg, st1, &st1_data[0], stack_base(&st1_stack[0], sizeof(st1_stack)/4)); + thread_group_add(thg, st2, &st2_data[0], stack_base(&st2_stack[0], sizeof(st2_stack)/4)); + thread_group_add(thg, st3, &st3_data[0], stack_base(&st3_stack[0], sizeof(st3_stack)/4)); + thread_group_add(thg, st4, &st4_data[0], stack_base(&st4_stack[0], sizeof(st4_stack)/4)); + // thread_group_add(thg, rx_w, &rx_data[0], stack_base(&rx_stack[0], sizeof(rx_stack)/4)); + thread_group_start(thg); + + rx_w(&rx_data[0]); + + thread_group_wait_and_free(thg); + chanend_free(xscope_chan); - chan_free(c_th0_to_th1); - chan_free(c_th1_to_th0); + chan_free(tx_to_st1); + chan_free(tx_to_rx); + chan_free(st1_to_st2); + chan_free(st2_to_st3); + chan_free(st3_to_st4); + chan_free(st4_to_rx); } diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py index 6f462f19..ab323899 100644 --- a/tests/pipeline/test_pipeline.py +++ b/tests/pipeline/test_pipeline.py @@ -12,7 +12,7 @@ import time, fcntl -def test_pipelines(test, record_property): +def test_pipelines(test, record_property, target_arch): wav_file = test[0] wav_name = wav_file.name arch = test[1] @@ -23,7 +23,7 @@ def test_pipelines(test, record_property): _, rate, samps, _ = get_wav_info(str(input_file)) print(f"Processing a {samps//rate}s track") t0 = time.time() - output_file, stdo = process_file(input_file, arch, target) + output_file, stdo = process_file(input_file, arch, target, target_arch) tot = time.time() - t0 print(f"Processing took {tot:.2f}s") diff --git a/tests/shared/python/test_wav.py b/tests/shared/python/test_wav.py index 7d4bd4c8..c5adfe3f 100644 --- a/tests/shared/python/test_wav.py +++ b/tests/shared/python/test_wav.py @@ -14,6 +14,7 @@ def test_wav( output_channels, output_frame_len, sample_rate=16000, + target="xs3a", **run_kwargs, # pass-through to run_dut and further ): """ @@ -68,7 +69,7 @@ def test_wav( # Run DUT print(f"input_q31.shape = {input_q31.shape}") - output_interleaved_q31, xcore_stdout = run_dut(input_q31, xe_path, **run_kwargs) + output_interleaved_q31, xcore_stdout = run_dut(input_q31, xe_path, target, **run_kwargs) # Deinterleave output to (channels, frames) format output_q31 = pvc.deinterleave_channel_frames( From 3de25d519dc047288481c7c5ce289919a7be8b7e Mon Sep 17 00:00:00 2001 From: Allan Skellett Date: Wed, 18 Mar 2026 10:49:43 +0000 Subject: [PATCH 15/17] initial vx4 --- tests/profile_mips/CMakeLists.txt | 6 ++- tests/profile_mips/conftest.py | 56 ++++++++++++--------- tests/profile_mips/lib_voice_mips.json | 20 ++++---- tests/profile_mips/lib_voice_mips_table.rst | 16 ++++-- tests/profile_mips/src/main.c | 16 ++++++ tests/profile_mips/src/main.xc | 41 --------------- tests/profile_mips/test_profile_mips.py | 22 ++++---- 7 files changed, 90 insertions(+), 87 deletions(-) delete mode 100644 tests/profile_mips/src/main.xc diff --git a/tests/profile_mips/CMakeLists.txt b/tests/profile_mips/CMakeLists.txt index e11de837..9d6e08a6 100644 --- a/tests/profile_mips/CMakeLists.txt +++ b/tests/profile_mips/CMakeLists.txt @@ -2,7 +2,11 @@ cmake_minimum_required(VERSION 3.21) include($ENV{XMOS_CMAKE_PATH}/xcommon.cmake) project(app_mips) -set(APP_HW_TARGET XK-EVK-XU316) +if(DEFINED XCORE_TARGET) + set(APP_HW_TARGET ${XCORE_TARGET}) +else() + set(APP_HW_TARGET XK-EVK-XU316) +endif() set(XMOS_SANDBOX_DIR ${CMAKE_CURRENT_LIST_DIR}/../../..) set(CONFIG_XSCOPE_PATH ${XMOS_SANDBOX_DIR}/lib_voice/tests/shared/file_utils) diff --git a/tests/profile_mips/conftest.py b/tests/profile_mips/conftest.py index 3fabfc5a..de41304a 100644 --- a/tests/profile_mips/conftest.py +++ b/tests/profile_mips/conftest.py @@ -5,15 +5,13 @@ import re def pytest_addoption(parser): - """ - Register custom pytest command-line option, --update. - When ``--update`` is provided: - - Reference MIPS files lib_voice_mips.json and lib_voice_mips_table.rst are regenerated. - - MIPS deviation checks are skipped during test execution. - - This flag is intended for controlled regeneration of baseline - profiling results after intentional performance changes. - """ + parser.addoption( + "--arch", + nargs="+", + default=["xs3a"], + help="One or more architectures to run on (e.g. --arch xs3a vx4b)", + choices=["xs3a", "vx4b"], + ) parser.addoption( "--update", action="store_true", @@ -23,36 +21,47 @@ def pytest_addoption(parser): def write_rst_table(configs: dict, outfile: Path): """ - Generate a reStructuredText table summarizing MIPS usage. + Generate a reStructuredText table summarizing MIPS usage per architecture. Parameters ---------- configs : dict - Dictionary mapping application name (e.g. 'app_mips_ns') - to measured MIPS value (float). + Nested dict mapping architecture name to a dict of app_name -> mips value. + e.g. {"xs3a": {"app_mips_ns": 20.79}, "vx4b": {"app_mips_ns": 19.1}} outfile : pathlib.Path Output path for generated RST file. """ + archs = sorted(configs.keys()) + all_apps = sorted({app for apps in configs.values() for app in apps}) + widths = " ".join(["8"] * (1 + len(archs))) lines = [ ".. _lib_voice_mips_usage:\n", ".. list-table:: CPU requirements (600 MHz system frequency, 120 MHz per HW thread)", " :header-rows: 1", - " :widths: 8 8 ", + f" :widths: {widths}", "", " * - Component", - " - MIPS use", ] - for app, mips in sorted(configs.items()): + for arch in archs: + lines.append(f" - MIPS use ({arch.upper()})") + for app in all_apps: m = re.search(r"app_mips_([^\s]+)", app) - assert(m), "Cannot parse app name. Should start with app_mips_" - if m: - print(m.group(1)) - app_name = (m.group(1)).upper() - + assert m, "Cannot parse app name. Should start with app_mips_" + app_name = m.group(1).upper() lines.append(f" * - {app_name}") - lines.append(f" - {mips}") + for arch in archs: + mips = configs[arch].get(app, "N/A") + lines.append(f" - {mips}") outfile.write_text("\n".join(lines)) +def pytest_generate_tests(metafunc): + if "target" in metafunc.fixturenames: + selected_arches = metafunc.config.getoption("arch") + if isinstance(selected_arches, str): + selected_arches = [selected_arches] + metafunc.parametrize("target", selected_arches) + + def pytest_sessionfinish(session, exitstatus): """ Perform final aggregation and reference update (if run with --update). @@ -70,11 +79,12 @@ def pytest_sessionfinish(session, exitstatus): if not hasattr(session.config, "workerinput"): update = session.config.getoption("--update") if update: # update needs happen in pytest_sessionfinish after all worker nodes have run and written their corresponding _mips_worker.json files - # read all worker JSON files here + # read all worker JSON files here — each contains {arch: {app: mips}} result_files = (Path(__file__).parent / "worker_logs").glob("*_mips_worker*.json") data = {} for f in result_files: - data.update(json.loads(f.read_text())) + for arch, apps in json.loads(f.read_text()).items(): + data.setdefault(arch, {}).update(apps) print(f"MIPS for all apps = {data}") # generate updated JSON diff --git a/tests/profile_mips/lib_voice_mips.json b/tests/profile_mips/lib_voice_mips.json index c01afaad..1a8729c9 100644 --- a/tests/profile_mips/lib_voice_mips.json +++ b/tests/profile_mips/lib_voice_mips.json @@ -1,11 +1,13 @@ { - "app_mips_adec": 1.53, - "app_mips_aec_std_arch_2threads": 40.85, - "app_mips_aec_std_arch_1thread": 62.89, - "app_mips_vnr": 1.84, - "app_mips_aec_alt_arch_1thread": 42.57, - "app_mips_ic": 13.21, - "app_mips_ns": 20.79, - "app_mips_aec_alt_arch_2threads": 30.7, - "app_mips_agc": 21.31 + "xs3a": { + "app_mips_adec": 1.53, + "app_mips_aec_alt_arch_1thread": 42.57, + "app_mips_aec_alt_arch_2threads": 30.7, + "app_mips_aec_std_arch_1thread": 62.89, + "app_mips_aec_std_arch_2threads": 40.85, + "app_mips_agc": 21.31, + "app_mips_ic": 13.21, + "app_mips_ns": 20.79, + "app_mips_vnr": 1.84 + } } \ No newline at end of file diff --git a/tests/profile_mips/lib_voice_mips_table.rst b/tests/profile_mips/lib_voice_mips_table.rst index 7117329a..8b6ba5a3 100644 --- a/tests/profile_mips/lib_voice_mips_table.rst +++ b/tests/profile_mips/lib_voice_mips_table.rst @@ -2,25 +2,35 @@ .. list-table:: CPU requirements (600 MHz system frequency, 120 MHz per HW thread) :header-rows: 1 - :widths: 8 8 + :widths: 8 8 8 * - Component - - MIPS use + - MIPS use (XS3A) + - MIPS use (VX4B) * - ADEC - 1.53 + - N/A * - AEC_ALT_ARCH_1THREAD - 42.57 + - N/A * - AEC_ALT_ARCH_2THREADS - 30.7 + - N/A * - AEC_STD_ARCH_1THREAD - 62.89 + - N/A * - AEC_STD_ARCH_2THREADS - 40.85 + - N/A * - AGC - 21.31 + - N/A * - IC - 13.21 + - N/A * - NS - 20.79 + - N/A * - VNR - - 1.84 \ No newline at end of file + - 1.84 + - N/A \ No newline at end of file diff --git a/tests/profile_mips/src/main.c b/tests/profile_mips/src/main.c index 59b805d3..9475887a 100644 --- a/tests/profile_mips/src/main.c +++ b/tests/profile_mips/src/main.c @@ -202,3 +202,19 @@ void wrapper_task(const char *input_file_name, const char *output_file_name) * * Done. */ + +#include +#include +#include + +#define IN_WAV_FILE_NAME "input.bin" +#define OUT_WAV_FILE_NAME "output.bin" + +int main(void) +{ + chanend_t xscope_chan = chanend_alloc(); + xscope_io_init(xscope_chan); + wrapper_task(IN_WAV_FILE_NAME, OUT_WAV_FILE_NAME); + _Exit(0); + return 0; +} diff --git a/tests/profile_mips/src/main.xc b/tests/profile_mips/src/main.xc deleted file mode 100644 index a712dc1a..00000000 --- a/tests/profile_mips/src/main.xc +++ /dev/null @@ -1,41 +0,0 @@ -// Copyright 2026 XMOS LIMITED. -// This Software is subject to the terms of the XMOS Public Licence: Version 1. -#include -#include -#include -#include -#ifdef __XC__ -#define chanend_t chanend -#else -#include -#endif - -extern "C" { -#include "xmath/xmath.h" -void wrapper_task(const char *input_file_name, const char *output_file_name); -#if TEST_WAV_XSCOPE - - #include "xscope_io_device.h" -#endif -} - -#define IN_WAV_FILE_NAME "input.bin" -#define OUT_WAV_FILE_NAME "output.bin" -int main (void) -{ - chan xscope_chan; - par - { -#if TEST_WAV_XSCOPE - xscope_host_data(xscope_chan); -#endif - on tile[0]: { -#if TEST_WAV_XSCOPE - xscope_io_init(xscope_chan); -#endif - wrapper_task(IN_WAV_FILE_NAME, OUT_WAV_FILE_NAME); - _Exit(0); - } - } - return 0; -} diff --git a/tests/profile_mips/test_profile_mips.py b/tests/profile_mips/test_profile_mips.py index 138194ff..acd8069c 100644 --- a/tests/profile_mips/test_profile_mips.py +++ b/tests/profile_mips/test_profile_mips.py @@ -86,7 +86,7 @@ }, } -def gen_input_and_run_dut(xe, module): +def gen_input_and_run_dut(xe, module, target="xs3a"): """ Generate input audio for a module and run the corresponding DUT xe. @@ -126,9 +126,10 @@ def gen_input_and_run_dut(xe, module): input_data, config["frame_advance"] ) - _, xcore_stdo = run_dut(input_data, xe) + _, xcore_stdo = run_dut(input_data, xe, target=target) return xcore_stdo + def find_apps(): """ Discover profiling executables matching registered modules. @@ -161,7 +162,7 @@ def find_apps(): APPS, ids=[xe.stem for xe, _ in APPS] ) -def test_measure_mips(xe, module, pytestconfig): +def test_measure_mips(xe, module, pytestconfig, target): """ Profile a single app (xe) and validate MIPS usage. @@ -193,7 +194,7 @@ def test_measure_mips(xe, module, pytestconfig): app = xe.stem # app name from executable print(f"app = {app}, module = {module}") log_file = Path(__file__).parent / f"{app}.log" - xcore_stdo = gen_input_and_run_dut(xe, module) + xcore_stdo = gen_input_and_run_dut(xe, module, target) with tempfile.TemporaryDirectory(dir=".", suffix=app) as tmp_folder: tmp = Path(tmp_folder) parse_profile_log( @@ -210,10 +211,10 @@ def test_measure_mips(xe, module, pytestconfig): assert m, (f"MIPS log file {log_file} doesnt seem to be formatted correctly. " f"file text = {text}") mips = float(m.group(1)) - # Dump {app: mips} in a json file, to be collected in pytest_sessionfinish, if reference update is required - out_file = Path(__file__).parent / "worker_logs" / f"{app}_mips_worker.json" + # Dump {target: {app: mips}} in a json file, to be collected in pytest_sessionfinish, if reference update is required + out_file = Path(__file__).parent / "worker_logs" / f"{app}_{target}_mips_worker.json" out_file.parent.mkdir(parents=True, exist_ok=True) # create missing dirs - out_file.write_text(json.dumps({app: mips})) + out_file.write_text(json.dumps({target: {app: mips}})) if not update: threshold = 0.1 # Allow upto 0.1 mips of variation @@ -221,13 +222,14 @@ def test_measure_mips(xe, module, pytestconfig): ref_json = Path(__file__).parent / "lib_voice_mips.json" with ref_json.open("r") as f: ref_data = json.load(f) - assert app in ref_data, (f"ERROR: App {app} not in reference json. " + assert target in ref_data and app in ref_data.get(target, {}), ( + f"ERROR: App {app} for arch {target} not in reference json. " "Run test with pytest test_profile_mips.py --update " "to regenerate the reference json and rst") - if abs(mips - ref_data[app]) > threshold: + if abs(mips - ref_data[target][app]) > threshold: fail_str = ( f"ERROR: App {app}, MIPS {mips} off by more than " - f"{threshold} MIPS compared to the reference {ref_data[app]}.\n" + f"{threshold} MIPS compared to the reference {ref_data[target][app]}.\n" "If this is expected, run the test with 'pytest test_profile_mips.py' --update to update the reference json and rst files.\n" ) pytest.fail(fail_str) From 6921b938b6695f141ccde7f9520ce966a0b17217 Mon Sep 17 00:00:00 2001 From: Allan Skellett Date: Wed, 18 Mar 2026 10:57:19 +0000 Subject: [PATCH 16/17] add profile mips to jenkins jenkins --- Jenkinsfile | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/Jenkinsfile b/Jenkinsfile index f5fe12bb..a7d1d93c 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -171,6 +171,9 @@ pipeline { dir("pipeline") { xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") } + dir("profile_mips") { + xcoreBuild(buildDir: "build_vx4b", archiveBins: false, toolsVersion: params.TOOLS_VX4_VERSION, cmakeOpts: "-DXCORE_TARGET=XK-EVK-XU416") + } stash name: 'vx4b_build_xcore', includes: '**/bin/**/*.xe' } } @@ -397,6 +400,13 @@ pipeline { } } } + dir("profile_mips") { + withEnv(["hydra_audio_PATH=/projects/hydra_audio"]) { + sh "pytest -n 2 --junitxml=pytest_result.xml --arch vx4b" + junit "pytest_result.xml" + archiveArtifacts artifacts: "lib_voice_mips.json", fingerprint: true, onlyIfSuccessful: true + } + } } } } From ce75fad1757b5a41b47ac805c112d0fa49cc0ba0 Mon Sep 17 00:00:00 2001 From: Allan Skellett Date: Wed, 18 Mar 2026 11:34:04 +0000 Subject: [PATCH 17/17] MIPS numbers --- tests/profile_mips/conftest.py | 22 ++++++++++++++++++++- tests/profile_mips/lib_voice_mips.json | 11 +++++++++++ tests/profile_mips/lib_voice_mips_table.rst | 22 ++++++++++----------- tests/shared/python/run_dut.py | 2 +- 4 files changed, 44 insertions(+), 13 deletions(-) diff --git a/tests/profile_mips/conftest.py b/tests/profile_mips/conftest.py index de41304a..bc1384d0 100644 --- a/tests/profile_mips/conftest.py +++ b/tests/profile_mips/conftest.py @@ -54,6 +54,19 @@ def write_rst_table(configs: dict, outfile: Path): lines.append(f" - {mips}") outfile.write_text("\n".join(lines)) +def pytest_sessionstart(session): + """Clean up stale worker JSON files at the start of an --update run (master only).""" + if hasattr(session.config, "workerinput"): + return # workers skip this + try: + update = session.config.getoption("--update") + except ValueError: + return + if update: + worker_logs = Path(__file__).parent / "worker_logs" + for f in worker_logs.glob("*_mips_worker*.json"): + f.unlink() + def pytest_generate_tests(metafunc): if "target" in metafunc.fixturenames: selected_arches = metafunc.config.getoption("arch") @@ -86,9 +99,16 @@ def pytest_sessionfinish(session, exitstatus): for arch, apps in json.loads(f.read_text()).items(): data.setdefault(arch, {}).update(apps) + # Merge with existing reference JSON so other architectures are preserved + ref_json = Path(__file__).parent / "lib_voice_mips.json" + if ref_json.exists(): + existing = json.loads(ref_json.read_text()) + for arch, apps in existing.items(): + if arch not in data: + data[arch] = apps + print(f"MIPS for all apps = {data}") # generate updated JSON - ref_json = Path(__file__).parent / "lib_voice_mips.json" with ref_json.open("w") as fp: json.dump(data, fp, indent=2) # generate updated RST diff --git a/tests/profile_mips/lib_voice_mips.json b/tests/profile_mips/lib_voice_mips.json index 1a8729c9..00c82ba0 100644 --- a/tests/profile_mips/lib_voice_mips.json +++ b/tests/profile_mips/lib_voice_mips.json @@ -1,4 +1,15 @@ { + "vx4b": { + "app_mips_adec": 1.17, + "app_mips_aec_alt_arch_1thread": 40.02, + "app_mips_aec_alt_arch_2threads": 29.11, + "app_mips_aec_std_arch_1thread": 62.64, + "app_mips_aec_std_arch_2threads": 41.89, + "app_mips_agc": 16.52, + "app_mips_ic": 12.42, + "app_mips_ns": 19.42, + "app_mips_vnr": 1.65 + }, "xs3a": { "app_mips_adec": 1.53, "app_mips_aec_alt_arch_1thread": 42.57, diff --git a/tests/profile_mips/lib_voice_mips_table.rst b/tests/profile_mips/lib_voice_mips_table.rst index 8b6ba5a3..5cf15cdb 100644 --- a/tests/profile_mips/lib_voice_mips_table.rst +++ b/tests/profile_mips/lib_voice_mips_table.rst @@ -5,32 +5,32 @@ :widths: 8 8 8 * - Component - - MIPS use (XS3A) - MIPS use (VX4B) + - MIPS use (XS3A) * - ADEC + - 1.17 - 1.53 - - N/A * - AEC_ALT_ARCH_1THREAD + - 40.02 - 42.57 - - N/A * - AEC_ALT_ARCH_2THREADS + - 29.11 - 30.7 - - N/A * - AEC_STD_ARCH_1THREAD + - 62.64 - 62.89 - - N/A * - AEC_STD_ARCH_2THREADS + - 41.89 - 40.85 - - N/A * - AGC + - 16.52 - 21.31 - - N/A * - IC + - 12.42 - 13.21 - - N/A * - NS + - 19.42 - 20.79 - - N/A * - VNR - - 1.84 - - N/A \ No newline at end of file + - 1.65 + - 1.84 \ No newline at end of file diff --git a/tests/shared/python/run_dut.py b/tests/shared/python/run_dut.py index 2b5a7f3c..a0972089 100644 --- a/tests/shared/python/run_dut.py +++ b/tests/shared/python/run_dut.py @@ -28,7 +28,7 @@ def get_binary_path(xe, target="xs3a"): assert 0, f"{target} target is unsupported" -def run_with_xscope_fileio(xe_path, cwd, target="xs3a", timeout=600): +def run_with_xscope_fileio(xe_path, cwd, target="xs3a", timeout=6): """ Run a .xe image on hardware via xscope_fileio, capturing device stdout.